Individual deviations from normative electroencephalographic connectivity predict antidepressant response.

Autor: Tong X; Department of Bioengineering, Lehigh University, Bethlehem, PA, USA., Xie H; Center for Neuroscience Research, Children's National Hospital, Washington, DC, USA; George Washington University School of Medicine, Washington, DC, USA., Wu W; Alto Neuroscience, Inc., Los Altos, CA, USA., Keller CJ; Department of Psychiatry and Behavioral Sciences, Stanford University, CA, USA; Veterans Affairs Palo Alto Healthcare System, Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, USA., Fonzo GA; Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, TX, USA., Chidharom M; Department of Psychology, Lehigh University, Bethlehem, PA, USA., Carlisle NB; Department of Psychology, Lehigh University, Bethlehem, PA, USA., Etkin A; Alto Neuroscience, Inc., Los Altos, CA, USA., Zhang Y; Department of Bioengineering, Lehigh University, Bethlehem, PA, USA; Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA. Electronic address: yuzi20@lehigh.edu.
Jazyk: angličtina
Zdroj: Journal of affective disorders [J Affect Disord] 2024 Apr 15; Vol. 351, pp. 220-230. Date of Electronic Publication: 2024 Jan 27.
DOI: 10.1016/j.jad.2024.01.177
Abstrakt: Background: Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo, partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment. Here we develop a novel normative modeling framework to quantify individual deviations in psychopathological dimensions that offers a promising avenue for the personalized treatment for psychiatric disorders.
Methods: We built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients (102 sertraline-medicated and 119 placebo-medicated). Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after the eight-week antidepressant treatment.
Results: We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between sertraline and placebo responses.
Conclusions: Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective personalized MDD treatment.
Trial Registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT#01407094.
Competing Interests: Declaration of competing interest G.A.F. received monetary compensation for consulting work for SynapseBio AI and owns equity in Alto Neuroscience. W.W. and A.E. report salary and equity from Alto Neuroscience. A.E. additionally holds equity in Akili Interactive and Mindstrong Health. The remaining authors declare no competing interests.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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