毕竟,大脑的“背景噪音”可能有意义

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2020年1月的一次睡眠研究研讨会上,詹娜·伦德纳提出了一些发现,这些发现暗示着一种观察人们大脑活动的方法,以了解清醒与无意识之间的界限。对于昏迷或处于麻醉状态的患者,医师正确进行区分非常重要。

伦德纳认为,答案不于规则的脑波,而是于科学家通常可能会忽略的神经活动方面:不稳定的背景噪声。“我说是。别人的声音是别人的信号。’”

2020年1月的一次睡眠研究研讨会上,詹娜·伦德纳(Janna Lendner)提出了一些发现,这些发现暗示着一种观察人们大脑活动的方法,以了解清醒与无意识之间的界限。对于昏迷或处于麻醉状态的患者,医师正确进行区分非常重要。但是,这样做比听起来要棘手得多,因为当某人处于快速眼动(REM)梦的梦境中时,他们的大脑会产生与清醒时一样熟悉的,平稳的振荡脑波。

伦德纳认为,答案不于规则的脑波,而是于科学家通常可能会忽略的神经活动方面:不稳定的背景噪声。

一些研究者似乎是不可思议的。 “他们说,'那么,你是告诉我,噪声中是否有诸如此类的信息?'”伦德纳说,他是德国图宾根大学医学中心的麻醉医师,最近加利福尼亚大学完成了博士后研究。 ,伯克利。 “我说是。别人的声音是别人的信号。’”

伦德纳(Lendner)是越来越多的神经科学家之一,他们对大脑电活动中的噪声可能为其内部运作提供新线索的想法感到鼓舞。曾经被视为令人讨厌的电视静态信号的神经学等效物,可能会对科学家如何研究大脑产生深远的影响。

怀疑论者曾经告诉神经科学家布拉德利·沃伊泰克(Bradley Voytek),大脑活动的这些嘈杂特征没有什么值得研究的。但是他自己对随着年龄增长而产生的电噪声变化的研究,以及有关不规则大脑活动的统计趋势的先前文献,都使他确信它们丢失了一些东西。因此,他花了多年的时间来研究一种方法,以帮助科学家重新思考他们的数据。

加州大学圣迭戈分校认知科学与数据科学副教授Voytek表示:“仅能站一群科学家面前说,'嘿,我认为我们做错了事情。'迭戈“您必须给他们一个新的工具来做事”,以不同或更好的方式。

与加州大学圣地亚哥分校和伯克利分校的神经科学家合作,Voytek开发了一种软件,该软件可隔离规则振动(例如,睡眠和清醒受试者中都经过大量研究的α波)隐藏大脑活动的非周期性部分中。这为神经科学家提供了一种剖析规则波和非周期性活动的新工具,以使它们行为,认知和疾病中的作用分离开。

Voytek和其他科学家正以各种方式进行研究的现象有很多名称。有人称其为“ 1 / f斜率”或“无标度活动”; Voytek已推动将其重命名为“非周期性信号”或“非周期性活动”。

这不仅是大脑的怪癖。 Lendner,Voytek和其他人寻找的模式与一种现象有关,科学家于1925年开始注意到整个自然世界和技术中的复杂系统。统计结构许多不同的背景下神秘地出现,有些科学家甚至认为它代表了未发现的自然法则。

尽管已发表的研究研究了心律失常性大脑活动已有20多年的历史,但没有人能够确定其真正含义。但是,现,科学家们有了更好的工具,可以新实验中隔离非周期性信号并寻找更深入的旧数据。得益于Voytek的算法和其他方法,最近几年进行的一系列研究都提出了这样的想法,即非周期性活动包含隐藏的宝藏,可以促进衰老,睡眠,儿童发育等方面的研究。

我们的身体适应熟悉的心跳和呼吸节奏,这是生存所必需的持续周期。但是大脑中同样重要的拍子似乎没有规律,它们可能包含有关行为和认知基础的新线索。

当一个神经元向另一个神经元发送一种称为谷氨酸的化学物质时,它会使接受者更容易开火。这种情况称为激发。相反,如果神经元吐出神经递质γ-氨基丁酸或GABA,则受体神经元发火的可能性降低;那是禁忌。两者中的任何一个都具有后果:兴奋失控导致癫痫发作,而抑制则表现为睡眠,更极端的情况下,则表现为昏迷。

为了研究兴奋与抑制之间的微妙平衡,科学家使用脑电图或EEG测量大脑的电活动。激发和抑制的循环形成了与不同精神状态有关的波。例如,大脑8到12赫兹左右的辐射会形成与睡眠有关的阿尔法波型。

但是大脑的电输出不能产生完美平滑的曲线。取而代之的是,这些线向上倾斜至峰谷而向下倾斜至谷点时抖动。有时大脑活动没有规律性,而看起来更像是电噪声。它的“白噪声”成分确实是随机的,如静态的,但其中一些具有更有趣的统计结构。

像Voytek这样的神经科学家所感兴趣的正是那些平滑度和噪音方面的缺陷。他说:“它是随机的,但是有不同的随机性。”

为了量化这种非周期性活动,科学家分解了原始的EEG数据,就像棱镜可以将光束分解成不同颜色的彩虹一样。他们首先采用了一种称为傅立叶分析的技术。随时间绘制的任何数据集都可以表示为三角函数之和,例如正弦波,可以用其频率和幅度表示。科学家可以称为功率谱的图中绘制不同频率处的波幅。

功率谱的幅度通常以对数坐标绘制,因为它们的值范围很广。对于纯随机的白噪声,功率谱曲线相对平坦且水平,斜率为零,因为所有频率上功率谱曲线大致相同。但是神经数据会产生具有负斜率的曲线,从而使较低的频率具有较高的振幅,并且强度对于较高的频率呈指数下降。将该形状称为1 / f,是指频率和幅度之间的逆关系。神经科学家对坡度的平坦或陡峭可能表明大脑的内部运作方式很感兴趣。

不列颠哥伦比亚大学的认知神经科学家劳伦斯·沃德(Lawrence Ward)解释说,以这种方式分析EEG数据类似于查看高速公路桥梁上的录音中的声波。随机驶过的汽车发出的嗡嗡声会产生非周期性的背景特征,但是附近每隔10分钟发出哨声的火车会产生一个周期性的信号,其数据峰值比背景大。突然的一次性事件(例如长号喇叭或车辆碰撞)会声波中产生明显的尖峰,从而导致整体1 / f斜率。

1 / f现象的意识可以追溯到贝尔电话实验室的J.B. Johnson于1925年发表的论文,当时他正研究真空管中的噪声。四年后,德国科学家汉斯·伯格(Hans Berger)发表了第一篇人类脑电图研究。随后几十年的神经科学研究主要集中大脑活动中的周期性周期性波动上。然而,各种电子噪声,股市活动,生物节奏甚至音乐片段中都发现了1 / f波动,而且没人知道为什么。

也许是因为它看起来如此普遍,所以许多生物学家不赞成这样的想法,即通过1 / f特性的镜头观察噪声会产生有用的信号。纽约大学格罗斯曼医学院神经病学,神经科学和生理学助理教授Biyu J. He2014年《认知科学趋势》一书中写道,他们认为这可能是所使用的科学仪器产生的一种噪音。

但是他和其他人通过控制仪器噪声的实验揭穿了这个想法,事实证明,这种噪声的大小远小于非周期性大脑活动。其于Neuron的2010年论文中,He和她的同事们还发现,尽管EEG读数,地面地震波和股市波动都呈现出1 / f趋势,但来自这些来源的数据却呈现出不同的高阶统计结构。这种洞察力削弱了一种观念,即单一的自然规律会所有事物中产生非周期性的信号。

但是,这不是一个完全解决的问题。沃德发现了不同情况下的数学共性,并相信幕后可能会发生一些根本性的事情。

无论哪种方式,沃德和他都认为值得大脑中进行更深入的探索。

他2014年的论文中写道:“数十年来,1 / f斜率中包含的大脑活动被认为不重要,并且经常被从分析中删除,以强调大脑的振动。” “但是,近年来,越来越多的证据表明,无标度的大脑活动会积极促进大脑功能。”

Voytek偶然地陷入了非周期性信号的话题:他最初想建模并从EEG数据中消除白噪声。但是,当他破解代码以消除噪音时,他开始更加关注其中的有趣之处。

Voytek2015年与他的博士生伯克利大学神经科学教授罗伯特·奈特(Robert Knight)进行的一项研究中发现,老年人的大脑似乎比年轻人的大脑更活跃。 Voytek和Knight观察到,随着大脑的衰老,它更多地受到白噪声的控制。他们还发现这种噪音与年龄相关的工作记忆下降之间存相关性。

Voytek希望神经科学家拥有能够更轻松,自动地隔离任何数据集(包括旧数据集)中的周期性和非周期性特征的软件,并帮助研究人员寻找有意义的1 / f趋势。因此,他和他的团队编写了一个程序来实现这一目的。

对这种工具的需求立即变得清晰起来。 Voytek及其同事于2018年4月11日将代码发布到网站biorxiv.org之后,它一个月内获得了近2,000次下载-这对利基神经科学计算工具而言是巨大的成功。那年11月,Voyytek神经科学协会会议上主持了关于如何使用它的仅限客厅演讲。由于它的受欢迎程度,他组织了最后一分钟的跟踪会议,他的实验室团队为数十名感兴趣的科学家提供了技术支持。教程和电子邮件的交换导致了新的合作。

其中一项合作是Lendner对睡眠中唤醒标记的研究,该研究发表2020年7月的线杂志eLife上。借助Voytek的软件,Lendner及其同事发现,测试对象的EEG的非周期性噪声中,高频活动下降了。快速眼动睡眠比清醒快。换句话说,功率谱的斜率更陡。

伦德纳(Lendner)和她的合著者论文中指出,非周期性信号可以作为衡量一个人的意识状态的独特标志。像这样的新的客观标记可以帮助改善麻醉和昏迷患者的治疗方法。

其他使用Voytek编码的已发表研究包括对ADHD药物功效的研究以及对自闭症患者大脑活动中基于性别的差异的研究。该代码于2020年11月首次同行评审期刊《自然神经科学》上发表;加州大学圣地亚哥分校的托马斯·唐诺休(Thomas Donoghue)和马塔尔·哈勒(Matar Haller)(当时伯克利)是该论文的第一作者,伯克利的Avgusta Shestyuk是Voytek的联合资深作者。他们和团队的其他成员模拟数据上演示了代码的性能以及揭示新发现的潜力。

她说,Voytek实验室的博士后研究员Natalie Schaworonkow通常研究诸如α波之类的规则振动,“它比非周期性信号更美丽,”她说,这使Voytek我们共同的Zoom通话中笑了起来。但是最近当她的兴趣转向婴儿的大脑以及作为其认知发展特征的电子模式时,她面临一个问题,因为婴儿不会产生这些优雅的阿尔法波。究竟何时以及如何开始出现波是一个悬而未决的问题。

她使用该算法分析了婴儿脑活动的开放式EEG数据集。《发展认知神经科学》上发表的一篇新论文中,Schaworonkow和Voytek发现生命的头七个月内,非周期性活动发生了巨大变化。但是,需要进行更多的研究,以确定这种活动是否反映出随着儿童的成长而更多地参与任务,或者仅仅是灰质密度的增加。

Voytek的代码已推动了许多最新研究,但它并不是镇上唯一用于非周期性噪声分析的游戏。 2015年,科技公司Nvidia的Haiguang Guang和密歇根大学的Liu Zhongming都普渡大学工作(温是研究助理,刘是副教授),他们发表了另一种方法来将周期与非周期隔离脑活动中的成分,称为不规则重采样自动光谱分析(IRASA)。同时,自从这两种工具出现之前,他就一直研究这个主题。已故的神经科学家沃尔特·弗里曼(Walter J. Freeman)也是如此,他的工作启发了Voytek。尽管这要花很多时间,但也可以手工完成这种工作。

具有使神经科学家能够轻松地根据周期性和非周期性信号检查其数据的工具非常重要,因为数据本身只是特定时间段内收集的一组数字。点状图本身并没有说明大脑的功能或功能失常。

“解释神经科学中很重要,对吗?因为这就是我们根据临床决策和药物开发以及所有这类东西做出的决定,” Voytek说。他说,以这种方式重新审视时,文献中的大量数据集有可能产生新的见解,而且“我们没有像我们应该的那样对它们进行充分的解释。”

科学家对这些非周期性特征进行探索的一个很大的限制是,没人会确切地从生理上了解导致它们的原因。麦吉尔大学神经病学和神经外科,生物医学工程和计算机科学教授西尔万·贝耶(Sylvain Baillet)说,需要进一步研究来阐明不同的神经递质,神经回路和大规模网络相互作用的各自贡献。

“原因和来源仍未确定,”贝耶特说。 “但是我们必须进行这项研究以积累知识和观察。”

一种理论是,非周期性信号以某种方式反映出大脑保持自身健康和活跃所需要的兴奋与抑制之间的微妙平衡。伦德纳说,过多的刺激可能会使大脑超负荷,而过多的抑制可能会使大脑入睡。

奈特认为,这种解释是正确的。他说:“我不想说这是抑制-激励比的变化,但我认为这是最简约的解释。”

另一种想法是,非周期性信号仅反映大脑的物理组织。

根据其他物理系统如何反映1 / f行为,沃德认为大脑中可能存某种结构性,层次关系,从而引起非周期性活动。例如,这可能是由于大量神经元将自身组织成组,然后形成更大的区域共同起作用的方式而产生的。

他说,与1 / f趋势有关的大脑活动可能非常适合自然环境中处理感官输入,因为它经常表现出1 / f型波动。她一封电子邮件中说,她2018年发表《神经科学杂志》上的研究探索了大脑如何对也具有1 / f特性的声音做出预测,这表明非周期性活动“与处理和预测自然主义刺激有关”。从爵士到巴赫的音乐也可以具有1 / f的特性,这对她并不奇怪-毕竟,音乐是人脑的创造。

Voytek说,要检验关于非周期性信号来自何处的假设,研究人员需要更仔细地研究哪种神经回路会引起非周期性信号。然后,神经科学家可以尝试将具有这些回路的部位与大脑的整体生理联系起来,以更好地了解哪种神经机制会产生特定的活动模式,并预测不同的大脑疾病中非周期性和周期性信号的表现。

Voytek还希望进行更多的大规模研究,将代码应用于现有数据集,以找出未开发的信号。

Lendner和Knight目前正阿拉巴马大学分析昏迷患者的数据,以了解非周期性活动是否与昏迷的发生有关。他们的预测是,如果一个人昏迷,大脑中高频活动的增加将显示为1 / f斜率的变化。伦德纳说,初步结果令人鼓舞。

对于贝叶特来说,大脑中的非周期性信号有点像暗物质,这是宇宙的隐形支架,仅通过重力与正常物质相互作用。我们不了解它的构成或属性,但是它位于天体的背景下,故意将银河系一起。

科学家们尚未弄清楚是什么原因导致了这些非周期性信号,但是它们也可能反映了我们头脑中对宇宙至关重要的支撑结构。某种神秘的事物可能会帮助我们摆脱沉睡的生活。


英文译文:

At a sleep research symposium in January 2020, Janna Lendner presented findings that hint at a way to look at people’s brain activity for signs of the boundary between wakefulness and unconsciousness. For patients who are comatose or under anesthesia, it can be all-important that physicians make that distinction correctly. Doing so is trickier than it might sound, however, because when someone is in the dreaming state of rapid-eye movement (REM) sleep, their brain produces the same familiar, smoothly oscillating brain waves as when they are awake.

Lendner argued, though, that the answer isn’t in the regular brain waves, but rather in an aspect of neural activity that scientists might normally ignore: the erratic background noise.

Some researchers seemed incredulous. “They said, ‘So, you’re telling me that there’s, like, information in the noise?’” said Lendner, an anesthesiology resident at the University Medical Center in Tübingen, Germany, who recently completed a postdoc at the University of California, Berkeley. “I said, ‘Yes. Someone’s noise is another one’s signal.’”

Lendner is one of a growing number of neuroscientists energized by the idea that noise in the brain’s electrical activity could hold new clues to its inner workings. What was once seen as the neurological equivalent of annoying television static may have profound implications for how scientists study the brain.

Skeptics used to tell the neuroscientist Bradley Voytek that there was nothing worth studying in these noisy features of brain activity. But his own studies of changes in electrical noise as people age, as well as previous literature on statistical trends in irregular brain activity, convinced him that they were missing something. So he spent years working on a way to help scientists rethink their data.

“It’s insufficient to go up in front of a group of scientists and say, ‘Hey, I think we’ve been doing things wrong,’” said Voytek, an associate professor of cognitive science and data science at the University of California, San Diego. “You’ve got to give them a new tool to do things” differently or better.

In collaboration with neuroscientists at UC San Diego and Berkeley, Voytek developed software that isolates regular oscillations—like alpha waves, which are studied heavily in both sleeping and waking subjects—hiding in the aperiodic parts of brain activity. This gives neuroscientists a new tool to dissect both the regular waves and the aperiodic activity in order to disentangle their roles in behavior, cognition and disease.

The phenomenon that Voytek and other scientists are investigating in a variety of ways goes by many names. Some call it “the 1/f slope” or “scale-free activity”; Voytek has pushed to rebrand it “the aperiodic signal” or “aperiodic activity.”

It’s not just a quirk of the brain. The patterns that Lendner, Voytek and others look for are related to a phenomenon that scientists started noticing in complex systems throughout the natural world and technology in 1925. The statistical structure crops up mysteriously in so many different contexts that some scientists even think it represents an undiscovered law of nature.

Although published studies have looked at arrhythmic brain activity for more than 20 years, no one has been able to establish what it really means. Now, however, scientists have better tools for isolating aperiodic signals in new experiments and looking more deeply older data, too. Thanks to Voytek’s algorithm and other methods, a flurry of studies published in the last few years have run with the idea that aperiodic activity contain hidden treasures that may advance the study of aging, sleep, childhood development and more.

Our bodies groove to the familiar rhythms of heartbeats and breaths—persistent cycles essential to survival. But there are equally vital drumbeats in the brain that don’t seem to have a pattern, and they may contain new clues to the underpinnings of behavior and cognition.

When a neuron sends a chemical called glutamate to another neuron, it makes the recipient more likely to fire; this scenario is called excitation. Conversely, if a neuron spits out the neurotransmitter gamma-aminobutyric acid, or GABA, the recipient neuron becomes less likely to fire; that’s inhibition. Too much of either has consequences: Excitation gone haywire leads to seizures, while inhibition characterizes sleep and, in more extreme cases, coma.

To study the delicate balance between excitation and inhibition, scientists measure the brain’s electrical activity with electroencephalography, or EEG. Cycles in excitation and inhibition form waves that have been linked to different mental states. Brain emissions at around 8 to 12 hertz, for example, form the alpha wave pattern associated with sleep.

But the brain’s electrical output doesn’t produce perfectly smooth curves. Instead, the lines jitter as they slope up toward peaks and down toward troughs. Sometimes brain activity has no regularity and instead looks more like electrical noise. The “white noise” component of this is truly random like static, but some of it has a more interesting statistical structure.

It’s those imperfections in the smoothness, and in the noise, that interest neuroscientists like Voytek. “It’s random, but there’s different kinds of random,” he said.

To quantify this aperiodic activity, scientists break down the raw EEG data, much as a prism can decompose a sunbeam into a rainbow of different colors. They first employ a technique called Fourier analysis. Any set of data plotted over time can be expressed as a sum of trigonometric functions like sine waves, which can be expressed in terms of their frequency and amplitude. Scientists can plot the wave amplitudes at different frequencies in a graph called a power spectrum.

The amplitudes for power spectra are usually plotted in logarithmic coordinates because of the wide range in their values. For purely random white noise, the power spectrum curve is relatively flat and horizontal, with a slope of zero, because it’s about the same at all frequencies. But neural data produces curves with a negative slope such that lower frequencies have higher amplitudes, and the intensity drops off exponentially for higher frequencies. This shape is called 1/f, referring to that inverse relationship between the frequency and the amplitude. Neuroscientists are interested in what the flatness or steepness of the slope might indicate about the brain’s inner workings.

Analyzing EEG data in this way is analogous to looking at the sound waves from an audio recording made on a bridge over a highway, explains Lawrence Ward, a cognitive neuroscientist at the University of British Columbia. The hum of the tires from random passing cars would produce aperiodic background features, but nearby trains that sound a whistle every 10 minutes would generate a periodic signal with peaks in the data louder than the background. A sudden one-time event like a long horn honk or a vehicle collision would produce a noticeable spike in the sound wave, contributing to the overall 1/f slope.

Awareness of the 1/f phenomenon dates back to a 1925 paper by J.B. Johnson of Bell Telephone Laboratories, who was looking at noise in vacuum tubes. The German scientist Hans Berger published the first human EEG study just four years later. Neuroscience research in subsequent decades focused heavily on the prominent periodic waves in brain activity. Yet 1/f fluctuations were found in all kinds of electrical noise, stock market activity, biological rhythms, and even pieces of music—and no one knew why.

Perhaps because it seemed so universal, many biologists dismissed the idea that looking at noise through the lens of 1/f characteristics could yield useful signals; they thought it might be a form of noise from the scientific instruments used, wrote Biyu J. He, an assistant professor of neurology, neuroscience and physiology at New York University Grossman School of Medicine, in a 2014 review in Trends in Cognitive Sciences.

But He and others debunked that idea through experiments controlling for instrument noise, which turned out to be much smaller in magnitude than aperiodic brain activity. In a 2010 paper in Neuron, He and her colleagues also found that while EEG readouts, seismic waves in the ground, and stock market fluctuations all exhibit 1/f trends, the data from these sources exhibits different higher-order statistical structures. That insight put a dent in the idea that a single law of nature generates aperiodic signals in everything.

However, it isn’t a completely settled question. Ward has found mathematical commonalities in different contexts and believes something fundamental could be going on behind the scenes.

Either way, both Ward and He agree it’s worth probing deeper in the brain.

“For decades, brain activity contained in the ‘1/f slope has been deemed unimportant and was often removed from analyses in order to emphasize brain oscillations,” He wrote in the 2014 paper. “However, in recent years, increasing evidence suggests that scale-free brain activity contributes actively to brain functioning.”

Voytek fell into the topic of aperiodic signals somewhat accidentally: He originally wanted to model and remove white noise from EEG data. But as he hacked away at a code to pull out noise, he started paying more attention to what was interesting within it.

The brains of older adults seem to have more aperiodic activity than those of younger adults, Voytek found in a 2015 study with his doctoral adviser Robert Knight, a professor of neuroscience at Berkeley. Voytek and Knight observed that as the brain ages, it is dominated more by white noise. They also found correlations between this noise and age-related working memory decline.

Voytek wanted neuroscientists to have software that could more easily and automatically isolate the periodic and aperiodic features in any data set, including old ones, and help researchers look for meaningful 1/f trends. So he and his team wrote a program for an algorithm that could do that.

The demand for a tool like this became clear immediately. After Voytek and colleagues posted their code to the website biorxiv.org on April 11, 2018, it received nearly 2,000 downloads within the month—a big hit for a niche neuroscience computational tool. In November of that year, Voytek moderated a standing-room-only talk at the Society for Neuroscience conference on how to use it. Because of its popularity, he organized a last-minute follow-up session, where his lab team provided tech support to dozens of interested scientists. The tutorial and e-mail exchanges led to new collaborations.

One of those collaborations was Lendner’s study of markers for arousal during sleep, published in the online journal eLife in July 2020. With Voytek’s software, Lendner and her colleagues found that in the aperiodic noise of test subjects’ EEGs, the high-frequency activity dropped off faster during REM sleep than when they were awake. In other words, the slope of the power spectrum was steeper.

In their paper, Lendner and her co-authors argue that aperiodic signals can serve as a unique signature to measure a person’s state of consciousness. A new objective marker like this could help to improve the practice of anesthesia and treatments for coma patients.

Other published studies that used Voytek’s code included investigations of ADHD medication efficacy and studies of sex-based differences in brain activity in people with autism. The code was published in a peer-reviewed journal for the first time—Nature Neuroscience—in November 2020; Thomas Donoghue of UC San Diego and Matar Haller (then at Berkeley) were co-first authors of the paper, with Avgusta Shestyuk of Berkeley serving as co-senior author with Voytek. They and other members of the team demonstrated the code’s performance on simulated data and its potential to reveal new findings.

Natalie Schaworonkow, a postdoctoral fellow in Voytek’s lab, usually researches regular oscillations like alpha waves, “which are more beautiful than the aperiodic signal,” she said, making Voytek laugh in our shared Zoom call. But when her interests recently turned to the infant brain and the electrical patterns that are signatures of its cognitive development, she was faced with a problem, because infants do not produce these elegant alpha waves; exactly when and how the waves start to appear is an open question.

She used the algorithm to analyze an open EEG data set of infant brain activity. In a new paper published in Developmental Cognitive Neuroscience, Schaworonkow and Voytek found large changes in aperiodic activity during the first seven months of life. More research is needed, however, to figure out whether this activity reflects greater engagement in tasks as children grow up or just increases in gray matter density.

Voytek’s code has driven a lot of recent research, but it isn’t the only game in town for aperiodic noise analysis. In 2015, when Haiguang Wen of the tech company Nvidia and Zhongming Liu of the University of Michigan both worked at Purdue University (Wen was a research assistant and Liu was an associate professor), they published a different approach to isolating the periodic from the aperiodic components in brain activity, called irregular-resampling auto-spectral analysis (IRASA). Meanwhile, Biyu He has been working on the topic since before either of these tools arrived on the scene; so too did the late neuroscientist Walter J. Freeman, whose work inspired Voytek. It’s possible to do this kind of work by hand, though it is far more time-consuming.

Having a tool that allows neuroscientists to easily examine their data in terms of periodic and aperiodic signals is important because the data itself is just a set of numbers gathered over a specific period of time. A graph of points by itself doesn’t say anything about brain functioning or malfunctioning.

“Interpretation is what matters in neuroscience, right? Because that’s what we make clinical decision-making off of and drug development and all of this kind of stuff,” Voytek said. A huge wealth of data sets in the literature has the potential to yield new insights when reexamined in this way, he said, and “we haven’t been interpreting them as richly as we should.”

A big limitation in scientists’ exploration of these aperiodic features is that no one knows exactly what causes them physiologically. More research is needed to clarify the respective contributions of different neurotransmitters, neural circuits and large-scale network interactions, said Sylvain Baillet, a professor of neurology and neurosurgery, biomedical engineering, and computer science at McGill University.

“The causes and the sources are still not identified,” Baillet said. “But we have to do this research to accumulate knowledge and observations.”

One theory is that aperiodic signals somehow reflect the delicate balance between excitation and inhibition that the brain needs to keep itself healthy and active. Too much excitation may overload the brain, while too much inhibition may put it to sleep, Lendner said.

Knight thinks that explanation is on the right track. “I wouldn’t want to say I’m positive it’s an inhibition-excitation ratio change, but I think it’s the most parsimonious explanation,” he said.

An alternative idea is that the aperiodic signals simply reflect the brain’s physical organization.

Based on how other physical systems reflect 1/f behaviors, Ward thinks there could be some kind of structural, hierarchical relationship in the brain that gives rise to the aperiodic activity. For example, this might arise from the way that huge numbers of neurons organize themselves into groups, which then form larger regions that work together.

Brain activity related to 1/f trends may be ideally suited to processing sensory input in the natural environment, since that often exhibits 1/f-type fluctuations, He said. Her 2018 study in The Journal of Neuroscience explores how the brain is able to make predictions about sounds that also have 1/f properties, suggesting that aperiodic activity “is involved in processing and predicting naturalistic stimuli,” she said in an email. It isn’t surprising to her that music, from jazz to Bach, can also have 1/f properties—after all, music is a creation of the human brain.

To test hypotheses about where aperiodic signals come from, Voytek said, researchers need to look more closely at what kinds of neural circuitry could give rise to them. Neuroscientists can then try to link sites with those circuits to the brain’s overall physiology for a better idea of which neural mechanisms generate specific activity patterns, and to predict how the aperiodic and periodic signals would look in different brain disorders.

Voytek is also hoping to do more large-scale studies that apply the code to existing data sets to tease out untapped signals.

Lendner and Knight are currently analyzing data on coma patients at the University of Alabama to see if aperiodic activity correlates with how a coma evolves. Their prediction is that if a person is coming out of a coma, a rise in high-frequency activity in the brain will show up as a change in the 1/f slope. The preliminary results are promising, Lendner said.

For Baillet, the aperiodic signals in the brain are a bit like dark matter, the invisible scaffolding of the universe that interacts with normal matter only through gravity. We don’t understand what it’s made of or what its properties are, but it’s out there in the celestial background, furtively holding the Milky Way together.

Scientists haven’t figured out what causes these aperiodic signals yet, but they too may reflect an essential support structure for the universe in our heads. Something mysterious may help tip our minds from waking life into slumber.


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