Individual deviations from normative electroencephalographic connectivity predict antidepressant response

Authors

Xiaoyu Tong, Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
Hua Xie, Center for Neuroscience Research, Children's National Hospital, Washington, DC, USA; George Washington University School of Medicine, Washington, DC, USA.
Wei Wu, Alto Neuroscience, Inc., Los Altos, CA, USA.
Corey J. Keller, 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.
Gregory A. Fonzo, Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, TX, USA.
Matthieu Chidharom, Department of Psychology, Lehigh University, Bethlehem, PA, USA.
Nancy B. Carlisle, Department of Psychology, Lehigh University, Bethlehem, PA, USA.
Amit Etkin, Alto Neuroscience, Inc., Los Altos, CA, USA.
Yu Zhang, Department of Bioengineering, Lehigh University, Bethlehem, PA, USA; Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA. Electronic address: yuzi20@lehigh.edu.

Document Type

Journal Article

Publication Date

4-15-2024

Journal

Journal of affective disorders

Volume

351

DOI

10.1016/j.jad.2024.01.177

Keywords

Antidepressant; EEG; Functional connectivity; Individual deviation; Major depressive disorder; Treatment outcome

Abstract

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.

APA Citation

Tong, Xiaoyu; Xie, Hua; Wu, Wei; Keller, Corey J.; Fonzo, Gregory A.; Chidharom, Matthieu; Carlisle, Nancy B.; Etkin, Amit; and Zhang, Yu, "Individual deviations from normative electroencephalographic connectivity predict antidepressant response" (2024). GW Authored Works. Paper 4725.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/4725

Department

Neurology