Altered Wiring of the Human Structural Connectome in Adults with Mild Traumatic Brain Injury

Relationship Between Structural and Functional Network Connectivity Changes for Patients With Traumatic Brain Injury and Chronic Health Symptoms

Combination of structural and functional brain connectivity methods provides a more complete and effective avenue into the investigation of cortical network responses to traumatic brain injury (TBI) and subtle alterations in brain connectivity associated with TBI. Structural connectivity (SC) can be measured using diffusion tensor imaging to evaluate white matter integrity, whereas functional connectivity (FC) can be studied by examining functional correlations within or between functional networks. In this study, the alterations of SC and FC were assessed for TBI patients, with and without chronic symptoms (TBIcs/TBIncs), compared with a healthy control group (CG). The correlation between global SC and FC was significantly increased for both TBI groups compared with CG. SC was significantly lower in the TBIcs group compared with CG, and FC changes were seen in the TBIncs group compared with CG. When comparing TBI groups, FC differences were observed in the TBIcs group compared with the TBIncs group. These observations show that the presence of chronic symptoms is associated with a distinct pattern of SC and FC changes including the atrophy of the SC and a mixture of functional hypoconnectivity and hyperconnectivity, as well as loss of segregation of functional networks.

Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury: predictors of patient prognosis

JOURNAL/nrgr/04.03/01300535-202407000-00035/figure1/v/2023-11-20T171125Z/r/image-tiff

Patients with mild traumatic brain injury have a diverse clinical presentation, and the underlying pathophysiology remains poorly understood. Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neurobiological markers after mild traumatic brain injury. This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury. Graph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function. However, most previous mild traumatic brain injury studies using graph theory have focused on specific populations, with limited exploration of simultaneous abnormalities in structural and functional connectivity. Given that mild traumatic brain injury is the most common type of traumatic brain injury encountered in clinical practice, further investigation of the patient characteristics and evolution of structural and functional connectivity is critical. In the present study, we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury. In this longitudinal study, we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 weeks of injury, as well as 36 healthy controls. Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis. In the acute phase, patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network. More than 3 months of follow-up data revealed signs of recovery in structural and functional connectivity, as well as cognitive function, in 22 out of the 46 patients. Furthermore, better cognitive function was associated with more efficient networks. Finally, our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury. These findings highlight the importance of integrating structural and functional connectivity in understanding the occurrence and evolution of mild traumatic brain injury. Additionally, exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.

Predictive Utility of Diffusion MRI After Mild Traumatic Brain Injury in Civilian Populations: A Systematic Review

OBJECTIVE

A considerable number of people experience persisting symptoms and functional limitations after mild traumatic brain injury (mTBI). It is unclear whether subtle white matter changes contribute to this phenomenon. In this systematic review, the authors evaluated whether microstructural white matter indices on advanced MRI are related to clinical dysfunction among patients without abnormalities on standard brain computed tomography (CT) or MRI (uncomplicated mTBI).

METHODS

A search of multiple databases was performed. Studies with individuals who experienced blast-related, sports-related, or multiple mTBIs were excluded. Diffusion tensor imaging (DTI) and susceptibility-weighted imaging (SWI) metrics and cognitive, neuropsychiatric, or functional outcome measures were extracted from each study.

RESULTS

Thirteen studies were selected (participants with mTBI, N=553; healthy control group, N=438). Seven DTI studies evaluated cognitive function, with five reporting significant correlations between reduced white matter integrity and deficits in attention, processing speed, and executive function at 6-12 months after injury (three studies included only individuals with uncomplicated mTBI). Four studies found significant correlations between DTI metrics and persistent postconcussive symptoms after 3-12 months (one study included only individuals with uncomplicated mTBI). Two SWI studies reported conflicting findings regarding the relationship between the presence of microbleeds and postconcussive symptoms.

CONCLUSIONS

The results revealed that indices of microstructural white matter integrity may relate to clinical presentation 3-12 months after injury in uncomplicated mTBI. However, analysis methods and brain regions studied varied across studies. Further research is needed to identify relationships between white matter indices in specific brain regions and symptom persistence beyond 12 months.

Altered grey matter structural covariance in chronic moderate-severe traumatic brain injury

Traumatic brain injury (TBI) alters brain network connectivity. Structural covariance networks (SCNs) reflect morphological covariation between brain regions. SCNs may elucidate how altered brain network topology in TBI influences long-term outcomes. Here, we assessed whether SCN organisation is altered in individuals with chronic moderate-severe TBI (≥ 10 years post-injury) and associations with cognitive performance. This case-control study included fifty individuals with chronic moderate-severe TBI compared to 75 healthy controls recruited from an ongoing longitudinal head injury outcome study. SCNs were constructed using grey matter volume measurements from T1-weighted MRI images. Global and regional SCN organisation in relation to group membership and cognitive ability was examined using regression analyses. Globally, TBI participants had reduced small-worldness, longer characteristic path length, higher clustering, and higher modularity globally (p < 0.05). Regionally, TBI participants had greater betweenness centrality (p < 0.05) in frontal and central areas of the cortex. No significant associations were observed between global network measures and cognitive ability in participants with TBI (p > 0.05). Chronic moderate-severe TBI was associated with a shift towards a more segregated global network topology and altered organisation in frontal and central brain regions. There was no evidence that SCNs are associated with cognition.

Altered longitudinal structural connectome in paediatric mild traumatic brain injury: an Advancing Concussion Assessment in Paediatrics study

Advanced diffusion-weighted imaging techniques have increased understanding of the neuropathology of paediatric mild traumatic brain injury (i.e. concussion). Most studies have examined discrete white-matter pathways, which may not capture the characteristically subtle, diffuse and heterogenous effects of paediatric concussion on brain microstructure. This study compared the structural connectome of children with concussion to those with mild orthopaedic injury to determine whether network metrics and their trajectories across time post-injury differentiate paediatric concussion from mild traumatic injury more generally. Data were drawn from of a large study of outcomes in paediatric concussion. Children aged 8-16.99 years were recruited from five paediatric emergency departments within 48 h of sustaining a concussion (n = 360; 56% male) or mild orthopaedic injury (n = 196; 62% male). A reliable change score was used to classify children with concussion into two groups: concussion with or without persistent symptoms. Children completed 3 T MRI at post-acute (2-33 days) and/or chronic (3 or 6 months, via random assignment) post-injury follow-ups. Diffusion-weighted images were used to calculate the diffusion tensor, conduct deterministic whole-brain fibre tractography and compute connectivity matrices in native (diffusion) space for 90 supratentorial regions. Weighted adjacency matrices were constructed using average fractional anisotropy and used to calculate global and local (regional) graph theory metrics. Linear mixed effects modelling was performed to compare groups, correcting for multiple comparisons. Groups did not differ in global network metrics. However, the clustering coefficient, betweenness centrality and efficiency of the insula, cingulate, parietal, occipital and subcortical regions differed among groups, with differences moderated by time (days) post-injury, biological sex and age at time of injury. Post-acute differences were minimal, whereas more robust alterations emerged at 3 and especially 6 months in children with concussion with persistent symptoms, albeit differently by sex and age. In the largest neuroimaging study to date, post-acute regional network metrics distinguished concussion from mild orthopaedic injury and predicted symptom recovery 1-month post-injury. Regional network parameters alterations were more robust and widespread at chronic timepoints than post-acutely after concussion. Results suggest that increased regional and local subnetwork segregation (modularity) and inefficiency occurs across time after concussion, emerging after post-concussive symptom resolve in most children. These differences persist up to 6 months after concussion, especially in children who showed persistent symptoms. While prognostic, the small to modest effect size of group differences and the moderating effects of sex likely would preclude effective clinical application in individual patients.

Structural-functional connectivity bandwidth predicts processing speed in mild traumatic brain Injury: A multiplex network analysis

An emerging body of work has revealed alterations in structural (SC) and functional (FC) brain connectivity following mild TBI (mTBI), with mixed findings. However, these studies seldom integrate complimentary neuroimaging modalities within a unified framework. Multilayer network analysis is an emerging technique to uncover how white matter organization enables functional communication. Using our novel graph metric (SC-FC Bandwidth), we quantified the information capacity of synchronous brain regions in 53 mild TBI patients (46 females; age mean = 40.2 years (y), σ = 16.7 (y), range: 18-79 (y). Diffusion MRI and resting state fMRI were administered at the acute and chronic post-injury intervals. Moreover, participants completed a cognitive task to measure processing speed (30 Seconds and Counting Task; 30-SACT). Processing speed was significantly increased at the chronic, relative to the acute post-injury intervals (p = <0.001). Nonlinear principal components of direct (t = -1.84, p = 0.06) and indirect SC-FC Bandwidth (t = 3.86, p = <0.001) predicted processing speed with a moderate effect size (R2 = 0.43, p < 0.001), while controlling for age. A subnetwork of interhemispheric edges with increased SC-FC Bandwidth was identified at the chronic, relative to the acute mTBI post-injury interval (pFDR = 0.05). Increased interhemispheric SC-FC Bandwidth of this network corresponded with improved processing speed at the chronic post-injury interval (partial r = 0.32, p = 0.02). Our findings revealed that mild TBI results in complex reorganization of brain connectivity optimized for maximum information flow, supporting improved cognitive performance as a compensatory mechanism. Moving forward, this measurement may complement clinical assessment as an objective marker of mTBI recovery.

MRI factors associated with cognitive functioning after acute onset brain injury: Systematic review and meta-analysis

Impairments of memory, attention, and executive functioning are frequently reported after acute onset brain injury. MRI markers hold potential to contribute to identification of patients at risk for cognitive impairments and clarification of mechanisms. The aim of this systematic review was to summarize and value the evidence on MRI markers of memory, attention, and executive functioning after acute onset brain injury. We included ninety-eight studies, on six classes of MRI factors (location and severity of damage (n = 15), volume/atrophy (n = 36), signs of small vessel disease (n = 15), diffusion-weighted imaging measures (n = 36), resting-state functional MRI measures (n = 13), and arterial spin labeling measures (n = 1)). Three measures showed consistent results regarding their association with cognition. Smaller hippocampal volume was associated with worse memory in fourteen studies (pooled correlation 0.58 [95% CI: 0.46-0.68] for whole, 0.11 [95% CI: 0.04-0.19] for left, and 0.34 [95% CI: 0.17-0.49] for right hippocampus). Lower fractional anisotropy in cingulum and fornix was associated with worse memory in six and five studies (pooled correlation 0.20 [95% CI: 0.08-0.32] and 0.29 [95% CI: 0.20-0.37], respectively). Lower functional connectivity within the default-mode network was associated with worse cognition in four studies. In conclusion, hippocampal volume, fractional anisotropy in cingulum and fornix, and functional connectivity within the default-mode network showed consistent associations with cognitive performance in all types of acute onset brain injury. External validation and cut off values for predicting cognitive impairments are needed for clinical implementation.

Diffusion-Weighted Imaging in Mild Traumatic Brain Injury: A Systematic Review of the Literature

There is evidence that diffusion-weighted imaging (DWI) is able to detect tissue alterations following mild traumatic brain injury (mTBI) that may not be observed on conventional neuroimaging; however, findings are often inconsistent between studies. This systematic review assesses patterns of differences in DWI metrics between those with and without a history of mTBI. A PubMed literature search was performed using relevant indexing terms for articles published prior to May 14, 2020. Findings were limited to human studies using DWI in mTBI. Articles were excluded if they were not full-length, did not contain original data, if they were case studies, pertained to military populations, had inadequate injury severity classification, or did not report post-injury interval. Findings were reported independently for four subgroups: acute/subacute pediatric mTBI, acute/subacute adult mTBI, chronic adult mTBI, and sport-related concussion, and all DWI acquisition and analysis methods used were included. Patterns of findings between studies were reported, along with strengths and weaknesses of the current state of the literature. Although heterogeneity of sample characteristics and study methods limited the consistency of findings, alterations in DWI metrics were most commonly reported in the corpus callosum, corona radiata, internal capsule, and long association pathways. Many acute/subacute pediatric studies reported higher FA and lower ADC or MD in various regions. In contrast, acute/subacute adult studies most commonly indicate lower FA within the context of higher MD and RD. In the chronic phase of recovery, FA may remain low, possibly indicating overall demyelination or Wallerian degeneration over time. Longitudinal studies, though limited, generally indicate at least a partial normalization of DWI metrics over time, which is often associated with functional improvement. We conclude that DWI is able to detect structural mTBI-related abnormalities that may persist over time, although future DWI research will benefit from larger samples, improved data analysis methods, standardized reporting, and increasing transparency.

Exploring personalized structural connectomics for moderate to severe traumatic brain injury

Graph theoretical analysis of the structural connectome has been employed successfully to characterize brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalized connectomics approach that examines structural brain alterations in five chronic patients with moderate to severe TBI who underwent anatomical and diffusion magnetic resonance imaging. We generated individualized profiles of lesion characteristics and network measures (including personalized graph metric GraphMe plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N = 12) to assess brain damage qualitatively and quantitatively at the individual level. Our findings revealed alterations of brain networks with high variability between patients. With validation and comparison to stratified, normative healthy control comparison cohorts, this approach could be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalized rehabilitation protocols based on their unique lesion load and connectome.

Chronic Mild Traumatic Brain Injury: Aberrant Static and Dynamic Connectomic Features Identified Through Machine Learning Model Fusion

Traumatic Brain Injury (TBI) is a frequently occurring condition and approximately 90% of TBI cases are classified as mild (mTBI). However, conventional MRI has limited diagnostic and prognostic value, thus warranting the utilization of additional imaging modalities and analysis procedures. The functional connectomic approach using resting-state functional MRI (rs-fMRI) has shown great potential and promising diagnostic capabilities across multiple clinical scenarios, including mTBI. Additionally, there is increasing recognition of a fundamental role of brain dynamics in healthy and pathological cognition. Here, we undertake an in-depth investigation of mTBI-related connectomic disturbances and their emotional and cognitive correlates. We leveraged machine learning and graph theory to combine static and dynamic functional connectivity (FC) with regional entropy values, achieving classification accuracy up to 75% (77, 74 and 76% precision, sensitivity and specificity, respectively). As compared to healthy controls, the mTBI group displayed hypoconnectivity in the temporal poles, which correlated positively with semantic (r = 0.43, p < 0.008) and phonemic verbal fluency (r = 0.46, p < 0.004), while hypoconnectivity in the right dorsal posterior cingulate correlated positively with depression symptom severity (r = 0.54, p < 0.0006). These results highlight the importance of residual FC in these regions for preserved cognitive and emotional function in mTBI. Conversely, hyperconnectivity was observed in the right precentral and supramarginal gyri, which correlated negatively with semantic verbal fluency (r=-0.47, p < 0.003), indicating a potential ineffective compensatory mechanism. These novel results are promising toward understanding the pathophysiology of mTBI and explaining some of its most lingering emotional and cognitive symptoms.

Differential Patterns of Change in Brain Connectivity Resulting from Severe Traumatic Brain Injury

Background: Traumatic brain injury (TBI) damages white matter tracts, disrupting brain network structure and communication. There exists a wide heterogeneity in the pattern of structural damage associated with injury, as well as a large heterogeneity in behavioral outcomes. However, little is known about the relationship between changes in network connectivity and clinical outcomes. Materials and Methods: We utilize the rat lateral fluid-percussion injury model of severe TBI to study differences in brain connectivity in 8 animals that received the insult and 11 animals that received only a craniectomy. Diffusion tensor imaging is performed 5 weeks after the injury and network theory is used to investigate changes in white matter connectivity. Results: We find that (1) global network measures are not able to distinguish between healthy and injured animals; (2) injury induced alterations predominantly exist in a subset of connections (subnetworks) distributed throughout the brain; and (3) injured animals can be divided into subgroups based on changes in network motifs-measures of local structural connectivity. In addition, alterations in predicted functional connectivity indicate that the subgroups have different propensities to synchronize brain activity, which could relate to the heterogeneity of clinical outcomes. Discussion: These results suggest that network measures can be used to quantify progressive changes in brain connectivity due to injury and differentiate among subpopulations with similar injuries, but different pathological trajectories.

Brain architecture-based vulnerability to traumatic injury

The white matter tracts forming the intricate wiring of the brain are subject-specific; this heterogeneity can complicate studies of brain function and disease. Here we collapse tractography data from the Human Connectome Project (HCP) into structural connectivity (SC) matrices and identify groups of similarly wired brains from both sexes. To characterize the significance of these architectural groupings, we examined how similarly wired brains led to distinct groupings of neural activity dynamics estimated with Kuramoto oscillator models (KMs). We then lesioned our networks to simulate traumatic brain injury (TBI) and finally we tested whether these distinct architecture groups’ dynamics exhibited differing responses to simulated TBI. At each of these levels we found that brain structure, simulated dynamics, and injury susceptibility were all related to brain grouping. We found four primary brain architecture groupings (two male and two female), with similar architectures appearing across both sexes. Among these groupings of brain structure, two architecture types were significantly more vulnerable than the remaining two architecture types to lesions. These groups suggest that mesoscale brain architecture types exist, and these architectural differences may contribute to differential risks to TBI and clinical outcomes across the population.

Early Changes in the White Matter Microstructure and Connectome Underlie Cognitive Deficit and Depression Symptoms After Mild Traumatic Brain Injury

Cognitive and emotional impairments are frequent among patients with mild traumatic brain injury (mTBI) and may reflect alterations in the brain structural properties. The relationship between microstructural changes and cognitive and emotional deficits remains unclear in patients with mTBI at the acute stage. The purpose of this study was to analyze the alterations in white matter microstructure and connectome of patients with mTBI within 7 days after injury and investigate whether they are related to the clinical questionnaires. A total of 79 subjects (42 mTBI and 37 healthy controls) underwent neuropsychological assessment and diffusion-tensor MRI scan. The microstructure and connectome of white matter were characterized by tract-based spatial statistics (TBSSs) and graph theory approaches, respectively. Mini-mental state examination (MMSE) and self-rating depression scale (SDS) were used to evaluate the cognitive function and depressive symptoms of all the subjects. Patients with mTBI revealed early increases of fractional anisotropy in most areas compared with the healthy controls. Graph theory analyses showed that patients with mTBI had increased nodal shortest path length, along with decreased nodal degree centrality and nodal efficiency, mainly located in the bilateral temporal lobe and right middle occipital gyrus. Moreover, lower nodal shortest path length and higher nodal efficiency of the right middle occipital gyrus were associated with higher SDS scores. Significantly, the strength of the rich club connection in the mTBI group decreased and was associated with the MMSE. Our study demonstrated that the neuroanatomical alterations of mTBI in the acute stage might be an initial step of damage leading to cognitive deficits and depression symptoms, and arguably, these occur due to distinct mechanisms.

Structural brain connectivity correlates with outcome in mild traumatic brain injury

We investigated the topology of structural brain connectivity networks and its association to outcome following mild traumatic brain injury, a major cause of permanent disability. Eighty-five patients with mild traumatic brain injury underwent MRI twice, about three weeks and eight months after injury, and 30 age-matched orthopedic trauma control subjects were scanned. Outcome was assessed with Extended Glasgow Outcome Scale on average eight months after injury. We performed constrained spherical deconvolution based probabilistic streamlines tractography on diffusion MRI data and parcellated cortical and subcortical gray matter into 84 regions based on T1-weighted data to reconstruct structural brain connectivity networks weighted by the number of streamlines. Graph theoretical methods were employed to measure network properties in both patients and controls, and correlations between these properties and outcome were calculated. We found no global differences in the network properties between patients with mild traumatic brain injury and orthopedic control subjects at either stage. However, we found significantly increased betweenness centrality of the right pars opercularis in the chronic stage compared to control subjects. Furthermore, both global and local network properties correlated significantly with outcome. Higher normalized global efficiency, degree, and strength as well as lower small-worldness were associated with better outcome. Correlations between the outcome and the local network properties were the most prominent in the left putamen and the left postcentral gyrus. Our results indicate that both global and local network properties provide valuable information about the outcome already in the acute/subacute stage, and therefore, are promising biomarkers for prognostic purposes in mild traumatic brain injury.

Quantitative multimodal imaging in traumatic brain injuries producing impaired cognition

PURPOSE OF REVIEW

Cognitive impairments are a devastating long-term consequence following traumatic brain injury (TBI). This review provides an update on the quantitative mutimodal neuroimaging studies that attempt to elucidate the mechanism(s) underlying cognitive impairments and their recovery following TBI.

RECENT FINDINGS

Recent studies have linked individual specific behavioural impairments and their changes over time to physiological activity and structural changes using EEG, PET and MRI. Multimodal studies that combine measures of physiological activity with knowledge of neuroanatomical and connectivity damage have also illuminated the multifactorial function-structure relationships that underlie impairment and recovery following TBI.

SUMMARY

The combined use of multiple neuroimaging modalities, with focus on individual longitudinal studies, has the potential to accurately classify impairments, enhance sensitivity of prognoses, inform targets for interventions and precisely track spontaneous and intervention-driven recovery.

White matter microstructure of the neural emotion regulation circuitry in mild traumatic brain injury

Emotion regulation is related to recovery after mild traumatic brain injury (mTBI). This longitudinal tractography study examined white matter tracts subserving emotion regulation across the spectrum of mTBI, with a focus on persistent symptoms. Four groups were examined: (a) symptomatic (n = 33) and (b) asymptomatic (n = 20) patients with uncomplicated mTBI (i.e., no lesions on computed tomography [CT]), (c) patients with CT-lesions in the frontal areas (n = 14), and (d) healthy controls (HC) (n = 20). Diffusion and conventional MRI were performed approximately 1- and 3-months post-injury. Whole-brain deterministic tractography followed by region of interest analyses was used to identify forceps minor (FM), uncinate fasciculus (UF), and cingulum bundle as tracts of interest. An adjusted version of the ExploreDTI Atlas Based Tractography method was used to obtain reliable tracts for every subject. Mean fractional anisotropy (FA), mean, radial and axial diffusivity (MD, RD, AD), and number of streamlines were studied per tract. Linear mixed models showed lower FA, and higher MD, and RD of the right UF in asymptomatic patients with uncomplicated mTBI relative to symptomatic patients and HC. Diffusion alterations were most pronounced in the group with frontal lesions on CT, particularly in the FM and UF; these effects increased over time. Within the group of patients with uncomplicated mTBI, there were no associations of diffusion measures with the number of symptoms nor with lesions on conventional MRI. In conclusion, mTBI can cause microstructural changes in emotion regulation tracts, however, no explanation was found for the presence of symptoms.

Traumatic Brain Injury Severity in a Network Perspective: A Diffusion MRI Based Connectome Study

Traumatic brain injury (TBI) is often characterized by alterations in brain connectivity. We explored connectivity alterations from a network perspective, using graph theory, and examined whether injury severity affected structural connectivity and modulated the association between brain connectivity and cognitive deficits post-TBI. We performed diffusion imaging network analysis on chronic TBI patients, with different injury severities and healthy subjects. From both global and local perspectives, we found an effect of injury severity on network strength. In addition, regions which were considered as hubs differed between groups. Further exploration of graph measures in the determined hub regions showed that efficiency of six regions differed between groups. An association between reduced efficiency in the precuneus and nonverbal abstract reasoning deficits (calculated using actual pre-injury scores) was found in the controls but was lost in TBI patients. Our results suggest that disconnection of network hubs led to a less efficient network, which in turn may have contributed to the cognitive impairments manifested in TBI patients. We conclude that injury severity modulates the disruption of network organization, reflecting a "dose response" relationship and emphasize the role of efficiency as an important diagnostic tool to detect subtle brain injury specifically in mild TBI patients.

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

Concussion is a significant public health problem affecting 1.6-2.4 million Americans annually. An alternative to reducing the burden of concussion is to reduce its incidence with improved protective equipment and injury mitigation systems. Finite element (FE) models of the brain response to blunt trauma are often used to estimate injury potential and can lead to improved helmet designs. However, these models have yet to incorporate how the patterns of brain connectivity disruption after impact affects the relay of information in the injured brain. Furthermore, FE brain models typically do not consider the differences in individual brain structural connectivities and their purported role in concussion risk. Here, we use graph theory techniques to integrate brain deformations predicted from FE modeling with measurements of network efficiency to identify brain regions whose connectivity characteristics may influence concussion risk. We computed maximum principal strain in 129 brain regions using head kinematics measured from 53 professional football impact reconstructions that included concussive and non-concussive cases. In parallel, using diffusion spectrum imaging data from 30 healthy subjects, we simulated structural lesioning of each of the same 129 brain regions. We simulated lesioning by removing each region one at a time along with all its connections. In turn, we computed the resultant change in global efficiency to identify regions important for network communication. We found that brain regions that deformed the most during an impact did not overlap with regions most important for network communication (Pearson's correlation, ρ = 0.07; p = 0.45). Despite this dissimilarity, we found that predicting concussion incidence was equally accurate when considering either areas of high strain or of high importance to global efficiency. Interestingly, accuracy for concussion prediction varied considerably across the 30 healthy connectomes. These results suggest that individual network structure is an important confounding variable in concussion prediction and that further investigation of its role may improve concussion prediction and lead to the development of more effective protective equipment.

Functional outcome is tied to dynamic brain states after mild to moderate traumatic brain injury

The current study set out to investigate the dynamic functional connectome in relation to long-term recovery after mild to moderate traumatic brain injury (TBI). Longitudinal resting-state functional MRI data were collected (at 1 and 3 months postinjury) from a prospectively enrolled cohort consisting of 68 patients with TBI (92% mild TBI) and 20 healthy subjects. Patients underwent a neuropsychological assessment at 3 months postinjury. Outcome was measured using the Glasgow Outcome Scale Extended (GOS-E) at 6 months postinjury. The 57 patients who completed the GOS-E were classified as recovered completely (GOS-E = 8; n = 37) or incompletely (GOS-E < 8; n = 20). Neuropsychological test scores were similar for all groups. Patients with incomplete recovery spent less time in a segregated brain state compared to recovered patients during the second visit. Also, these patients moved less frequently from one meta-state to another as compared to healthy controls and recovered patients. Furthermore, incomplete recovery was associated with disruptions in cyclic state transition patterns, called attractors, during both visits. This study demonstrates that poor long-term functional recovery is associated with alterations in dynamics between brain networks, which becomes more marked as a function of time. These results could be related to psychological processes rather than injury-effects, which is an interesting area for further work. Another natural progression of the current study is to examine whether these dynamic measures can be used to monitor treatment effects.

Longitudinal increases in structural connectome segregation and functional connectome integration are associated with better recovery after mild TBI

Traumatic brain injury damages white matter pathways that connect brain regions, disrupting transmission of electrochemical signals and causing cognitive and emotional dysfunction. Connectome-level mechanisms for how the brain compensates for injury have not been fully characterized. Here, we collected serial MRI-based structural and functional connectome metrics and neuropsychological scores in 26 mild traumatic brain injury subjects (29.4 ± 8.0 years, 20 males) at 1 and 6 months postinjury. We quantified the relationship between functional and structural connectomes using network diffusion (ND) model propagation time, a measure that can be interpreted as how much of the structural connectome is being utilized for the spread of functional activation, as captured via the functional connectome. Overall cognition showed significant improvement from 1 to 6 months (t25 = -2.15, p = .04). None of the structural or functional global connectome metrics was significantly different between 1 and 6 months, or when compared to 34 age- and gender-matched controls (28.6 ± 8.8 years, 25 males). We predicted longitudinal changes in overall cognition from changes in global connectome measures using a partial least squares regression model (cross-validated R2 = .27). We observe that increased ND model propagation time, increased structural connectome segregation, and increased functional connectome integration were related to better cognitive recovery. We interpret these findings as suggesting two connectome-based postinjury recovery mechanisms: one of neuroplasticity that increases functional connectome integration and one of remote white matter degeneration that increases structural connectome segregation. We hypothesize that our inherently multimodal measure of ND model propagation time captures the interplay between these two mechanisms.

The structural connectome in traumatic brain injury: A meta-analysis of graph metrics

Although recent structural connectivity studies of traumatic brain injury (TBI) have used graph theory to evaluate alterations in global integration and functional segregation, pooled analysis is needed to examine the robust patterns of change in graph metrics across studies. Following a systematic search, 15 studies met the inclusion criteria for review. Of these, ten studies were included in a random-effects meta-analysis of global graph metrics, and subgroup analyses examined the confounding effects of severity and time since injury. The meta-analysis revealed significantly higher values of normalised clustering coefficient (gö=ö1.445, CI=[0.512, 2.378], pö=ö0.002) and longer characteristic path length (gö=ö0.514, CI=[0.190, 0.838], pö=ö0.002) in TBI patients compared with healthy controls. Our findings suggest that the TBI structural network has shifted away from the balanced small-world network towards a regular lattice. Therefore, these graph metrics may be useful markers of neurocognitive dysfunction in TBI. We conclude that the pattern of change revealed by our analysis should be used to guide hypothesis-driven research into the role of graph metrics as diagnostic and prognostic biomarkers.

Dynamic Computational Model of the Human Spinal Cord Connectome

Connectomes abound, but few for the human spinal cord. Using anatomical data in the literature, we constructed a draft connectivity map of the human spinal cord connectome, providing a template for the many calibrations of specialized behavior to be overlaid on it and the basis for an initial computational model. A thorough literature review gleaned cell types, connectivity, and connection strength indications. Where human data were not available, we selected species that have been studied. Cadaveric spinal cord measurements, cross-sectional histology images, and cytoarchitectural data regarding cell size and density served as the starting point for estimating numbers of neurons. Simulations were run using neural circuitry simulation software. The model contains the neural circuitry in all ten Rexed laminae with intralaminar, interlaminar, and intersegmental connections, as well as ascending and descending brain connections and estimated neuron counts for various cell types in every lamina of all 31 segments. We noted the presence of highly interconnected complex networks exhibiting several orders of recurrence. The model was used to perform a detailed study of spinal cord stimulation for analgesia. This model is a starting point for workers to develop and test hypotheses across an array of biomedical applications focused on the spinal cord. Each such model requires additional calibrations to constrain its output to verifiable predictions. Future work will include simulating additional segments and expanding the research uses of the model.

Mild Traumatic Brain Injury Induces Structural and Functional Disconnection of Local Neocortical Inhibitory Networks via Parvalbumin Interneuron Diffuse Axonal Injury

Diffuse axonal injury (DAI) plays a major role in cortical network dysfunction posited to cause excitatory/inhibitory imbalance after mild traumatic brain injury (mTBI). Current thought holds that white matter (WM) is uniquely vulnerable to DAI. However, clinically diagnosed mTBI is not always associated with WM DAI. This suggests an undetected neocortical pathophysiology, implicating GABAergic interneurons. To evaluate this possibility, we used mild central fluid percussion injury to generate DAI in mice with Cre-driven tdTomato labeling of parvalbumin (PV) interneurons. We followed tdTomato+ profiles using confocal and electron microscopy, together with patch-clamp analysis to probe for DAI-mediated neocortical GABAergic interneuron disruption. Within 3 h post-mTBI tdTomato+ perisomatic axonal injury (PSAI) was found across somatosensory layers 2-6. The DAI marker amyloid precursor protein colocalized with GAD67 immunoreactivity within tdTomato+ PSAI, representing the majority of GABAergic interneuron DAI. At 24 h post-mTBI, we used phospho-c-Jun, a surrogate DAI marker, for retrograde assessments of sustaining somas. Via this approach, we estimated DAI occurs in ~9% of total tdTomato+ interneurons, representing ~14% of pan-neuronal DAI. Patch-clamp recordings of tdTomato+ interneurons revealed decreased inhibitory transmission. Overall, these data show that PV interneuron DAI is a consistent and significant feature of experimental mTBI with important implications for cortical network dysfunction.

Role of Graph Architecture in Controlling Dynamical Networks with Applications to Neural Systems

Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviors such as synchronization. While descriptions of these behaviors are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behavior. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain's control performance by removing single edges in the network. Generally, our results ground the expectation of a control system's behavior in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

High angular resolution diffusion-weighted imaging in mild traumatic brain injury

We sought to investigate white matter abnormalities in mild traumatic brain injury (mTBI) using diffusion-weighted magnetic resonance imaging (DW-MRI). We applied a global approach based on tract-based spatial statistics skeleton as well as constrained spherical deconvolution tractography. DW-MRI was performed on 102 patients with mTBI within two months post-injury and 30 control subjects. A robust global approach considering only the voxels with a single-fiber configuration was used in addition to global analysis of the tract skeleton and probabilistic whole-brain tractography. In addition, we assessed whether the microstructural parameters correlated with age, time from injury, patient's outcome and white matter MRI hyperintensities. We found that whole-brain global approach restricted to single-fiber voxels showed significantly decreased fractional anisotropy (FA) (p = 0.002) and increased radial diffusivity (p = 0.011) in patients with mTBI compared with controls. The results restricted to single-fiber voxels were more significant and reproducible than those with the complete tract skeleton or the whole-brain tractography. FA correlated with patient outcomes, white matter hyperintensities and age. No correlation was observed between FA and time of scan post-injury. In conclusion, the global approach could be a promising imaging biomarker to detect white matter abnormalities following traumatic brain injury.