Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology.
Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy.
Duke Scholars
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- Experimental Psychology
- 5204 Cognitive and computational psychology
- 5202 Biological psychology
- 3209 Neurosciences
- 1702 Cognitive Sciences
- 1701 Psychology
- 1109 Neurosciences
Citation
Published In
DOI
ISSN
Publication Date
Volume
Start / End Page
Location
Related Subject Headings
- Experimental Psychology
- 5204 Cognitive and computational psychology
- 5202 Biological psychology
- 3209 Neurosciences
- 1702 Cognitive Sciences
- 1701 Psychology
- 1109 Neurosciences