| Title: | Multiplex brain network architecture in psychoses |
| Lead Institution: | University of Pittsburgh |
| Principal investigator: | Dr Konasale Prasad |
Application 68923
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About
The human brain is organized into multiple network layers interacting with each other. These layers comprise of structural networks derived from white matter data obtained through diffusion-weighted imaging and functional networks derived from brain oxygenation level dependent signals on functional MRI. Many studies suggest that the structural "wiring" determines functional networks; modulation of structural connections through functional network alterations is also reported. Alterations in interactions between these networks may underlie many psychiatric disorders. Here, we focus on psychotic disorders, such as schizophrenia, and mood disorders, such as bipolar and major depressive disorders. Despite decades of research, psychotic illnesses are continuing to be treated symptomatically with poor long-term outcomes, resulting in tremendous cost to the society. In the US alone, schizophrenia costs nearly $125 billion annually in both direct and indirect costs. Biological basis of these disorders is unclear that is a major obstacle to develop curative treatments. Characterizing precise network interactions could help accomplish that goal. Several studies including ours suggest structural and functional disconnections, but the findings are inconsistent, that impedes precise understanding of pathophysiology and design novel treatments. An integrated investigation of characteristics of these networks and their interactions in a large sample provides the best shot at understanding complexities of brain functions. We use various measurement techniques to identify corresponding representations of brain organization that relate to aspects contributing to brain function. To accomplish this, we created a construct called, "multiplex network layers" of structural and functional networks that are intrinsically organized and interact with each other to efficiently model the network using mathematical and statistical approaches and examine their relevance to cognition and behavior. Our studies on modest samples showed that the strengths of structural connections and functional connections were significantly positively correlated despite these networks being fundamentally dissimilar. There were several common core connections in functional network between healthy subjects and schizophrenia, but it was non-core connections that predicted performance on behavioral tasks. We defined core connections as those that exist in both healthy subjects and schizophrenia subjects; non-core connections were unique to each group. Core connections in functional networks were better-defined in healthy subjects but not in schizophrenia patients. Structural networks had common core connections between patients and controls and non-core edges did not predict cognitive performance. We have identified specific core and non-core connections to test the characteristics of these connections to generate hypothesis for new interventions and network pathology.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 12097 | On Inference for Modularity Statistics in Structured Networks | Anirban Mitra (+2) | 2024 | Journal of Computational and Graphical Statistics |
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