Stephanie R. Jones, PhD
Dr. Stephanie Jones received her doctorate in Mathematics, with a concentration in Dynamical Systems Theory, from Boston University in 2001. She pursued post-doctoral training in human brain imaging focusing on MEG/EEG at the Martinos Center for Biomedical Imaging at Massachusetts General Hospital. Integrating her mathematical and experimental training, she currently designs biophysically principled mathematical models of neural circuits to study the mechanisms and meaning of human brain dynamics. Her focus is on developing data-constrained models that are translationally relevant. She joined the faculty in the Department of Neuroscience at Brown University in 2011. Dr. Jones trains a diverse group of post-doctoral, graduate and undergraduate students including those from the Neuroscience, Applied Mathematics and Biomedical Engineering Departments.
Samuel Neymotin, PhD
In collaboration with Stephanie Jones and researchers at MGH/Yale, I am working on the Human Neocortical Neurosolver (HNN) project. HNN is an open-source neural modeling tool designed to help researchers/clinicians interpret the neural origin of their human EEG, MEG, or ECoG imaging data. Our goal is to design HNN to be useful to researchers with no formal computational neural modeling experience who want to develop and test hypotheses on the cellular/circuit level generators of source localized human data. I also develop data driven multiscale models of the brain and methods of analysis for electrophysiological data in order to better understand neural dynamics and computation.
Simona Temereanca Ibanescu, PhD
My work investigates brain mechanisms and brain-body interactions in meditative movement practice, with a focus on clinical outcomes in patient populations. In collaboration with Dr. Stephanie Jones and a team of investigators, I design clinical trials and develop data analysis methods to study the impact of qigong on fatigue in cancer survivors, integrating multi-modal physiological and clinical measures including EEG, EMG, EKG, blood markers, and behavioral assays.
Robert Law, PhD
In collaboration with Professor Stephanie Jones, I am using a computational model of somatosensory cortex to address how beta-band events affect perception. The energy in the beta band before a tactile stimulus (e.g. a fingertap) is a strong indicator of whether that stimulus will be reported, and the neural activity putatively underlying that perception also leaves a trace in the event-related potential: a high level of beta not only blocks perception but affects this potential in a consistent way. Our modeling efforts are aimed at disentangling the neural mechanisms responsible for this effect.
Shane Lee, PhD
I am primarily interested in understanding the neural pathology of Essential Tremor (ET) and Parkinson's Disease (PD). My work, here with Stephanie's group and the lab of Wael Asaad, combines behaviorally-framed intraoperative and postoperative electrophysiology and biophysically-relevant computational modeling to understand (1) how motor activity is represented in the cerebellar motor system (via cerebellar motor thalamus and motor cortical areas) and (2) markers of disease in ET and PD. Specifically, I am interested in cerebellar thalamocortical and basal gangliar representations of tremor, error correction, and movement. My computational modeling of the thalamocortical circuit is focused on understanding mechanisms of deep brain stimulation (DBS), toward optimization of DBS protocols to improve efficacy for patients.
Another active area of research includes understanding functional implications of oscillatory activity in laminar neocortex. With others in the lab, we are modeling the genesis of short-time beta periodic events to understand how they may be expressed and used in somatosensory cortex. In a collaboration with Nancy Kopell and Miles Whittington, I am also modeling auditory cortical gamma oscillations to understand how sensory signals may be routed through distinct cortical lamina, with implications for repetition priming and suppression.
Non-invasive, low-intensity, electric stimulation has been used as a therapeutic treatment for neurological disorders since the early 1900's. While it is strongly accepted that this type of stimulation regulates neural activity to elicit some effect, the specific mechanisms of how such externally generated electric fields interact with ongoing neural activity is not well defined. To this point, we are studying the effects of transcranial alternating current stimulation (tACS) on normal cutaneous sensory processing. Using both human electrophysiology studies (EEG) and neural modeling techniques we are piecing together how the perturbation of groups of neurons leads to unique dynamics that produce perceived behaviors.
My research focuses on using non-invasive stimulation to probe and modulate somatosensory processing and perception in humans. Transcranial magnetic stimulation (TMS) is a safe and non-invasive tool that is used clinically for treatment-resistant depression and pre-surgical motor and language mapping. It also shows great potential for treatment of several other disorders; of particular interest, this includes autism, chronic pain, and Parkinson's disease, which are associated with aberrant somatosensory processing and/or rhythmic brain activity. Our goal is to understand how TMS affects cortical excitability at the circuit level, leading to large scale changes in macroscopic activity, and how this relates to augmentation and inhibition of tactile perception. To accomplish this, we utilize a multimodal approach, incorporating simultaneous human electrophysiology (EEG) and TMS informed by MRI and computational modeling.
My research focuses on the neocortical processing of sensory perception. I am specifically interested in the role of gamma oscillations (30-80Hz component of local field potential) in sensory encoding. To this end, I am conducting chronic extracellular electrophysiology recordings in the barrel cortex of mice performing a vibrissae deflection detection task. Detection at the perceptual threshold level is a process known to be dependent on the primary sensory area of the neocortex. Gamma oscillations have been widely observed in correlation to higher cognitive processes such as attention and learning. Notably, gamma in the primary sensory area has been causally linked to enhanced performance in threshold level detection. The goal of my study is to probe the mechanistic details underlying gamma-mediated enhancement of sensory encoding. In addition to electrophysiology experiments, I am constructing a biophysically plausible model of the barrel column microcircuitry governing sensory encoding. I aim to employ computational modeling to constrain plausible inverse solutions that can explain observed electrophysiological phenomena.
I am currently working on a collaborative project between the Frank and the Jones labs, combining systems level and biophysical modeling to understand the modulation of theta power in the Subthalamic Nucleus during response conflict.
We are also investigating the possibility that the cortico-subthalamic topography may allow the Subthalamic Nucleus to integrate cortical information to detect response conflict itself, and thereby prevent gating of motor responses in the cortico-thalamic loop via the Basal ganglia.
I am working with the Jones lab on the Human Neocortical Neurosolver (HNN) software, which is an open-source neuromodeling tool that allows for interpretation of potential origins of source-localized MEG/EEG/ECoG data based on the biophysical properties of the underlying neural circuits. I am also involved in the lab's research investigating the brain-body interactions that arise from meditative movement practices such as qigong.
Cooper is a Senior at Brown, his primary focus is on utilizing complex systems analysis to understand mind body therapies.
Sarah is a sophomore concentrating in Applied Mathematics-Biology with an emphasis in neuroscience.
HNN Software Research and Development
The following undegraduate students are assisting in the development of various features of the human neocortical neurosolver (HNN) software tool:
Oussama Ben Abdelbaki
Alexandre Gramfort, ParisTech
Charles Schroeder, Columbia U.
Matti Hamalainen, MGH
Saski Haegens, Columbia U./Donders