Dataset Information

Changes in community structure of resting state functional connectivity in unipolar depression.

ABSTRACT: Major depression is a prevalent disorder that imposes a significant burden on society, yet objective laboratory-style tests to assist in diagnosis are lacking. We employed network-based analyses of "resting state" functional neuroimaging data to ascertain group differences in the endogenous cortical activity between healthy and depressed subjects.We additionally sought to use machine learning techniques to explore the ability of these network-based measures of resting state activity to provide diagnostic information for depression. Resting state fMRI data were acquired from twenty two depressed outpatients and twenty two healthy subjects matched for age and gender. These data were anatomically parcellated and functional connectivity matrices were then derived using the linear correlations between the BOLD signal fluctuations of all pairs of cortical and subcortical regions.We characterised the hierarchical organization of these matrices using network-based matrics, with an emphasis on their mid-scale "modularity" arrangement. Whilst whole brain measures of organization did not differ between groups, a significant rearrangement of their community structure was observed. Furthermore we were able to classify individuals with a high level of accuracy using a support vector machine, primarily through the use of a modularity-based metric known as the participation index.In conclusion, the application of machine learning techniques to features of resting state fMRI network activity shows promising potential to assist in the diagnosis of major depression, now suggesting the need for validation in independent data sets.

SUBMITTER: Lord A

PROVIDER: S-EPMC3423402 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Changes in community structure of resting state functional connectivity in unipolar depression.

Lord Anton A Horn Dorothea D Breakspear Michael M Walter Martin M

PloS one 20120820 8

Major depression is a prevalent disorder that imposes a significant burden on society, yet objective laboratory-style tests to assist in diagnosis are lacking. We employed network-based analyses of "resting state" functional neuroimaging data to ascertain group differences in the endogenous cortical activity between healthy and depressed subjects.We additionally sought to use machine learning techniques to explore the ability of these network-based measures of resting state activity to provide d ...[more]

PMID: 22916105

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Project description:ImportanceMajor depressive disorder (MDD) frequently emerges during adolescence and can lead to persistent illness, disability, and suicide. The maturational changes that take place in the brain during adolescence underscore the importance of examining neurobiological mechanisms during this time of early illness. However, neural mechanisms of depression in adolescents have been understudied. Research has implicated the amygdala in emotion processing in mood disorders, and adult depression studies have suggested amygdala-frontal connectivity deficits. Resting-state functional magnetic resonance imaging is an advanced tool that can be used to probe neural networks and identify brain-behavior relationships.ObjectiveTo examine amygdala resting-state functional connectivity (RSFC) in adolescents with and without MDD using resting-state functional magnetic resonance imaging as well as how amygdala RSFC relates to a broad range of symptom dimensions.Design, setting, and participantsA cross-sectional resting-state functional magnetic resonance imaging study was conducted within a depression research program at an academic medical center. Participants included 41 adolescents and young adults aged 12 to 19 years with MDD and 29 healthy adolescents (frequency matched on age and sex) with no psychiatric diagnoses.Main outcomes and measuresUsing a whole-brain functional connectivity approach, we examined the correlation of spontaneous fluctuation of the blood oxygen level-dependent signal of each voxel in the whole brain with that of the amygdala.ResultsAdolescents with MDD showed lower positive RSFC between the amygdala and hippocampus, parahippocampus, and brainstem (z >2.3, corrected P < .05); this connectivity was inversely correlated with general depression (R = -.523, P = .01), dysphoria (R = -.455, P = .05), and lassitude (R = -.449, P = .05) and was positively correlated with well-being (R = .470, P = .03). Patients also demonstrated greater (positive) amygdala-precuneus RSFC (z >2.3, corrected P < .05) in contrast to negative amygdala-precuneus RSFC in the adolescents serving as controls.Conclusions and relevanceImpaired amygdala-hippocampal/brainstem and amygdala-precuneus RSFC have not previously been highlighted in depression and may be unique to adolescent MDD. These circuits are important for different aspects of memory and self-processing and for modulation of physiologic responses to emotion. The findings suggest potential mechanisms underlying both mood and vegetative symptoms, potentially via impaired processing of memories and visceral signals that spontaneously arise during rest, contributing to the persistent symptoms experienced by adolescents with depression.

Dataset Information

Changes in community structure of resting state functional connectivity in unipolar depression.

Publications

Changes in community structure of resting state functional connectivity in unipolar depression.

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