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Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury.

ABSTRACT: Mild traumatic brain injury (mTBI) may affect normal cognition and behavior by disrupting the functional connectivity networks that mediate efficient communication among brain regions. In this study, we analyzed brain connectivity profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 31 mTBI patients and 55 normal controls. We used phase-locking value estimates to compute functional connectivity graphs to quantify frequency-specific couplings between sensors at various frequency bands. Overall, normal controls showed a dense network of strong local connections and a limited number of long-range connections that accounted for approximately 20% of all connections, whereas mTBI patients showed networks characterized by weak local connections and strong long-range connections that accounted for more than 60% of all connections. Comparison of the two distinct general patterns at different frequencies using a tensor representation for the connectivity graphs and tensor subspace analysis for optimal feature extraction showed that mTBI patients could be separated from normal controls with 100% classification accuracy in the alpha band. These encouraging findings support the hypothesis that MEG-based functional connectivity patterns may be used as biomarkers that can provide more accurate diagnoses, help guide treatment, and monitor effectiveness of intervention in mTBI.

SUBMITTER: Dimitriadis SI

PROVIDER: S-EPMC4632071 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury.

Dimitriadis Stavros I SI Zouridakis George G Rezaie Roozbeh R Babajani-Feremi Abbas A Papanicolaou Andrew C AC

NeuroImage. Clinical 20150921

Mild traumatic brain injury (mTBI) may affect normal cognition and behavior by disrupting the functional connectivity networks that mediate efficient communication among brain regions. In this study, we analyzed brain connectivity profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 31 mTBI patients and 55 normal controls. We used phase-locking value estimates to compute functional connectivity graphs to quantify frequency-specific couplings between sensors at vario ...[more]

PMID: 26640764

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Project description:Mental fatigue in healthy individuals is typically observed under conditions of high cognitive demand, particularly when effort is required to perform a task for a long period of time-thus the concepts of fatigue and effort are closely related. In brain injured individuals, mental fatigue can be a persistent and debilitating symptom. Presence of fatigue after brain injury is prognostic for return to work/school and engagement in activities of daily life. As such, it should be a high priority for treatment in this population, but because there is little understanding of its behavioral and neural underpinnings, the target for such treatment is unknown. Here, the neural underpinnings of fatigue and effort are investigated in active duty military service members with mild traumatic brain injury (mTBI) and demographically-matched orthopedic controls. Participants performed a Constant Effort task for which they were to hold a pre-defined effort level constant for long durations during fMRI scanning. The task allowed for investigation of the neural systems underlying fatigue and their relationship with sense of effort. While brain activation associated with effort and fatigue did not differentiate the mTBI and controls, functional connectivity amongst active brain regions did. The mTBI group demonstrated immediate hyper-connectivity that increased with effort level but diminished quickly when there was a need to maintain effort. Controls, in contrast, demonstrated a similar pattern of hyper-connectivity, but only when maintaining effort over time. Connectivity, particularly between the left anterior insula, rostral anterior cingulate cortex, and right-sided inferior frontal regions, correlated with effort-level and state fatigue in mTBI participants. These connections also correlated with effort level in the Control group, but only the connection between the left insula and superior medial frontal gyrus correlated with fatigue, suggesting a differing pattern of connectivity. These findings align, in part, with the dopamine imbalance, and neural efficiency hypotheses that pose key roles for medial frontal connections with insular or striatal regions in motivating or optimizing performance. Sense of effort and fatigue are closely related. As people fatigue, sense of effort increases systematically. The data propose a complex link between sense of effort, fatigue, and mTBI that is centered in what may be an inefficient neural system due to brain trauma that warrants further investigation.

Project description:To investigate longitudinal changes in global and regional brain volume in patients 1 year after mild traumatic brain injury (MTBI) and to correlate such changes with clinical and neurocognitive metrics.This institutional review board-approved study was HIPAA compliant. Twenty-eight patients with MTBI (with 19 followed up at 1 year) with posttraumatic symptoms after injury and 22 matched control subjects (with 12 followed up at 1 year) were enrolled. Automated segmentation of brain regions to compute regional gray matter (GM) and white matter (WM) volumes was performed by using three-dimensional T1-weighted 3.0-T magnetic resonance imaging, and results were correlated with clinical metrics. Pearson and Spearman rank correlation coefficients were computed between longitudinal brain volume and neurocognitive scores, as well as clinical metrics, over the course of the follow-up period.One year after MTBI, there was measurable global brain atrophy, larger than that in control subjects. The anterior cingulate WM bilaterally and the left cingulate gyrus isthmus WM, as well as the right precuneal GM, showed significant decreases in regional volume in patients with MTBI over the 1st year after injury (corrected P < .05); this was confirmed by means of cross-sectional comparison with data in control subjects (corrected P < .05). Left and right rostral anterior cingulum WM volume loss correlated with changes in neurocognitive measures of memory (r = 0.65, P = .005) and attention (r = 0.60, P = .01). At 1-year follow-up, WM volume in the left cingulate gyrus isthmus correlated with clinical scores of anxiety (Spearman rank correlation r = -0.68, P = .007) and postconcussive symptoms (Spearman rank correlation r = -0.65, P = .01).These observations demonstrate structural changes to the brain 1 year after injury after a single concussive episode. Regional brain atrophy is not exclusive to moderate and severe traumatic brain injury but may be seen after mild injury. In particular, the anterior part of the cingulum and the cingulate gyrus isthmus, as well as the precuneal GM, may be distinctively vulnerable 1 year after MTBI.

Project description:IntroductionDynamic functional network connectivity (dFNC), derived from magnetic resonance imaging (fMRI), is an important technique in the search for biomarkers of brain diseases such as mild traumatic brain injury (mTBI). At the individual level, mTBI can affect cognitive functions and change personality traits. Previous research aimed at detecting significant changes in the dFNC of mTBI subjects. However, one of the main concerns in dFNC analysis is the appropriateness of methods used to correct for subject movement. In this work, we focus on the effect that rearranging movement correction at different points of the processing pipeline has in dFNC analysis utilizing mTBI data.MethodsThe sample cohort consists of 50 mTBI patients and matched healthy controls. A 5-min resting-state run was completed by each participant. Data were preprocessed using different pipeline alternatives varying with the place where motion-related variance was removed. In all pipelines, group-independent component analysis (gICA) followed by dFNC analysis was performed. Additional tests were performed varying the detection of temporal spikes, the number of gICA components, and the sliding-window size. A linear support vector machine was used to test how each pipeline affects classification accuracy.ResultsResults suggest that correction for motion variance before spatial smoothing, but leaving correction for spiky time courses after gICA produced the best mean classification performance. The number of gICA components and the sliding-window size were also important in determining classification performance. Variance in spikes correction affected some pipelines more than others with fewer significant differences than the other parameters.ConclusionThe sequence of preprocessing steps motion regression, smoothing, gICA, and despiking produced data most suitable for differentiating mTBI from healthy subjects. However, the selection of optimal preprocessing parameters strongly affected the final results.

Dataset Information

Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury.

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Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury.

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