Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma

• Published in: Human brain mapping Vol. 39; no. 1; pp. 264 - 287
• Main Authors: Rangaprakash, D., Dretsch, Michael N., Venkataraman, Archana, Katz, Jeffrey S., Denney, Thomas S., Deshpande, Gopikrishna
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.01.2018; John Wiley and Sons Inc
• Subjects: Aberration; Adolescent; Adult; Amygdala; Brain; Brain - diagnostic imaging; Brain - physiopathology; Brain mapping; Classification; Cognition; Connectome - methods; disease foci; dynamic connectivity; effective connectivity; Frontal gyrus; Functional magnetic resonance imaging; Functional MRI; Head injuries; Hippocampus; Humans; Information flow; Learning algorithms; machine learning; Magnetic Resonance Imaging; Male; Memory; Middle Aged; mild traumatic brain injury; Military; Military Personnel; Military psychology; Neural networks; Neural Pathways - diagnostic imaging; Neural Pathways - physiopathology; Post traumatic stress disorder; posttraumatic stress disorder; Psychiatric Status Rating Scales; Rest; Sensitivity and Specificity; Severity of Illness Index; Stress Disorders, Post-Traumatic - diagnostic imaging; Stress Disorders, Post-Traumatic - physiopathology; Support Vector Machine; Traumatic brain injury; United States; War Exposure; Young Adult; dynamic connectivity; effective connectivity; disease foci; Functional MRI; machine learning; mild traumatic brain injury; posttraumatic stress disorder
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.23841

Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region‐based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time‐varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting‐state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine‐learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal‐subcortical‐parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal‐variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less‐variable connectivity in subcortical regions, followed by a similar effect on parietal memory‐related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re‐experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264–287, 2018. © 2017 Wiley Periodicals, Inc.