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

Abstract

7Alabama Advanced Imaging Consortium, USA r r Abstract: 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 regionbased 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 5 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

Hemodynamic response function (HRF) variability confounds resting‐state fMRI functional connectivity

“…We used the method proposed by Wu et al, which has gained increasing popularity and acceptance owing to its interpretability, robustness, simplicity, validity, and an increasing awareness on the importance of deconvolution. Many recent works have used this method . Briefly, the method relies on modeling resting‐state fMRI data using point processes as event‐related time series with randomly occurring events .…”

“…Many recent works have used this method. 5,[22][23][24][25][26][27][28][29] Briefly, the method relies on modeling resting-state fMRI data using point processes as event-related time series with randomly occurring events. 30,31 A temporal mask with frame-wise displacement (FD) <0.3 was added to avoid pseudo point process events induced by motion artifacts.…”

Estimated hemodynamic response function parameters obtained from resting state BOLD fMRI signals in subjects with autism spectrum disorder and matched healthy subjects

“…The three voxel-specific parameters that represented the shape of the HRF (RH, TTP, and FWHM) were obtained, and the latent neuronal events were estimated as well. This technique has been increasingly applied in fMRI studies, and the robust and valid estimation of HRF has been demonstrated [16] , [17] , [18] , [22] , [23] .…”

Saliva microRNA Biomarkers of Cumulative Concussion

“…An alternative hypothesis involves dysregulation of the neurometabolic cascade, caused by indiscriminate release of excitatory neurotransmitters and perturbation of the ion pumps that regulate neuronal membrane potential [ 22 ]. Regardless of the responsible mechanisms, evidence shows that individuals with a history of concussive injury experience disrupted neural functioning associated with cognition and regulatory processes [ 23 , 24 ].…”