Resting state brain network function in major depression – Depression symptomatology, antidepressant treatment effects, future research

Resting state functional connectivity MRI (fcMRI) is widely used to investigate brain networks that exhibit correlated fluctuations. While fcMRI does not provide direct measurement of anatomic connectivity, accumulating evidence suggests it is sufficiently constrained by anatomy to allow the architecture of distinct brain systems to be characterized. fcMRI is particularly useful for characterizing large-scale systems that span distributed areas (e.g., polysynaptic cortical pathways, cerebro-cerebellar circuits, cortical-thalamic circuits) and has complementary strengths when contrasted with the other major tool available for human connectomics-high angular resolution diffusion imaging (HARDI). We review what is known about fcMRI and then explore fcMRI data reliability, effects of preprocessing, analysis procedures, and effects of different acquisition parameters across six studies (n = 98) to provide recommendations for optimization. Run length (2-12 min), run structure (1 12-min run or 2 6-min runs), temporal resolution (2.5 or 5.0 s), spatial resolution (2 or 3 mm), and the task (fixation, eyes closed rest, eyes open rest, continuous word-classification) were varied. Results revealed moderate to high test-retest reliability. Run structure, temporal resolution, and spatial resolution minimally influenced fcMRI results while fixation and eyes open rest yielded stronger correlations as contrasted to other task conditions. Commonly used preprocessing steps involving regression of nuisance signals minimized nonspecific (noise) correlations including those associated with respiration. The most surprising finding was that estimates of correlation strengths stabilized with acquisition times as brief as 5 min. The brevity and robustness of fcMRI positions it as a powerful tool for large-scale explorations of genetic influences on brain architecture. We conclude by discussing the strengths and limitations of fcMRI and how it can be combined with HARDI techniques to support the emerging field of human connectomics.

This review considers recent advances in the application of resting-state functional magnetic resonance imaging to the study of neuropsychiatric disorders. Resting-state functional magnetic resonance imaging is a relatively novel technique that has several potential advantages over task-activation functional magnetic resonance imaging in terms of its clinical applicability. A number of research groups have begun to investigate the use of resting-state functional magnetic resonance imaging in a variety of neuropsychiatric disorders including Alzheimer's disease, depression, and schizophrenia. Although preliminary results have been fairly consistent in some disorders (for example, Alzheimer's disease) they have been less reproducible in others (schizophrenia). Resting-state connectivity has been shown to correlate with behavioral performance and emotional measures. It's potential as a biomarker of disease and an early objective marker of treatment response is genuine but still to be realized. Resting-state functional magnetic resonance imaging has made some strides in the clinical realm but significant advances are required before it can be used in a meaningful way at the single-patient level.

Neuroimaging studies have shown that major depressive disorder (MDD) is accompanied by structural and functional abnormalities in specific brain regions and connections; yet, little is known about alterations of the topological organization of whole-brain networks in MDD patients. Thirty drug-naive, first-episode MDD patients and 63 healthy control subjects underwent a resting-state functional magnetic resonance imaging scan. The whole-brain functional networks were constructed by thresholding partial correlation matrices of 90 brain regions, and their topological properties (e.g., small-world, efficiency, and nodal centrality) were analyzed using graph theory-based approaches. Nonparametric permutation tests were further used for group comparisons of topological metrics. Both the MDD and control groups showed small-world architecture in brain functional networks, suggesting a balance between functional segregation and integration. However, compared with control subjects, the MDD patients showed altered quantitative values in the global properties, characterized by lower path length and higher global efficiency, implying a shift toward randomization in their brain networks. The MDD patients exhibited increased nodal centralities, predominately in the caudate nucleus and default-mode regions, including the hippocampus, inferior parietal, medial frontal, and parietal regions, and reduced nodal centralities in the occipital, frontal (orbital part), and temporal regions. The altered nodal centralities in the left hippocampus and the left caudate nucleus were correlated with disease duration and severity. These results suggest that depressive disorder is associated with disruptions in the topological organization of functional brain networks and that this disruption may contribute to disturbances in mood and cognition in MDD patients. Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.