A Dynamic Approach to Depression Treatment Prediction

Abstract

Major depressive disorder is often treated clinically as a single disorder, but it represents a highly heterogeneous phenomenon ( 1 Malhi G.S. Mann J.J. Depression. Lancet. 2018; 392: 2299-2312 Abstract Full Text Full Text PDF PubMed Scopus (954) Google Scholar ). Symptom profiles can differ greatly between individuals, and episodes may last for weeks, months, or even years. Treatment response is also highly variable and currently unpredictable. Unpacking the neural basis of depression’s heterogeneity might one day allow for individualized treatment. Resting-state connectivity offers a method for characterizing human brain networks. The method relies on correlated fluctuations of intrinsic brain activity when individuals are at rest without any particular task to perform and is commonly measured via blood oxygen level–dependent signal during functional magnetic resonance imaging. These correlations between sets of regions reveal resting-state networks connecting anatomically and functionally related areas. Resting-state networks are relatively stable within individuals and consistent across groups ( 2 Gratton C. Laumann T.O. Nielsen A.N. Greene D.J. Gordon E.M. Gilmore A.W. et al. Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron. 2018; 98: 439-452.e5 Abstract Full Text Full Text PDF PubMed Scopus (338) Google Scholar ). As a result, resting-state functional magnetic resonance imaging has rapidly gained popularity for relating human brain networks to both normal function and pathological dysfunction. SEE CORRESPONDING ARTICLE ON PAGE 533 SEE CORRESPONDING ARTICLE ON PAGE 533 SEE CORRESPONDING ARTICLE ON PAGE 533 Dynamic Resting-State Network Biomarkers of Antidepressant Treatment ResponseBiological PsychiatryVol. 92Issue 7PreviewDelivery of effective antidepressant treatment has been hampered by a lack of objective tools for predicting or monitoring treatment response. This study aimed to address this gap by testing novel dynamic resting-state functional network markers of antidepressant response. Full-Text PDF