Objective: In the cognitive and clinical neurosciences, the past decade has been marked by dramatic growth in a literature examining brain "connectivity" using noninvasive methods. We offer a critical review of the blood oxygen level dependent functional MRI (BOLD fMRI) literature examining neural connectivity changes in neurological disorders with focus on brain injury and dementia. The goal is to demonstrate that there are identifiable shifts in local and large-scale network connectivity that can be predicted by the degree of pathology. We anticipate that the most common network response to neurological insult is hyperconnectivity but that this response depends upon demand and resource availability. Method: To examine this hypothesis, we initially reviewed the results from 1,426 studies examining functional brain connectivity in individuals diagnosed with multiple sclerosis, traumatic brain injury, mild cognitive impairment, and Alzheimer's disease. Based upon inclusionary criteria, 126 studies were included for detailed analysis. Results: Results from 126 studies examining local and whole brain connectivity demonstrated increased connectivity in traumatic brain injury and multiple sclerosis. This finding is juxtaposed with findings in mild cognitive impairment and Alzheimer's disease where there is a shift to diminished connectivity as degeneration progresses. Conclusion: This summary of the functional imaging literature using fMRI methods reveals that hyperconnectivity is a common response to neurological disruption and that it may be differentially observable across brain regions. We discuss the factors contributing to both hyper-and hypoconnectivity results after neurological disruption and the implications these findings have for network plasticity.

Whereas traumatic brain injury (TBI) results in widespread disruption of neural networks, changes in regional resting-state functional connectivity patterns after insult remain unclear. Specifically, little is known about the chronology of emergent connectivity alterations and whether they persist after a critical recovery window. We used resting-state functional magnetic resonance imaging and seed-voxel correlational analyses in both cross-sectional and longitudinal designs to probe intrinsic connectivity patterns involving the posterior cingulate cortex (PCC) and hippocampi, regions shown to be important in the default mode network (DMN) and vulnerable to neuropathology. A total of 22 participants in the chronic stage of moderateto-severe TBI and 18 healthy controls were included for cross-sectional study. Longitudinal analyses included 13 individuals in the TBI group for whom data approximately 3 months after injury (subacute) were available. Overall, results indicated dissociable connectivity trajectories of the PCC and hippocampi during recovery from TBI, with PCC alterations characterized by early hypersynchrony with the anterior DMN that is gradually reduced, and hippocampal changes marked by increasing synchrony with proximal cortex and subcortex. The PCC also showed increasing antiphase synchrony with posterior attentional regions, and the hippocampi showed decreasing antiphase synchrony with frontal attentional regions. Antiphase synchrony of the hippocampus and dorsolateral prefrontal cortex at the subacute stage of TBI was positively associated with attentional performance on neuropsychological tests at both the subacute and chronic stages. Our findings highlight the heterogeneity of regional whole-brain connectivity changes after TBI, and suggest that residual connectivity alterations exist in the clinically stable phase of TBI. Parallels between the chronicity of the observed effects and findings in neurodegenerative disease are discussed in the context of potential long-term outcomes of TBI.

ObjectiveChanges in functional network connectivity following traumatic brain injury (TBI) have received increasing attention in recent neuroimaging literature. This study sought to understand how disrupted systems adapt to injury during resting and goal-directed brain states. Hyperconnectivity has been a common finding, and dedifferentiation (or loss of segregation of networks) is one possible explanation for this finding. We hypothesized that individuals with TBI would show dedifferentiation of networks (as noted in other clinical populations) and these effects would be associated with cognitive dysfunction.MethodsGraph theory was implemented to examine functional connectivity during periods of task and rest in 19 individuals with moderate/severe TBI and 14 healthy controls (HCs). Using a functional brain atlas derived from 83 functional imaging studies, graph theory was used to examine network dynamics and determine whether dedifferentiation accounts for changes in connectivity. Regions of interest were assigned to one of three groups: task-positive, default mode, or other networks. Relationships between these metrics were then compared with performance on neuropsychological tests.ResultsHyperconnectivity in TBI was most commonly observed as increased within-network connectivity. Network strengths within networks that showed differences between TBI and HCs were correlated with performance on five neuropsychological tests typically sensitive to deficits commonly reported in TBI. Hyperconnectivity within the default mode network (DMN) during task was associated with better performance on Digit Span Backward, a measure of working memory [R2(18) = 0.28, p = 0.02]. In other words, increased differentiation of networks during task was associated with better working memory. Hyperconnectivity within the task-positive network during rest was not associated with behavior. Negative correlation weights were not associated with behavior.ConclusionThe primary hypothesis that hyperconnectivity occurs through dedifferentiation was not supported. Instead, enhanced connectivity post injury was observed within network. Results suggest that the relationship between increased connectivity and cognitive functioning may be both state (rest or task) and network dependent. High-cost network hubs were identical for both rest and task, and cost was negatively associated with performance on measures of psychomotor speed and set-shifting.

Objective Patients with Alzheimer’s disease (AD) demonstrate deficits in cross-cortical feature binding distinct from age-related changes in selective attention. This may have consequences for driving performance given its demands on multisensory integration. We examined the relationship of visuospatial search and binding to driving in patients with early AD and elderly controls (EC). Method Participants (42 AD, 37 EC) completed search tasks requiring either luminance-motion (L-M) or color-motion (C-M) binding, analogs of within and across visual processing stream binding, respectively. Standardized road test (RIRT) and naturalistic driving data (CDAS) were collected alongside clinical screening measures. Results Patients performed worse than controls on most cognitive and driving indices. Visual search and clinical measures were differentially related to driving behavior across groups. L-M search and Trail Making Test (TMT-B) were associated with RIRT performance in controls, while C-M binding, TMT-B errors, and Clock Drawing correlated with CDAS performance in patients. After controlling for demographic and clinical predictors, L-M reaction time significantly predicted RIRT performance in controls. In patients, C-M binding made significant contributions to CDAS above and beyond demographic and clinical predictors. RIRT and C-M binding measures accounted for 51% of variance in CDAS performance in patients. Conclusions Whereas selective attention is associated with driving behavior in EC, cross-cortical binding appears most sensitive to driving in AD. This latter relationship may emerge only in naturalistic settings, which better reflect patients’ driving behavior. Visual integration may offer distinct insights into driving behavior, and thus has important implications for assessing driving competency in early AD.

Ergodicity can be assumed when the structure of data is consistent across individuals and time. Neural network approaches do not frequently test for ergodicity in data which holds important consequences for data integration and intepretation. To demonstrate this problem, we present several network models in healthy and clinical samples where there exists considerable heterogeneity across individuals. We offer suggestions for the analysis, interpretation, and reporting of neural network data. The goal is to arrive at an understanding of the sources of non-ergodicity and approaches for valid network modeling in neuroscience.

Moderate-severe traumatic brain injury (TBI) may result in difficulty with emotion recognition, which has negative implications for social functioning. As aspects of social cognition have been linked to resting-state functional connectivity (RSFC) in the default mode network (DMN), we sought to determine whether DMN connectivity strength predicts emotion recognition and level of social integration in TBI. To this end, we examined emotion recognition ability of 21 individuals with TBI and 27 healthy controls in relation to RSFC between DMN regions. Across all participants, decreased emotion recognition ability was related to increased connectivity between dorsomedial prefrontal cortex (dmPFC) and temporal regions (temporal pole and parahippocampal gyrus). Furthermore, within the TBI group, connectivity between dmPFC and parahippocampal gyrus predicted level of social integration on the Community Integration Questionnaire, an important index of post-injury social functioning in TBI. This finding was not explained by emotion recognition ability, indicating that DMN connectivity predicts social functioning independent of emotion recognition. These results advance our understanding of the neural underpinnings of emotional and social processes in both healthy and injured brains, and suggest that RSFC may be an important marker of social outcomes in individuals with TBI.

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Objective: Functional brain networks converge on areas of heteromodal processing such as lateral posterior parietal cortex (PPC). Traumatic brain injury (TBI) alters global connectivity patterns secondary to both focal and diffuse damage, but little is known about how it impacts regional environments. We examined local PPC functioning in individuals with moderate-severe TBI and controls during resting-state functional magnetic resonance imaging (rs-fMRI). Method: Eighteen individuals with moderate-severe TBI and 19 healthy controls underwent rs-fMRI and neurocognitive testing. Seed-based analyses characterized remote connectivity of PPC subregions. Voxelwise graph theoretical approaches were used to probe local PPC connectivity and modularity within and between groups, and to examine relationships between local functioning and cognition. Results: Seed-based findings included increased connectivity from left and right hemispheric subregions to right-lateralized default mode and frontoparietal control networks in TBI compared to controls. Graph theoretical analyses revealed increased connection strength within right PPC relative to the contralateral region in TBI. Across groups, right PPC also showed decreased betweenness centrality compared with left PPC. Groups did not differ in the extent of modularity within left or right PPC, but there was less interindividual variability in modular structure within the TBI group. Right PPC modularity significantly predicted individual differences in cognitive performance. Conclusions: Our findings substantiate hyperconnectivity on both local and global levels after TBI and propose a special role for local right hemispheric functioning in supporting cognition independent of neurologic status. Hyperconnectivity does not appear to result from breakdown in local modular organization and may reflect shared responses to neurologic disruption among those with TBI.