Complexity of resting cortical activity predicts neurophysiological responses to theta-burst stimulation but fails to generalize: A rigorous machine-learning approach.

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Title: Complexity of resting cortical activity predicts neurophysiological responses to theta-burst stimulation but fails to generalize: A rigorous machine-learning approach.
Authors: Ning, Matthew Herbert1 (AUTHOR), Sun, Haoqi2 (AUTHOR), Passera, Brice1 (AUTHOR), Das, Duygu Bagci1 (AUTHOR), Westover, Brandon2 (AUTHOR), Pascual-Leone, Alvaro3 (AUTHOR), Santarnecchi, Emiliano4 (AUTHOR), Shafi, Mouhsin M.1 (AUTHOR), Ozdemir, Recep A.1 (AUTHOR) rozdemir@bidmc.harvard.edu
Source: PLoS Computational Biology. 4/30/2026, Vol. 22 Issue 4, p1-23. 23p.
Database: Academic Search Ultimate
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  Data: Complexity of resting cortical activity predicts neurophysiological responses to theta-burst stimulation but fails to generalize: A rigorous machine-learning approach.
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  Data: <searchLink fieldCode="JN" term="%22PLoS+Computational+Biology%22">PLoS Computational Biology</searchLink>. 4/30/2026, Vol. 22 Issue 4, p1-23. 23p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=193468675
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