Widge, A.S., Boggess, M., Rockhill, A.P., Mullen, A., Sheopory, S., Loonis, R. Freeman, D.K., and Miller, E.K. (2018) Altering alpha-frequency brain oscillations with rapid analog feedback-driven neurostimulation. PLOS ONE
Oscillations of the brain’s local field potential (LFP) may coordinate neural ensembles and brain networks. It has been difficult to causally test this model or to translate its implications into treatments because there are few reliable ways to alter LFP oscillations. We developed a closed-loop analog circuit to enhance brain oscillations by feeding them back into cortex through phase-locked transcranial electrical stimulation. We tested the system in a rhesus macaque with chronically implanted electrode arrays, targeting 8–15 Hz (alpha) oscillations. Ten seconds of stimulation increased alpha oscillatory power for up to 1 second after stimulation offset. In contrast, open-loop stimulation decreased alpha power. There was no effect in the neighboring 15–30 Hz (beta) LFP rhythm or on a neighboring array that did not participate in closed-loop feedback. Analog closed-loop neurostimulation might thus be a useful strategy for altering brain oscillations, both for basic research and the treatment of neuro-psychiatric disease
An article in Scientific American discusses the debate about whether brain waves have a function or whether they are an epiphenomenon:
Do Brain Waves Conduct Neural Activity Like a Symphony? There are so many things wrong with this debate.
We know so little about how the brain functions. To dismiss observable signals as an epiphenomenon assumes a level of knowledge that no one has. It is merely a defense of the status quo paradigm, straight out of Thomas Kuhn. The attitude boils down to “they don’t play a role because we kind of already know how the brain works”. No, you don’t. No one does.
What do neurons do?: They spike when their membrane potential reaches the spiking threshold. What do oscillations do? They move the membrane potential towards and away from the spiking threshold. You start with the assumption that oscillations matter, not that they don’t. The latter is just a defense of what you think you already know. That attitude holds back progress.
The article says that “critics point out that oscillations arise everywhere one looks in nature” so therefore brain oscillations are not functional. To my mind, that is an argument *for* a functional role. Evolution builds on what is already available.
Critics also say that there is not a lot of evidence for a link between oscillations and mental states. Yes, there is. There are more and more papers published each week. There might be more evidence for spiking but that is just because spiking has been studied longer. The same type of correlational evidence is there for both spikes and oscillations. As for the lack of a causal role between oscillations and brain function, the evidence for a causal role between spiking and function is equally flimsy. Spiking is not the gold standard just because it is the first and easiest thing we could measure when we only had single-channel amplifiers and slow or no computers.
Skepticism is a good thing but where is the skepticism about the notion that spikes do it all? For example, consider the following quote from the article: “evidence amassed so far is not based on rigorous tests looking for a cause-and-effect relationship between gamma waves and specific neural processes.” The same statement is equally true for cause-and-effect between spikes and function. Somehow that gets a free pass? The oscillation naysayers accept their spikes-only model without question but set a high bar for others. I refer you back to Thomas Kuhn.
Spikes vs oscillations is not an either/or thing. They both work together. Indeed, it is hard to imagine how one would decouple them. The bottom line is that both spikes and oscillations are both signals. No one knows enough about how the brain works to dismiss the measuring of a signal. Since when is more data a bad thing?
Widge AS, Boggess M, Rockhill AP, Mullen A, Sheopory S, Loonis R, Freeman DK, Miller EK (in press) Altering alpha-frequency brain oscillations with rapid analog feedback-driven neurostimulation. PLOS ONE
Stayed tuned for the exciting details.
Check out this National Public Radio piece on the debate about our new model of working memory:
Neuroscientists Debate A Simple Question: How Does The Brain Store A Phone Number? I have a few follow-up points.
1. In the piece, Christos Constantinidis says: “The problem with the theory is that so far there has been no experimental evidence linking this critical variable with behavior,”
This is false. We have a paper entirely devoted to experimental evidence linking the theory to behavior:
Lundqvist, M., Herman, P. Warden, M.R., Brincat, S.L., and Miller, E.K. (2018) Gamma and beta bursts during working memory read-out suggest roles in its volitional control. Nature Communications. 9, 394 View PDF
2. Christos also says: Miller’s contention that working memory is linked to long-term memory seems at odds with doctors’ experience with patients whose brains have been injured.
This totally misses the point. Working memory can exploit some of the same mechanisms as long-term memory (synaptic weight changes) while at the same time rely on different brain areas. The fact that you can have brain damage that disrupts working memory without disrupting long-term memory is completely irrelevant.
3. Most importantly, the people who support the old model of persistent activity have not done the crucial test. All the evidence for the old model of persistent activity averages activity across trials. You cannot do this. Averaging creates an illusion of persistence. You must examine activity on single trials. Unless you do that, you are not addressing the issue.
If you want to read about our new working memory, check out this paper:
Miller, E.K., Lundqvist, L., and Bastos, A.M. (2018) Working Memory 2.0 Neuron, DOI:https://doi.org/10.1016/j.neuron.2018.09.023 View PDF
Our new paper describing a new model of working memory. Actually, not so much a new model as an update to the classic model. The classic model posited that we hold thoughts “in mind” (i.e., in working memory) via the persistent spiking of neurons. That is not wrong. It is right to a certain level of approximation. There is little doubt that spikes help maintain working memories. However, a closer examination revealed that there is much more going on than persistent spiking.
It is important to keep in mind (pun intended) that virtually all evidence for persistent spiking comes from experiments that averaged neural activity across trials. The assumption was that averaging boosts signal and decreases noise (“noise” meaning changes in activity from trial to trial). But what if that noise was not noise but real neural dynamics? We don’t always think about the same thing in the same way. Averaging assumes we do.
With this in mind, we and others have been leveraging multiple-electrode recording to examine neural activity in “real time” (on individual trials). This has revealed that working memory-related spiking occurs in sparse, coordinated bursts of activity. It also revealed oscillatory dynamics between brain waves in two frequency bands, beta and gamma. Gamma seems to act as a carrier wave that maintains the contents of working memory. Beta seems carry the top-down control signals that allow us to exert volitional control over working memory.
Miller, E.K., Lundqvist, M., and Bastos, A.M. Working Memory 2.0 Neuron DOI:https://doi.org/10.1016/j.neuron.2018.09.023 (Download a *free* copy for the next 50 days)
Here’s the abstract:
Working memory is the fundamental function by which we break free from reflexive input-output reactions to gain control over our own thoughts. It has two types of mechanisms: online maintenance of information and its volitional or executive control. Classic models proposed persistent spiking for maintenance but have not explicitly addressed executive control. We review recent theoretical and empirical studies that suggest updates and additions to the classic model. Synaptic weight changes between sparse bursts of spiking strengthen working memory maintenance. Executive control acts via interplay between network oscillations in gamma (30–100 Hz) in superficial cortical layers (layers 2 and 3) and alpha and beta (10–30 Hz) in deep cortical layers (layers 5 and 6). Deep-layer alpha and beta are associated with top-down information and inhibition. It regulates the flow of bottom-up sensory information associated with superficial layer gamma. We propose that interactions between different rhythms in distinct cortical layers underlie working memory maintenance and its volitional control.
V1-V4 gamma coherence before stimulus change predicts reaction time to detect the change while deviations from the phase relation increases reaction times. Nice.
Gamma Synchronization between V1 and V4 Improves Behavioral Performance
Gustavo Rohenkohl, Conrado Arturo Bosman, Pascal Fries
A trial by trial analysis showed that beta bursts, as opposed to power averaged across trials, is a good predictor of variations in motor behavior.
Torrecillos, F., Tinkhauser, G., Fischer, P., Green, A. L., Aziz, T. Z., Foltynie, T., … & Tan, H. (2018). Modulation of beta bursts in the subthalamic nucleus predicts motor performance. Journal of Neuroscience, 38(41), 8905-8917.
Rodu, J., Klein, N., Brincat, S. L., Miller, E. K., & Kass, R. E. (2018). Detecting Multivariate Cross-Correlation Between Brain Regions. Journal of neurophysiology.
The problem of identifying functional connectivity from multiple time series data recorded in each of two or more brain areas arises in many neuroscientific investigations. For a single stationary time series in each of two brain areas statistical tools such as cross-correlation and Granger causality may be applied. On the other hand, to examine multivariate interactions at a single time point, canonical correlation, which finds the linear combinations of signals that maximize the correlation, may be used. We report here a new method that produces interpretations much like these standard techniques and, in addition, 1) extends the idea of canonical correlation to 3-way arrays (with dimensionality number of signals by number of time points by number of trials), 2) allows for nonstationarity, 3) also allows for nonlinearity, 4) scales well as the number of signals increases, and 5) captures predictive relationships, as is done with Granger causality. We demonstrate the effectiveness of the method through simulation studies and illustrate by analyzing local field potentials recorded from a behaving primate.
When MIT neuroscientist Earl Miller was in graduate school at Princeton, he was inspired by the lectures of George A. Miller, an influential psychologist who helped to spark the young student’s interest in working memory. Now, as the newly named 2019 recipient of the George A. Miller Prize in Cognitive Neuroscience, Earl Miller is set to deliver a lecture honoring his teacher at the annual meeting of the Cognitive Neuroscience Society in San Francisco in March.
Read more here.
Nice review from Miler Lab alumnus Joni Wallis arguing for the importance of single-trial analyses. Variability across trials may not be noise, it may be cognition. Joni argues that ensembles, not single neurons, are the fundamental unit in the brain. One needs to record from many neurons simultaneously to understand cognitive processes.
Wallis, J. D. (2018). Decoding Cognitive Processes from Neural Ensembles. Trends in Cognitive Sciences.