Brain Connectivity Measures Improve Modeling of Functional Outcome After Acute Ischemic Stroke

Hub overload and failure as a final common pathway in neurological brain network disorders

Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.

Differential Impact of Brain Network Efficiency on Poststroke Motor and Attentional Deficits

BACKGROUND

Most studies on stroke have been designed to examine one deficit in isolation; yet, survivors often have multiple deficits in different domains. While the mechanisms underlying multiple-domain deficits remain poorly understood, network-theoretical methods may open new avenues of understanding.

METHODS

Fifty subacute stroke patients (7±3days poststroke) underwent diffusion-weighted magnetic resonance imaging and a battery of clinical tests of motor and cognitive functions. We defined indices of impairment in strength, dexterity, and attention. We also computed imaging-based probabilistic tractography and whole-brain connectomes. To efficiently integrate inputs from different sources, brain networks rely on a rich-club of a few hub nodes. Lesions harm efficiency, particularly when they target the rich-club. Overlaying individual lesion masks onto the tractograms enabled us to split the connectomes into their affected and unaffected parts and associate them to impairment.

RESULTS

We computed efficiency of the unaffected connectome and found it was more strongly correlated to impairment in strength, dexterity, and attention than efficiency of the total connectome. The magnitude of the correlation between efficiency and impairment followed the order attention>dexterity ≈ strength (strength: |r|=.03, P=0.02, dexterity: |r|=.30, P=0.05, attention: |r|=.55, P<0.001). Network weights associated with the rich-club were more strongly correlated to efficiency than non-rich-club weights.

CONCLUSIONS

Attentional impairment is more sensitive to disruption of coordinated networks between brain regions than motor impairment, which is sensitive to disruption of localized networks. Providing more accurate reflections of actually functioning parts of the network enables the incorporation of information about the impact of brain lesions on connectomics contributing to a better understanding of underlying stroke mechanisms.

The relevance of rich club regions for functional outcome post-stroke is enhanced in women

This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich-club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI-GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non-rich club regions. In spatial specificity analyses, we randomized the split into "rich club" and "non-rich club" regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex-specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female-specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club-combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long-term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.

Early effect of thrombolysis on structural brain network organisation after anterior‐circulation stroke in the randomized WAKE‐UP trial

The symptoms of acute ischemic stroke can be attributed to disruption of the brain network architecture. Systemic thrombolysis is an effective treatment that preserves structural connectivity in the first days after the event. Its effect on the evolution of global network organisation is, however, not well understood. We present a secondary analysis of 269 patients from the randomized WAKE‐UP trial, comparing 127 imaging‐selected patients treated with alteplase with 142 controls who received placebo. We used indirect network mapping to quantify the impact of ischemic lesions on structural brain network organisation in terms of both global parameters of segregation and integration, and local disruption of individual connections. Network damage was estimated before randomization and again 22 to 36 h after administration of either alteplase or placebo. Evolution of structural network organisation was characterised by a loss in integration and gain in segregation, and this trajectory was attenuated by the administration of alteplase. Preserved brain network organization was associated with excellent functional outcome. Furthermore, the protective effect of alteplase was spatio‐topologically nonuniform, concentrating on a subnetwork of high centrality supported in the salvageable white matter surrounding the ischemic cores. This interplay between the location of the lesion, the pathophysiology of the ischemic penumbra, and the spatial embedding of the brain network explains the observed potential of thrombolysis to attenuate topological network damage early after stroke. Our findings might, in the future, lead to new brain network‐informed imaging biomarkers and improved prognostication in ischemic stroke.

Structural disconnectome mapping of cognitive function in poststroke patients

BACKGROUND AND PURPOSE

Sequalae following stroke represents a significant challenge in current rehabilitation. The location and size of focal lesions are only moderately predictive of the diverse cognitive outcome after stroke. One explanation building on recent work on brain networks proposes that the cognitive consequences of focal lesions are caused by damages to anatomically distributed brain networks supporting cognition rather than specific lesion locations.

METHODS

To investigate the association between poststroke structural disconnectivity and cognitive performance, we estimated individual level whole-brain disconnectivity probability maps based on lesion maps from 102 stroke patients using normative data from healthy controls. Cognitive performance was assessed in the whole sample using Montreal Cognitive Assessment, and a more comprehensive computerized test protocol was performed on a subset (n = 82).

RESULTS

Multivariate analysis using Partial Least Squares on the disconnectome maps revealed that higher disconnectivity in right insular and frontal operculum, superior temporal gyrus and putamen was associated with poorer MoCA performance, indicating that lesions in regions connected with these brain regions are more likely to cause cognitive impairment. Furthermore, our results indicated that disconnectivity within these clusters was associated with poorer performance across multiple cognitive domains.

CONCLUSIONS

These findings demonstrate that the extent and distribution of structural disconnectivity following stroke are sensitive to cognitive deficits and may provide important clinical information predicting poststroke cognitive sequalae.

Rich-club reorganization of functional brain networks in acute mild traumatic brain injury with cognitive impairment

BACKGROUND

Mild traumatic brain injury (mTBI) is typically characterized by temporally limited cognitive impairment and regarded as a brain connectome disorder. Recent findings have suggested that a higher level of organization named the "rich-club" may play a central role in enabling the integration of information and efficient communication across different systems of the brain. However, the alterations in rich-club organization and hub topology in mTBI and its relationship with cognitive impairment after mTBI have been scarcely elucidated.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 88 patients with mTBI and 85 matched healthy controls (HCs). Large-scale functional brain networks were established for each participant. Rich-club organizations and network properties were assessed and analyzed between groups. Finally, we analyzed the correlations between the cognitive performance and changes in rich-club organization and network properties.

RESULTS

Both mTBI and HCs groups showed significant rich-club organization. Meanwhile, the rich-club organization was aberrant, with enhanced functional connectivity (FC) among rich-club nodes and peripheral regions in acute mTBI. In addition, significant differences in partial global and local network topological property measures were found between mTBI patients and HCs (P<0.01). In patients with mTBI, changes in rich-club organization and network properties were found to be related to early cognitive impairment after mTBI (P<0.05).

CONCLUSIONS

Our findings suggest that such patterns of disruption and reorganization will provide the basic functional architecture for cognitive function, which may subsequently be used as an earlier biomarker for cognitive impairment after mTBI.

Brain Functional Topology Alteration in Right Lateral Occipital Cortex Is Associated With Upper Extremity Motor Recovery

Stroke is a chief cause of sudden brain damage that severely disrupts the whole-brain network. However, the potential mechanisms of motor recovery after stroke are uncertain and the prognosis of poststroke upper extremity recovery is still a challenge. This study investigated the global and local topological properties of the brain functional connectome in patients with subacute ischemic stroke and their associations with the clinical measurements. A total of 57 patients, consisting of 29 left-sided and 28 right-sided stroke patients, and 32 age- and gender-matched healthy controls (HCs) were recruited to undergo a resting-state functional magnetic resonance imaging (rs-fMRI) study; patients were also clinically evaluated with the Upper Extremity Fugl-Meyer Assessment (FMA_UE). The assessment was repeated at 15 weeks to assess upper extremity functional recovery for the patient remaining in the study (12 left- 20 right-sided stroke patients). Global graph topological disruption indices of stroke patients were significantly decreased compared with HCs but these indices were not significantly associated with FMA_UE. In addition, local brain network structure of stroke patients was altered, and the altered regions were dependent on the stroke site. Significant associations between local degree and motor performance and its recovery were observed in the right lateral occipital cortex (R LOC) in the right-sided stroke patients. Our findings suggested that brain functional topologies alterations in R LOC are promising as prognostic biomarkers for right-sided subacute stroke. This cortical area might be a potential target to be further validated for non-invasive brain stimulation treatment to improve poststroke upper extremity recovery.

Selectively disrupted sensorimotor circuits in chronic stroke with hand dysfunction

Aim

To investigate the directional and selective disconnection of the sensorimotor cortex (SMC) subregions in chronic stroke patients with hand dysfunction.

Methods

We mapped the resting‐state fMRI effective connectivity (EC) patterns for seven SMC subregions in each hemisphere of 65 chronic stroke patients and 40 healthy participants and correlated these patterns with paretic hand performance.

Results

Compared with controls, patients demonstrated disrupted EC in the ipsilesional primary motor cortex_4p, ipsilesional primary somatosensory cortex_2 (PSC_2), and contralesional PSC_3a. Moreover, we found that EC values of the contralesional PSC_1 to contralesional precuneus, the ipsilesional inferior temporal gyrus to ipsilesional PSC_1, and the ipsilesional PSC_1 to contralesional postcentral gyrus were correlated with paretic hand performance across all patients. We further divided patients into partially (PPH) and completely (CPH) paretic hand subgroups. Compared with CPH patients, PPH patients demonstrated decreased EC in the ipsilesional premotor_6 and ipsilesional PSC_1. Interestingly, we found that paretic hand performance was positively correlated with seven sensorimotor circuits in PPH patients, while it was negatively correlated with five sensorimotor circuits in CPH patients.

Conclusion

SMC neurocircuitry was selectively disrupted after chronic stroke and associated with diverse hand outcomes, which deepens the understanding of SMC reorganization.

OUP accepted manuscript

Analyses of alterations of brain networks have gained an increasing interest in stroke rehabilitation research. Compared with functional networks derived from resting-state analyses, there is limited knowledge of how structural network topology might undergo changes after stroke and, more importantly, if structural network information obtained early after stroke could enhance recovery models to infer later outcomes. The present work re-analysed cross-sectional structural imaging data, obtained within the first 2 weeks, of 45 acute stroke patients (22 females, 24 right-sided strokes, age 68 ± 13 years). Whole-brain tractography was performed to reconstruct structural connectomes and graph-theoretical analyses were employed to quantify global network organization with a focus on parameters of network integration and modular processing. Graph measures were compared between stroke patients and 34 healthy controls (15 females, aged 69 ± 10 years) and they were integrated with four clinical scores of the late subacute stage, covering neurological symptom burden (National Institutes of Health Stroke Scale), global disability (modified Rankin Scale), activity-related disability (Barthel Index) and motor functions (Upper-Extremity Score of the Fugl-Meyer Assessment). The analyses were employed across the complete cohort and, based on clustering analysis, separately within subgroups stratified in mild to moderate (n = 21) and severe (n = 24) initial deficits. The main findings were (i) a significant reduction of network’s global efficiency, specifically in patients with severe deficits compared with controls (P = 0.010) and (ii) a significant negative correlation of network efficiency with the extent of persistent functional deficits at follow-up after 3–6 months (P ≤ 0.032). Specifically, regression models revealed that this measure was capable to increase the explained variance in future deficits by 18% for the modified Rankin Scale, up to 24% for National Institutes of Health Stroke Scale, and 16% for Barthel Index when compared with models including the initial deficits and the lesion volume. Patients with mild to moderate deficits did not exhibit a similar impact of network efficiency on outcome inference. Clustering coefficient and modularity, measures of segregation and modular processing, did not exhibit comparable structure–outcome relationships, neither in severely nor in mildly affected patients. This study provides empirical evidence that structural network efficiency as a graph-theoretical marker of large-scale network topology, quantified early after stroke, relates to recovery. Notably, this contribution was only evident in severely but not mildly affected stroke patients. This suggests that the initial clinical deficit might shape the dependency of recovery on global network topology after stroke.

Calhoun V, Grefkes C, Rost NS. Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke

The aim of the current study was to explore the whole-brain dynamic functional connectivity patterns in acute ischemic stroke (AIS) patients and their relation to short and long-term stroke severity. We investigated resting-state functional MRI-based dynamic functional connectivity of 41 AIS patients two to five days after symptom onset. Re-occurring dynamic connectivity configurations were obtained using a sliding window approach and k-means clustering. We evaluated differences in dynamic patterns between three NIHSS-stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we built Bayesian hierarchical models to evaluate the predictive capacity of dynamic connectivity and examine the interrelation with clinical measures, such as white matter hyperintensity lesions. Finally, we established correlation analyses between dynamic connectivity and AIS severity as well as 90-day neurological recovery (ΔNIHSS). We identified three distinct dynamic connectivity configurations acutely post-stroke. More severely affected patients spent significantly more time in a configuration that was characterized by particularly strong connectivity and isolated processing of functional brain domains (three-level ANOVA: p < .05, post hoc t tests: p < .05, FDR-corrected). Configuration-specific time estimates possessed predictive capacity of stroke severity in addition to the one of clinical measures. Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson's r = -.68, p = .003, FDR-corrected). Our findings demonstrate transiently increased isolated information processing in multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first 3 months poststroke.

Disconnectomics of the Rich Club Impacts Motor Recovery After Stroke

BACKGROUND AND PURPOSE

Structural brain networks possess a few hubs, which are not only highly connected to the rest of the brain but are also highly connected to each other. These hubs, which form a rich-club, play a central role in global brain organization. To investigate whether the concept of rich-club sheds new light on poststroke recovery, we applied a novel network-theoretical quantification of lesions to patients with stroke and compared the outcomes with what lesion size alone would indicate.

METHODS

Whole-brain structural networks of 73 patients with ischemic stroke were reconstructed using diffusion-weighted imaging data. Disconnectomes, a new type of network analyses, were constructed using only those fibers that pass through the lesion. Fugl-Meyer upper extremity scores and their changes were used to determine whether the patients show natural recovery or not.

RESULTS

Cluster analysis revealed 3 patient clusters: small-lesion-good-recovery, midsized-lesion-poor-recovery (MLPR), and large-lesion-poor-recovery (LLPR). The small-lesion-good-recovery consisted of subjects whose lesions were small, and whose prospects for recovery were relatively good. To explain the nondifference in recovery between the MLPR and LLPR clusters despite the difference (LLPR>MLPR) in lesion volume, we defined the [Formula: see text] metric to be the sum of the entries in the disconnectome and, more importantly, the [Formula: see text] to be the sum of all entries in the disconnectome corresponding to edges with at least one node in the rich-club. Unlike lesion volume and corticospinal tract damage (MLPR<LLPR), for [Formula: see text], this relationship was reversed (MLPR>LLPR) or showed no difference for [Formula: see text].

CONCLUSIONS

Smaller lesions that focus on the rich-club can be just as devastating as much larger lesions that do not focus on the rich-club, pointing to the role of the rich-club as a backbone for functional communication within brain networks and for recovery from stroke.

Relation between rich-club organization versus brain functions and functional recovery after acute ischemic stroke

Studies have shown the brain's rich-club organization may underpin brain function and be associated with various brain disorders. In this study, we aimed to investigate the relation between poststroke brain functions and functional recovery versus the rich-club organization of the structural brain network of patients after first-time acute ischemic stroke. A cohort of 16 acute ischemic stroke patients (11 males) was recruited. Structural brain networks were measured using diffusion tensor imaging within 1 week and at 1, 3 and 6 months after stroke. Motor impairment was assessed using the Upper-Extremity Fugl-Meyer motor scale and activities of daily living using the Barthel Index at the same time points as MRI. The rich-club regions that were stable over the course of stroke recovery included the bilateral dorsolateral superior frontal gyri, right supplementary motor area, and left median cingulate and paracingulate gyri. The network properties that correlated with poststroke brain functions were mainly the ratio between communication cost ratio and density ratio of rich-club, feeder and local connections. The recovery of both motor functions and activities of daily living were correlated with higher normalized rich club coefficients and a shorter length of local connections within a week after stroke. The communication cost ratio of feeder connections, the length of rich-club and local connections, and normalized rich club coefficients were found to be potential prognostic indicators of stroke recovery. Our results provide additional support to the notion that different types of network connections play different roles in brain functions as well as functional recovery.

Exploring the predictive value of lesion topology on motor function outcomes in a porcine ischemic stroke model

Harnessing the maximum diagnostic potential of magnetic resonance imaging (MRI) by including stroke lesion location in relation to specific structures that are associated with particular functions will likely increase the potential to predict functional deficit type, severity, and recovery in stroke patients. This exploratory study aims to identify key structures lesioned by a middle cerebral artery occlusion (MCAO) that impact stroke recovery and to strengthen the predictive capacity of neuroimaging techniques that characterize stroke outcomes in a translational porcine model. Clinically relevant MRI measures showed significant lesion volumes, midline shifts, and decreased white matter integrity post-MCAO. Using a pig brain atlas, damaged brain structures included the insular cortex, somatosensory cortices, temporal gyri, claustrum, and visual cortices, among others. MCAO resulted in severely impaired spatiotemporal gait parameters, decreased voluntary movement in open field testing, and higher modified Rankin Scale scores at acute timepoints. Pearson correlation analyses at acute timepoints between standard MRI metrics (e.g., lesion volume) and functional outcomes displayed moderate R values to functional gait outcomes. Moreover, Pearson correlation analyses showed higher R values between functional gait deficits and increased lesioning of structures associated with motor function, such as the putamen, globus pallidus, and primary somatosensory cortex. This correlation analysis approach helped identify neuroanatomical structures predictive of stroke outcomes and may lead to the translation of this topological analysis approach from preclinical stroke assessment to a clinical biomarker.

Electrophysiological signatures of resting state networks predict cognitive deficits in stroke

Localized damage to different brain regions can cause specific cognitive deficits. However, stroke lesions can also induce modifications in the functional connectivity of intrinsic brain networks, which could be responsible for the behavioral impairment. Though resting state networks (RSNs) are typically mapped using fMRI, it has been recently shown that they can also be detected from high-density EEG. We build on a state-of-the-art approach to extract RSNs from 64-channels EEG activity in a group of right stroke patients and to identify neural predictors of their cognitive performance. Fourteen RSNs previously found in fMRI and high-density EEG studies on healthy participants were successfully reconstructed from our patients' EEG recordings. We then correlated EEG-RSNs functional connectivity with neuropsychological scores, first considering a wide frequency band (1-80 Hz) and then specific frequency ranges in order to examine the association between each EEG rhythm and the behavioral impairment. We found that visuo-spatial and motor impairments were primarily associated with the dorsal attention network, with contribution dependent on the specific EEG band. These findings are in line with the hypothesis that there is a core system of brain networks involved in specific cognitive domains. Moreover, our results pave the way for low-cost EEG-based monitoring of intrinsic brain networks' functioning in neurological patients to complement clinical-behavioral measures.

Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge

Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI (rsfMRI) time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided Challenge participants with demographic information of age, sex, IQ, and handedness. The second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Classification methodologies were submitted in a standardized format as containerized Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 metrics: accuracy, area under the curve, F1-score, false discovery rate, false negative rate, false omission rate, false positive rate, geometric mean, informedness, markedness, Matthew's correlation coefficient, negative predictive value, optimized precision, precision, sensitivity, and specificity. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each methodology. Five participants submitted their method for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are still needed to reach the clinical translation of functional connectomics. We have kept the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of connectomics.

Functional Connectivity Decreases with Metabolic Stress in Sickle Cell Disease

OBJECTIVE

Children with sickle cell disease (SCD) experience cognitive deficits even when unaffected by stroke. Using functional connectivity magnetic resonance imaging (MRI) as a potential biomarker of cognitive function, we tested our hypothesis that children with SCD would have decreased functional connectivity, and that children experiencing the greatest metabolic stress, indicated by elevated oxygen extraction fraction, would have the lowest connectivity.

METHODS

We prospectively obtained brain MRIs and cognitive testing in healthy controls and children with SCD.

RESULTS

We analyzed data from 60 participants (20 controls and 40 with sickle cell disease). There was no difference in global cognition or cognitive subdomains between cohorts. However, we found decreased functional connectivity within the sensory-motor, lateral sensory-motor, auditory, salience, and subcortical networks in participants with SCD compared with controls. Further, as white matter oxygen extraction fraction increased, connectivity within the visual (p = 0.008, parameter estimate = -0.760 [95% CI = -1.297, -0.224]), default mode (p = 0.012, parameter estimate = -0.417 [95% CI = -0.731, -0.104]), and cingulo-opercular (p = 0.009, parameter estimate = -0.883 [95% CI = -1.517, -0.250]) networks decreased.

INTERPRETATION

We conclude that there is diminished functional connectivity within these anatomically contiguous networks in children with SCD compared with controls, even when differences are not seen with cognitive testing. Increased white matter oxygen extraction fraction was associated with decreased connectivity in select networks. These data suggest that elevated oxygen extraction fraction and disrupted functional connectivity are potentially presymptomatic neuroimaging biomarkers for cognitive decline in SCD. ANN NEUROL 2020;88:995-1008.