Pretreatment Brain Connectome Fingerprint Predicts Treatment Response in Major Depressive Disorder

Background Major depressive disorder (MDD) treatment is characterized by low remission rate and often involves weeks to months of treatment. Identification of pretreatment biomarkers of response may play a critical role in novel drug development, in enhanced prognostic predictions, and perhaps in pr...

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Main Authors: Siyan Fan, Samaneh Nemati, Teddy J. Akiki, Jeremy Roscoe, Christopher L. Averill, Samar Fouda, Lynnette A. Averill, Chadi G. Abdallah
Format: Article
Language:English
Published: SAGE Publishing 2020-12-01
Series:Chronic Stress
Online Access:https://doi.org/10.1177/2470547020984726
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spelling doaj-0a29598b8a894b9a902793ce19c0e8962020-12-30T00:33:25ZengSAGE PublishingChronic Stress2470-54702020-12-01410.1177/2470547020984726Pretreatment Brain Connectome Fingerprint Predicts Treatment Response in Major Depressive DisorderSiyan FanSamaneh NematiTeddy J. AkikiJeremy RoscoeChristopher L. AverillSamar FoudaLynnette A. AverillChadi G. AbdallahBackground Major depressive disorder (MDD) treatment is characterized by low remission rate and often involves weeks to months of treatment. Identification of pretreatment biomarkers of response may play a critical role in novel drug development, in enhanced prognostic predictions, and perhaps in providing more personalized medicine. Using a network restricted strength predictive modeling (NRS-PM) approach, the goal of the current study was to identify pretreatment functional connectome fingerprints (CFPs) that (1) predict symptom improvement regardless of treatment modality and (2) predict treatment specific improvement. Methods Functional magnetic resonance imaging and behavioral data from unmedicated patients with MDD (n = 200) were investigated. Participants were randomized to daily treatment of sertraline or placebo for 8 weeks. NRS-PM with 1000 iterations of 10 cross-validation were implemented to identify brain connectivity signatures that predict percent improvement in depression severity at week-8. Results The study identified a pretreatment CFP that significantly predicts symptom improvement independent of treatment modality but failed to identify a treatment specific CFP. Regardless of treatment modality, improved antidepressant response was predicted by high pretreatment connectivity between modules in the default mode network and the rest of the brain, but low external connectivity in the executive network. Moreover, high pretreatment internal nodal connectivity in the bilateral caudate predicted better response. Conclusions The identified CFP may contribute to drug development and ultimately to enhanced prognostic predictions. However, the results do not assist with providing personalized medicine, as pretreatment functional connectivity failed to predict treatment specific response.https://doi.org/10.1177/2470547020984726
collection DOAJ
language English
format Article
sources DOAJ
author Siyan Fan
Samaneh Nemati
Teddy J. Akiki
Jeremy Roscoe
Christopher L. Averill
Samar Fouda
Lynnette A. Averill
Chadi G. Abdallah
spellingShingle Siyan Fan
Samaneh Nemati
Teddy J. Akiki
Jeremy Roscoe
Christopher L. Averill
Samar Fouda
Lynnette A. Averill
Chadi G. Abdallah
Pretreatment Brain Connectome Fingerprint Predicts Treatment Response in Major Depressive Disorder
Chronic Stress
author_facet Siyan Fan
Samaneh Nemati
Teddy J. Akiki
Jeremy Roscoe
Christopher L. Averill
Samar Fouda
Lynnette A. Averill
Chadi G. Abdallah
author_sort Siyan Fan
title Pretreatment Brain Connectome Fingerprint Predicts Treatment Response in Major Depressive Disorder
title_short Pretreatment Brain Connectome Fingerprint Predicts Treatment Response in Major Depressive Disorder
title_full Pretreatment Brain Connectome Fingerprint Predicts Treatment Response in Major Depressive Disorder
title_fullStr Pretreatment Brain Connectome Fingerprint Predicts Treatment Response in Major Depressive Disorder
title_full_unstemmed Pretreatment Brain Connectome Fingerprint Predicts Treatment Response in Major Depressive Disorder
title_sort pretreatment brain connectome fingerprint predicts treatment response in major depressive disorder
publisher SAGE Publishing
series Chronic Stress
issn 2470-5470
publishDate 2020-12-01
description Background Major depressive disorder (MDD) treatment is characterized by low remission rate and often involves weeks to months of treatment. Identification of pretreatment biomarkers of response may play a critical role in novel drug development, in enhanced prognostic predictions, and perhaps in providing more personalized medicine. Using a network restricted strength predictive modeling (NRS-PM) approach, the goal of the current study was to identify pretreatment functional connectome fingerprints (CFPs) that (1) predict symptom improvement regardless of treatment modality and (2) predict treatment specific improvement. Methods Functional magnetic resonance imaging and behavioral data from unmedicated patients with MDD (n = 200) were investigated. Participants were randomized to daily treatment of sertraline or placebo for 8 weeks. NRS-PM with 1000 iterations of 10 cross-validation were implemented to identify brain connectivity signatures that predict percent improvement in depression severity at week-8. Results The study identified a pretreatment CFP that significantly predicts symptom improvement independent of treatment modality but failed to identify a treatment specific CFP. Regardless of treatment modality, improved antidepressant response was predicted by high pretreatment connectivity between modules in the default mode network and the rest of the brain, but low external connectivity in the executive network. Moreover, high pretreatment internal nodal connectivity in the bilateral caudate predicted better response. Conclusions The identified CFP may contribute to drug development and ultimately to enhanced prognostic predictions. However, the results do not assist with providing personalized medicine, as pretreatment functional connectivity failed to predict treatment specific response.
url https://doi.org/10.1177/2470547020984726
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