Static and Dynamic Intrinsic Connectivity following Mild Traumatic Brain Injury

Abstract

Mild traumatic brain injury (mTBI) is the most common neurological disorder and is typically characterized by temporally limited cognitive impairment and emotional symptoms. Previous examinations of intrinsic resting state networks in mTBI have primarily focused on abnormalities in static functional connectivity, and deficits in dynamic functional connectivity have yet to be explored in this population. Resting-state data was collected on 48 semi-acute (mean = 14 days post-injury) mTBI patients and 48 matched healthy controls. A high-dimensional independent component analysis (N = 100) was utilized to parcellate intrinsic connectivity networks (ICN), with a priori hypotheses focusing on the default-mode network (DMN) and sub-cortical structures. Dynamic connectivity was characterized using a sliding window approach over 126 temporal epochs, with standard deviation serving as the primary outcome measure. Finally, distribution-corrected z-scores (DisCo-Z) were calculated to investigate changes in connectivity in a spatially invariant manner on a per-subject basis. Following appropriate correction for multiple comparisons, no significant group differences were evident on measures of static or dynamic connectivity within a priori ICN. Reduced (HC > mTBI patients) static connectivity was observed in the DMN at uncorrected (p < 0.005) thresholds. Finally, a trend (p = 0.07) for decreased dynamic connectivity in patients across all ICN was observed during spatially invariant analyses (DisCo-Z). In the semi-acute phase of recovery, mTBI was not reliably associated with abnormalities in static or dynamic functional connectivity within the DMN or sub-cortical structures.

Detection of Mild Traumatic Brain Injury by Machine Learning Classification Using Resting State Functional Network Connectivity and Fractional Anisotropy

“…The DTI and resting state data from this cohort have been used before in which four subjects were excluded because of increased head motion. 7,26 The current work represents a re-analysis of the published data. In the current instance, an additional pair of subjects was excluded based on the quality of the DTI data.…”

“…26 A total of 47 sex and age (up to three years) matched healthy controls (HC) were included. Subjects classified as mTBI had a Glasgow Coma Scale between 13 and 15 at first contact with medical staff, a maximum of 30 min loss of consciousness (if present), and a maximum of 24 h post-traumatic amnesia (if present).…”

“…36 In addition, these regressors are not part of the standard pre-processing procedure followed in gICA. 26,27,31 A full width half maximum Gaussian kernel of 6 mm was then used for spatial smoothing.…”

“…26 However, no individual subject classification was performed. The motivation for re-analyzing the data is based on recent studies that suggest there may be important differences depending on the order in which motion and anomalous points are corrected for in resting state analyses.…”

“…26 We followed the suggestion that resting state functional network connectivity (rsFNC) bias could be minimized by processing head motion variance before using gICA 27 rather than after gICA as has been typically done previously in the ICA literature. 20,26,[28][29][30] After investigating the effects of several pre-processing pipelines, Vergara and associates 31 found that pre-processing head movement before gICA leads to more accurate detection of group differences and higher classification accuracy after testing using both simulated and real data. Simulations in the same study also suggest that preprocessing head movement after gICA results in higher residual correlation with head movement and higher spatial variance between subjects.…”