Dataset Information

Abnormal connectivity in the sensorimotor network predicts attention deficits in traumatic brain injury.

ABSTRACT: The aim of this study was to explore modifications of functional connectivity in multiple resting-state networks (RSNs) after moderate to severe traumatic brain injury (TBI) and evaluate the relationship between functional connectivity patterns and cognitive abnormalities. Forty-three moderate/severe TBI patients and 34 healthy controls (HC) underwent resting-state fMRI. Group ICA was applied to identify RSNs. Between-subject analysis was performed using dual regression. Multiple linear regressions were used to investigate the relationship between abnormal connectivity strength and neuropsychological outcome. Forty (93%) TBI patients showed moderate disability, while 2 (5%) and 1 (2%) upper severe disability and low good recovery, respectively. TBI patients performed worse than HC on the domains attention and language. We found increased connectivity in sensorimotor, visual, default mode (DMN), executive, and cerebellar RSNs after TBI. We demonstrated an effect of connectivity in the sensorimotor RSN on attention (p < 10^-3) and a trend towards a significant effect of the DMN connectivity on attention (p = 0.058). A group-by-network interaction on attention was found in the sensorimotor network (p = 0.002). In TBI, attention was positively related to abnormal connectivity within the sensorimotor RSN, while in HC this relation was negative. Our results show altered patterns of functional connectivity after TBI. Attention impairments in TBI were associated with increased connectivity in the sensorimotor network. Further research is needed to test whether attention in TBI patients is directly affected by changes in functional connectivity in the sensorimotor network or whether the effect is actually driven by changes in the DMN.

SUBMITTER: Shumskaya E

PROVIDER: S-EPMC5315712 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Abnormal connectivity in the sensorimotor network predicts attention deficits in traumatic brain injury.

Shumskaya Elena E van Gerven Marcel A J MA Norris David G DG Vos Pieter E PE Kessels Roy P C RP

Experimental brain research 20161124 3

The aim of this study was to explore modifications of functional connectivity in multiple resting-state networks (RSNs) after moderate to severe traumatic brain injury (TBI) and evaluate the relationship between functional connectivity patterns and cognitive abnormalities. Forty-three moderate/severe TBI patients and 34 healthy controls (HC) underwent resting-state fMRI. Group ICA was applied to identify RSNs. Between-subject analysis was performed using dual regression. Multiple linear regressi ...[more]

PMID: 27885406

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Project description:ObjectiveTraumatic brain injury (TBI) is a heterogeneous disease with multiple neurological deficits that evolve over time. It is also associated with an increased incidence of neurodegenerative diseases. Accordingly, clinicians need better tools to predict a patient's long-term prognosis.MethodsDiffusion-weighted and anatomical MRI data were collected from 17 adolescents (mean age = 15y8mo) with moderate-to-severe TBI and 19 healthy controls. Using a network diffusion model (NDM), we examined the effect of progressive deafferentation and gray matter thinning in young TBI patients. Moreover, using a novel automated inference method, we identified several injury epicenters in order to determine the neural degenerative patterns in each TBI patient.ResultsWe were able to identify the subject-specific patterns of degeneration in each patient. In particular, the hippocampus, temporal cortices, and striatum were frequently found to be the epicenters of degeneration across the TBI patients. Orthogonal transformation of the predicted degeneration, using principal component analysis, identified distinct spatial components in the temporal-hippocampal network and the cortico-striatal network, confirming the vulnerability of these networks to injury. The NDM model, best predictive of the degeneration, was significantly correlated with time since injury, indicating that NDM can potentially capture the pathological progression in the chronic phase of TBI.InterpretationThese findings suggest that network spread may help explain patterns of distant gray matter thinning, which would be consistent with Wallerian degeneration of the white matter connections (i.e., "diaschisis") from diffuse axonal injuries and multifocal contusive injuries, and the neurodegenerative patterns of abnormal protein aggregation and transmission, which are hallmarks of brain changes in TBI. NDM approaches could provide highly subject-specific biomarkers relevant for disease monitoring and personalized therapies in TBI.

Dataset Information

Abnormal connectivity in the sensorimotor network predicts attention deficits in traumatic brain injury.

Publications

Abnormal connectivity in the sensorimotor network predicts attention deficits in traumatic brain injury.

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