Pupillometry and intracranial recordings in Psychiatry Research
Thursday, April 7, 2022 – 4:00 – 5:00 pm
Dr. Alfred P. Kaye and Dr. Eyiyemisi Damisah, Department of Psychiatry, Yale University School of Medicine
Computational psychiatry as a paradigm for understanding mental health conditions. Yet our ability to understand and access the neurophysiological basis of computational psychiatry constructs still has important barriers in terms of spatiotemporal resolution. The past 5 years have seen a surge in publications in approaches utilizing invasive intracranial electrophysiology in humans to understand either the fundamental computations underlying emotional behaviors or psychiatric illness. This seminar aims to open a dialogue on how these two innovative paradigms can interact to produce improvements in our understanding of the neurobiology of mental illness.
In this talk, we approach the question of how invasive electrophysiological recordings in humans may yield insights into the basis of mental disorders. Although we focus on what has been done in mood and in a study of anxiety, these fundamental approaches may be translatable across behavioral constructs and disorders.
Neurophysiological recording and stimulation techniques can be categorized in terms of their spatiotemporal resolution. Neuroscience requires integration of multiple levels of analysis from cellular to large-scale networks. This technique allows access to these more local circuit levels in humans at a more precise spatiotemporal scale and including deep brain regions which may be involved in psychiatric illness.
This approach is still in its infancy in the field of psychiatry. While it has generated great interest, there are relatively few groups in the country performing invasive intracranial electrophysiology in humans. Despite this high level of interest and significant complementarity with existing human neuroimaging approaches (fMRI, EEG, MEG), this approach may require some different methodological and conceptual perspectives.
Prerequisites: This approach employs both standard techniques of neurophysiological analysis (time-frequency analysis) as well as computational psychiatry approaches (statistical modeling of behavior).
Most importantly, it requires access to neurosurgical iEEG data from human subjects.
Datasets: There are several repositories of open-source iEEG datasets:
An excellent review: Parvizi, J., & Kastner, S. (2018). Human intracranial EEG: promises and limitations. Nature neuroscience, 21(4), 474.
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