Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma
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 framew...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
D. Rangaprakash [verfasserIn] Michael N. Dretsch [verfasserIn] Jeffrey S. Katz [verfasserIn] Thomas S. Denney Jr. [verfasserIn] Gopikrishna Deshpande [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Frontiers in Neuroscience - Frontiers Media S.A., 2008, 13(2019) |
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| Übergeordnetes Werk: |
volume:13 ; year:2019 |
| Links: |
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| DOI / URN: |
10.3389/fnins.2019.00803 |
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| Katalog-ID: |
DOAJ040191044 |
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| 520 | |a 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. | ||
| 650 | 4 | |a functional MRI | |
| 650 | 4 | |a network dynamics | |
| 650 | 4 | |a complex network modeling | |
| 650 | 4 | |a effective connectivity | |
| 650 | 4 | |a dynamic connectivity | |
| 650 | 4 | |a posttraumatic stress disorder | |
| 653 | 0 | |a Neurosciences. Biological psychiatry. Neuropsychiatry | |
| 700 | 0 | |a D. Rangaprakash |e verfasserin |4 aut | |
| 700 | 0 | |a Michael N. Dretsch |e verfasserin |4 aut | |
| 700 | 0 | |a Michael N. Dretsch |e verfasserin |4 aut | |
| 700 | 0 | |a Michael N. Dretsch |e verfasserin |4 aut | |
| 700 | 0 | |a Jeffrey S. Katz |e verfasserin |4 aut | |
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| 700 | 0 | |a Thomas S. Denney Jr. |e verfasserin |4 aut | |
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Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma |
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D. Rangaprakash |
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D. Rangaprakash Michael N. Dretsch Jeffrey S. Katz Thomas S. Denney Jr. Gopikrishna Deshpande |
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dynamics of segregation and integration in directional brain networks: illustration in soldiers with ptsd and neurotrauma |
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Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma |
| abstract |
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. |
| abstractGer |
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. |
| abstract_unstemmed |
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. |
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Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma |
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https://doi.org/10.3389/fnins.2019.00803 https://doaj.org/article/bc76594a892b4f629b261ad79f9fe04c https://www.frontiersin.org/article/10.3389/fnins.2019.00803/full https://doaj.org/toc/1662-453X |
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D. Rangaprakash Michael N. Dretsch Jeffrey S. Katz Thomas S. Denney Jr. Gopikrishna Deshpande |
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