Good legs 'control' paralysed partners

• 09:30 29 August 2002
• Exclusive from New Scientist Print Edition
• Duncan Graham-Rowe  

The new implant could help some stroke patients to walk again (Photo: FSP/GAMMA)

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Two men paralysed on one side of their body can walk again, thanks to an ingenious implant that uses signals from a healthy leg to control a paralysed one.

Both men, aged 47 and 64, had been paralysed by strokes. Previously neither could walk unaided. But after sensors were placed over certain muscle groups on the healthy leg and stimulators implanted in the paralysed leg, they can now walk, stand and sit.

The unique therapy allows a patient to move their paralysed leg in a natural way without being aware that they are doing it, says Wenwei Yu, who developed the technique at Hokkaido University in Sapporo, Japan. But it could be another five years or more before the technology becomes available, he says.

In Yu's system, muscle sensors monitor signals from the patient's able leg. These are used to trigger pre-programmed electrical impulses in 11 electrodes implanted near nerves in the paralysed leg. This lets the paralysed leg do what the patient wants it to do - by taking its cue from the good leg.

Abnormal behaviour

Producing movement in limbs by electrically stimulating muscles or nerves is known as functional electrical stimulation. One of the difficulties of using conventional FES, says Paul Taylor, a clinical engineer at Salisbury District Hospital in Wiltshire, is overcoming "spasticity" - involuntary muscular spasms normally suppressed by the brain.

"So even if you had appropriate signals in the appropriate muscles it may not behave normally," he says. "There will also be stiffness, the muscles will be weak and activity from other muscles might be working against what you're trying to do," says Taylor.

Another problem with conventional FES, says Yu, is that patients have to activate the electrodes using their upper body, either through hand-held switches or sensors in their arms.

A certain wrist action, for example, could make a leg move. But this is far from practical, as they may want to make that same arm movement for other reasons. And while researchers have been trying out FES for many decades, much of the work on legs has focused on paraplegia, where both legs are paralysed. But hemiplegia, where only one leg is paralysed, is far more prevalent.

Falling danger

By taking advantage of the working leg to control the paralysed one, Yu avoids the problem of using the upper body to activate the electrodes. And he avoids any spasticity by tuning the electrical stimulations and their timing so that the muscles work in concert with each other to produce smooth coordinated movements.

Not only that, but the electrical stimulation itself has a therapeutic effect, preventing the leg muscles from getting stiff.

Gerald Loeb, an expert in FES at the University of Southern California in Los Angeles, says Yu and his team will have to ensure their technique is safe because many hemiplegic people are elderly. One fall and they could break a hip.

Yu acknowledges these dangers but says his system uses a learning program that tailors itself to the individual patient's muscle contractions. This means it can get almost perfect recognition of the patient's intentions, which should reduce the risk of falling.

World's first brain prosthesis revealed

• 19:00 12 March 2003
• Exclusive from New Scientist Print Edition
• Duncan Graham-Rowe

Enlarge image

Hippocampus replacement

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• Neuroscience, University of Southern California
• Sam Deadwyler, Wake Forest University
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• Bibliography, Bernard Williams

 

The world's first brain prosthesis - an artificial hippocampus - is about to be tested in California. Unlike devices like cochlear implants, which merely stimulate brain activity, this silicon chip implant will perform the same processes as the damaged part of the brain it is replacing.

The prosthesis will first be tested on tissue from rats' brains, and then on live animals. If all goes well, it will then be tested as a way to help people who have suffered brain damage due to stroke, epilepsy or Alzheimer's disease.

Any device that mimics the brain clearly raises ethical issues. The brain not only affects memory, but your mood, awareness and consciousness - parts of your fundamental identity, says ethicist Joel Anderson at Washington University in St Louis, Missouri.

The researchers developing the brain prosthesis see it as a test case. "If you can't do it with the hippocampus you can't do it with anything," says team leader Theodore Berger of the University of Southern California in Los Angeles. The hippocampus is the most ordered and structured part of the brain, and one of the most studied. Importantly, it is also relatively easy to test its function.

The job of the hippocampus appears to be to "encode" experiences so they can be stored as long-term memories elsewhere in the brain. "If you lose your hippocampus you only lose the ability to store new memories," says Berger. That offers a relatively simple and safe way to test the device: if someone with the prosthesis regains the ability to store new memories, then it's safe to assume it works.

Model, build, interface

The inventors of the prosthesis had to overcome three major hurdles. They had to devise a mathematical model of how the hippocampus performs under all possible conditions, build that model into a silicon chip, and then interface the chip with the brain.

No one understands how the hippocampus encodes information. So the team simply copied its behaviour. Slices of rat hippocampus were stimulated with electrical signals, millions of times over, until they could be sure which electrical input produces a corresponding output. Putting the information from various slices together gave the team a mathematical model of the entire hippocampus.

They then programmed the model onto a chip, which in a human patient would sit on the skull rather than inside the brain. It communicates with the brain through two arrays of electrodes, placed on either side of the damaged area. One records the electrical activity coming in from the rest of the brain, while the other sends appropriate electrical instructions back out to the brain.

The hippocampus can be thought of as a series of similar neural circuits that work in parallel, says Berger, so it should be possible to bypass the damaged region entirely (see graphic).

Memory tasks

Berger and his team have taken nearly 10 years to develop the chip. They are about to test it on slices of rat brain kept alive in cerebrospinal fluid, they will tell a neural engineering conference in Capri, Italy, next week.

"It's a very important step because it's the first time we have put all the pieces together," he says. The work was funded by the US National Science Foundation, Office of Naval Research and Defense Advanced Research Projects Agency.

If it works, the team will test the prosthesis in live rats within six months, and then in monkeys trained to carry out memory tasks. The researchers will stop part of the monkey's hippocampus working and bypass it with the chip. "The real proof will be if the animal's behaviour changes or is maintained," says Sam Deadwyler of Wake Forest University in Winston-Salem, North Carolina, who will conduct the animal trials.

The hippocampus has a similar structure in most mammals, says Deadwyler, so little will have to be changed to adapt the technology for people. But before human trials begin, the team will have to prove unequivocally that the prosthesis is safe.

Collateral damage

One drawback is that it will inevitably bypass some healthy brain tissue. But this should not affect the patient's memories, says Berger. "It would be no different from removing brain tumours," where there is always some collateral damage, says Bernard Williams, a philosopher at Britain's University of Oxford, who is an expert in personal identity.

Anderson points out that it will take time for people to accept the technology. "Initially people thought heart transplants were an abomination because they assumed that having the heart you were born with was an important part of who you are."

While trials on monkeys will tell us a lot about the prosthesis's performance, there are some questions that will not be answered. For example, it is unclear whether we have any control over what we remember. If we do, would brain implants of the future force some people to remember things they would rather forget?

The ethical consequences of that would be serious. "Forgetting is the most beneficial process we possess," Williams says. It enables us to deal with painful situations without actually reliving them.

Another ethical conundrum concerns consent to being given the prosthesis, says Anderson. The people most in need of it will be those with a damaged hippocampus and a reduced ability to form new memories. "If someone can't form new memories, then to what extent can they give consent to have this implant?"

　　这名女子名叫艾米·韦斯塔尔，今年20岁，是医学人员所开发的用于治疗帕金森氏症、抑郁症甚至瘫痪等疾病的实验性疗法的最生动案例。大脑线路“重新连接”是指将电极植 入负责特定功能的区域。移植到颈骨下面的电池向外输送小股电流，同时电流通过电线连接到大脑本身的电极上。韦斯塔尔来自英国莱斯特郡的麦尔登毛伍伯瑞，从小就患上一种称为肌张力障碍的遗传病。肌张力障碍是一种神经系统常见疾病，患者会普遍感到肌肉僵硬，不听使唤，而且经常痉挛，令患者痛不欲生，几乎不可能行走。

　　英国伦敦神经病学研究所功能神经外科学教授马尔旺·哈利兹对韦斯塔尔进行了长期的实验性手术。哈利兹教授将比人发还细小的细丝植入韦斯塔尔德大脑，输送可拦截某些令其肌肉陷入痉挛状态信号的电流，结果产生了神奇的效果。韦斯塔尔说：“难以用语言形容手术对我生活的巨大改变。在手术前，我在轮椅上度过了七年痛不欲生的时光。如今，我不仅会走，还能在业余歌手表演比赛上唱歌跳舞。”

　　以前，韦斯塔尔不仅要面对日渐增加的痛苦，甚至还有可能在未体会到人间欢乐之前便匆匆离去这个世界。现在，她是一所学校的助理，过去由于疾病缠身，她无法到学校接受教育，今天她正试图通过一切途径来弥补自己的这一遗憾。

　　患者和医生长期以来对大脑移植技术存在警惕心理，因为许多人不由得将这种手术与脑白质切除术联系起来。脑白质切除术实施起来危险重重，而且成功率极低。不过大脑移植方面的先驱指出，这种手术的疗效具有可逆性，因为切断设备电源便可以。在对世界各地的偏头痛及妄想强迫症患者进行的手术中，他们已经取得了极富前景的结果。

　　哈利兹及同事如今认为，抑郁症可能是下一个大量患者从这项技术中受益的重病。美国众多患者报告说，在移植手术后，他们的情绪普遍得到显著改善。目前，英国方面的第一项此类研究也在进行当中。英国布里斯托尔大学的一项研究正在确定研究对象。

　　在此项研究中，8名30岁左右的重度抑郁症患者将在今年做手术，把四个电极移植到大脑的不同区域。领导此项研究的精神病学家安德烈·马里奇亚然后会对哪个电极和哪种频率在抑制引起抑郁症的异常信号方面最为有效进行深入研究。马里奇亚说：“高达5%的人患有周期性重度抑郁症。这种手术有可能令其中许多人的生活从此发生根本性改变。”

　　另一项研究计划正在详细分析将电极植入帕金森氏综合症患者大脑后产生的效果。帕金森氏综合症会使患者丧失对肌肉的控制。现在，每年有百余名患者接受费用高达三万英镑的手术治疗。南安普顿居民迈克·罗宾斯就是其中的代表，他55岁患上了帕金森氏综合症，不久后便考虑做手术。

　　罗宾斯说：“我浑身上下哆嗦得厉害，不能做任何事儿，我甚至一度产生过自杀的念头。”牛津拉德克利夫 医院的医生在对罗宾斯进行局部麻醉后，用手钻刺穿他的头骨。谈起当时的手术情景，现年62岁的罗宾斯至今心有余悸：“医生在寻找致病位置时我感到惊恐异常。可当他们找到确切位置，我的颤抖立即停止了，从此就再也没有复发。这种手术真是令人惊讶不已。”(杨孝文）

英国瘫痪少女脑中被植电极后恢复行走自如
http://www.sina.com.cn 2006年03月06日09:18 上海青年报
本报讯据《泰晤士报》5日报道，20岁的英国女孩艾美·维斯因患“帕金森症”下肢瘫痪，与轮椅相伴7年。但不可思议的是，自从医生在她的大脑植入了几枚电极后，借助于遥控器调节，现在她不仅站立起来行走自如，甚至还可以唱歌跳舞。 　　瘫痪7年与轮椅为伴 　　据报道，这名女孩名叫艾美·维斯托，现年20岁。据悉，艾美原本活泼可爱，但大约7年前她罹患帕金森症，从此肌肉关节僵硬，肢体挛缩畸形，终日与轮椅为伴。与此同时，她还患上了精神抑郁症。 　　伦敦神经学协会的功能性神经外科教授马尔万·哈里兹大夫在对艾美进行了长期认真的观察实验之后，决定为她尝试一种大胆创新的手术———在大脑中植入电极。该手术大体步骤为：第一，在患者脑壳上钻上小洞，植入数枚微型电极；第二，在患者锁骨下植入一枚微型电池；第三，用比发丝还细的电线连接电极和电池。 　　手术后竟下地走路 　　不可思议的是，术后异常成功。电极植入大脑后，艾美不仅肌肉痉挛症状消失了，而且还奇迹般站立了起来。据悉，幸运康复后的艾美如今不仅自愿成为一名教室辅导员，而且正努力补上由于长年治疗而丢下的学校课程。 　　她激动地说：“我的生活获得了巨大改变，这种喜悦无法用语言表达。手术之前，我与轮椅相伴7年之久，现在我不仅可以行走自如，而且还可以在表演秀上歌唱跳舞。” 　　据悉，目前大脑电极植入术也被用于治疗偏头痛以及精神强迫性紊乱等疾病，效果喜人。哈里兹及其同事们认为，为数众多的精神抑郁症患者接下来也有可能从这项新技术中受益。（袁海 木子） 　　遥控器控制脑内电流 　　手术后，医生通过体外遥控器调节患者颅内电极电流大小，运用强弱不同的电子脉冲信号控制肢体肌肉甚至调节情绪。医生说：“我们在艾美身上运用的这种技术被用于治疗帕金森症已经10到15年了，可是最近才获得突破性进展。”随着科技的发展，越来越多的先锋者指出，在人脑中植入电极手术效果是可逆的，因为只需切断电源，脑内被植入的装置便会立即停止工作。

鸟类智商乌鸦排第一
http://www.sina.com.cn 2005年03月04日 14:25 环球时报
文/吴惟 　　鸟类脑容量较小，一直被人们认为智商很低，以至于中国很早就有“呆鸟”的说法。但加拿大科学家日前推出了世界上第一份鸟类智商排行榜，它们的智慧令人刮目相看。 　　根据取食创造性判断智商 　　这份“鸟类智商指数”是由加拿大麦吉尔大学的路易斯·拉菲波尔博士编排的。他 研究了20世纪30年代以来全球鸟类爱好者记载的有关鸟类在野生环境下取食行为的2000份报告，通过科学的方法对这些例子进行分类统计，然后根据它们取食行为的创造性来判断智商。这项研究日前在“美国科学进步协会”2005年年会上进行了交流。 　　拉菲波尔博士指出，不管是鸟类还是哺乳动物，它们大脑中负责取食方面的创新、使用工具等部分都是前脑，智商高的鸟类前脑也更大。“在动物进化过程中，尽管鸟类和哺乳动物的祖先在3亿年前就分道扬镳了，但大脑的这部分功能始终是相同的。” 　　乌鸦把铁丝制成钩子钩食物 　　按照这份排名，最聪明的鸟类前五名依次是乌鸦、猎隼、秃鹰、啄木鸟和苍鹭。乌鸦的智慧是众所周知的，大家都听说过那个乌鸦向瓶里扔石子、最终喝到水的故事。但你也许有所不知，乌鸦还会“制造工具”。一只名叫“贝蒂”的乌鸦就会用铁丝做成钩子，钩瓶子里的食物。苏格兰乌鸦则会用树叶做成某种形状的工具，捉树上的昆虫。 　　秃鹰外表冷酷，一看就工于心计，这一点在它们觅食过程中暴露得一览无遗。津巴布韦饱经战乱，至今还有许多雷区。一些秃鹰学会了“守株待兔”，专门守候在雷区附近，等瞪羚等食草动物触雷后美餐一顿。苍鹭则是“钓鱼高手”。它们会捉来一只昆虫，把它放在溪流表面将鱼引来，然后一口将鱼吃掉。如果一次不成功，它们也不浪费诱饵，会用它再试一次。 　　海鸥在英语中有“傻瓜”、“容易上当受骗者”的意思，其实它们也不笨。渔民们早就发现，它们会将贝类动物扔到岩石上将壳摔裂，吃里面的肉。一种生活在南极的贼鸥，甚至能混到海豹幼崽中偷吃母海豹的乳汁。 　　鹦鹉觅食没创意 　　鹦鹉虽然“会说人话”，大脑比例在鸟类中也是最大的，但它们在觅食方面的机灵劲儿并不足，一点儿创意也没有。澳大利亚鹦鹉稍微强一些，人们经常看到它们在火车站啄开装谷物的麻袋偷吃里面的粮食。 　　最笨的鸟有鸸鹋(澳大利亚一种体型大而不会飞的鸟)、鸵鸟、北美鹑等。▲

乌鸦、猎鹰最聪明，鹌鹑、鸵鸟最笨———

　　你喜爱的长尾小鹦鹉有多聪明？你家后院树上乌鸦的IQ是多少？请教一下加拿大蒙特利尔麦吉尔大学的路易斯·莱菲布维博士吧，他研究出了世界上惟一的测量鸟类IQ的方法。他研究出的鸟类IQ指数不仅仅把相关要素从测量大型鸟类的大脑重量中分离出来，也为解释鸟类的长途迁徙以及研究灵长类动物和鸟类大脑的平行进化提供了线索。

　　2月21日，一位由加拿大自然科学和工程研究所支持的生物学家在华盛顿举行的2005年美国联合科学进步会议上陈述了自己的最新发现，列举出了一个鸟类智慧的排行榜。他就是加拿大蒙特利尔麦吉尔大学的路易斯·莱菲布维博士。

　　他说：“我们尽可能地从鸟类学研究论文中汇集关于鸟类取食行为的各种例子，其中有些可能是现在人们从没有看到过的或是很稀少的。通过科学的方法对这些例子进行分类统计，列出鸟类的智商排名。”

　　鸟类智商排序的数据并不是取自饲养在笼中的某一只小鸟，而是来自2000份自然界中鸟类创新取食的观察报告和75年内在世界各国发表的鸟类研究论文中所搜集到的例子。

　　路易斯·莱菲布维博士在1997年首次发表了鸟类智商曲线体系。但是，他对自己研究出的体系并不满意。

　　“坦率地说，我最初并不认为这是一种有效的方法。”莱菲布维博士说：“具有严谨科学态度的研究者是不会专注于轶闻趣事性质的事例。所以，如果可以从一件这样的事例中得出什么结果，为什么不能在2000个例子中找出更加有效的模式呢？我一直在等待，希望有一天能够出现使这个体系失效的证据，但是还没有发现。”

　　新的鸟类IQ指数着力研究了世界范围内对于鸟类各种有创意的取食行为的观察。专业的和业余的鸟类观察者和猎鸟者都报告了他们不同寻常的发现，这些报告刊登在一些鸟类学期刊上，比如美国的《威尔森公报》，以及《英国鸟类》。这些观测是作为简讯发表的，莱菲布维博士的创新指数运用了1930年至今为期75年的相关简讯，以此为基础计算在野生环境中不同种群鸟类的各种有创意的取食行为。

　　山雀会开启牛奶瓶，贼鸥会偷食母海豹的乳汁，秃鹰会借助地雷“捕猎”……

　　莱菲布维博士说：“各种新颖的取食方法对于人类来说看似普通，但对于鸟类却都是非凡的创造发明，甚至一些鸟类的捕食创意令人惊讶万分。”

从报告中看来，这些鸟类确实具有相当聪明的取食技巧。其中最著名的是一份1949年关于英国山雀的报告，有人发现它们学会了开启被人们遗忘在门口平台上的牛奶瓶。还有褐色贼鸥，这是一种生活在南极的鸟类，能混入到海豹幼崽中偷食母海豹的乳汁。

　　莱菲布维博士最喜爱的是来自津巴布韦解放战争前线的一位鸟类观察者的报告。这名士兵观察到，秃鹰会守候在地雷区铁丝网附近，它们四处徘徊，等到瞪羚和其他草食动物在吃草时踩到地雷、炸得粉身碎骨，然后这些秃鹰就可以大吃一顿已经为它们“切割”好了的美味大餐。不过秃鹰也有玩火自焚的时候，偶尔也会自己触雷丧了性命。

　　莱菲布维博士说，鸟类IQ指数尽量避免一些与人类IQ指数相互触及的因素，比如文化偏向等。从统计学上说，指数吸收了大量来自不同的观察者对一些常见鸟类的报告，比如对乌鸦的观察。当然，其中也包括一些十分少见的特殊观察报告。他说，尽管如此，鸟类的创新能力仍然具有一个清晰的分层。乌鸦名列第一，猎鹰紧随其后位居次席，这两者排名靠前，处于智商分级的最高层；其后分别是老鹰、啄木鸟和苍鹭。而排在最后的是鹌鹑、鸵鸟等鸟类。

　　进入新世纪，创新取食指数方法获得更高发展。

　　2002年，创新取食指数方法论获得了科学上更高层次的发展，它被英国剑桥大学的研究人员西蒙·雷德博士和凯文·纳兰德博士延伸到了对于灵长类的测试。

　　莱菲布维博士说，这个结果证明了鸟类和灵长类的大脑创新能力相关的进化过程有一定的联系。“所以，这为我们提供了一个趋同进化的画面。尽管灵长类和鸟类的祖先在3亿年前就已经分化了，但是，相似的大脑认识组织的解决方案却在这两种动物中都得到了发展。”他说。

内中有许多相关报道，

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Robo-pups created with curiosity in mind

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Robotic racers gear up for desert challenge

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Self-cloning robots are a chip off the old block

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Technology - 11 May 2005

'Robotic' dental drill to be tested on humans

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Breaking News - 20 April 2005

Robotic camel riders are ready to race

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Breaking News - 11 April 2005

Shape-shifting tetrabots tumble into action

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Technology - 09 April 2005

Robot finds life in a Mars-like desert

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Personalised robot aircraft for US soldiers

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Technology - 26 March 2005

Japanese robot Expo will wow the crowds

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Breaking News - 24 March 2005

3D printer to churn out copies of itself

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Breaking News - 18 March 2005

Robotic rover detects life in the driest desert

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Breaking News - 16 March 2005

Robotic aircraft could map forest fires

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Technology - 12 March 2005

Arm wrestling robots beaten by a teenaged girl

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Breaking News - 08 March 2005

Twin Mars rovers in instrument mix-up

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Breaking News - 04 March 2005

New robots waddle with human efficiency

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Technology - 26 February 2005

Rambling robots show human efficiency

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Breaking News - 17 February 2005

Robots inspired by Segway balancing act

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Features - 12 February 2005

Spherical robot provides rolling security cover

The ball-shaped bot could automatically patrol the perimeter of a building using GPS navigation, radar sensing and wide angle cameras

Breaking News - 28 January 2005

Lunar colony to run on moon dust and robots

Simulated moon dust has been used to make a key component of a working solar cell, giving a boost to US plans for a future lunar base

Breaking News - 24 January 2005

Robotic baby rocker to relieve tired parents

The Robopax BabySitter's motorised platform will take a pram or baby seat and rock it at the perfect frequency to calm restive infants

Technology - 08 January 2005

http://www.bioon.com/biology/advance/neuroscience/200410/80793.html

资料图片：大脑究竟是如何来计算时间
点击此处查看全部科技图片

　　据《新科学家》杂志2月2日报道，一些人老觉得时间不够用，也有一些人感觉度日如年。对于同样长短的一段时间，为什么不同的人会感觉长短不已？我们的大脑究竟是如何来计算时间的？最新的科学试验揭开了时间感知的神秘面纱。

　　依靠三个“域”

　　迈克·哈尔一直在研究如何将时间伸长。他利用他的力量，已经将自己打造成一名优秀的壁球手。他说：“这很难具体描述，但它是一种静止的感觉，就象我不再受线性的时间束缚一样。球仍然在弹来弹去，但它依据不同的情况，在场地里以不同的速度运动。”

　　哈尔是英国爱丁堡的一名运动教练，他说的是一种被称为“域”的思想状态。他把自己目前具有的这种能力归功于对中国武术太极长达12年的研究，现在教授运动员如何做到“欲快先慢”成为他主要的收入来源。

　　对多数人来说，在工作和家庭中进入“域”的状态并不现实，但他的将时间伸长(至少可以更好地对时间进行控制)的想法却很吸引人。在这方面，我们真的可以做许多事情。而且科学家发现了越来越多的证据显示我们的大脑如何计算时间的流失，说明我们完全可以更加有意识地控制时间。

　　生物学家传统上将我们大脑的计时能力分成三个“域”。一端是生理节奏“域”，控制24小时周期内的睡眠和清醒等；另一端是毫秒计时“域”，负责计算精细的运动任务；中间部分——由秒到分的区域——被称作间隔计时“域”，这是我们可以感知时间流失的系统区域。

　　英国斯塔福德郡基勒大学的约翰·威尔登教授说，迄今为止，“间隔计时”仍是心理学上的“一潭死水”。生理节奏“域”和毫秒计时“域”的生物原理都已被搞得相当明白，但谁都找不出个“生物记停表”来让我们间隔计时，好发现时间的流失。结果，许多人就认为“时间感知”只不过是“总体感知”的一种副作用，拒绝将其当作独立科目进行研究。可如今既然人的大脑已经划分出专门负责计时的各个区域，那我们就忍不住要看看究竟是什么在给我们“滴答”计时。

　　研究“间隔计时”的生物原理开始往往采用一种所谓的“起搏累加器”模式。假设人的大脑有某种起搏器，它有规律地发出脉冲，并临时存储在累加存储器里。当我们需要估算一下过了多长时间，比如等了多长时间的公交车或一壶茶多长时间泡好，我们就去查一下“累加器”里的内容。

　　内置式记停表

　　用“起搏累加器”模式比较容易预测和解释人们做的判断一个音调或一次闪光持续时间之类的“行为实验”。随着人脑研究的不断进步，这种方法已被指责为过于简单，尤其是它既不能判明人脑起搏器的“身份”，也无法说明人脑的哪些区域参与“间隔计时”。

　　过去几年，神经学家开始利用脑电波测量和MRI成像技术来研究人脑的计时原理，也研究了那些时间感知因疾病或脑损伤而失真的病人。研究结果发现，间隔计时是一种非常复杂的称作“巧合检波”的模式。

　　去年，美国北卡莱罗纳州杜克大学的沃伦·梅克和凯特林·布胡西两人，将研究成果汇总发表了论文。他们认为“间隔计时系统”处在人脑的一个“条纹区”，属基神经节的一部分，但尚不能简单地认为“条纹区”就是人脑起搏器，他们认为它只是控制人脑其它区域包括额皮层的活动。这些区域的大脑神经元负责人的运动、注意、记忆等活动，它们产生的脑电波被“条纹区”检波识别，整合成时间流失量的估量值。

　　“巧合检波”模式仍有待于不断研究深化，但有一点正逐渐明了，那就是我们感知时间流失的方式是何等的灵活！这一点也许不应该感到奇怪，因为本来就可以通过药物或不同思维方式(抑郁、觉醒、沉思等)来改变时间感知，这是一个常识性的问题。每个人都懂得，当全神贯专注某项事情时，会觉得时间过得飞快，而百无聊赖时，觉得时间过得真慢。现在科学家开始懂得时间随人主观情感而改变的原因，有些科学家甚至认为，总有一天人们将随心所欲地控制自己的时间感知。

　　那么我们将如何改变时间感知呢？首选可能是控制脑化学反应，尤其是(控制脑神经的)多巴胺系统。该系统紊乱的病人，如帕金森氏综合症、亨廷顿氏综合症或精神分裂症患者，他们的时间感知也是紊乱的，因为他们的脑神经化学反应，尤其是多巴胺系统，某种程度上改变了体内主观时钟的速度。“精神分裂症患者大脑中的多巴胺活动太强，因而时钟速度快得让其觉得整个世界都疯狂，”沃伦·梅克说：“如果用药物将其大脑里的多巴胺感受器阻住，那就能把其时钟速度降回可以接受的正常水平。”

　　影响多巴胺系统的娱乐消遣类药物也能改变我们的时间感知。比如可卡因、咖啡因和尼古丁之类的刺激品可以使我们觉得时间过得快，大麻之类的镇静剂则使我们觉得时间过得慢。那么多巴胺系统会不会就是寻求改变我们时间感知的药物的出发点呢？也许是吧。梅克认为这里确有药物学道理，他说：“我认为可能研制出一种既可产生同样效果而且不会让人上瘾的妙药来。我确信如果有市场需求，就能做出来。”

　　我们等待这种“妙药”问世之前，还有控制我们体内时钟的更多的自然方法吗？至于说到用思维能力控制时间感知，那么我们关注时间流失就是最重要的因素之一。根据梅克的观点，虽然我们很少关注时间流失，但我们下意识地一直在检查自己的“间隔计时”系统，也时不时地接触到这一信息。正是这种时不时的注意力保持着我们的时间感知“喀嚓喀嚓”地持续下去。

　　当时光飞逝

　　一旦我们因为某些原因放松了对体内时钟的关注，那我们的时间感就会迷失。这正好应了那句古老格言：“时间逝于玩乐”，确切地说是“时间逝于关注时间流失之外的事情”。人们沉思时，时间走得慢，是因为没有集中注意体内时钟。当你从沉思中回过神来，感觉流失的时间比实际时间长。

　　虽然这似乎有点怪，但一些实验演示出了我们对时间流失关注与否确实能影响到时间感知。而且答案还取决于我们是“当时”还是“事后”在想时间流失的事。测试主观时间流失(远景计时)的标准方法是，让你在做某事之前意识到时间的重要。比如，有人对你讲：“我要弹一个音调，你告诉我它持续多长时间？”这就是一个典型的“间隔计时”实验，只是有些人为因素。毕竟你在现实世界中不会经常去有意识地给某个事物计时的。

　　一些科学家，包括英国的威尔登教授，已经开始进行另外两种主观时间流失实验。一个是“回想计时”实验，即在你事先不晓得某事情前，先对其作个事后评估，估计它能持续多长时间。比如，这本杂志你已经读了多长时间(虽尚未读完)？另一个实验被威尔登称作“时间流失评判”实验，即你做某事情过了一段时间后，估计一下与正常时间相比，这段时间似乎过得有多快。

　　大约一年前左右，威尔登做了这两种时间流失测试实验。在“世界末日实验”中，他把志愿者分成两组。一组看电影《世界末日》看了9分钟，另一组同时在等候室等待同样的时间。正如预料的那样，当让他们做“时间流失评判”时，看电影的一组说他们觉得时间过得比平时快，而等待的一组认为时间过得慢；而让两组做个“回想计时”，评判持续了多长时间，却得出了完全相反的结果：看电影的一组评判出的时间比等待的一组评判的时间长10%。奇怪的是，两组人估计的时间都少于实际的9分钟时间。

　　威尔登说，原因就是实验对象基于他们在实验中处理的信息量或发生事件数量的记忆，而给出他们的二次评判。“在等候室内，没什么更多的事情发生，也就觉得时间过得较慢，”他说：“而回想过去时，因为没什么内容(没有事情发生)，所以这段时间就显得短。看电影这段时间，在看时显得快，而在回想时，由于用作时间评判的事情多，就显得慢。它就是这么奇怪的东西。”

　　正是早期这些为数不多的类似实验，帮助人们揭开了时间感知的某些神秘面纱。比如为什么上了年纪的人感觉时间过得慢，而年轻人觉得时间飞逝而过。很可能就是因为老年人较少事做，更多的时间花在了关注时间流失上；而当他们回首往事的时候，由于大脑没有处理过更多的信息，也就评判出往事过得真快。

　　威尔登指出，这些实验并没有在老年人身上做过，也许他们的时间感知失真更有别的解释。比如人们已经知道，记忆力和智商会随年龄增长而衰退，这或许影响时间感知。

　　“某种意义上讲，我们在做一种真空中的理论研究，”他说，“我们认为我们也知道有些什么问题，但就是没有一种研究来解释老年人所抱怨的东西。”而且，对任何想调整自己生活节奏的人来说，威尔登“世界末日实验”的结果将人置于一种进退两难的境地：你可以伸长你的时间感知，但你只能作好在一个相当于等候室的地方打发时光的准备。也许最好的选择是，你尽管放心接受现代生活的快节奏，但至少也要努力认真地度过一些无所事事的时光。

　　这听起来就象一般常识。但美国加州州立弗勒斯诺大学的社会心理学家罗伯特·列文认为，一般常识才值得我们好好记住。“时间可是我们最宝贵的财富，”他说，“在生物医学家们让我们长生不老之前，我们能接触到的最近的东西就是将时间伸长。”因此，花上10年的时间学会入“域”，虽说可能是一笔很大的投资，但我们要勇于面对它，我们许多人连这点时间都还没有呢。(杨孝文）

http://tech.sina.com.cn/d/2006-02-07/1043834698.shtml

原文

http://www.newscientist.com/channel/being-human/mg18925371.700

Teach your brain to stretch time

• 04 February 2006
• Caroline Williams
• Magazine issue 2537

MIKE HALL has taught himself to stretch time. He uses his powers to make him a better squash player. "It's hard to describe, but it's a feeling of stillness, like I'm not trapped in sequential time any more," he says. "The ball still darts around, but it moves around the court at different speeds depending on the circumstances. It's like I've stepped out of linear time."

Hall, a sports coach from Edinburgh, UK, is talking about a state of mind known as "the zone". He puts his abilities down to 12 years of studying the martial art t'ai chi, and now makes a living teaching other sportspeople how to "go faster by going slower".

For most people, getting into "the zone" at work or home isn't a realistic option. But the idea of stretching time - or at least having more control over its frantic pace - is an attractive

日本正研制遥控机器人蟑螂（附图）

    日本政府政府正投巨资进行遥控蟑螂的研究。研究人员给蟑螂装上电子背包，再将电子背包与蟑螂的大脑连接起来。当进行适当的刺激时，蟑螂就能根据需要向左转或者向右转，向前移动或者向后退。

    日本政府将向东京大学的这项研究计划投资340万英镑。

    科学家们希望能研制出可携带照相机的机器人蟑螂，用于灾难发生后的搜索与救援工作。

    每个微处理器背包都与植入昆虫头部的脉冲发射电极相连结。电子背包收到信号之后，刺激电极使蟑螂移动。

    目前，这个系统还不完善，所以植入脉冲发射电极的蟑螂仍然按自己的意志行动，甚至对脉冲信号毫无反应。

    助理教授Isao Shimoyama说：“昆虫能够做许多人类不能做的事情。这项工作的潜在应用对人类有巨大的意义。”(东缘)

http://japan.people.com.cn/2001/09/21/riben20010921_11760.html

• 21 May 2005
• Celeste Biever
• Magazine issue 2500

James McLurkin has a novel party trick - he can coax 20 small autonomous wheeled robots to form herds, disperse again, wheel in neat circles, sing a harmonic rendition of the theme from Star Wars, and automatically recharge from a power station.

McLurkin, a postgraduate student at the Massachusetts Institute of Technology, is trying to design robots that will work together and make collective decisions. If he succeeds, swarms of robots could one day be put to work in the home, in space and by the military. "A swarm or a team can collaborate to overcome what a single robot might not be able to do," explains Paolo Gaudiano, who works on swarms at Icosystem in Cambridge, Massachusetts.

Soon teams of up to 40 robots could be employed as border security guards and outside airports. Frontline Robotics in Ottawa, Canada, has installed collaborative software on its vacuum cleaner-sized PC-bots and ...

http://www.avianbrain.org/

Profile: Erich Jarvis

The work of neuroscientist Erich Jarvis demonstrates the power of open-mindedness in the lab.

http://www.pbs.org/wgbh/nova/sciencenow/3214/03.html

Profile: Erich Jarvis Links The Jarvis Lab www.jarvislab.net The Web site for Erich Jarvis' lab at Duke University Medical Center offers detailed information on their bird brain studies and full text of every major research paper its members have published. Avian Brain Nomenclature Exchange avianbrain.org Explore the nomenclature of the avian brain, which has recently undergone a complete makeover spearheaded by Erich Jarvis. The Life of Birds www.pbs.org/lifeofbirds Follow Sir David Attenborough as he explores bird intelligence, behavior, evolution, and more on this PBS Web site. Bird Brains www.theconnection.org/shows/2003/01/20030114_b_main.asp Hear a 2003 interview with neurobiologist and bird specialist Erich Jarvis on NPR's The Connection. Modern Bird Anatomy hoopermuseum.earthsci.carleton.ca/birds/modern.htm Learn about our feathered friends from the inside out on this bird anatomy Web site. Bird Mag Dot Com www.birdmag.com Read about the art and science of bird care through this online magazine. Features include interviews, articles, Webcams, and more. Inside the Animal Mind www.pbs.org/wnet/nature/animalmind This Web site from the PBS series "Nature" takes a look inside the animal mind. Explore the science of animal emotion, social consciousness, and more. Books Bird Brains: The Intelligence of Crows, Ravens, Magpies, and Jays by Candace Savage. Sierra Club Books, 1997. The Parrot's Lament: And Other True Tales of Animal Intrigue, Intelligence, and Ingenuity by Eugene Linden. Plume Books, 2001. Inside the Animal Mind: A Groundbreaking Exploration of Animal Intelligence by George Page. Broadway Books, 2001. Recent Article "Minds of Their Own: Birds Gain Respect" by Sandra Blakeslee. The New York Times, February 1, 2005 http://www.jarvislab.net/index.html

keywords :  brain, Neurobiology date :  1/31/2005 media contact :  Dennis Meredith , (919) 681-8054 or (919) 417-6581 dennis.meredith@duke.edu Note: An accompanying video interview with Erich Jarvis can be viewed here. DURHAM, N.C. -- An international consortium of 29 neuroscientists has proposed a drastic renaming of the structures of the bird brain to correctly portray birds as more comparable to mammals in their cognitive ability. The scientists assert that the century-old traditional nomenclature is outdated and does not reflect new molecular, genetic and behavioral studies that reveal the brainpower of birds. For example, they identified behavioral studies demonstrating that pigeons can discriminate cubist from impressionistic styles of painting; that crows can make useful tools and pass on their skills to other birds, and that parrots can not only learn human words but use them to communicate with humans. The researchers emphasize that the old view of evolution as progressive and linear is outdated, pointing out that so-called "primitive" animals such as birds evolved some 50 to 100 million years after mammals. The Avian Brain Nomenclature Consortium published a report on the rationale for the proposed revised nomenclature in the February 2005 issue of Nature Reviews Neuroscience. A technical report detailing the revisions was published in the May 2004 issue of the Journal of Comparative Neurology. The consortium's efforts were supported by the National Institutes of Health and the National Science Foundation, including the NSF's Waterman Award for young researchers to the Nature Reviews Neuroscience paper's first author, Duke University Medical Center neurobiologist Erich Jarvis. "We believe that names have a powerful influence on the experiments we do and the way in which we think," wrote the consortium members in their paper. "For this reason, and in the light of new evidence about the function and evolution of the vertebrate brain, the international consortium of neuroscientists has reconsidered the traditional 100-year-old terminology that is used to describe the avian cerebrum. "Our current understanding of the avian brain -- in particular the neocortex-like cognitive functions of the avian pallium -- requires a new terminology that better reflects these functions and the homologies between avian and mammalian brains." The consortium members asserted that the old terminology -- which implied that the avian brain was more primitive than the mammalian brain -- has hindered scientific understanding. They concluded that "The inaccurate evolution-based terminology for the vertebrate brain that was used throughout the twentieth century became a severe impediment to the communication of scientific discoveries and the generation of new insights." The consortium's revision of the nomenclature for avian brains is aimed at replacing the century-old system developed in the 19th century by Ludwig Edinger, considered the father of comparative neuroanatomy. Edinger's system was based on the then-common practice of combining Darwin's recent theory of evolution and Aristotle's old concept that there exists a natural "scale" of creatures from lowest to highest. The result were the views that evolution was progressive from organisms with "lower" intelligence to those with "higher" intelligence and that evolution had a purpose -- the generation of humans. The resulting nomenclature used prefixes such as palaeo- ("oldest") and archi- ("archaic") to designate structures in the avian brain and neo- ("new") to designate supposedly new structures, particularly in the mammalian brain. "According to this theory, the avian cerebrum is almost entirely composed of basal ganglia, the basal ganglia is involved only in instinctive behavior, and the malleable behavior that is thought to typify mammals exclusively requires the so-called neocortex," wrote the researchers. However, said Jarvis, "We have to get rid of the idea that mammals -- and humans in particular -- are the pinnacle of evolution. We have to stop using words like 'lower vertebrates' and 'higher vertebrates.' We also have to understand that evolution is not linear, but an intricate branching process. So, we can't automatically expect to track a structure in the human brain back to other current vertebrate species." According to Jarvis, new research "debunks the theory that the brain evolved in stages, like the laying down of geological sediments layer by layer. There is no evidence to show that there was a primordial brain structure to which so-called higher brain structures were systematically added." In the Nature Reviews Neuroscience paper, the authors described studies by other researchers and their own studies demonstrating that the so-called "primitive" regions of avian brains were actually sophisticated processing regions homologous to those in mammals. Those studies, which included tracing of neural pathways and behavioral studies, showed that such avian brain regions carried out sensory processing, motor control and sensorimotor learning just as did the mammalian neocortex. Also, wrote the scientists, molecular studies have shown that the avian and mammalian brain regions are comparable in their genetic and biochemical machinery. The neocortex and related areas in the mammalian brain are derived from a region in the embryonic cerebrum called the pallium, which means mantle or covering. Edinger thought, however, that most of this region in the bird cerebrum was part of the basal ganglia. Accordingly, he gave them names that ended in the basal ganglia term "-striatum", a practice he also employed in naming the parts of the mammalian basal ganglia. As a result of the recent studies, the consortium has recommended such changes as renaming the avian brain region called the "archistriatum" as the "arcopallium," (arched pallium); and renaming the region that includes part of the true basal ganglia in birds, the "palaeostriatum primitivum" and the "ventral palaeostriatum" which sits below the pallium as the "pallidum" (pallidal or pale domain). The consortium's work began in 1997 and was organized by Jarvis, Anton Reiner of the University of Tennessee Health Science Center in Memphis, Martin Wild of the University of Auckland in New Zealand, and other neurobiologists, dubbing themselves the ThinkTank. Jarvis recalled that "there were people in the field of avian neurobiology who knew the real structures behind these names and knew the names were wrong. And as a member of the younger generation of neurobiologists, I just felt that it was against my conscience to continue to use terminology that I knew was wrong and would mislead scientists." For example, said Jarvis, researchers not familiar with the growing body of scientific literature demonstrating the sophistication of the avian brain could not understand how birds could exhibit sophisticated cognitive abilities with brains that held only what the nomenclature designated as the equivalent of the human basal ganglia. The result of the scientists' objections led to a seven-year effort, which steadily recruited new participants. This effort culminated in an intensive international scientific forum at Duke in 2002, in which the new nomenclature was developed. "We knew that we were doing something that may have an impact, not only on the immediate conduct of research in neuroscience, but on neuroscience for the next hundred years," said Jarvis. "And, this nomenclature will help people understand that evolution has created more than one way to generate complex behavior -- the mammal way and the bird way. And they're comparable to one another. In fact, some birds have evolved cognitive abilities that are far more complex than in many mammals." Besides Jarvis, other co-authors of the paper were -- Onur G黱t黵k黱, Ruhr-Universit鋞 Bochum, Germany, -- Laura Bruce, Creighton University School of Medicine, -- Andr醩 Csillag, Semmelweis University, Hungary, -- Harvey Karten, University of California San Diego, -- Wayne Kuenzel, University of Arkansas -- Loreta Medina, University of Murcia, Spain, -- George Paxinos, Prince of Wales Medical Research Institute, Australia, -- David J. Perkel, University of Washington, -- Toru Shimizu, University of South Florida, -- Georg Striedter, University of California at Irvine, -- Martin Wild, University of Auckland, New Zealand, -- Gregory F. Ball, Johns Hopkins University, -- Jennifer Dugas-Ford, University of Chicago, -- Sarah Durand, CUNY-LaGuardia, -- Gerald Hough, Rowan University, -- Scott Husband, University of South Florida, -- Lubica Kubikova, Duke University Medical Center, -- Diane Lee, California State University Long Beach, -- Claudio Mello, Oregon Health & Science University, -- Alice Powers, St. John's University, -- Connie Siang, Duke University Medical Center, -- Tom Smulders, University of Newcastle, United Kingdom, -- Kazuhiro Wada, Duke University Medical Center, -- Stephanie White, University of California, Los Angeles, -- Keiko Yamamoto, University of Tennessee Health Science Center, -- Jing Yu, Duke University Medical Center, -- Anton Reiner, University of Tennessee Health Science Center, -- Ann Butler, George Mason University. contact sources : Erich Jarvis Ph.D. , (919) 681-1680 jarvis@neuro.duke.edu

http://news.mc.duke.edu/news/article.php?id=8401

Bird Talk: Probing the Avian Instrument
Tweet Mystery of Life
(Originally published in the July-August 1994 issue)

To fathom the intricacies of bird song, a Duke zoologist has concocted experiments using paraphernalia that range from the most sophisticated--soundproof chambers, videotape recorders, audiotape players, and computers--to the humblest--lollipop sticks, rubber bands, and helium.

Steve Nowicki is about to become a father. Before dawn on a spring morning, he and his graduate students rise from warm beds in the rustic cabin, shivering and sipping hot coffee in the chilly darkness. They don layers of clothing against the cold and sleepily pull on stiff hip waders, emerging from the cabin as the sun rises dimly over a still lake. They drive bouncing and swerving along rutted back roads in the damp Pennsylvania woods until they reach the swamp. Swatting at breakfast-bent mosquitoes, bracing themselves against the cold water, they wade into the slough, binoculars at the ready, ears cocked for the faintest sound.

With luck, over the next hours they will spot an evanescent flash of brown feathers or hear a tell-tale twitter that marks their quarry--a swamp sparrow carrying food. If they are luckier still, they will pinpoint a sparrow's nest of newly hatched baby birds. Gently, the will ease both nest and birds into a cloth bag and return to their cabin. There, Nowicki and his students will become the birds' fathers and mothers, every half-hour faithfully feeding the baby birds "meat glop"--a health-giving mix of ground sirloin, tofu, baby carrots, vitamins, and minerals.

Thus does Nowicki, a Duke associate professor of zoology, obtain the fascinating animals whose complex trills, warbles, and chirps, and exquisitely pure tones he seeks to decipher. He and his colleagues concoct experiments using paraphernalia that range from the most sophisticated--soundproof chambers, videotape recorders, audiotape players, and computers--to the humblest--lollipop sticks, rubber bands, and helium. From those experiments have come intriguing insights into the talents of nature's most accomplished musicians.

What's more, Nowicki's own fine-tuned teaching abilities have brought students flocking to his rigorous undergraduate courses on neuroscience and animal communication. An ex-trombonist and adept juggler, he has developed his own brand of scientific street theater to jump-start students' minds and drive home intellectual points.

To illustrate a discussion of auditory processing, he's had a rock band perform. To make points about evolution and the mind, he's picked arguments with actors dressed as Darwin, Freud, and a giant frog who invaded his class. Rumor has it that, to illustrate the fallibility of perception, he even showed up for class once dressed in drag. (He points out proudly that his wife, Susan Peters, also a professional biologist, expertly sewed the frog's head. He is mum about where he got his dress.) Because of his determination to communicate, his clear compelling lectures, his imaginative exams, and his commitment to his students, he was awarded the 1992-1993 Robert B. Cox Trinity College Distinguished Teaching Award.

His baby birds even like him. After all, they luxuriate in a sort of sparrow spa in his laboratory. They get free food, a comfortable soundproof chamber, and free singing lessons via tape recordings. In fact, other scientists' studies of the solitary birds find that they experience lower stress and longer lives in captivity than in the wild. (Incidentally, nor are the swamp-sparrow parents particularly upset at the lost of the nest and its nestlings. They typically immediately rebuild the next and lay more eggs.)

To Nowicki, songbirds represent stunningly complex examples of animal communication. A typical bird song, lasting about two seconds, is a rapid-fire aria of fifty or more "notes," each as short as ten thousandths of a second. The bird can rattle off these notes up to five times faster than a human can speak syllables. Besides their songs, birds may have a repertoire of five to twenty calls--a collection of bird war whoops, alarm calls, love songs, and lullabies to their offspring. Even weirder is that birds are fully capable of singing duets with themselves. Their vocal organ, the syrinx, has two vibrating membranes that can somehow produce and modulate two independent tones at the same time.

Such extraordinary abilities can teach humans about their own speech abilities, says Nowicki, which is one reason his studies are funded by the National Institutes of Health. What's more, he says, bird song can yield powerful insight into the intricate mysteries of animal communication--the process by which an animal encodes and transmits a "thought" across space to another animal, where it is decoded and transformed back into thought.

If bird song seems remarkable on its surface, Nowicki's deeper studies of this tweet mystery of life have revealed it to be even more amazing. Until Nowicki's work, most scientists believed that the syrinx, located just beneath the bird's breastbone, was the only part of the bird's "instrument" that figured in its song-production. To Nowicki, such a theory was like arguing that the only important part of a clarinet was the reed. He believed that birds use their beak and throat to change the resonances of their song, much like humans singers use their mouths and throats to control their song harmonics. To test his theory, Nowicki had his captive songbirds sing solos in a harmless helium atmosphere. If only the syrinx mattered, helium wouldn't affect how it vibrated because the birds' song would remain unchanged. But if the windpipe were important, the song would resonate differently, just humans chattering away with a lungful of helium sound like Alvin the Chipmunk.

In fact, the helium did change the birds' song, causing new harmonic overtones to appear. The discover marked a critical new understanding that a bird's instrument consists of practically its whole breathing apparatus. "That discovery had very important implications for how a bird's songis wired up neurobiologically, how the bird learns his song," says Nowicki. "It adds a level of complexity to the problem of motor control that was previously unexplored." But like all good science, the experiment raised even more questions. "Having, I think, demonstrated that something is going on, we are now left in the position of trying to figure out what exactly it is," he says.

So, Nowicki and his students began high-speed videotaping of their birds singing, attempting to understand how a bird alters its head, throat, and beak to create its song. They also began experiments in which they fit the birds with "braces" to understand the beak's role. For brief periods at a time, they insert a small lollipop stick in a bird's bill and hold it in place with a rubber band. Their object is to fix the bird's beak open at a certain angle. "We think the bird does use its beak to change the effective length of its vocal tract, thereby changing its natural frequencies," says Nowicki. The birds remain unflappable during the procedure, quickly commencing to sing with their braces on.

So far, the researchers have found that the beak is, indeed, a critical part of the bird's song. The next step will be the daunting technical challenge of understanding specifically how the bird changes the shape of its vocal tract to control its sound.

While such studies probe the nature of the avian instrument, Nowicki is also trying to understand the new meaning behind bird song. "Ever since Darwin, bird song has been cited as an example of an exaggerated male trait, like the peacock's tail," he says. "Darwin proposed a distinct form of selection to account for these exaggerated traits--sexual selection as opposed to a natural selection." This sexual selection has to do with traits that evolve either to better attract mates or otherwise increase reproductive success. Whether such traits be peacock tails or ram horns, bigger is better up to a point, says Nowicki. "Now, the interesting question is what is 'bigger' in bird song?" he asks. Perhaps, scientists believe, the most successful male birds sweep females off their skinny bird feet by singing more kinds of songs--a correlation that they have found in some bird species but not others.

But the most fascinating discovery by Nowicki and graduate student Jeffrey Podos is that songbirds basically "wing it" when they sing: "If you listen to a song sparrow, you'll hear an individual sing about eight or twelve basic song types. But if you record those song types and analyze them closely, you'll realize that almost every time a bird sings a song, even of the same type, it does something slightly different." Thus, like human cabaret singers belting out old hits, birds add a little variety each time they sing even a standard song. This variety is very likely an important spice of bird life, says Nowicki. He and his students have discovered that their isolated baby birds learn to sing a multitude of song variations, to see how accomplished the birds can become.

But to really explore the meaning of this song complexity, Nowicki decided to "ask" the birds in the field what they hear when songs vary. "You know, we can measure in the lab until the cowbirds come home, but ultimately the question has to be validated perceptually by the birds," says Nowicki. So this summer, Nowicki and University of Miami zoologist William Searcy mounted an expedition to Pennsylvania whose aim was, basically, to mildly annoy male birds. His equipment: wooden marking poles, binoculars, a tape recorder, a loudspeaker, and an infinite amount of patience. His technique involves first pinpointing a male songbird's territory and installing a loudspeaker at its center. Then the scientists play a variety of songs, measuring how the male bird reacts.

At the first chirp of a recorded song, the bird will aggressively fly close to the loudspeaker. But it soon grows used to the song, wandering away until it renews its threat when the zoologists begin to play a subtly different version. By measuring the bird's response to different variations, the scientists can begin to understand what the bird perceives.

Nowicki and Searcy's summer expedition was also meant to make bird love, not just war. The scientists planned to capture adult female birds, give them a bit of hormone to put them in a loving mood, and play them recorded songs of avian amour. If the male songs are alluring, the female will fluff her feathers and adopt a ready-for-love "precopulatory" posture. "So, with these experiments, we'll get to see if this kind of variation has functional significance for courtship," says Nowicki.

Nowicki brings the same studious observation to his teaching as he does to his field work. He does something slightly embarrassing but highly useful the first week of his popular undergraduate course in neuroscience: Before class begins, he stands in front of the room with pictures of all 100 or so students spread in front of him. As student enter, he points to them, reciting their names, with the goal of eventually learning all of them. "I do it partly because it just gives me an excuse to stare at the students a bit," says Nowicki. "I try to get a sense pretty quickly who are the students who have a lot of scientific background, who are the students who are just intrinsically sharp, who are the students who are going to need more help. I also try to look into their eyes when I lecture."

As his class has grown from forty to seventy to 115, such individual attention has become more difficult, but he has persevered. His course's popularity has skyrocketed not because of its easiness. It's a rigorous semester that spans the breadth of neurobiology from molecule to neuron, and from jellyfish to the human consciousness.

Says zoology major David Finley, "It's an extremely interesting class, but it's not easy at all. The information he gives you, he doesn't expect you to just spit back on an exam. For example, on tests, he invents new organisms and asks you to use what you know to tell him what experiments you could do to answer questions about them." But it's not just the message that attracts students, says Finley, it's the medium of Nowicki's delivery. "He created a class dynamic that was just amazing," says Finley. "He commanded your complete attention; nobody flipped through the Chronicle or did crossword puzzles. It was how vivacious and dynamic he was in the classroom that caused you to be interested in what he said."

Nowicki's ambitious aim in the course is to help students begin to grasp the vast intellectual realm that attempts to explain how mind arises from body. "We want to do no less than help students try to understand the fullness of our own mental experiences, from their feelings about their mother to their feelings about Beethoven's Fifth Symphony. Of course, science is nowhere near that goal, but we don't want to forget it's the ultimate goal of neuroscience."

Nowicki's course also emphasizes how mind evolved from primitive forms. "We take the perspective that the higher-level processes that we're doing in class--teaching and learning--are in some way related to a toad catching a worm."

Certainly, Nowicki's street theater assures that nobody falls asleep. "I want to break down their intellectual complacency," he says of his thespian efforts. "I want them on the edge of their chairs, wonder what will happen next." Along with that suspense comes learning, he says. "The point isn't just to have a good time; the point is to also try to hammer home the complexity of some of these issues." Only once, he says, has a bit of theater fallen really flat. For a discussion of body structure, he had a fake Hollywood "wound" installed on his arm under fake skin, so he could dramatically rip off the skin to reveal the tendons and arteries beneath. The moment came, he ripped off the skin, the sight was satisfyingly yucky, and--laughter. "I was expecting fainting or worse," says a disappointed Nowicki. Perhaps, he theorizes, the MTV generation is too used to gore in its entertainment.

Besides his large neurobiology class, Nowicki teaches a smaller seminar on animal communication. He and University of North Carolina zoologist Haven Wiley have informally joined their Duke and UNC classes to explore scientific literature on animal communication. The students read assigned scientific papers and then report on and discuss them in class. A typical class might range through reports on communications in frogs, toads, damsel flies, warblers, and crickets.

Nowicki finds that the smaller class leads to more personal involvement by the student. "When this class works at its best, the students get very motivated and they get very excited. In fact, the students are just hard to shut up," he says with obvious pleasure. "The students get very passionate about whether animals can think or not. And they get passionate about our concern for animals and animal rights; because if animals can think, then what are our responsibilities and obligations to them? And they get passionate about human evolution and the origin of our own sense of self-awareness."

Nowicki sees the zoology department as a gateway for students into science, as well as into intellectual inquiry in general. "I really think that this department is an asset to Duke, partly because we happen to have many faculty who think broadly, even artistically, in a way that could become very highly integrated into a liberal education." Department chair Fred Nijhout, who studies butterfly wing patterns, is also an artist; professor Stephen Wainwright, who studies animal structure, is also a sculptor.

"This department also really values teaching, which makes me feel good about working on trying to be a good teacher," says Nowicki. "And what also excites me is just the wonder of knowing about things, and ours is a department where that kind of wonder is valued."

--by Dennis Meredith

Methodological Issues in Event-Related Brain Potential and Magnetic Field Studies

Walton T. Roth, Judith M. Ford, Adolf Pfefferbaum, and Thomas R. Elbert

Psychiatry in its search for the roots of abnormal thoughts, feelings, and behavior has again turned its attention to the human brain and is trying to apply the methods of the many scientific disciplines that have cast light on normal brain functioning-disciplines such as neuroanatomy and histology, biochemistry and molecular biology, and electrophysiology. This chapter concentrates on ways of maximizing what can be learned from noninvasive electrophysiology, a technique that is singular in its ability to record millisecond-by-millisecond changes in the brain following repeated external or internal events. Although the triggering events are often simple sensory stimuli, the cognitive processes that follow them and leave their trace in fluctuating voltage or magnetic fields can be quite complex. In the last decade competing noninvasive techniques such as positron emission tomography (PET) have challenged the preeminence of electrophysiology, particularly in spatial localization of brain processes. This challenge has stimulated a number of technological and methodological developments in acquiring, analyzing, and presenting brain electrical and magnetic data. But before we review these developments, we remind you of some basic principles and give examples of their relevance to psychiatry (see also A Critical Analysis of Neurochemical Methods for Monitoring Transmitter Dynamics in the Brain, Electrophysiology, and Pharmacology and Physiology of Central Noradrenergic Systems for related discussion).

Nerve cells generate extracellular current flow by fluctuations in the slower changing membrane potentials of dendrites and cell bodies. Postsynaptic potentials cause an outflow of negative (excitatory) or positive (inhibitory) ionic charges into extracellular fluid, which are then pumped back into the cell. This current flow, when summated, results in volume-conducted potentials recorded at the scalp as the electroencephalogram (EEG). Event-related potentials (ERPs) are EEG changes that are time-locked to sensory, motor, or cognitive events. They have provided a way to evaluate brain functioning in mental disorders and the effects of psychoactive drugs. Recent conceptual and technical developments have greatly expanded our capability to understand and document the mechanisms underlying surface recordings. Particular attention has been paid to identifying the location, orientation, and distribution of current dipoles (pairs of opposite charges) that may be the sources of scalp-recorded electrical activity.

Nerve cells also generate intracellular current flow from dendrites to cell body. This flow results in a magnetic field that can be detected at the scalp as a magnetoencephalogram (MEG), even though it is a billionfold less intense than the earth's magnetic field. Event-related magnetic fields (ERFs) can be elicited and time-locked to specific events and are analogous to ERPs. Magnetoencephalograms and ERFs convey different information than EEG and ERPs. This is because voltage fields on the surface of a sphere, which the skull enclosing the brain approximates, are produced equally well by dipoles oriented radially and tangentially with respect to a radius of the sphere. In contrast, 90% of the magnetic field at the skull can be ascribed to tangential dipoles alone. This is a consequence of the geometrical orientation of masses of nerve cells and of magnetic sensors. Fig. 1 illustrates how dipole orientation can be either correlated or random for different gyri and sulci. Parallel dipoles lying tangentially on sulcal walls contribute much more to the MEG than random dipoles or dipoles lying radially along the crowns of gyri.

Why are the methodological issues that this chapter addresses relevant to psychiatrists and psychologists? First, ERPs and ERFs are theoretically relevant because they provide ways of testing theories of abnormal brain functioning that no other methods can offer. For example, unlike ordinary behavioral tests of cognitive processing, ERPs give an index of the processing of task-irrelevant events, distracting stimuli, or events subjects have been told to ignore. The topographic distribution of ERPs and ERFs gives clues as to what parts of the brain are active during a particular cognitive activity. Second, ERPs and to a less extent ERFs have been demonstrated empirically to be relevant. ERP abnormalities have been repeatedly observed in psychiatric disorders, notably in the P300 and P50 components. The P or N signifies positive or negative and the number is the mean peak latency in milliseconds. Thus, the P300 component is a positive potential that occurs approximately 300 msec after a stimulus that is infrequent and in some way relevant. The most venerable and consistent psychiatric ERP finding is that of reduced P300 amplitude in schizophrenics (60), although this is not specific to schizophrenia (see refs. 59 and 22 for reviews). For instance, a longitudinal study demonstrated that lower P300 amplitude at age 15 was predictive of poorer global personality functioning at age 25 (66). Latency at P300 is generally greater in patients with dementia than in normals or in patients with schizophrenia or depression (28, 54). Recently, psychiatric attention has been directed to P50, an ERP component to auditory stimuli whose amplitude is suppressed if the eliciting stimulus is paired with another that precedes it by one-half second. Schizophrenics show less P50 suppression than controls (25) as indicated by smaller amplitude ratios (P50 to the second stimulus of a pair divided by P50 to the first), although again this finding is not limited to schizophrenia (4).

Abnormalities of ERPs in psychiatric patients can be interpreted in light of a considerable amount of knowledge that has accumulated about the significance of certain ERP components in normal human information processing. For example, P300 is known to reflect the categorization of events, depending jointly on stimulus probability, stimulus significance, and the information value of the event (36). Probably, P300 has multiple, partially asynchronous generators (58). Components occurring 60 to 100 msec after onset of auditory stimuli, including N100, have been shown to reflect selective attention to auditory stimulus channels (42). In contrast, auditory ERPs with latencies less than 10 msec are insensitive to attention effects but give a unique assessment of the intactness of brainstem circuitry (32).

The literature on ERFs in normal subjects is quite extensive although magnetic recording techniques have been available only a relatively short time. Much of that literature has documented the existence of ERF components that parallel those established by invasive and noninvasive ERP recording. However, to date, most clinical MEG studies have been done in neurological rather than psychiatric patients, although that is likely to change in the near future. Reite et al. (57) recorded ERFs in six medicated, paranoid schizophrenic patients and six normal controls. The M100 component (analogous to the N100 of the ERP) showed less interhemispheric asymmetry in schizophrenics and had different source orientations in the left hemisphere. Tiihonen et al. (68) compared the M100 component in two schizophrenic patients when they were experiencing auditory hallucinations and when they were not. During hallucinations, M100 peaked approximately 20 msec later, an effect similar to that of external masking noise in normals.

We now turn to methodological trends that are transforming ERP and ERF research. Specific topics include data acquisition, signal averaging, ocular artifact, choice of reference electrodes, digital filtering, measuring components including dipole modeling, and statistical and diagnostic considerations.

Older electroencephalographic tube-based amplifiers have been completely replaced with high impedance solid-state amplifiers with electronically controlled amplification and filter settings. In many laboratories, pen-chart recorders have been replaced with electronic data storage and display systems, but paper records are still widely used for visual analysis of diagnostic EEGs and sleep. Laboratory computers are constantly evolving toward faster, cheaper, and more powerful models. New storage media based on tape or magnetic or optical disks permit archiving of data from many subjects in an easily retrievable form. As welcome as these advances have been, they have generated difficult new choices for researchers. Should they buy commercial EEG and ERP hardware and software systems or develop their own? Which commercial systems or routes to laboratory-program development are satisfactory? Commercial systems tend to be limited in flexibility, details of data analysis may be a trade secret (which is unacceptable scientifically), and access to raw data for special analyses may be difficult. Laboratory-developed systems require deciding among manifold hardware and software possibilities, and then allocating many hours to programming. As will be learned from this chapter, methodologically up-to-date ERP analysis requires much more than eye-movement artifact rejection and signal averaging.

Whereas the conventional 10–20 system of Jasper (35) used 19 electrodes with a typical distance of 6 cm between them, some investigators have greatly expanded the electrode arrays in order to record more of the spatial detail present in the EEG. Thus arrays of 124, or even 256 electrodes, which yield interelectrode distances of 2.25 and 1.6 cm, are now being advocated (27) and have been shown to enhance localization. The application of multiple electrodes is a lengthy, labor-intensive process, which requires care in scalp preparation and accuracy in electrode placement. For localization studies relating EEG or MEG data to brain structures visualized by magnetic resonance imaging (MRI), it is important that electrodes be aligned correctly according to skull landmarks, and fiducial markers visible in MRI scans are used. (Vitamin E capsules are easily available and the right size.)

Electrode application entails a potential health risk to both subject and technician if the intactness of the scalp is compromised by procedures to reduce electrical resistance between electrode and scalp or by skin lesions. Acquired immunodeficiency syndrome and hepatitis B can both be transmitted by this route, so it is absolutely essential that proper precautions be taken. Putnam et al. (56) give recommendations for disinfecting reusable electrodes and for protecting the technician.

The recording of the MEG has been made practical by the development of superconducting quantum interference devices (SQUIDs) that are sensitive to minute magnetic fields. The MEG technology is much more expensive than the EEG technology. Not only are the SQUIDs themselves expensive, but they require provision for liquid helium at 4.2°K to cool them, and a recording room shielded with a high-permeability material against magnetic fields and with aluminum against eddy currents. The liquid helium is kept in a vacuum-insulated container called a dewar. Locating magnetic sources requires recording from multiple sites, preferably simultaneously. Otherwise, separate stimulation runs must be made, moving sensors from one location to another between runs. More runs take more recording time and increase the likelihood that the subject's mental state will change, altering the sources. A MEG system with over 30 channels costs approximately $3,000,000, 100 times more than the same number of EEG channels. Because MEG prices reflect the cost of research and development more than construction of the apparatus, the price per unit would drop if more units were sold. In one system, 37 sensors are placed 2.2 cm apart to cover a single hemisphere (12).

An advantage of MEG sensors is that they do not touch the head, so transmission of infectious agents is of less concern. Fixation of head position is critical so that sensors can be aligned according to skull landmarks. Modern SQUID technology allows recording of signals that vary slowly over a minute, undisturbed by electrode drift. A new method for recording even slower or static magnetic fields converts such fields to more rapidly changing fields by having the subject lie on a mechanically driven platform that executes a circular movement of a few centimeters at 0.2 Hz (26). Auditory and visual stimulation cannot be given by conventional earphones or CRT displays because of their magnetic properties. Instead, sounds have to be delivered from outside the testing chamber through hollow tubes and visual stimuli projected through a window in the magnetic shield or delivered fiber optically.

Both ERPs and ERFs benefit greatly from signal averaging to enhance their signal-to-noise ratio (SNR). Data are generally digitized at a fixed rate to fill a data array, and a stimulus or other synchronizing event defines the time epoch of interest within this array. The event is repeated (each repetition is called a trial), and a time-locked signal (ensemble) average is calculated across trials epochs for each time point of the epoch. If Xj(t) is the electrical potential (voltage) or magnetic field strength at some electrode or sensor location at time t and trial j, the signal average is defined as

If Xjt is considered the sum of true signal mt and random noise Njt (background EEG and measurement error), signal averaging improves the SNR. Unbiased estimates of signal power , noise power , and SNR can be calculated as follows (71).

One of the assumptions of signal averaging is that the signal is invariant across trials. This assumption is violated when the amplitude of the ERP component of interest habituates or when its latency varies from trial to trial, as is clearly the case for components related to certain cognitive processes, such as the P300. One way of dealing with component latency variability is to locate the signal on each trial and align the trials on these signals rather than on the eliciting stimulus. Woody (75) proposed an iterative procedure (an adaptive filter) that located the signal on each single trial by moving a template (initially the signal average) by time increments along the trial to find the latency of maximum correlation. A new average was then formed by aligning trials on the identified signal latencies, and the new average was used as a new template. If the SNR is too low, this procedure produces results that simply reflect random noise. Gratton et al. (31) tested the procedure with simulated signals and background EEG noise and demonstrated that iterations (up to three) were important only when the original template had a wavelength on the order of two times longer than the signal.

Roth et al. (56) used this procedure to analyze ERPs elicited from schizophrenics and controls performing an auditory choice reaction time paradigm in order to test whether P300 amplitude reduction in schizophrenics could be attributed to latency variability. They found that individual trial P300 latency was indeed more variable in schizophrenics but that schizophrenic P300 amplitude was still smaller than control amplitude after latency adjustment. To reduce distortions due to noise, Pfefferbaum and Ford (53) modified the procedure by only including trials whose covariance is greater in the part of the epoch where signal is expected than in the part where noise is expected, and whose correlation with the template (initially a half-sine wave) exceeds a set threshold. Using this modified procedure, Ford et al. (23) replicated the Roth et al. (61) finding that schizophrenic P300 remained smaller. Furthermore, schizophrenics had more trials that did not pass the covariance–correlation screen than controls. Trials that did not qualify for latency adjustment had longer reaction times, showing that they were deviant behaviorally as well as electrophysiologically. In addition, Ford et al. calculated for each subject the covariance of P300 signal average across trials with that subject's EEG in single signal epochs and in single nonsignal epochs. The ratio of mean signal covariance to mean noise covariance was significantly smaller in the schizophrenics. Because trials were filtered with a bandpass of 0.5 to 4.4 Hz, noise was EEG activity in the frequency range of P300 rather than higher frequency like a, b, or muscle activity.

Another assumption of signal averaging is that background EEG noise is random noise. This is only an approximation to the truth, as a study of event-related spectral perturbation indicates (41). In normal subjects, auditory tone pips reliably produced momentary increases in spectral power in the 2- to 8-Hz and 10- to 40-Hz bands.

Eye movement and blinks produce electrical potentials and magnetic fields that are often much larger than those deriving from brain sources. The magnetic fields are more restricted to the vicinity of the eye than are the electrical fields and for this reason are less troublesome if unsynchronized with events of experimental interest. Synchronized eye artifact can cause major errors in peak measurement or source localization. Attempts to control this artifact by instructing subjects to fixate their gaze on a point or not to blink are often ineffective, particularly if the subject is psychotic or cognitively impaired. Thus methods for removing eye artifact from the ERP or ERF need to be applied. Many are based on determining the coefficients Ak in the equation

V(k,t) = Ak * EOG(t) + EEG(k,t)

where V(k,t) is the voltage observed in lead k at time t, and EOG(t) and EEG(k,t) are the true EOG and EEG voltage contributions at that time.

Spatial-temporal dipole models of eye movements and blinks make it clear that the same correction cannot be used for both (6). Thus eye-correction procedures should include at a minimum the following steps: (a) Separate blinks from movements on the basis of their temporal properties, (b) calculate separate linear regressions for the propagation of artifacts from each, and (c) correct EEG leads by the amount predicted by the regression coefficients. Gratton et al. (29), whose method has been used by a number of investigators, adds an additional step of subtracting signal averages from individual trials to avoid distortions resulting from ERP effects in both EEG and EOG records. A computerized implementation of this procedure that adjusts for both a vertical and a horizontal EOG channel, has been developed (43). Although certain technical issues in implementing EOG corrections remain unresolved—the proper number and position of EOG electrodes, the error attendant upon assuming a linear relationships between the EOG signal and EEG artifacts, the implications of the presence of EEG artifacts in EOG leads, how to deal with overlapping eye movement and blinks, and instability of individual propagation factors between sessions and even between tasks within a session (19)—the use of such off-line procedures have greatly increased the number of trials available for analysis in clinical studies.

Whereas MEG sensors detect the absolute magnetic field at a given location in space and need no reference in the body, the EEG must be measured as voltage differences between two points on or in the organism. Ideally one point should be close to the biological voltage source under investigation, and the other should be a reference point with constant voltage or at least a voltage not correlated with the source voltage. Traditional references for human ERP have been linked mastoids, linked ears, or the nose; unfortunately none of these is unaffected by brain sources. Special disadvantages of linked ear references include the possibility that shorting can reduce asymmetry if resistance is low, and the possibility that artifactual spatial asymmetry will result if resistances at the two ears are not equal (48). Shorting is not a serious consideration as long as skin-electrode resistance at each ear is greater than 5 kW, because in that case scalp path resistance is reduced less than 5% (44). Resistance at the two ears can be balanced with a potentiometer, or one ear (say A1) can be used as a reference and recorded as a separate channel. Then a linked ear reference for say Cz, a scalp electrode in the 10–20 system, can be created algebraically, (Cz - A1) - (A2 - A1)/2 = Cz - (A1 + A2)/2.

To avoid active reference electrodes on the head, some investigators have turned to noncephalic (e.g., sternovertebral) electrodes (67). Unfortunately these electrodes are liable to pick up heart activity even when adjusted to be at right angles to the main vector of voltage during the cardiac cycle, since cardiac depolarization and repolarization vectors do not maintain a perfectly constant direction over the cycle.

Another solution is to use an average reference. At each time point, an average reference defines zero over C electrodes in a data array A as

A limitation of the average reference is that when electrodes are not densely and equally spaced around the brain, for example, there are none at the bottom of the head (69), the sum in the formula above is generally different from true zero. For example, Desmedt et al. (16) have shown that P14 of the somatosensory evoked response, which is present with a linked ears reference, disappears when a zero reference based on 27 scalp electrodes is applied, becoming surrounded by "ghost" negativities. A linked-ear reference reflects more accurately the medial lemniscal volley that is the presumed basis of P14. In addition, local changes can be mistaken for global changes with a zero reference. These distortions are less likely to affect tangential than radial dipoles.

In conclusion, there is no perfect reference for all cases. As a general principle, a known local source should be referred to an electrode distant from it.

Before measurements are made on ERPs or ERFs, it is useful to apply SNR-enhancing filters that incorporate assumptions about frequency, timing, and spatial distribution of the component of interest. For example, the ERP P300 component may be expected from experiments in the literature to have a frequency lower than 2 Hz (30), to peak in a range of 280 to 400 msec (in a simple auditory choice reaction time task in young adults) and to be maximal at Pz, another electrode in the 10–20 system. Though signal averaging attenuates unsynchronized noise at every frequency as it improves SNR, frequency filters are commonly applied prior to component measurement. These filters are useful whenever the frequency of the noise is different from that of the signal.

Digital frequency filters (11) have the advantage over analog filters of being able to operate without introducing distorting phase shifts into the signal. The most commonly used digital filter has been the moving average or boxcar filter, in which each point of the signal is replaced by an average of that point and a certain number of prior and subsequent points. This is only possible for stored data, because it makes use of future time points to calculate current output. Farwell et al. (20) have shown that a simple moving average filter does not prepare average and single-trial waveforms as well for P300 peak-picking as does a filter designed by an optimizing algorithm. Such an algorithm determines a set of weights that are able to reduce deviations (ripple or ringing) in the passband and stopband of the filter. Optimized filters have less tendency to reduce P300 amplitude or distort shape and, in the case of averages, gave more stable latency measurements. For P300, the authors recommend that the optimum filter have a passband cut-off frequency of 6 Hz, a stopband cut-off frequency of 8 or 8.5 Hz, and use 490/n points, where n is the sampling interval in milliseconds. It should be emphasized that analog filters still have a place in data acquisition prior to digital filtering—a low-pass analog filter with a half-power frequency below but close to half the sampling rate prevents aliasing, and, for P300 recording, a high-pass analog filter with a half-power frequency of less than 0.16 Hz minimizes irrelevant baseline shifts (20).

Current source density maps (also called surface Laplacian or radial current estimate maps) act as spatial filters emphasizing localized components with a high spatial frequency. For this to work well of course, electrodes must be placed with a high spatial frequency. Maps can be made of unaveraged activity such as epileptic spikes or of signal averages. Sensory ERP components show a more localized distribution using this approach than in voltage maps. For example, Nagamine et al. (46) compared voltage and current source density maps on the scalp ERPs obtained by tibial nerve stimulation. The results for a single subject presented in Fig. 2 demonstrate better localization for P40, N50, and P60 for the current source density map. The equation for calculating current source density is I = r(d2V/dx2 + d2V/dy2), where V is the voltage, x and y the surface location on the x–y plane, and r the charge density. In addition, r = k * d2, where d is the distance between electrodes and k is a constant for all electrodes within a subject. The Laplacian operator can give limits for finding equivalent dipoles. It has a physical interpretation—local radial current flow from the brain into the scalp and vice versa—but it is different from dipole modeling (described below) and is free of dipole modeling's ambiguities.

In the Laplacian calculation, surface contours can be generated by a method called spherical spline interpolation, which is based on physical principles for minimizing the deformation energy of a thin sphere constrained to pass through known points (51). This produces a smooth surface running through the data values and filling in between them, even when electrodes are irregularly placed on the scalp. Spherical splines have advantages over plate splines, which are based on deformation of an infinite thin plate. As might be expected from the fact that interpolated values at any point are derived from data from other locations, coherence (a measure of covariation) is inflated by interpolation. Nearest-neighbor interpolations are less smooth and inferior for locating extrema (peaks and troughs must lie on an electrode site) but do not inflate coherence.

Gevins et al. (27) have demonstrated a method of current source density mapping they call finite element model deblurring that they believe is superior to the Laplacian method. Mathematically, it is a less computationally demanding version of dipole modeling known as spatial deconvolution, which assumes that all dipoles are located on a cortical surface. Gevins et al. use the subject's head MRI to provide information about conducting volumes between scalp and cortical surfaces.

A simpler spatial filter, the vector filter (30), has been used for component measurement. Its output is the weighted sum of data points at different electrodes. Conceptually, measuring a component at one lead is the same as applying a vector filter with weight 1 assigned to values at that lead and weight 0 to values at all other leads. Vector filtering assumes that the distribution of the component to be measured is constant despite changes in amplitude or latency. The crux of the procedure is how to specify the weights: using three 10–20 system scalp electrodes, Fz, Cz, and Pz, weights of 0.15 for Fz, -0.53 for Cz, and 0.83 for Pz were found to produce optimal discrimination in an oddball paradigm between rare trials, which contain substantial P300s, and frequent trials, which do not (30). Thus, optimum weights do not necessarily correspond to component distribution, because P300 is larger at Cz than at Fz. Dipole modeling, which is described below, can act as both a spatial and temporal filter.

A component can be defined as electrical or magnetic activity associated with a specific neurological or psychological process, for example, a motor act such as moving one's finger, a sensory process such as the reaction to a light flash, or a cognitive process such as categorizing a stimulus as target or nontarget. In a statistical sense a component explains experimental variance. The details of the experimental method are part of the operational definition of a component. As more experiments are done, theoretical expectations about components develop into generalizations. For example, many experiments in which subjects performed a fixed foreperiod reaction time task have resulted in a parietal–central negative shift prior to the button press. A natural generalization is that the parietal–central shift represents preparation for a motor act. Furthermore, because the source of the recorded data is a physical location within the brain, the ultimate description of a component must include reference to the specific brain structures activated. Some leads or sensors will pick up activity from those structures better than others, particularly when sources are multiple with overlapping influences. In the case of ERPs, the choice of voltage reference influences how electrical activity from a source appears in the EEG recording.

Measurement procedures include peak picking, area measurement, waveform subtraction, principal components analysis, template correlation, and dipole modeling. Peak picking means finding maxima or minima in specified latency ranges and determining peak latency and amplitude with respect to a prestimulus baseline. This is the simplest method of component evaluation, but can be biased when latency ranges are selected after an inspection of the data, and is perhaps unduly restricted in that it considers only peaks among other waveform features. In addition, it is often based on only one point, which may be influenced by noise or overlapping components. With multiple leads, another limitation of peak picking becomes obvious: what appears by shape to be a single component has maxima at different time points in different leads, and it is not clear how best to resolve the discrepancies. Furthermore, the choice of reference electrodes can determine when peaks and troughs appear.

Area measurement is sometimes used when the component is believed to be more rectangular than peaked. Area is measured in a specified latency range, and is thus based on multiple points, but area measurement, like peak picking, can be biased and is influenced by overlapping components.

Waveform subtraction can be used before peak picking or area measurement to reduce the effects of component overlap. For example, consider a paradigm where tones of two pitches are given in an unpredictable sequence and one occurs less frequently and is designated as the target of some task. The ERP to the rare tone can be considered a combination of the sensory effects of the tone and the cognitive effects of the tone being a rare target. By subtracting the ERP to the frequent tones from the ERP to the infrequent tones, the sensory effects are removed leaving behind the cognitive effects. This assumes that the sensory responses to the two tones are identical and that cognitive and sensory effects are additive, an assumption that is not always warranted. For example, frequency-specific temporal recovery of the auditory N100, a noncognitive effect, makes the response of N100 to frequents smaller than the response of N100 to rares.

Principal components analysis (PCA) is another approach to ERP component measurement, which uses the time points on waveforms from different subjects, different electrodes, and different experimental conditions to define components. In statistical terms PCA identifies orthogonal axes of maximal variance in a multidimensional space defined by the variables. Generally these axes are rotated according to the varimax procedure. Less arbitrary than peak picking, PCA makes no assumption about the latency range in which specific components will be found but only that they have a fixed latency across conditions and subjects. It has some ability to separate overlapping components. However, PCA is not completely free from arbitrariness. First, PCA solutions are not unique. Many rotations of the factors are possible. Second, results depend to a certain extent on what experimental conditions are chosen and how many leads are included. Variance from electrodes, subjects, conditions, and correlated noise are all treated the same. Furthermore, each experiment gives slightly different factor structures, and there is no established criterion for deciding whether these differences are significant or not. Thus, it is uncertain how many statistical components to interpret, and how to identify these components with ones previously described.

Template correlation assesses the similarity of a template of the component to the waveform to be evaluated. The template may be based on prior knowledge of the component shape or on signal averages (see the iterative Woody filter procedure described above). The template is usually compared to waveforms at specified intervals over a designated latency range to identify the latency of maximum correlation (or in one variation, maximum covariance). This time point is defined as the peak. The sum of cross products at this time point or the difference between amplitude at this point and a baseline can define amplitude.

Interpreting latency data under different experimental conditions can be difficult when multiple leads are involved. Latency may vary at different leads and topography may vary under different conditions, implying different components whose latency cannot be compared. To solve these problems, Brandeis et al. (8) spatially generalized the Woody filter procedure using an average reference map, and applying a measure they call global field power (GFP) defined by the following formula for an array A consisting of data from C electrodes:

Further, global dissimilarity (GD) is defined as the root mean square (rms) power of the difference maps calculated by subtracting two normalized GFP maps. The procedure is as follows: (a) Grand averages are used to form template GFP maps, from which component model maps at single latencies near 100, 200, and 400 msec are derived, corresponding to P1, N1, and P3 (see ref. 8 for details). (b) Component model maps are moved in specified latency ranges around the latency of each model's component. The minimum of GD multiplied by sequential dissimilarity (GD between current and previous map: a stability constraint) is calculated, and the minimum of this function (best fit) is defined as the map latency for that component. (c) In an iteration, the average of all normalized maps at their latencies of best fit is used as a new model, and the search window is set around the new mean latency. The results show that components can be identified by topography alone, without respect to amplitude or time. However, this method does not take into account possible overlapping components and would fail if such components influenced topographies. Furthermore, average references for P300, which is widely distributed on the top of the head, may be inferior to a noncephalic reference.

Dipole modeling is a method for reducing data from multilead EEG or multisource MEG by deducing the dipole sources that may have produced them. Although the forward problem (calculating scalp distribution from known dipoles) has a unique solution whose accuracy is limited only by the approximations of skull geometry and conductivities, the inverse problem has multiple mathematically valid solutions as was pointed about by Helmholz more than a century ago (33). The reason is that a single scalp distribution can be produced by different numbers of dipoles in different combinations of locations and orientations. Thus, various constraints on the number of sources allowed and their approximate location must be applied to reach a solution. Sometimes these constraints are so severe as to specify that the source be a single dipole located somewhere in the brain.

At an abstract level, dipole modeling is like PCA in that an equation U = C * S must be solved where U is an array of k electrodes at t times that represents the linear superimposition of the array S of m sources at t times multiplied by C weighing coefficients at k electrodes for m sources (62). Whereas PCA determines C and S from mathematical constraints, dipole modeling assumes that C depends on volume conduction from j dipoles at certain locations, assuming Ckj = f(rj,oj,ek ), where f is a nonlinear function of the electrode location vector ek and of the geometry of the source and the head. The dipole has a location vector rj and the orientation vector oj. Equations defining a 3-shell sphere model of the head with differing conductivities for scalp, skull, and brain are found in the appendix to this chapter. Using these equations to model dipoles at various depths, Pfefferbaum (52) demonstrated how increasing the thickness of the superficial extrasulcal subarachnoid layer of cerebrospinal fluid (CSF) or skull thickness might affect scalp ERP amplitudes and topographic distributions.

One procedure for the dipole modeling of ERPs was developed by Scherg and Berg (64). Their software is available commercially as brain electrical source analysis (BESA, from Neuroscan, Inc.). It models a window of points, assuming a finite number of equivalent dipoles with fixed location and orientation. In its recent version, it does not assume a parametric dipole magnitude function (like the decaying sinusoid of ref. 70) but computes a varying magnitude function over the window of points for each dipole. The BESA model is applied iteratively, calculating at each step the residual variance (percentage of recorded data not explained by the model). The first step looks for the inverse solution by calculating parameters of a plausible dipole from an EEG or MEG data map. Then forward solutions calculate resultant EEG or MEG maps from those dipoles. Hundreds of iterations may take place, stopping when the change in residual variance is less than some criterion, such as 0.001%. When more than one dipole is modeled, some may be fixed in position (but not in amplitude) while a new dipole is optimized. The results of these procedures depend among other things on the starting location and other parameters of a dipole. An iterative procedure may find topographically local optima that would not be optima if all locations and orientations were tested. Scherg and Berg (64) explained that multiple-source solutions are less arbitrary if spatial and temporal constraints are added. For example, two sources may be required to have a symmetry between hemispheres, radial and tangential dipoles, or lie in the supratemporal plane. How this method works is illustrated in Fig. 3 and Fig. 4, Figure 3 shows ERPs to clicks and resultant dipoles that were inferred from these ERPs. Figure 4 shows how well four models account for the data. The model that explains the greatest amount of the variance (99.4%) and corresponds best to anatomic reality assumes six dipoles: one central, two bilaterally symmetrical pairs, and one unilateral, coming from the postauricular muscle. Of course, some of the 99.4% may be noise rather than signal.

Other procedures are possible. Turetsky et al. (70) developed a method called the dipole components model, which simultaneously fits multilead data from a time window in multiple averages, pooling noise estimates. It assumes that the component shape is a decaying sinusoid and that the skull is a sphere of homogeneous conductivity. Turetsky et al. (70) applied it to P300 elicited in an auditory oddball paradigm and found four dipoles in two dimensions, three of which varied with experimental conditions. Cardenas et al. (9) applied it to the P50 suppression paradigm in the reliability study described below.

A single dipole modeled at brief intervals can mathematically generate a moving trajectory of loci. The two main alternatives to single dipole modeling are multiple dipole models and fully distributed models (34). For the second, a probability density is generated for widely distributed current sources. In addition, cylinders rather than points may be modeled. A distinction between a point source and a region can only be made if the region is of a size comparable to the distance between sensors.

For a MEG, it is not necessary to employ a layer model because magnetic permeability is unaffected by variations in conductivity. In practice only the radial component of the field is measured because it is convenient to place pickup coils parallel to the scalp (reviewed in ref. 39). Although generally it is assumed that the source is composed of similarly oriented and concurrently active neurons, this simplification is clearly wrong in certain cases, such as the folds of the visual cortex, which are better modeled by a cross-shaped arrangement of dipoles. The strength of the resultant dipole detected at the scalp depends very much on the symmetry. Synchronization (as with the appearance of a waves) may actually be a periodic breaking of symmetry of activation of the component dipoles (39).

Often, ERF analyses use peak data to model the dipole, because the SNR is likely to be highest there. An example of a dipole analysis in which the results were coordinated with MRI scan data is the work of Pantev et al. (50). They analyzed ERFs elicited by auditory tones of varying pitches and based on at least 96 trials for each pitch from each of 60 measuring positions at the M100 peak (in this case at 88 msec) for a single current dipole source. Fig. 5 shows isofield contour plots of the ERF at 88 msec for a single subject and the positions of the dipoles associated with each pitch. Fig. 6 shows the coronal MRI section with the dipole locations for that subject. They lie just below the surface of the transverse temporal gyrus (Heschl), the assumed location of the primary auditory cortex, and are ordered in depth by pitch. Modeling before and after the peak may give somewhat different dipoles, but it is hard to exclude the possibility that they are spurious. To accentuate the onset of activation of weak secondary dipoles, Moran et al. (45) calculated dipoles associated with auditory ERFs on the basis of differences in magnetic fields in 4-msec intervals between 0 and 300 msec. This interval selects for components of a frequency high enough to change during it. Using this method, the authors found evidence for a source spatially separate from N1m but coactive with it. A distributed source in Heschl's gyrus and adjacent areas could also produce such a result.

The number of sensors (SQUIDs or electrodes) is important. For ERFs we need to know n * 5 parameters if n is the number of sources and, for ERPs, n *6 (65). For ERPs, this means a minimum of (n * 6) + 1 electrodes. Thus, the conventional 19 electrodes allow only 1 or 2 generators to be determined. The results of dipole modeling can be ambiguous in that substantially different models provide only trivially inferior fits. It is more important to analyze the number of sources and their gross location than their exact location. A good initial approximation escapes local minima in residual (unexplained) variance but begs the question. Noise, particularly if it is spatiotemporally organized, can distort solutions by creating local minima. Achim et al. (1) created simulations and used a variety of procedures to analyze them. By using several initial approximations (rather than simply reinitializing with a previous solution) and a multiplicity of optimizations, they managed largely to escape local minima. Precise localization is prevented by the presence of background EEG noise. Errors ranged from 2.5% to 13% of sphere radius. The authors developed a residual orthogonality test for testing the presence of signal in residues after modeling.

Sensory ERPs and ERFs are more likely to be amenable to dipole modeling than more complex cognitive ones. Witt et al. (74) applied dipole modeling to brainstem auditory evoked potentials (BAEPs). These authors recorded simultaneously from 12 electrodes constituting three three-channel bipolar montages. Data from all montages were transformed to fit the same central dipole. The authors concluded that a tetrahedral montage equivalent to Einthoven's Triangle for the EKG is adequate for clinical work, although it is slightly inaccurate because the dipole is known to move over time.

In an investigation of a nonsensory ERF, Elbert et al. (17) measured the magnetic field prior to button response in a go–no-go reaction time task. With this task, the EEG shows a negative shift prior to the button press called the contingent negative variation (CNV). The magnetic equivalent, which they called the contingent magnetic variation (CMV), was larger for go than no-go conditions, but a moving single dipole model accounted for less than 80% of the variance in four of eight subjects. The authors conclude that the later parts of the CMV are particularly dependent on distributed sources in motor, sensory, and association areas. Another component that is likely to have multiple sources is P300 (37). Turetsky et al. (70) applied their dipole model to model electrical P300 in 18 subjects using data from the oddball two-tone choice reaction time task. Using four dipoles in the midsaggital plane, they could explain approximately two-thirds of the total variance across subjects, conditions, and electrodes.

An unsolved problem with dipole estimation is how to decide if dipoles are equivalent. For example, experimenters may want to statistically compare dipoles modeled from individual subjects to draw general conclusions valid for a group, yet each dipole will vary somewhat in its location and orientation from every other. In the approach of Turetsky et al. (70), a single solution encompasses all subjects and conditions in an experiment, but it is still important to be able to compare dipoles between experiments. A related problem is how many of the multiple component dipoles generated in a given application of a model should be considered valid. This is analogous to the problem of how many PCA components to accept in a given analysis.

The reliability and accuracy of certain computerized methods for measuring P300 has been assessed for averages and single trials. Reliability of automated measurement is a function of two factors that are often difficult to untangle: the stability of the underlying component being measured over time and the effects of electrical sources other than the component (background EEG and muscle and eye artifact). Whatever its cause, unreliability reduces a measure's usefulness.

Recent parametric studies have illuminated some of the variables underlying unreliability. Fabiani et al. (19) found that P300 latency estimates of averages had split-half reliabilities between 0.63 and 0.88, and in most paradigms was rather similar for peak picking and template correlation. Amplitude estimates of P300 were most reliable (between 0.90 and 0.96) when based on covariance with a full-cycle 2-Hz cosinusoidal wave. Making measurements at Pz alone was almost as good as using the output of a vector filter based on Fz, Cz, and Pz. Subtracting averages of frequent trials from infrequent trials led to more reliable measurement of the probability effect than when the two types of trials were measured separately. Test–retest reliabilities of both amplitude and latency were lower between than within sessions, probably because of changes in P300 over time. Gratton et al. (31) did a simulation study of P300 single-trial latency estimation, embedding known signals in noise from actual EEG records adjusted to give various SNRs. Peak picking and several methods of template correlation were compared after data were prepared by frequency filtering with various lowpass parameters (in some comparisons, 6.29 to 2.38 Hz) and sometimes by vector filtering. Accuracy of latency estimation increased exponentially with the template SNR. Regardless of the SNR, template cross-correlation was better than peak picking. Vector filtering helped, but with lower lowpass frequencies, the differences were rather small (in one comparison, the optimum lowpass cutoff was 1.76 Hz). Vector filtering was most useful when overlapping components of different distributions were simulated.

P50 is a more difficult component to measure than P300 because its amplitude is 10% to 25% that of P300. Typically measurements have been made by human observers picking peaks from averages of 32 trials. The ratio of P50 amplitudes to paired conditioning (S1) and testing (S2) stimuli is calculated. Ratios are less reliable than measurement of the numerator or denominator alone because ratios combine the statistically independent noise of both measures (3, 9). Two studies have found the reliabilities of P50 amplitude ratios to be less than 0.15 (7, 38). Freedman (24) has emphasized the importance of using only moderate intensity clicks and recording with the subject in a supine position for minimizing muscle artifact. Cardenas et al. (9) showed that the reliability of S2/S1 could be improved by applying the dipole modeling method of Turetsky et al. (70) to averages of 110 to 120 trials filtered with a 10- to 50-Hz bandpass. Even though reliability for peak picking was only 0.27 (interclass correlation of 6 repetitions), it was 0.63 for a model that fit a single source simultaneously to P50s evoked by S1 and S2. One caveat about reliabilities from dipole modeling is that complex computational methods can achieve results that turn out to be artifactual in simulations. These checks have yet to be made.

To accurately locate brain sources a number of known error sources must be controlled. Electrodes or magnetic sensors must be accurately placed in relation to the skull. A precise alignment of dipole and structural brain images must be made and the SNR must be enhanced. Assumptions of mathematical models for computing the dipole must be met, including assumptions about sphericity, conductivity (in the case of an EEG), and the temporal stability of sources. The size of the error made by the assumption of a spherical head shape was explored by Law and Nunez (40). Using a three-dimensional digitizer, they located 62 positions on an electrode cap. An ellipsoidal shape fit the electrode positions better than a sphere. Law and Nunez described a method for determining by tape measure the three axes of the shape conforming best to the head of an individual subject.

One presumed advantage of a MEG over an EEG was that the former affords more precise localization of sources. Controversy about this point, stimulated by a report by Cohen et al. (10), reached the news section of the magazine Science (12). Cohen et al. created an artificial source by passing subthreshold current through depth electrodes implanted in three patients for seizure monitoring. The exact locations of the electrodes could be determined from roentgenographs, and these locations were compared to those calculated for dipoles based on MEG and EEG recordings, each from 16 head locations. The average error for a MEG was 8 mm and for an EEG, 10 mm, thus showing no significant advantage for the MEG. In a follow-up study from the same research group, Cuffin et al. (13) calculated additional EEG dipoles using the same method and found an average localization error of 11 mm.

The studies above used artificial sources. Baumann et al. (5) tested the between-session reliability of dipole parameters from the P1m (50 msec), N1m (100 msec), and P2m (165 msec) components of an auditory ERF. Spatial parameters had an absolute difference of 3 to 10 mm. Errors were attributed to changes in attention, SNR, and local asymmetries in head shape. The sizes of sources detected by MEG after sensory stimulation have been estimated by Williamson and Kaufman (73) to be between 40 and 400 mm2. These are intermediate in size between macrocolumns of the visual cortex and a full sensory area, which can be several square centimeters.

A consensus statement by a group of scientists (2) pointed out that EEG and MEG should be considered complementary, because their different sensitivity to dipoles of different direction and depth gives valuable information about neural organization. The MEG is most sensitive to activity in fissures of the cortex where currents flow tangentially and to superficial sources, whereas the EEG is sensitive to both radial and tangential currents and is more sensitive than the MEG to deep sources, since in the MEG there is minimal magnetic field spreading by volume condition. The MEG has the advantage of being independent of inhomogeneities in concentric conductivities, whereas localization by an EEG depends on how accurately these conductivities can be approximated. Information from MRI and models of the real geometry of the head are needed. Additional advantages of the MEG are that it requires no electrode placement and permits very slow frequencies to be measured. On the other hand, it is not portable and is sensitive to ambient noise. Until recently, the MEG has had a limited number of channels, and its sensors have been relatively large, with diameters of 3 cm or more positioned at least 1 cm from the scalp.

EEG and MEG localization is comparable to the best 15O positron emission tomography (PET) resolution (6 to 100 mm), but both EEGs and MEGs have certain advantages over PET: the sample time of O15PET is 45 to 60 sec in contrast to the millisecond resolution of an EEG or MEG, PET requires administration of radioactive materials, and PET facilities are much more expensive than even MEG facilities (27). In addition, important neural events may not be concentrated enough to increase blood flow regionally. For example, Eulitz et al. (18) had subjects respond to nouns every 6 sec by silently articulating related verbs. Subjects repeated the task during separate sessions of MEG recording and PET imaging. In two regions, one in Wernicke's area and one in Broca's area, cerebral blood flow was increased on PET. Analysis of the MEG showed that during the first 200 msec of the 6-sec interval, a single current dipole was present in the primary cortex, but thereafter multiple dipoles appeared that were not confined to the regions of increased blood flow. Of course, it is somewhat misleading to cast PET and EEG/MEG as direct competitors because the two methods are most valid in different realms. Only PET assesses blood flow, disturbances of which are often the primary cause of brain dysfunction.

A framework for combining from EEG, MEG, and MRI data has been provided by Dale and Sereno (15). Such a combination of data makes possible the identification of plausible multiple cortical sources with a spatial resolution as good as PET but with a much finer temporal resolution. When available, PET and functional MRI data, can be added to the reconstruction.

Modern multichannel EEG and MEG recording have expanded many fold the amount of data recorded from each subject, leading to problems of statistical inference. This can be seen graphically, for example, when the probability of statistical difference between two groups is plotted across electrode sites (this has been called significance probability mapping). Groups usually differ by at least one electrode, and if they differ at one electrode, they tend to differ at adjacent electrodes, creating regions of significant difference. Of course, because there are multiple electrodes and because data at adjacent electrodes tend to correlate, the extent of significant difference often appears greater than it is. For correct statistical inference, the number of variables must somehow be reduced. Because data between time points and between topographic locations are often highly correlated, breakdown into components, factors, or dipoles as outlined above is possible. Even then, too many variables may remain for the number of subjects that can be tested.

The best way to avoid type I errors (rejecting the null hypothesis when it is true) is by replication of initial findings on a second data set, distinguishing between exploratory and confirmatory data analysis. In the exploratory phase of research, it would be foolish to limit data collection to a few variables chosen to test definitively a few a priori hypotheses. For clinical studies, the second data set needs to come from an independent clinical sample. Less satisfactory than the two-step approach of confirmation of exploratory findings is the application to a single data set of Bonferroni corrections or leave-one-out (jackknifing) methods. The latter sequentially leaves out one subject from the data set and determines how well a discriminant function based on the other subjects classifies the one. The cost of the Bonferroni correction is high, since it increases the likelihood of type 2 errors (accepting the null hypothesis when it is false). It should be noted that demonstrations of statistically significant replicability do not guarantee that significant neural events have been observed—artifact can be highly replicable too.

The application of evoked MEG and EEG tests to clinical diagnosis has the same requirements as for other clinical tests. To establish the usefulness of a test, well-accepted standards should be used to define the disease, the test should be evaluated on a population different from the one used to derive the test, and the test should have a low false-positive rate, or if it is meant to exclude a diagnosis, a low false-negative rate (49). A few definitions need to be kept in mind: a true positive (TP) is a positive test in a patient with the disease, whereas a false positive (FP) is a positive test in a patients without the disease. A true negative (TN) is a negative test in a person without the disease, and a false negative (FN) is a negative test in a person with the disease. Sensitivity = TP/(TP + FN) and specificity = TN/(TN + FP). Positive predictive power = TP/(TP + FP) and negative predictive power = TN/(TN + FN).

In psychiatric contexts, ERPs have generally been considered a way to investigate cognitive or biological differences between already-diagnosed patients and controls, rather than a way to make a diagnosis. This has been the case even for the most replicable ERP findings such as P300 amplitude reduction in schizophrenia and P300 latency prolongation in dementia. Occasionally, the diagnostic usefulness of ERPs in psychiatry has been debated as in the pair of articles discussing the pros (28) and cons (54) of P300 latency in assessing dementia. Goodin (28) points out that in neurology, brainstem auditory ERPs are very sensitive in diagnosing cerebellopontine angle tumor, with a false-negative rate of less than 3%. The EEG is useful in diagnosing suspected epileptics, although its sensitivity is only 52% because it is 96% specific. However, P300 latency is limited for diagnosing dementia because its sensitivity in some studies is less than 60%, but since its false-negative rate is low, a negative result can give valuable information in some contexts. Of course P300's usefulness presumes that it can be elicited reliably in the population to be tested, which some studies affirm (more than 95% of subjects had adequate P300s) and one denies (less than 20% had adequate P300s) (54).

Pfefferbaum et al. (54) argue that better discrimination between demented and nondemented patients can be made if the effects of age itself are taken into account by regression analysis. They point out that the sensitivity and specificity of a test depends on the cutoff used to define abnormality and the prevalence of the disease in the population. The trade-offs between sensitivity and specificity at various cutoffs can be depicted in a receiver operating-characteristics graph. In the data of Pfefferbaum et al. (54) a statistically optimal cutoff for discrimination between demented and nondemented neurological and psychiatric patients yielded a specificity of 93% and a sensitivity of 38%. Thus, P300 latency is unsuitable for screening because of the low sensitivity, but might be more useful for confirmation of diagnosis because of its higher specificity. In a low-risk population, however, the specificity of P300 is likely to be even lower. A fundamental problem in dementia testing with P300 is that the paradigm used so far to elicit P300 requires the subject to perform a task that severely demented patients may be unable to do, or do in a way that results in P300s with low SNRs. ERP or ERF components less dependent on subject cooperation may play a greater role in clinical assessment in the future.

Ford et al. (21) did a sensitivity–specificity (receiver operating characteristics) analysis of the utility of P300 in diagnosing schizophrenia. Using data originally reported in Pfefferbaum et al. (55) they expressed P300 amplitudes of 20 schizophrenics, 34 depressed, 37 demented, and 9 nondemented patients as age-corrected z-scores based on P300 data from 115 control subjects. Diagnosis of schizophrenia on the basis of P300 amplitude was less successful than the diagnosis of dementia on the basis of P300 latency: a specificity of 90% corresponded to a sensitivity of only 15%. However, P300 amplitude could be used to rule out schizophrenia in certain cases: no patient with a z-score above 1.6 was schizophrenic.

The methodology of evoked brain potential and magnetic field studies is in a phase of rapid technical evolution. A 122-channel MEG system is already on-line in Finland (72). In the future more and more studies will coordinate EEG and MEG data with data from MRI, PET, and SPECT scans. The claims of analysis methods to identify actual brain sources will be tested. Electrical and magnetic localization and other imaging methods will vie with each other in precision. Not just the sources of ERP and ERF components to simple stimuli will be localized, but also those reflecting more complex cognitive processes. The application of these new methods, particularly magnetic field measurement, to psychiatric disorders has hardly begun. We hope and expect that this situation will change in the near future.

To calculate the potential on the surface of a sphere, the following equations must be satisfied (14). For a Px dipole located along the radial projection at distance f from the center of a sphere

}

For a Py dipole located along the radial projection at distance f from the center of a sphere

For a Pz dipole located along the radial projection at distance f from the center of a sphere

where Pn and {} are Legendre polynomials and for n = 1 to 30 iterations

and where

Preparation of this chapter was supported by the National Institute of Mental Health, grants MH30854 and MH40052, and by the Department of Veterans Affairs.

We thank Margaret J. Rosenbloom for her critical comments.

published 2000

转载自http://www.cingulate.ibms.sinica.edu.tw/ftpshare/Lab_Seminar/gic/ERPs/Methodological%20Issues%20in%20Event-Related%20Brain%20Potential%20and%20Magnetic%20Field%20Studies.htm

Methodological Issues in Event-Related Brain Potential and Magnetic Field Studies

Walton T. Roth, Judith M. Ford, Adolf Pfefferbaum, and Thomas R. Elbert

Psychiatry in its search for the roots of abnormal thoughts, feelings, and behavior has again turned its attention to the human brain and is trying to apply the methods of the many scientific disciplines that have cast light on normal brain functioning-disciplines such as neuroanatomy and histology, biochemistry and molecular biology, and electrophysiology. This chapter concentrates on ways of maximizing what can be learned from noninvasive electrophysiology, a technique that is singular in its ability to record millisecond-by-millisecond changes in the brain following repeated external or internal events. Although the triggering events are often simple sensory stimuli, the cognitive processes that follow them and leave their trace in fluctuating voltage or magnetic fields can be quite complex. In the last decade competing noninvasive techniques such as positron emission tomography (PET) have challenged the preeminence of electrophysiology, particularly in spatial localization of brain processes. This challenge has stimulated a number of technological and methodological developments in acquiring, analyzing, and presenting brain electrical and magnetic data. But before we review these developments, we remind you of some basic principles and give examples of their relevance to psychiatry (see also A Critical Analysis of Neurochemical Methods for Monitoring Transmitter Dynamics in the Brain, Electrophysiology, and Pharmacology and Physiology of Central Noradrenergic Systems for related discussion).

Nerve cells generate extracellular current flow by fluctuations in the slower changing membrane potentials of dendrites and cell bodies. Postsynaptic potentials cause an outflow of negative (excitatory) or positive (inhibitory) ionic charges into extracellular fluid, which are then pumped back into the cell. This current flow, when summated, results in volume-conducted potentials recorded at the scalp as the electroencephalogram (EEG). Event-related potentials (ERPs) are EEG changes that are time-locked to sensory, motor, or cognitive events. They have provided a way to evaluate brain functioning in mental disorders and the effects of psychoactive drugs. Recent conceptual and technical developments have greatly expanded our capability to understand and document the mechanisms underlying surface recordings. Particular attention has been paid to identifying the location, orientation, and distribution of current dipoles (pairs of opposite charges) that may be the sources of scalp-recorded electrical activity.

Nerve cells also generate intracellular current flow from dendrites to cell body. This flow results in a magnetic field that can be detected at the scalp as a magnetoencephalogram (MEG), even though it is a billionfold less intense than the earth's magnetic field. Event-related magnetic fields (ERFs) can be elicited and time-locked to specific events and are analogous to ERPs. Magnetoencephalograms and ERFs convey different information than EEG and ERPs. This is because voltage fields on the surface of a sphere, which the skull enclosing the brain approximates, are produced equally well by dipoles oriented radially and tangentially with respect to a radius of the sphere. In contrast, 90% of the magnetic field at the skull can be ascribed to tangential dipoles alone. This is a consequence of the geometrical orientation of masses of nerve cells and of magnetic sensors. Fig. 1 illustrates how dipole orientation can be either correlated or random for different gyri and sulci. Parallel dipoles lying tangentially on sulcal walls contribute much more to the MEG than random dipoles or dipoles lying radially along the crowns of gyri.

Why are the methodological issues that this chapter addresses relevant to psychiatrists and psychologists? First, ERPs and ERFs are theoretically relevant because they provide ways of testing theories of abnormal brain functioning that no other methods can offer. For example, unlike ordinary behavioral tests of cognitive processing, ERPs give an index of the processing of task-irrelevant events, distracting stimuli, or events subjects have been told to ignore. The topographic distribution of ERPs and ERFs gives clues as to what parts of the brain are active during a particular cognitive activity. Second, ERPs and to a less extent ERFs have been demonstrated empirically to be relevant. ERP abnormalities have been repeatedly observed in psychiatric disorders, notably in the P300 and P50 components. The P or N signifies positive or negative and the number is the mean peak latency in milliseconds. Thus, the P300 component is a positive potential that occurs approximately 300 msec after a stimulus that is infrequent and in some way relevant. The most venerable and consistent psychiatric ERP finding is that of reduced P300 amplitude in schizophrenics (60), although this is not specific to schizophrenia (see refs. 59 and 22 for reviews). For instance, a longitudinal study demonstrated that lower P300 amplitude at age 15 was predictive of poorer global personality functioning at age 25 (66). Latency at P300 is generally greater in patients with dementia than in normals or in patients with schizophrenia or depression (28, 54). Recently, psychiatric attention has been directed to P50, an ERP component to auditory stimuli whose amplitude is suppressed if the eliciting stimulus is paired with another that precedes it by one-half second. Schizophrenics show less P50 suppression than controls (25) as indicated by smaller amplitude ratios (P50 to the second stimulus of a pair divided by P50 to the first), although again this finding is not limited to schizophrenia (4).

Abnormalities of ERPs in psychiatric patients can be interpreted in light of a considerable amount of knowledge that has accumulated about the significance of certain ERP components in normal human information processing. For example, P300 is known to reflect the categorization of events, depending jointly on stimulus probability, stimulus significance, and the information value of the event (36). Probably, P300 has multiple, partially asynchronous generators (58). Components occurring 60 to 100 msec after onset of auditory stimuli, including N100, have been shown to reflect selective attention to auditory stimulus channels (42). In contrast, auditory ERPs with latencies less than 10 msec are insensitive to attention effects but give a unique assessment of the intactness of brainstem circuitry (32).

The literature on ERFs in normal subjects is quite extensive although magnetic recording techniques have been available only a relatively short time. Much of that literature has documented the existence of ERF components that parallel those established by invasive and noninvasive ERP recording. However, to date, most clinical MEG studies have been done in neurological rather than psychiatric patients, although that is likely to change in the near future. Reite et al. (57) recorded ERFs in six medicated, paranoid schizophrenic patients and six normal controls. The M100 component (analogous to the N100 of the ERP) showed less interhemispheric asymmetry in schizophrenics and had different source orientations in the left hemisphere. Tiihonen et al. (68) compared the M100 component in two schizophrenic patients when they were experiencing auditory hallucinations and when they were not. During hallucinations, M100 peaked approximately 20 msec later, an effect similar to that of external masking noise in normals.

We now turn to methodological trends that are transforming ERP and ERF research. Specific topics include data acquisition, signal averaging, ocular artifact, choice of reference electrodes, digital filtering, measuring components including dipole modeling, and statistical and diagnostic considerations.

Older electroencephalographic tube-based amplifiers have been completely replaced with high impedance solid-state amplifiers with electronically controlled amplification and filter settings. In many laboratories, pen-chart recorders have been replaced with electronic data storage and display systems, but paper records are still widely used for visual analysis of diagnostic EEGs and sleep. Laboratory computers are constantly evolving toward faster, cheaper, and more powerful models. New storage media based on tape or magnetic or optical disks permit archiving of data from many subjects in an easily retrievable form. As welcome as these advances have been, they have generated difficult new choices for researchers. Should they buy commercial EEG and ERP hardware and software systems or develop their own? Which commercial systems or routes to laboratory-program development are satisfactory? Commercial systems tend to be limited in flexibility, details of data analysis may be a trade secret (which is unacceptable scientifically), and access to raw data for special analyses may be difficult. Laboratory-developed systems require deciding among manifold hardware and software possibilities, and then allocating many hours to programming. As will be learned from this chapter, methodologically up-to-date ERP analysis requires much more than eye-movement artifact rejection and signal averaging.

Whereas the conventional 10–20 system of Jasper (35) used 19 electrodes with a typical distance of 6 cm between them, some investigators have greatly expanded the electrode arrays in order to record more of the spatial detail present in the EEG. Thus arrays of 124, or even 256 electrodes, which yield interelectrode distances of 2.25 and 1.6 cm, are now being advocated (27) and have been shown to enhance localization. The application of multiple electrodes is a lengthy, labor-intensive process, which requires care in scalp preparation and accuracy in electrode placement. For localization studies relating EEG or MEG data to brain structures visualized by magnetic resonance imaging (MRI), it is important that electrodes be aligned correctly according to skull landmarks, and fiducial markers visible in MRI scans are used. (Vitamin E capsules are easily available and the right size.)

Electrode application entails a potential health risk to both subject and technician if the intactness of the scalp is compromised by procedures to reduce electrical resistance between electrode and scalp or by skin lesions. Acquired immunodeficiency syndrome and hepatitis B can both be transmitted by this route, so it is absolutely essential that proper precautions be taken. Putnam et al. (56) give recommendations for disinfecting reusable electrodes and for protecting the technician.

The recording of the MEG has been made practical by the development of superconducting quantum interference devices (SQUIDs) that are sensitive to minute magnetic fields. The MEG technology is much more expensive than the EEG technology. Not only are the SQUIDs themselves expensive, but they require provision for liquid helium at 4.2°K to cool them, and a recording room shielded with a high-permeability material against magnetic fields and with aluminum against eddy currents. The liquid helium is kept in a vacuum-insulated container called a dewar. Locating magnetic sources requires recording from multiple sites, preferably simultaneously. Otherwise, separate stimulation runs must be made, moving sensors from one location to another between runs. More runs take more recording time and increase the likelihood that the subject's mental state will change, altering the sources. A MEG system with over 30 channels costs approximately $3,000,000, 100 times more than the same number of EEG channels. Because MEG prices reflect the cost of research and development more than construction of the apparatus, the price per unit would drop if more units were sold. In one system, 37 sensors are placed 2.2 cm apart to cover a single hemisphere (12).

An advantage of MEG sensors is that they do not touch the head, so transmission of infectious agents is of less concern. Fixation of head position is critical so that sensors can be aligned according to skull landmarks. Modern SQUID technology allows recording of signals that vary slowly over a minute, undisturbed by electrode drift. A new method for recording even slower or static magnetic fields converts such fields to more rapidly changing fields by having the subject lie on a mechanically driven platform that executes a circular movement of a few centimeters at 0.2 Hz (26). Auditory and visual stimulation cannot be given by conventional earphones or CRT displays because of their magnetic properties. Instead, sounds have to be delivered from outside the testing chamber through hollow tubes and visual stimuli projected through a window in the magnetic shield or delivered fiber optically.

Both ERPs and ERFs benefit greatly from signal averaging to enhance their signal-to-noise ratio (SNR). Data are generally digitized at a fixed rate to fill a data array, and a stimulus or other synchronizing event defines the time epoch of interest within this array. The event is repeated (each repetition is called a trial), and a time-locked signal (ensemble) average is calculated across trials epochs for each time point of the epoch. If Xj(t) is the electrical potential (voltage) or magnetic field strength at some electrode or sensor location at time t and trial j, the signal average is defined as

If Xjt is considered the sum of true signal mt and random noise Njt (background EEG and measurement error), signal averaging improves the SNR. Unbiased estimates of signal power , noise power , and SNR can be calculated as follows (71).

One of the assumptions of signal averaging is that the signal is invariant across trials. This assumption is violated when the amplitude of the ERP component of interest habituates or when its latency varies from trial to trial, as is clearly the case for components related to certain cognitive processes, such as the P300. One way of dealing with component latency variability is to locate the signal on each trial and align the trials on these signals rather than on the eliciting stimulus. Woody (75) proposed an iterative procedure (an adaptive filter) that located the signal on each single trial by moving a template (initially the signal average) by time increments along the trial to find the latency of maximum correlation. A new average was then formed by aligning trials on the identified signal latencies, and the new average was used as a new template. If the SNR is too low, this procedure produces results that simply reflect random noise. Gratton et al. (31) tested the procedure with simulated signals and background EEG noise and demonstrated that iterations (up to three) were important only when the original template had a wavelength on the order of two times longer than the signal.

Roth et al. (56) used this procedure to analyze ERPs elicited from schizophrenics and controls performing an auditory choice reaction time paradigm in order to test whether P300 amplitude reduction in schizophrenics could be attributed to latency variability. They found that individual trial P300 latency was indeed more variable in schizophrenics but that schizophrenic P300 amplitude was still smaller than control amplitude after latency adjustment. To reduce distortions due to noise, Pfefferbaum and Ford (53) modified the procedure by only including trials whose covariance is greater in the part of the epoch where signal is expected than in the part where noise is expected, and whose correlation with the template (initially a half-sine wave) exceeds a set threshold. Using this modified procedure, Ford et al. (23) replicated the Roth et al. (61) finding that schizophrenic P300 remained smaller. Furthermore, schizophrenics had more trials that did not pass the covariance–correlation screen than controls. Trials that did not qualify for latency adjustment had longer reaction times, showing that they were deviant behaviorally as well as electrophysiologically. In addition, Ford et al. calculated for each subject the covariance of P300 signal average across trials with that subject's EEG in single signal epochs and in single nonsignal epochs. The ratio of mean signal covariance to mean noise covariance was significantly smaller in the schizophrenics. Because trials were filtered with a bandpass of 0.5 to 4.4 Hz, noise was EEG activity in the frequency range of P300 rather than higher frequency like a, b, or muscle activity.

Another assumption of signal averaging is that background EEG noise is random noise. This is only an approximation to the truth, as a study of event-related spectral perturbation indicates (41). In normal subjects, auditory tone pips reliably produced momentary increases in spectral power in the 2- to 8-Hz and 10- to 40-Hz bands.

Eye movement and blinks produce electrical potentials and magnetic fields that are often much larger than those deriving from brain sources. The magnetic fields are more restricted to the vicinity of the eye than are the electrical fields and for this reason are less troublesome if unsynchronized with events of experimental interest. Synchronized eye artifact can cause major errors in peak measurement or source localization. Attempts to control this artifact by instructing subjects to fixate their gaze on a point or not to blink are often ineffective, particularly if the subject is psychotic or cognitively impaired. Thus methods for removing eye artifact from the ERP or ERF need to be applied. Many are based on determining the coefficients Ak in the equation

V(k,t) = Ak * EOG(t) + EEG(k,t)

where V(k,t) is the voltage observed in lead k at time t, and EOG(t) and EEG(k,t) are the true EOG and EEG voltage contributions at that time.

Spatial-temporal dipole models of eye movements and blinks make it clear that the same correction cannot be used for both (6). Thus eye-correction procedures should include at a minimum the following steps: (a) Separate blinks from movements on the basis of their temporal properties, (b) calculate separate linear regressions for the propagation of artifacts from each, and (c) correct EEG leads by the amount predicted by the regression coefficients. Gratton et al. (29), whose method has been used by a number of investigators, adds an additional step of subtracting signal averages from individual trials to avoid distortions resulting from ERP effects in both EEG and EOG records. A computerized implementation of this procedure that adjusts for both a vertical and a horizontal EOG channel, has been developed (43). Although certain technical issues in implementing EOG corrections remain unresolved—the proper number and position of EOG electrodes, the error attendant upon assuming a linear relationships between the EOG signal and EEG artifacts, the implications of the presence of EEG artifacts in EOG leads, how to deal with overlapping eye movement and blinks, and instability of individual propagation factors between sessions and even between tasks within a session (19)—the use of such off-line procedures have greatly increased the number of trials available for analysis in clinical studies.

Whereas MEG sensors detect the absolute magnetic field at a given location in space and need no reference in the body, the EEG must be measured as voltage differences between two points on or in the organism. Ideally one point should be close to the biological voltage source under investigation, and the other should be a reference point with constant voltage or at least a voltage not correlated with the source voltage. Traditional references for human ERP have been linked mastoids, linked ears, or the nose; unfortunately none of these is unaffected by brain sources. Special disadvantages of linked ear references include the possibility that shorting can reduce asymmetry if resistance is low, and the possibility that artifactual spatial asymmetry will result if resistances at the two ears are not equal (48). Shorting is not a serious consideration as long as skin-electrode resistance at each ear is greater than 5 kW, because in that case scalp path resistance is reduced less than 5% (44). Resistance at the two ears can be balanced with a potentiometer, or one ear (say A1) can be used as a reference and recorded as a separate channel. Then a linked ear reference for say Cz, a scalp electrode in the 10–20 system, can be created algebraically, (Cz - A1) - (A2 - A1)/2 = Cz - (A1 + A2)/2.

To avoid active reference electrodes on the head, some investigators have turned to noncephalic (e.g., sternovertebral) electrodes (67). Unfortunately these electrodes are liable to pick up heart activity even when adjusted to be at right angles to the main vector of voltage during the cardiac cycle, since cardiac depolarization and repolarization vectors do not maintain a perfectly constant direction over the cycle.

Another solution is to use an average reference. At each time point, an average reference defines zero over C electrodes in a data array A as

A limitation of the average reference is that when electrodes are not densely and equally spaced around the brain, for example, there are none at the bottom of the head (69), the sum in the formula above is generally different from true zero. For example, Desmedt et al. (16) have shown that P14 of the somatosensory evoked response, which is present with a linked ears reference, disappears when a zero reference based on 27 scalp electrodes is applied, becoming surrounded by "ghost" negativities. A linked-ear reference reflects more accurately the medial lemniscal volley that is the presumed basis of P14. In addition, local changes can be mistaken for global changes with a zero reference. These distortions are less likely to affect tangential than radial dipoles.

In conclusion, there is no perfect reference for all cases. As a general principle, a known local source should be referred to an electrode distant from it.

Before measurements are made on ERPs or ERFs, it is useful to apply SNR-enhancing filters that incorporate assumptions about frequency, timing, and spatial distribution of the component of interest. For example, the ERP P300 component may be expected from experiments in the literature to have a frequency lower than 2 Hz (30), to peak in a range of 280 to 400 msec (in a simple auditory choice reaction time task in young adults) and to be maximal at Pz, another electrode in the 10–20 system. Though signal averaging attenuates unsynchronized noise at every frequency as it improves SNR, frequency filters are commonly applied prior to component measurement. These filters are useful whenever the frequency of the noise is different from that of the signal.

Digital frequency filters (11) have the advantage over analog filters of being able to operate without introducing distorting phase shifts into the signal. The most commonly used digital filter has been the moving average or boxcar filter, in which each point of the signal is replaced by an average of that point and a certain number of prior and subsequent points. This is only possible for stored data, because it makes use of future time points to calculate current output. Farwell et al. (20) have shown that a simple moving average filter does not prepare average and single-trial waveforms as well for P300 peak-picking as does a filter designed by an optimizing algorithm. Such an algorithm determines a set of weights that are able to reduce deviations (ripple or ringing) in the passband and stopband of the filter. Optimized filters have less tendency to reduce P300 amplitude or distort shape and, in the case of averages, gave more stable latency measurements. For P300, the authors recommend that the optimum filter have a passband cut-off frequency of 6 Hz, a stopband cut-off frequency of 8 or 8.5 Hz, and use 490/n points, where n is the sampling interval in milliseconds. It should be emphasized that analog filters still have a place in data acquisition prior to digital filtering—a low-pass analog filter with a half-power frequency below but close to half the sampling rate prevents aliasing, and, for P300 recording, a high-pass analog filter with a half-power frequency of less than 0.16 Hz minimizes irrelevant baseline shifts (20).

Current source density maps (also called surface Laplacian or radial current estimate maps) act as spatial filters emphasizing localized components with a high spatial frequency. For this to work well of course, electrodes must be placed with a high spatial frequency. Maps can be made of unaveraged activity such as epileptic spikes or of signal averages. Sensory ERP components show a more localized distribution using this approach than in voltage maps. For example, Nagamine et al. (46) compared voltage and current source density maps on the scalp ERPs obtained by tibial nerve stimulation. The results for a single subject presented in Fig. 2 demonstrate better localization for P40, N50, and P60 for the current source density map. The equation for calculating current source density is I = r(d2V/dx2 + d2V/dy2), where V is the voltage, x and y the surface location on the x–y plane, and r the charge density. In addition, r = k * d2, where d is the distance between electrodes and k is a constant for all electrodes within a subject. The Laplacian operator can give limits for finding equivalent dipoles. It has a physical interpretation—local radial current flow from the brain into the scalp and vice versa—but it is different from dipole modeling (described below) and is free of dipole modeling's ambiguities.

In the Laplacian calculation, surface contours can be generated by a method called spherical spline interpolation, which is based on physical principles for minimizing the deformation energy of a thin sphere constrained to pass through known points (51). This produces a smooth surface running through the data values and filling in between them, even when electrodes are irregularly placed on the scalp. Spherical splines have advantages over plate splines, which are based on deformation of an infinite thin plate. As might be expected from the fact that interpolated values at any point are derived from data from other locations, coherence (a measure of covariation) is inflated by interpolation. Nearest-neighbor interpolations are less smooth and inferior for locating extrema (peaks and troughs must lie on an electrode site) but do not inflate coherence.

Gevins et al. (27) have demonstrated a method of current source density mapping they call finite element model deblurring that they believe is superior to the Laplacian method. Mathematically, it is a less computationally demanding version of dipole modeling known as spatial deconvolution, which assumes that all dipoles are located on a cortical surface. Gevins et al. use the subject's head MRI to provide information about conducting volumes between scalp and cortical surfaces.

A simpler spatial filter, the vector filter (30), has been used for component measurement. Its output is the weighted sum of data points at different electrodes. Conceptually, measuring a component at one lead is the same as applying a vector filter with weight 1 assigned to values at that lead and weight 0 to values at all other leads. Vector filtering assumes that the distribution of the component to be measured is constant despite changes in amplitude or latency. The crux of the procedure is how to specify the weights: using three 10–20 system scalp electrodes, Fz, Cz, and Pz, weights of 0.15 for Fz, -0.53 for Cz, and 0.83 for Pz were found to produce optimal discrimination in an oddball paradigm between rare trials, which contain substantial P300s, and frequent trials, which do not (30). Thus, optimum weights do not necessarily correspond to component distribution, because P300 is larger at Cz than at Fz. Dipole modeling, which is described below, can act as both a spatial and temporal filter.

A component can be defined as electrical or magnetic activity associated with a specific neurological or psychological process, for example, a motor act such as moving one's finger, a sensory process such as the reaction to a light flash, or a cognitive process such as categorizing a stimulus as target or nontarget. In a statistical sense a component explains experimental variance. The details of the experimental method are part of the operational definition of a component. As more experiments are done, theoretical expectations about components develop into generalizations. For example, many experiments in which subjects performed a fixed foreperiod reaction time task have resulted in a parietal–central negative shift prior to the button press. A natural generalization is that the parietal–central shift represents preparation for a motor act. Furthermore, because the source of the recorded data is a physical location within the brain, the ultimate description of a component must include reference to the specific brain structures activated. Some leads or sensors will pick up activity from those structures better than others, particularly when sources are multiple with overlapping influences. In the case of ERPs, the choice of voltage reference influences how electrical activity from a source appears in the EEG recording.

Measurement procedures include peak picking, area measurement, waveform subtraction, principal components analysis, template correlation, and dipole modeling. Peak picking means finding maxima or minima in specified latency ranges and determining peak latency and amplitude with respect to a prestimulus baseline. This is the simplest method of component evaluation, but can be biased when latency ranges are selected after an inspection of the data, and is perhaps unduly restricted in that it considers only peaks among other waveform features. In addition, it is often based on only one point, which may be influenced by noise or overlapping components. With multiple leads, another limitation of peak picking becomes obvious: what appears by shape to be a single component has maxima at different time points in different leads, and it is not clear how best to resolve the discrepancies. Furthermore, the choice of reference electrodes can determine when peaks and troughs appear.

Area measurement is sometimes used when the component is believed to be more rectangular than peaked. Area is measured in a specified latency range, and is thus based on multiple points, but area measurement, like peak picking, can be biased and is influenced by overlapping components.

Waveform subtraction can be used before peak picking or area measurement to reduce the effects of component overlap. For example, consider a paradigm where tones of two pitches are given in an unpredictable sequence and one occurs less frequently and is designated as the target of some task. The ERP to the rare tone can be considered a combination of the sensory effects of the tone and the cognitive effects of the tone being a rare target. By subtracting the ERP to the frequent tones from the ERP to the infrequent tones, the sensory effects are removed leaving behind the cognitive effects. This assumes that the sensory responses to the two tones are identical and that cognitive and sensory effects are additive, an assumption that is not always warranted. For example, frequency-specific temporal recovery of the auditory N100, a noncognitive effect, makes the response of N100 to frequents smaller than the response of N100 to rares.

Principal components analysis (PCA) is another approach to ERP component measurement, which uses the time points on waveforms from different subjects, different electrodes, and different experimental conditions to define components. In statistical terms PCA identifies orthogonal axes of maximal variance in a multidimensional space defined by the variables. Generally these axes are rotated according to the varimax procedure. Less arbitrary than peak picking, PCA makes no assumption about the latency range in which specific components will be found but only that they have a fixed latency across conditions and subjects. It has some ability to separate overlapping components. However, PCA is not completely free from arbitrariness. First, PCA solutions are not unique. Many rotations of the factors are possible. Second, results depend to a certain extent on what experimental conditions are chosen and how many leads are included. Variance from electrodes, subjects, conditions, and correlated noise are all treated the same. Furthermore, each experiment gives slightly different factor structures, and there is no established criterion for deciding whether these differences are significant or not. Thus, it is uncertain how many statistical components to interpret, and how to identify these components with ones previously described.

Template correlation assesses the similarity of a template of the component to the waveform to be evaluated. The template may be based on prior knowledge of the component shape or on signal averages (see the iterative Woody filter procedure described above). The template is usually compared to waveforms at specified intervals over a designated latency range to identify the latency of maximum correlation (or in one variation, maximum covariance). This time point is defined as the peak. The sum of cross products at this time point or the difference between amplitude at this point and a baseline can define amplitude.

Interpreting latency data under different experimental conditions can be difficult when multiple leads are involved. Latency may vary at different leads and topography may vary under different conditions, implying different components whose latency cannot be compared. To solve these problems, Brandeis et al. (8) spatially generalized the Woody filter procedure using an average reference map, and applying a measure they call global field power (GFP) defined by the following formula for an array A consisting of data from C electrodes:

Further, global dissimilarity (GD) is defined as the root mean square (rms) power of the difference maps calculated by subtracting two normalized GFP maps. The procedure is as follows: (a) Grand averages are used to form template GFP maps, from which component model maps at single latencies near 100, 200, and 400 msec are derived, corresponding to P1, N1, and P3 (see ref. 8 for details). (b) Component model maps are moved in specified latency ranges around the latency of each model's component. The minimum of GD multiplied by sequential dissimilarity (GD between current and previous map: a stability constraint) is calculated, and the minimum of this function (best fit) is defined as the map latency for that component. (c) In an iteration, the average of all normalized maps at their latencies of best fit is used as a new model, and the search window is set around the new mean latency. The results show that components can be identified by topography alone, without respect to amplitude or time. However, this method does not take into account possible overlapping components and would fail if such components influenced topographies. Furthermore, average references for P300, which is widely distributed on the top of the head, may be inferior to a noncephalic reference.

Dipole modeling is a method for reducing data from multilead EEG or multisource MEG by deducing the dipole sources that may have produced them. Although the forward problem (calculating scalp distribution from known dipoles) has a unique solution whose accuracy is limited only by the approximations of skull geometry and conductivities, the inverse problem has multiple mathematically valid solutions as was pointed about by Helmholz more than a century ago (33). The reason is that a single scalp distribution can be produced by different numbers of dipoles in different combinations of locations and orientations. Thus, various constraints on the number of sources allowed and their approximate location must be applied to reach a solution. Sometimes these constraints are so severe as to specify that the source be a single dipole located somewhere in the brain.

At an abstract level, dipole modeling is like PCA in that an equation U = C * S must be solved where U is an array of k electrodes at t times that represents the linear superimposition of the array S of m sources at t times multiplied by C weighing coefficients at k electrodes for m sources (62). Whereas PCA determines C and S from mathematical constraints, dipole modeling assumes that C depends on volume conduction from j dipoles at certain locations, assuming Ckj = f(rj,oj,ek ), where f is a nonlinear function of the electrode location vector ek and of the geometry of the source and the head. The dipole has a location vector rj and the orientation vector oj. Equations defining a 3-shell sphere model of the head with differing conductivities for scalp, skull, and brain are found in the appendix to this chapter. Using these equations to model dipoles at various depths, Pfefferbaum (52) demonstrated how increasing the thickness of the superficial extrasulcal subarachnoid layer of cerebrospinal fluid (CSF) or skull thickness might affect scalp ERP amplitudes and topographic distributions.

One procedure for the dipole modeling of ERPs was developed by Scherg and Berg (64). Their software is available commercially as brain electrical source analysis (BESA, from Neuroscan, Inc.). It models a window of points, assuming a finite number of equivalent dipoles with fixed location and orientation. In its recent version, it does not assume a parametric dipole magnitude function (like the decaying sinusoid of ref. 70) but computes a varying magnitude function over the window of points for each dipole. The BESA model is applied iteratively, calculating at each step the residual variance (percentage of recorded data not explained by the model). The first step looks for the inverse solution by calculating parameters of a plausible dipole from an EEG or MEG data map. Then forward solutions calculate resultant EEG or MEG maps from those dipoles. Hundreds of iterations may take place, stopping when the change in residual variance is less than some criterion, such as 0.001%. When more than one dipole is modeled, some may be fixed in position (but not in amplitude) while a new dipole is optimized. The results of these procedures depend among other things on the starting location and other parameters of a dipole. An iterative procedure may find topographically local optima that would not be optima if all locations and orientations were tested. Scherg and Berg (64) explained that multiple-source solutions are less arbitrary if spatial and temporal constraints are added. For example, two sources may be required to have a symmetry between hemispheres, radial and tangential dipoles, or lie in the supratemporal plane. How this method works is illustrated in http://www.sina.com.cn 2005年12月04日 14:47 南方都市报
 
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