Research




Linking human MEG/EEG signals to the underlying cellular- and circuit-level events via biophysically principled computational neural modeling

We have developed a unique model of neocortical circuitry that is designed to interpret the neural origin of human brain dynamics measured with MEG/EEG based on the biophysics of these signals. The model simulates electrical activity across the neocortical layers and includes individual cell types and layer specific synaptic inputs and outputs enabling a direct connection between the extracranially measured activity and the underlying cellular and circuit level events. We have employed this model to study the mechanisms of low frequency human brain rhythms and sensory evoked responses, and changes in these signals with perception, attention, and healthy aging. With funding from the NIH BRAIN Initiative, we have turned this model into a user-friendly software tool, Human Neocortical Neurosolver, from which researchers can develop and test hypotheses on the circuit-level mechanisms underlying their human electrophysiological data.



Mechanisms and meaning of low-frequency brain rhythms in human perception and attention

We are integrating our human MEG/EEG imaging and computational modeling approaches to study the mechanisms and meaning of low frequency brain rhythms (<100Hz), which are among the most common signatures of brain activity measured non-invasively in humans. We have observed that the power of low frequency rhythms in the alpha (7-14Hz) and beta (15-29Hz) shift with attention, predict perception, and are modulated with learning and meditative practice. We are applying our unique modeling techniques to interpret the network processes creating these rhythms and working closely with animal electrophysiologists, including Christopher Moore in the Neuroscience Department at Brown, and clinicians to test and refine model-derived predictions.



Integrated EEG, transcranial electrical current stimulation (tACS) and magnetic stimulation (TMS), and modeling for the rational design of non-invasive brain stimulation paradigms

A new direction of research in our group is in combing EEG and modeling techniques to develop rationally-designed non-invasive electrical brain stimulation paradigms (tACS, TMS). To this end, we have recently developed a novel integrated EEG/tACS device that enables simultaneous recording and stimulation based on the OpenEphys electrophysiology platform (www.openephys.org) and applied this system to study the impact of tACS on somatosensory perception. We are currently combining EEG/TMS and HNN modeling to study the impact of TMS on brain circuits and perception. Our goal is to design biophysically principled closed-loop stimulation paradigms to improve brain function.



Brain-body interactions in meditative movement practice

We are continuing work pioneered by our long-term collaborator and friend, Dr. Catherine Kerr, investigating brain-body interactions during qi-gong and other mind-body practices that lead to improved symptoms in patient populations, with a current focus on symptoms of fatigue in cancer survivors. This work is supported by a generous gift bestowed from the Landis-Berkman Family Fund.



Basal-ganglia modeling of the mechanisms and meaning of theta rhythms in conflict resolution

In collaboration with Dr. Michael Frank in the Cognitive and Linguistic Sciences Department at Brown, we are developing a model of basal-ganglia circuitry to study the mechanisms and meaning of (4-8 Hz) theta frequency rhythms known to increase in basal-ganglia and frontal cortex areas during conflict resolution. Our goal is to integrate systems level modeling of the circuitry involved in conflict resolution, pioneered in the Frank lab, with the biophysically detailed circuit level modeling developed in the Jones lab to bridge system level phenomena to the circuit level underpinnings.



Thalamic mechanisms of tremor oscillations and the rational design of deep brain stimulation

In collaboration with Brown Neurosurgeon Dr. Wael Asaad, we are developing a data-constrained model of a motor thalamic circuit to study the cellular mechanisms underlying tremor frequency oscillations (6-12Hz) in Essential Tremor patients. We are working with Dr. Asaad in the analysis of intracranial thalamic and cortical (ECoG) data from human patients who undergo thalamic deep brain stimulation surgery (DBS) for tremor. Recorded data is used to refine development of a biophysically principled thalamocortical circuit with an ultimate goal of designing rational DBS stimulation paradigms.