Apr 24, 2020

Common brain networks between major depressive disorder and symptoms of depression that are validated for independent cohorts

BioRxiv : the Preprint Server for Biology
Ayumu YamashitaH. Imamizu

Abstract

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable major depressive disorder (MDD) brain network markers which would distinguish patients from healthy controls (a classifier) or would predict symptom severity (a prediction model) based on resting state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD brain network markers. The classifier achieved 70% generalization accuracy, and the prediction model moderately well predicted symptom severity for an independent validation dataset with 449 participants from 4 different imaging sites. Finally, we found common 2 functional connections between those related to MDD diagnosis and those related to depression symptoms. The successful generalization to the perfectly independ...Continue Reading

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Mentioned in this Paper

Genome
Inversion Mutation Abnormality
Synaptic Vesicles
Genomics
Massively-Parallel Sequencing
Tandem
Chromosome Inversion
Simulation
Statistical Technique
Gene Duplication Abnormality

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