Cortical Physiology Laboratory
Sydney S. Cash
Wang Ambulatory Care Center
55 Fruit Street, WACC 730
Boston, MA 02114
Explore This Lab
The research in the Cortical Physiology Laboratory is, broadly speaking, dedicated to trying to understand normal and abnormal brain activity, particularly oscillations, using multi-modal and multi-scalar approaches with long term goals of improving therapies for patients with epilepsy.
We are combining novel microelectrode approaches with both invasive and non-invasive techniques such as electroencephalography and magnetoencephalography to record directly from both human and animal cortex and subcortical structures.
Our projects are built on a multi-scalar / multi-modal foundation of combined microelectrode, macroelectrode and non-invasive recording techniques that span information from the level of single action potentials to aggregate activity of millions of neurons. Intensive signal processing and computational techniques are employed to analyze these data sets and correlate them with imaging data. Collaborative activities are a hallmark of the lab with involvement of neurologists, neuroscientists, mathematicians, engineers from multiple universities.
Neurophysiology of Epilepsy
One group within the lab studies the neurophysiology of epilepsy; this group studies how seizures start and stop and tries to understand how they might be predicted and ultimately terminated. Our final goal is to have a more thorough knowledge of the mechanisms of epilepsy and to use this information to design better treatments for patients suffering from seizures. We use both animal models and information collected from patients with epilepsy. These questions overlap with investigations into the mechanisms of sleep, normal language, auditory and other cognitive processing.
Understanding Human Cognition
We are also studying some of the basic mechanisms of how the brain works. We are particularly interested in a deeper knowledge of how language, emotion and auditory processing occur.
Understanding Sleep and Dreams
While human cognition during the waking state is of obvious interest, it is equally fascinating what happens while we are asleep. Despite an enormous literature on this topic remarkable little is known about the fundamental mechanisms of sleep activity in the human brain or the purpose of those activities. Our current research is focused on understanding how some of the characteristic rhythms and elements of sleep arise in the human cortex. Projects which we are just beginning, delve more deeply into what is occurring during dreaming.
Fundamentals of Cortical and Subcortical Oscillations
Interwoven with all of our investigations is an interest in the ongoing oscillatory and rhythmic activity of the brain. These are features, which are obviously present during sleeping and dreaming, make a fundamental component of active cognition and have gone pathologically askew during epilepsy. We are investigating the mechanisms and importance of different oscillatory activity during many different brain states.
Brain-Computer Interface Research
The largely basic science issues which we focus on in much of our work comes to a practical launching point with our work on brain-computer interfaces. The focus of these projects is on mechanisms through which recording and therapeutic systems can be interfaced with the nervous system – a form of brain-machine interface research. Ultimately, all of these projects aim toward the creation of both invasive and non-invasive mechanisms for restoring damaged neuronal function.
Clinical Research and Trials
The lab itself is not focused on clinical trials per se. But, the Epilepsy Service of the Massachusetts General Hospital maintains an active research program, and some patients will have the opportunity to enroll in research or clinical trials. For information about ongoing studies and the ability to participate, please call 617-726-5904.
Read about and apply for residency, fellowship and observership programs in neurology.
All applicants should register with the Massachusetts General Hospital careers page.
Local and distant cortical responses to single pulse intracranial stimulation in the human brain are differentially modulated by specific stimulation parameters.
Paulk AC, Zelmann R, Crocker B, Widge AS, Dougherty DD, Eskandar EN, Weisholtz DS, Richardson RM, Cosgrove GR, Williams ZM, Cash SS. Brain Stimul. 2022 Mar 2:S1935-861X(22)00045-6. doi: 10.1016/j.brs.2022.02.017.
Human brain mapping with multithousand-channel PtNRGrids resolves spatiotemporal dynamics.
Tchoe Y, Bourhis AM, Cleary DR, Stedelin B, Lee J, Tonsfeldt KJ, Brown EC, Siler DA, Paulk AC, Yang JC, Oh H, Ro YG, Lee K, Russman SM, Ganji M, Galton I, Ben-Haim S, Raslan AM, Dayeh SA. Sci Transl Med. 2022 Jan 19;14(628):eabj1441. doi: 10.1126/scitranslmed.abj1441.
Optimal Spindle Detection Parameters for Predicting Cognitive Performance.
Adra N, Sun H, Ganglberger W, Ye EM, Dümmer LW, Tesh RA, Westmeijer M, Cardoso MDS, Kitchener E, Ouyang A, Salinas J, Rosand J, Cash SS, Thomas RJ, Westover MB. Sleep. 2022 Jan 4:zsac001. doi: 10.1093/sleep/zsac001.
Allometric rules for mammalian cortical layer 5 neuron biophysics.
Beaulieu-Laroche L, Brown NJ, Hansen M, Toloza EHS, Sharma J, Williams ZM, Frosch MP, Cosgrove GR, Cash SS, Harnett MT. Nature. 2021 Dec;600(7888):274-278. doi: 10.1038/s41586-021-04072-3.
The development of microfabricated solenoids with magnetic cores for micromagnetic neural stimulation.
Khalifa A, Zaeimbashi M, Zhou TX, Abrishami SM, Sun N, Park S, Šumarac T, Qu J, Zohar I, Yacoby A, Cash S, Sun NX. Microsyst Nanoeng. 2021 Nov 12;7:91. doi: 10.1038/s41378-021-00320-8.
Closed-loop enhancement and neural decoding of cognitive control in humans.
Basu I, Yousefi A, Crocker B, Zelmann R, Paulk AC, Peled N, Ellard KK, Weisholtz DS, Cosgrove GR, Deckersbach T, Eden UT, Eskandar EN, Dougherty DD, Cash SS, Widge AS. Nat Biomed Eng. 2021 Nov 1. doi: 10.1038/s41551-021-00804-y.
Dynamical ergodicity DDA reveals causal structure in time series.
Lainscsek C, Cash SS, Sejnowski TJ, Kurths J. Chaos. 2021 Oct;31(10):103108. doi: 10.1063/5.0063724.
Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.
Thangavel P, Thomas J, Peh WY, Jing J, Yuvaraj R, Cash SS, Chaudhari R, Karia S, Rathakrishnan R, Saini V, Shah N, Srivastava R, Tan YL, Westover B, Dauwels J. Int J Neural Syst. 2021 Aug;31(8):2150032. doi: 10.1142/S0129065721500325.