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

Dynamic Dysregulation of the Triple Network of the Brain in Mild Traumatic Brain Injury and Its Relationship With Cognitive Performance

A triple network model consisting of a default network, a salience network, and a central executive network has recently been used to understand connectivity patterns in cognitively normal versus dysfunctional brains. This study aimed to explore changes in the dynamic connectivity of triplet network in mild traumatic brain injury (mTBI) and its relationship to cognitive performance. In this work, we acquired resting-state functional magnetic resonance imaging (fMRI) data from 30 mTBI patients and 30 healthy controls (HCs). Independent component analysis, sliding time window correlation, and k-means clustering were applied to resting-state fMRI data. Further, we analyzed the relationship between changes in dynamic functional connectivity (FC) parameters and clinical variables in mTBI patients. The results showed that the dynamic functional connectivity of the brain triple network was clustered into five states. Compared with HC, mTBI patients spent longer in state 1, which is characterized by weakened dorsal default mode network (DMN) and anterior salience network (SN) connectivity, and state 3, which is characterized by a positive correlation between DMN and SN internal connectivity. Mild TBI patients had fewer metastases in different states than HC patients. In addition, the mean residence time in state 1 correlated with Montreal Cognitive Assessment scores in mTBI patients; the number of transitions between states correlated with Glasgow Coma Score in mTBI patients. Taken together, our findings suggest that the dynamic properties of FC in the triple network of mTBI patients are abnormal, and provide a new perspective on the pathophysiological mechanism of cognitive impairment from the perspective of dynamic FC.

ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

Dynamic Functional Connectivity in Pediatric Mild Traumatic Brain Injury

Resting-state fMRI can be used to identify recurrent oscillatory patterns of functional connectivity within the human brain, also known as dynamic brain states. Alterations in dynamic brain states are highly likely to occur following pediatric mild traumatic brain injury (pmTBI) due to the active developmental changes. The current study used resting-state fMRI to investigate dynamic brain states in 200 patients with pmTBI (ages 8-18 years, median = 14 years) at the subacute (∼1-week post-injury) and early chronic (∼ 4 months post-injury) stages, and in 179 age- and sex-matched healthy controls (HC). A k-means clustering analysis was applied to the dominant time-varying phase coherence patterns to obtain dynamic brain states. In addition, correlations between brain signals were computed as measures of static functional connectivity. Dynamic connectivity analyses showed that patients with pmTBI spend less time in a frontotemporal default mode/limbic brain state, with no evidence of change as a function of recovery post-injury. Consistent with models showing traumatic strain convergence in deep grey matter and midline regions, static interhemispheric connectivity was affected between the left and right precuneus and thalamus, and between the right supplementary motor area and contralateral cerebellum. Changes in static or dynamic connectivity were not related to symptom burden or injury severity measures, such as loss of consciousness and post-traumatic amnesia. In aggregate, our study shows that brain dynamics are altered up to 4 months after pmTBI, in brain areas that are known to be vulnerable to TBI. Future longitudinal studies are warranted to examine the significance of our findings in terms of long-term neurodevelopment.

Visuo-vestibular and cognitive connections of the vestibular neuromatrix are conserved across age and injury populations

BACKGROUND AND PURPOSE

Given the prevalence of vestibular dysfunction in pediatric concussion, there is a need to better understand pathophysiological disruptions within vestibular and associated cognitive, affective, and sensory-integrative networks. Although current research leverages established intrinsic connectivity networks, these are nonspecific for vestibular function, suggesting that a pathologically guided approach is warranted. The purpose of this study was to evaluate the generalizability of the previously identified "vestibular neuromatrix" in adults with and without postconcussive vestibular dysfunction to young athletes aged 14-17.

METHODS

This retrospective study leveraged resting-state functional MRI data from two sites. Site A included adults with diagnosed postconcussive vestibular impairment and healthy adult controls and Site B consisted of young athletes with preseason, postconcussion, and postseason time points (prospective longitudinal data). Adjacency matrices were generated from preprocessed resting-state data from each sample and assessed for overlap and network structure in MATLAB.

RESULTS

Analyses indicated the presence of a conserved "core" network of vestibular regions as well as areas subserving visual, spatial, and attentional processing. Other vestibular connections were also conserved across samples but were not linked to the "core" subnetwork by regions of interest included in this study.

CONCLUSIONS

Our results suggest that connections between central vestibular, visuospatial, and known intrinsic connectivity networks are conserved across adult and pediatric participants with and without concussion, evincing the significance of this expanded, vestibular-associated network. Our findings thus support this network as a workable model for investigation in future studies of dysfunction in young athlete populations.

Extracting transition features among brain states based on coarse-grained similarity measurement for autism spectrum disorder analysis

BACKGROUND

The abnormal brain functional connectivity (FC) of patients with mental diseases is closely linked to the transition features among brain states. However, the current research on state transition will produce certain division deviations in the measurement method of state division, and also ignore the transition features among multiple states that contain more abundant information for analyzing brain diseases.

PURPOSE

To investigate the potential of the proposed method based on coarse-grained similarity measurement to solve the problem of state division, and consider the transition features among multiple states to analyze the FC abnormalities of autism spectrum disorder (ASD) patients.

METHODS

We used resting-state functional magnetic resonance imaging to examine 45 ASD and 47 healthy controls (HC). The FC between brain regions was calculated by the sliding window and correlation algorithm, and a novel coarse-grained similarity measure method was used to cluster the FC networks into five states, and then extract the features both of the state itself and the transition features among multiple states for analysis and diagnosis.

RESULTS

(1) The state as divided by the coarse-grained measurement method improves the diagnostic performance of individuals with ASD compared with previous methods. (2) The transition features among multiple states can provide complementary information to the features of the state itself in the ASD analysis and diagnosis. (3) ASD individuals have different brain state transitions than HC. Specifically, the abnormalities in intra- and inter-network connectivity of ASD patients mainly occur in the default mode network, the visual network, and the cerebellum.

CONCLUSIONS

Such results demonstrate that our approach with new measurements and new features is effective and promising in brain state analysis and ASD diagnosis.

Static and dynamic topological organization of brain functional connectome in acute mild traumatic brain injury

BACKGROUND

Prior studies have detected topological changes of brain functional networks in patients with acute mild traumatic brain injury (mTBI). However, the alterations of dynamic topological characteristics in mTBI have been scarcely elucidated.

PURPOSE

To evaluate static and dynamic functional connectivity topological networks in patients with acute mTBI using resting-state functional magnetic resonance imaging (fMRI).

MATERIAL AND METHODS

A total of 55 patients with acute mTBI and 55 age-, sex-, and education-matched healthy controls (HCs) were enrolled in this study. All participants underwent resting-state fMRI scans, and data were analyzed using graph-theory methods and a sliding window approach. Post-traumatic cognitive performance and resting-state fMRI data were collected within one week after injury. Static and dynamic functional connectivity patterns were determined by independent component analysis. Spearman's correlation analysis was further performed between fMRI changes and Montreal cognitive assessment (MoCA) scores.

RESULTS

Global efficiency was lower (P = 0.02), and local efficiency (P < 0.001) and mean Cp (P < 0.001) were higher in patients with acute mTBI than in HCs. Local efficiency was correlated with visuospatial/executive performance (r = -0.421; P = 0.002) in patients with acute mTBI. Significant differences in nodal efficiency and node degree centrality (P < 0.01) were found between the mTBI and HC groups. For dynamic properties, patients with mTBI showed higher variance (P = 0.016) in global efficiency than HCs.

CONCLUSIONS

The present study shows that patients with mTBI have abnormal brain functional connectome topology, especially the dynamic graph theory characteristics, which provide new insights into the role of topological network properties in patients with acute mTBI.

Robust dynamic brain coactivation states estimated in individuals

A confluence of evidence indicates that brain functional connectivity is not static but rather dynamic. Capturing transient network interactions in the individual brain requires a technology that offers sufficient within-subject reliability. Here, we introduce an individualized network-based dynamic analysis technique and demonstrate that it is reliable in detecting subject-specific brain states during both resting state and a cognitively challenging language task. We evaluate the extent to which brain states show hemispheric asymmetries and how various phenotypic factors such as handedness and gender might influence network dynamics, discovering a right-lateralized brain state that occurred more frequently in men than in women and more frequently in right-handed versus left-handed individuals. Longitudinal brain state changes were also shown in 42 patients with subcortical stroke over 6 months. Our approach could quantify subject-specific dynamic brain states and has potential for use in both basic and clinical neuroscience research.

Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning

Major depressive disorder (MDD) is a severe brain disease associated with a significant risk of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to explore the use of dynamic functional network connectivity (dFNC) for suicidality detection in MDD patients. A total of 173 MDD patients, including 48 without suicide risk (NS), 74 with suicide ideation (SI), and 51 having attempted suicide (SA), participated in the present study. Thirty-eight healthy controls were also recruited for comparison. A sliding window approach was used to derive the dFNC, and the K-means clustering method was used to cluster the windowed dFNC. A linear support vector machine was used for classification, and leave-one-out cross-validation was performed for validation. Other machine learning methods were also used for comparison. MDD patients had widespread hypoconnectivity in both the strongly connected states (states 2 and 5) and the weakly connected state (state 4), while the dysfunctional connectivity within the weakly connected state (state 4) was mainly driven by suicidal attempts. Furthermore, dFNC matrices, especially the weakly connected state, could be used to distinguish MDD from healthy controls (area under curve [AUC] = 82), and even to identify suicidality in MDD patients (AUC = 78 for NS vs. SI, AUC = 88 for NS vs. SA, and AUC = 74 for SA vs. SI), with vision-related and default-related inter-network connectivity serving as important features. Thus, the dFNC abnormalities observed in this study might further improve our understanding of the neural substrates of suicidality in MDD patients.

Dynamic connectivity predicts acute motor impairment and recovery post-stroke

Thorough assessment of cerebral dysfunction after acute lesions is paramount to optimize predicting clinical outcomes. We here built random forest classifier-based prediction models of acute motor impairment and recovery post-stroke. Predictions relied on structural and resting-state fMRI data from 54 stroke patients scanned within the first days of symptom onset. Functional connectivity was estimated via static and dynamic approaches. Motor performance was phenotyped in the acute phase and 6 months later. A model based on the time spent in specific dynamic connectivity configurations achieved the best discrimination between patients with and without motor impairments (out-of-sample area under the curve, 95% confidence interval: 0.67 ± 0.01). In contrast, patients with moderate-to-severe impairments could be differentiated from patients with mild deficits using a model based on the variability of dynamic connectivity (0.83 ± 0.01). Here, the variability of the connectivity between ipsilesional sensorimotor cortex and putamen discriminated the most between patients. Finally, motor recovery was best predicted by the time spent in specific connectivity configurations (0.89 ± 0.01) in combination with the initial impairment. Here, better recovery was linked to a shorter time spent in a functionally integrated configuration. Dynamic connectivity-derived parameters constitute potent predictors of acute impairment and recovery, which, in the future, might inform personalized therapy regimens to promote stroke recovery.

Resting state functional connectivity responses post-mild traumatic brain injury: a systematic review

Mild traumatic brain injuries (mTBI) are associated with functional network connectivity alterations throughout recovery. Yet, little is known about the adaptive or maladaptive nature of post-mTBI connectivity and which networks are predisposed to altered function and adaptation. The objective of this review was to determine functional connectivity changes post-mTBI and to determine the adaptive or maladaptive nature of connectivity through direct comparisons of connectivity and behavioral data. Literature was systematically searched and appraised for methodological quality. A total of 16 articles were included for review. There was conflicting evidence of post-mTBI connectivity responses as decreased connectivity was noted in 4 articles, 6 articles reported increased connectivity, 5 reported a mixture of increased and decreased connectivity, while 1 found no differences in connectivity. Supporting evidence for adaptive post-mTBI increases in connectivity were found, particularly in the frontoparietal, cerebellar, and default mode networks. Although initial results are promising, continued longitudinal research that systematically controls for confounding variables and that standardizes methodologies is warranted to adequately understand the neurophysiological recovery trajectory of mTBI.

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.

Altered Posterior Midline Activity in Patients with Jerky and Tremulous Functional Movement Disorders

Objective: To explore changes in resting-state networks in patients with jerky and tremulous functional movement disorders (JT-FMD). Methods: Resting-state functional magnetic resonance imaging data from seventeen patients with JT-FMD and seventeen age-, sex-, and education-matched healthy controls (HC) were investigated. Independent component analysis was used to examine the central executive network (CEN), salience network, and default mode network (DMN). Frequency distribution of network signal fluctuations and intra- and internetwork functional connectivity were investigated. Symptom severity was measured using the Clinical Global Impression-Severity scale. Beck Depression Inventory and Beck Anxiety Inventory scores were collected to measure depression and anxiety in FMD, respectively. Results: Compared with HC, patients with JT-FMD had significantly decreased power of lower range (0.01-0.10 Hz) frequency fluctuations in a precuneus and posterior cingulate cortex component of the DMN and in the dorsal attention network (DAN) component of the CEN (false discovery rate-corrected p < 0.05). No significant group differences were found for intra- and internetwork functional connectivity. In patients with JT-FMD, symptom severity was not significantly correlated with network measures. Depression scores were weakly correlated with intranetwork functional connectivity in the medial prefrontal cortex, while anxiety was not found to be related to network connectivity. Conclusions: Given the changes in the posterodorsal components of the DMN and DAN, we postulate that the JT-FMD-related functional alterations found in these regions could provide support for the concept that particularly attentional dysregulation is a fundamental disturbance in these patients.

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.

Identification of minimal hepatic encephalopathy based on dynamic functional connectivity

To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic patients without any hepatic encephalopathy (noHE) and healthy controls (HCs). Resting-state functional MRI data were acquired from 62 patients with cirrhosis (MHE, n = 30; noHE, n = 32) and 41 HCs. We used the sliding time window approach and functional connectivity analysis to extract the time-varying properties of brain connectivity. Three DFC characteristics (i.e., strength, stability, and variability) were calculated. For comparison, we also calculated the static functional connectivity (SFC). A linear support vector machine was used to differentiate MHE patients from noHE and HCs using DFC and SFC metrics as classification features. The leave-one-out cross-validation method was used to estimate the classification performance. The strength of DFC (DFC-Dstrength) achieved the best accuracy (MHE vs. noHE, 72.5%; MHE vs. HCs, 84%; and noHE vs. HCs, 88%) compared to the other dynamic features. Compared to static features, the classification accuracies of the DFC-Dstrength feature were improved by 10.5%, 8%, and 14% for MHE vs. noHE, MHE vs. HC, and noHE vs. HCs, respectively. Based on the DFC-Dstrength, seven nodes were identified as the most discriminant features to classify MHE from noHE, including left inferior parietal lobule, left supramarginal gyrus, left calcarine, left superior frontal gyrus, left cerebellum, right postcentral gyrus, and right insula. In summary, DFC characteristics have a higher classification accuracy in identifying MHE from cirrhosis patients. Our findings suggest the usefulness of DFC in capturing neural processes and identifying disease-related biomarkers important for MHE identification.

Post-traumatic Headache and Mild Traumatic Brain Injury: Brain Networks and Connectivity

PURPOSE OF REVIEW

Post-traumatic headache (PTH) consequent to mild traumatic brain injury (mTBI) is a complex, multidimensional, chronic neurological disorder. The purpose of this review is to evaluate the current neuroimaging studies on mTBI and PTH with a specific focus on brain networks and connectivity patterns.

RECENT FINDINGS

We present findings on PTH incidence and prevalence, as well as the latest neuroimaging research findings on mTBI and PTH. Additionally, we propose a new strategy in studying PTH following mTBI. The diversity and heterogeneity of pathophysiological mechanisms underlying mild traumatic brain injury pose unique challenges on how we interpret neuroimaging findings in PTH. Evaluating alterations in the intrinsic brain network connectivity patterns using novel imaging and analytical techniques may provide additional insights into PTH disease state and therefore inform effective treatment strategies.

Autophagy Activation Represses Pyroptosis through the IL-13 and JAK1/STAT1 Pathways in a Mouse Model of Moderate Traumatic Brain Injury

The newly highlighted research into programmed cell death (PCD), autophagy dependent cell death and pyroptotic cell death, has shown that these processes are both strongly correlated with the pathological progression of traumatic brain injury (TBI). However, their cross-talk in TBI remains unclear. Here, a moderate TBI model was established to explore the relationship between autophagy and pyroptosis. Rapamycin was used to activate the process of autophagy, which was impaired in the moderate TBI model, and this treatment reversed the expression of pyroptosis associated proteins, interleukin-13 (IL-13) and the pJAK-1 pathway, which were upregulated significantly after TBI. The level of IL-13 was downregulated, and the JAK-1 pathway was blocked to reveal the molecular mechanisms by which autophagy inhibits pyroptosis; these two treatments reduced the expression levels of pyroptosis associated proteins. In addition, these three interventions reduced the formation of neuronal NLRP3, the extent of brain edema, and the degree of cortical neuron degeneration. Furthermore, the deficit in motor function post-TBI was also markedly alleviated. Collectively, our results demonstrated that autophagy activation exerts a neuroprotective effect by inhibiting pyroptotic cell death in the moderate TBI model, and the inhibitory effect was dependent on the downregulation of IL-13 and repression of the JAK-1-STAT-1 signaling pathway.

The chronnectome as a model for Charcot's 'dynamic lesion' in functional movement disorders

This exploratory study set out to investigate dynamic functional connectivity (dFC) in patients with jerky and tremulous functional movement disorders (JT-FMD). The focus in this work is on dynamic brain states, which represent distinct dFC patterns that reoccur in time and across subjects. Resting-state fMRI data were collected from 17 patients with JT-FMD and 17 healthy controls (HC). Symptom severity was measured using the Clinical Global Impression-Severity scale. Depression and anxiety were measured using the Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI), respectively. Independent component analysis was used to extract functional brain components. After computing dFC, dynamic brain states were determined for every subject using k-means clustering. Compared to HC, patients with JT-FMD spent more time in a state that was characterized predominantly by increasing medial prefrontal, and decreasing posterior midline connectivity over time. They also tended to visit this state more frequently. In addition, patients with JT-FMD transitioned significantly more often between different states compared to HC, and incorporated a state with decreasing medial prefrontal, and increasing posterior midline connectivity in their attractor, i.e., the cyclic patterns of state transitions. Altogether, this is the first study that demonstrates altered functional brain network dynamics in JT-FMD that may support concepts of increased self-reflective processes and impaired sense of agency as driving factors in FMD.

Tools of the trade: estimating time-varying connectivity patterns from fMRI data

Given the dynamic nature of the brain, there has always been a motivation to move beyond 'static' functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain's dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.

Acute ischaemic stroke alters the brain's preference for distinct dynamic connectivity states

Acute ischaemic stroke disturbs healthy brain organization, prompting subsequent plasticity and reorganization to compensate for the loss of specialized neural tissue and function. Static resting state functional MRI studies have already furthered our understanding of cerebral reorganization by estimating stroke-induced changes in network connectivity aggregated over the duration of several minutes. In this study, we used dynamic resting state functional MRI analyses to increase temporal resolution to seconds and explore transient configurations of motor network connectivity in acute stroke. To this end, we collected resting state functional MRI data of 31 patients with acute ischaemic stroke and 17 age-matched healthy control subjects. Stroke patients presented with moderate to severe hand motor deficits. By estimating dynamic functional connectivity within a sliding window framework, we identified three distinct connectivity configurations of motor-related networks. Motor networks were organized into three regional domains, i.e. a cortical, subcortical and cerebellar domain. The dynamic connectivity patterns of stroke patients diverged from those of healthy controls depending on the severity of the initial motor impairment. Moderately affected patients (n = 18) spent significantly more time in a weakly connected configuration that was characterized by low levels of connectivity, both locally as well as between distant regions. In contrast, severely affected patients (n = 13) showed a significant preference for transitions into a spatially segregated connectivity configuration. This configuration featured particularly high levels of local connectivity within the three regional domains as well as anti-correlated connectivity between distant networks across domains. A third connectivity configuration represented an intermediate connectivity pattern compared to the preceding two, and predominantly encompassed decreased interhemispheric connectivity between cortical motor networks independent of individual deficit severity. Alterations within this third configuration thus closely resembled previously reported ones originating from static resting state functional MRI studies post-stroke. In summary, acute ischaemic stroke not only prompted changes in connectivity between distinct networks, but it also caused characteristic changes in temporal properties of large-scale network interactions depending on the severity of the individual deficit. These findings offer new vistas on the dynamic neural mechanisms underlying acute neurological symptoms, cortical reorganization and treatment effects in stroke patients.

Determining the number of states in dynamic functional connectivity using cluster validity indexes

BACKGROUND

Clustering analysis is employed in brain dynamic functional connectivity (dFC) to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several cluster validity index (CVI) methods to determine the best clustering partition exists, the appropriateness of methods to apply in the case of dynamic connectivity analysis has not been determined.

NEW METHOD

Currently employed indexes do not provide a crisp answer on what is the best number of clusters. In addition, there is a lack of CVI testing in the context of dFC data. This work tests a comprehensive set of twenty four cluster validity indexes applied to addiction data and suggest the best ones for clustering dynamic functional connectivity.

RESULTS

Out of the twenty four considered CVIs, Davies-Bouldin and Ray-Turi were the most suitable methods to find the number of clusters in both simulation and real data. The solution for these two CVIs is to find a local minimum critical point, which can be automated using computational algorithms.

COMPARISON WITH EXISTING METHODS

Elbow-Criterion, Silhouette and GAP-Statistic methods have been widely used in dFC studies. These methods are included among the tested CVIs where the performances of all twenty four CVIs are compared.

CONCLUSIONS

Davies-Bouldin and Ray-Turi CVIs showed better performance among a group of twenty four CVIs in determining the number of clusters to use in dFC analysis.

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.