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Intrinsic Functional Brain Connectivity Predicts Onset of Major Depression Disorder in Adolescence: A Pilot Study.

ABSTRACT: Children with familial risk for major depressive disorder (MDD) have elevated risk for developing depression as adolescents. Here, we investigated longitudinally whether resting-state functional connectivity (RSFC) could predict the onset of MDD. In this pilot study, we followed a group of never-depressed children with familial risk for MDD and a group of age-matched controls without familial risk who had undergone an MRI study at 8-14 years of age. Participants were reassessed 3-4 years later with diagnostic interviews. We first investigated group differences in RSFC from regions in the emotion regulation, cognitive control, and default mode networks in the children who later developed MDD (converted), the children who did not develop MDD (nonconverted), and the control group. We then built a prediction model based on baseline RSFC that was independent of the group differences to classify the individuals who later developed MDD. Compared with the nonconverted group, the converted group exhibited hypoconnectivity between subgenual anterior cingulate cortex (sgACC) and inferior parietal lobule (IPL) and between left and right dorsolateral prefrontal cortices. The nonconverted group exhibited higher sgACC-IPL connectivity than did both the converted and control groups, suggesting a possible resilience factor to MDD. Classification between converted and nonconverted individuals based on baseline RSFC yielded high predictive accuracy with high sensitivity and specificity that was superior to classification based on baseline clinical rating scales. Intrinsic brain connectivity measured in healthy children with familial risk for depression has the potential to predict MDD onset, and it can be a useful neuromarker in early identification of children for preventive treatment.

SUBMITTER: Hirshfeld-Becker DR

PROVIDER: S-EPMC6909691 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Intrinsic Functional Brain Connectivity Predicts Onset of Major Depression Disorder in Adolescence: A Pilot Study.

Hirshfeld-Becker Dina R DR Gabrieli John D E JDE Shapero Benjamin G BG Biederman Joseph J Whitfield-Gabrieli Susan S Chai Xiaoqian J XJ

Brain connectivity 20190411 5

Children with familial risk for major depressive disorder (MDD) have elevated risk for developing depression as adolescents. Here, we investigated longitudinally whether resting-state functional connectivity (RSFC) could predict the onset of MDD. In this pilot study, we followed a group of never-depressed children with familial risk for MDD and a group of age-matched controls without familial risk who had undergone an MRI study at 8-14 years of age. Participants were reassessed 3-4 years later w ...[more]

PMID: 30848160

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Project description:Background:Electroconvulsive therapy (ECT) is arguably the most effective available treatment for severe depression. Recent studies have used MRI data to predict clinical outcome to ECT and other antidepressant therapies. One challenge facing such studies is selecting from among the many available metrics, which characterize complementary and sometimes non-overlapping aspects of brain function and connectomics. Here, we assessed the ability of aggregated, functional MRI metrics of basal brain activity and connectivity to predict antidepressant response to ECT using machine learning. Methods:A radial support vector machine was trained using arterial spin labeling (ASL) and blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) metrics from n?=?46 (26 female, mean age 42) depressed patients prior to ECT (majority right-unilateral stimulation). Image preprocessing was applied using standard procedures, and metrics included cerebral blood flow in ASL, and regional homogeneity, fractional amplitude of low-frequency modulations, and graph theory metrics (strength, local efficiency, and clustering) in BOLD data. A 5-repeated 5-fold cross-validation procedure with nested feature-selection validated model performance. Linear regressions were applied post hoc to aid interpretation of discriminative features. Results:The range of balanced accuracy in models performing statistically above chance was 58-68%. Here, prediction of non-responders was slightly higher than for responders (maximum performance 74 and 64%, respectively). Several features were consistently selected across cross-validation folds, mostly within frontal and temporal regions. Among these were connectivity strength among: a fronto-parietal network [including left dorsolateral prefrontal cortex (DLPFC)], motor and temporal networks (near ECT electrodes), and/or subgenual anterior cingulate cortex (sgACC). Conclusion:Our data indicate that pattern classification of multimodal fMRI metrics can successfully predict ECT outcome, particularly for individuals who will not respond to treatment. Notably, connectivity with networks highly relevant to ECT and depression were consistently selected as important predictive features. These included the left DLPFC and the sgACC, which are both targets of other neurostimulation therapies for depression, as well as connectivity between motor and right temporal cortices near electrode sites. Future studies that probe additional functional and structural MRI metrics and other patient characteristics may further improve the predictive power of these and similar models.

Dataset Information

Intrinsic Functional Brain Connectivity Predicts Onset of Major Depression Disorder in Adolescence: A Pilot Study.

Publications

Intrinsic Functional Brain Connectivity Predicts Onset of Major Depression Disorder in Adolescence: A Pilot Study.

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