TY - JOUR
T1 - Invasive Computational Psychiatry
AU - Saez, Ignacio
AU - Gu, Xiaosi
N1 - Funding Information:
This work is supported by the National Institute of Mental Health (Grant Nos. R01MH124763 [to IS]; R21MH120789, R01MH122611, R01MH123069, and R01MH124115 [to XG]), the Office of Naval Research (Grant No. N00014-19-S-B001 to [to IS]), the National Institute on Drug Abuse (Grant Nos. R01DA043695 and R21DA049243 [to XG]), Simons Foundation (SFARI, Simons Foundation Autism Research Initiative [to XG]), and the Wellcome Leap as part of the MultiChannel Psych Program for Computational Roles of Dopamine and Serotonin Transients in Anhedonia in Humans with Treatment Resistant Depression (to XG).
Publisher Copyright:
© 2022 Society of Biological Psychiatry
PY - 2023/4/15
Y1 - 2023/4/15
N2 - Computational psychiatry, a relatively new yet prolific field that aims to understand psychiatric disorders with formal theories about the brain, has seen tremendous growth in the past decade. Despite initial excitement, actual progress made by computational psychiatry seems stagnant. Meanwhile, understanding of the human brain has benefited tremendously from recent progress in intracranial neuroscience. Specifically, invasive techniques such as stereotactic electroencephalography, electrocorticography, and deep brain stimulation have provided a unique opportunity to precisely measure and causally modulate neurophysiological activity in the living human brain. In this review, we summarize progress and drawbacks in both computational psychiatry and invasive electrophysiology and propose that their combination presents a highly promising new direction—invasive computational psychiatry. The value of this approach is at least twofold. First, it advances our mechanistic understanding of the neural computations of mental states by providing a spatiotemporally precise depiction of neural activity that is traditionally unattainable using noninvasive techniques with human subjects. Second, it offers a direct and immediate way to modulate brain states through stimulation of algorithmically defined neural regions and circuits (i.e., algorithmic targeting), thus providing both causal and therapeutic insights. We then present depression as a use case where the combination of computational and invasive approaches has already shown initial success. We conclude by outlining future directions as a road map for this exciting new field as well as presenting cautions about issues such as ethical concerns and generalizability of findings.
AB - Computational psychiatry, a relatively new yet prolific field that aims to understand psychiatric disorders with formal theories about the brain, has seen tremendous growth in the past decade. Despite initial excitement, actual progress made by computational psychiatry seems stagnant. Meanwhile, understanding of the human brain has benefited tremendously from recent progress in intracranial neuroscience. Specifically, invasive techniques such as stereotactic electroencephalography, electrocorticography, and deep brain stimulation have provided a unique opportunity to precisely measure and causally modulate neurophysiological activity in the living human brain. In this review, we summarize progress and drawbacks in both computational psychiatry and invasive electrophysiology and propose that their combination presents a highly promising new direction—invasive computational psychiatry. The value of this approach is at least twofold. First, it advances our mechanistic understanding of the neural computations of mental states by providing a spatiotemporally precise depiction of neural activity that is traditionally unattainable using noninvasive techniques with human subjects. Second, it offers a direct and immediate way to modulate brain states through stimulation of algorithmically defined neural regions and circuits (i.e., algorithmic targeting), thus providing both causal and therapeutic insights. We then present depression as a use case where the combination of computational and invasive approaches has already shown initial success. We conclude by outlining future directions as a road map for this exciting new field as well as presenting cautions about issues such as ethical concerns and generalizability of findings.
KW - Algorithmic targeting
KW - Computational psychiatry
KW - DBS
KW - ECoG
KW - Intracranial neuroscience
KW - sEEG
UR - http://www.scopus.com/inward/record.url?scp=85146449119&partnerID=8YFLogxK
U2 - 10.1016/j.biopsych.2022.09.032
DO - 10.1016/j.biopsych.2022.09.032
M3 - Review article
C2 - 36641365
AN - SCOPUS:85146449119
SN - 0006-3223
VL - 93
SP - 661
EP - 670
JO - Biological Psychiatry
JF - Biological Psychiatry
IS - 8
ER -