Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma
| Autor: | D. Rangaprakash, Michael N. Dretsch, Jeffrey S. Katz, Thomas S. Denney Jr., Gopikrishna Deshpande |
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| Rok vydání: | 2019 |
| Předmět: |
effective connectivity
Poison control dynamic connectivity 050105 experimental psychology lcsh:RC321-571 Brain functioning 03 medical and health sciences 0302 clinical medicine mild traumatic brain injury Injury prevention 0501 psychology and cognitive sciences Association (psychology) lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Original Research General Neuroscience 05 social sciences Flexibility (personality) Network dynamics network dynamics Posttraumatic stress machine learning complex network modeling posttraumatic stress disorder functional MRI Psychology Neurocognitive Neuroscience 030217 neurology & neurosurgery |
| Zdroj: | Frontiers in Neuroscience, Vol 13 (2019) Frontiers in Neuroscience |
| ISSN: | 1662-453X |
| DOI: | 10.3389/fnins.2019.00803 |
| Popis: | Brain functioning relies on various segregated/specialized neural regions functioning as an integrated-interconnected network (i.e., metastability). Various psychiatric and neurologic disorders are associated with aberrant functioning of these brain networks. In this study, we present a novel framework integrating the strength and temporal variability of metastability in brain networks. We demonstrate that this approach provides novel mechanistic insights which enables better imaging-based predictions. Using whole-brain resting-state fMRI and a graph-theoretic framework, we integrated strength and temporal-variability of complex-network properties derived from effective connectivity networks, obtained from 87 U.S. Army soldiers consisting of healthy combat controls (n = 28), posttraumatic stress disorder (PTSD; n = 17), and PTSD with comorbid mild-traumatic brain injury (mTBI; n = 42). We identified prefrontal dysregulation of key subcortical and visual regions in PTSD/mTBI, with all network properties exhibiting lower variability over time, indicative of poorer flexibility. Larger impairment in the prefrontal-subcortical pathway but not prefrontal-visual pathway differentiated comorbid PTSD/mTBI from the PTSD group. Network properties of the prefrontal-subcortical pathway also had significant association (R2 = 0.56) with symptom severity and neurocognitive performance; and were also found to possess high predictive ability (81.4% accuracy in classifying the disorders, explaining 66–72% variance in symptoms), identified through machine learning. Our framework explained 13% more variance in behaviors compared to the conventional framework. These novel insights and better predictions were made possible by our novel framework using static and time-varying network properties in our three-group scenario, advancing the mechanistic understanding of PTSD and comorbid mTBI. Our contribution has wide-ranging applications for network-level characterization of healthy brains as well as mental disorders. |
| Databáze: | OpenAIRE |
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