Our lab started out by primarily studying neural oscillations—or “brain waves”. Despite oscillations being one of the first-ever signals observed in the human brain back in 1929, we know surprisingly little about where neural oscillations come from, what they do, or even if they’re functional or “accidental”.
We’ve since expanded to try to understand not only oscillations, but also the “noise” in which they’re embedded. We believe that much of this non-oscillatory activity is also signal. we have shown that this aperiodic, non-oscillatory signal is also linked age-related cognitive decline, and physiologically may be related to the balance of neural excitation and inhibition.
In order to better understand the physiology and function of periodic oscillations and of aperiodic activity, we have developed several open-source Python toolboxes for simulating and parameterizing these kinds of data. Given that neural oscillations are correlated with nearly every aspect of healthy brain function and neurological/psychiatric disease, ranging from memory, attention, and motor control to depression, Alzheimer’s disease, and ADHD, it is important to make sure we’re measuring what we think we’re measuring.
Therefore, our lab steps back and asks two very simple questions:
Starting from those questions, we build our work forward to learn about the functional/computational role that oscillations play in human cognition, aging, and disease.
We do this from two complementary angles. From the experimental side, we conduct human cognitive experiments while recording either non-invasive electrical brain activity using EEG, or invasively by working with patients undergoing brain surgery (usually for epilepsy). We also collaborate extensively with researchers in a variety or other domains, and make considerable use of publicly available brain data (which we help catalog here).
We combine these experimental approaches with large scale data-mining, data science approaches, and machine-learning techniques to discover neural features—oscillatory and otherwise—that are informative of cognitive states.
From the computational side, we develop new methods for measuring and quantifying neural oscillations in both the time- and frequency domains (see our publications for our latest research). We also build models of neural circuitry to probe the computational capabilities of neural populations to investigate how oscillations impact computation and communication. Additionally, we model how these circuits contribute to the electrical brain signals we record. We validate these models by comparing to a wide range of electrophysiological data, including single-unit recordings and intracranial field potentials from humans and animals.
Ultimately, our research goal is to bridge these two approaches and construct an understanding of cognition built on the first principles of neurophysiology. Rather than asking, “What brain regions correlate with working memory or attentional load?” we ask, “Given what we know about the computational properties of neurons and neural systems, how can neural systems interact to give rise to cognitive phenomena we equate with ‘attention’ and ‘working memory’, and what are the behavioral and cognitive limitations and consequences of these biological constraints?”
In collaboration with his wife, Jessica Bolger Voytek, Voytek built and published brainSCANr, an algorithmic approach to aggregating information from more than 2 million peer-reviewed neuroscience articles. This project was his first attempt at automated science: using machine-learning tools to algorithmically generate novel hypotheses. Our philosophy with regards to the role of data-driven approaches to neuroscience is that large scale data analytics can complement and guide in-lab experimental research, but should not replace it.