Stroke Recovery-Related Changes in Cortical Reactivity Based on Modulation of Intracortical Inhibition.

BACKGROUND: Cortical excitation/inhibition dynamics have been suggested as a key mechanism occurring after stroke. Their supportive or maladaptive role in the course of recovery is still not completely understood. Here, we used transcranial magnetic stimulation (TMS)-electroencephalography coupling to study cortical reactivity and intracortical GABAergic inhibition, as well as their relationship to residual motor function and recovery longitudinally in patients with stroke. METHODS: Electroencephalography responses evoked by TMS applied to the ipsilesional motor cortex were acquired in patients with stroke with upper limb motor deficit in the acute (1 week), early (3 weeks), and late subacute (3 months) stages. Readouts of cortical reactivity, intracortical inhibition, and complexity of the evoked dynamics were drawn from TMS-evoked potentials induced by single-pulse and paired-pulse TMS (short-interval intracortical inhibition). Residual motor function was quantified through a detailed motor evaluation. RESULTS: From 76 patients enrolled, 66 were included (68.2±13.2 years old, 18 females), with a Fugl-Meyer score of the upper extremity of 46.8±19. The comparison with TMS-evoked potentials of healthy older revealed that most affected patients exhibited larger and simpler brain reactivity patterns (Pcluster<0.05). Bayesian ANCOVA statistical evidence for a link between abnormally high motor cortical excitability and impairment level. A decrease in excitability in the following months was significantly correlated with better motor recovery in the whole cohort and the subgroup of recovering patients. Investigation of the intracortical GABAergic inhibitory system revealed the presence of beneficial disinhibition in the acute stage, followed by a normalization of inhibitory activity. This was supported by significant correlations between motor scores and the contrast of local mean field power and readouts of signal dynamics. CONCLUSIONS: The present results revealed an abnormal motor cortical reactivity in patients with stroke, which was driven by perturbations and longitudinal changes within the intracortical inhibition system. They support the view that disinhibition in the ipsilesional motor cortex during the first-week poststroke is beneficial and promotes neuronal plasticity and recovery.

White adipose tissue distribution and amount are associated with increased white matter connectivity.

Obesity represents a significant public health concern and is linked to various comorbidities and cognitive impairments. Previous research indicates that elevated body mass index (BMI) is associated with structural changes in white matter (WM). However, a deeper characterization of body composition is required, especially considering the links between abdominal obesity and metabolic dysfunction. This study aims to enhance our understanding of the relationship between obesity and WM connectivity by directly assessing the amount and distribution of fat tissue. Whole-body magnetic resonance imaging (MRI) was employed to evaluate total adipose tissue (TAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT), while MR liver spectroscopy measured liver fat content in 63 normal-weight, overweight, and obese males. WM connectivity was quantified using microstructure-informed tractography. Connectome-based predictive modeling was used to predict body composition metrics based on WM connectomes. Our analysis revealed a positive dependency between BMI, TAT, SAT, and WM connectivity in brain regions involved in reward processing and appetite regulation, such as the insula, nucleus accumbens, and orbitofrontal cortex. Increased connectivity was also observed in cognitive control and inhibition networks, including the middle frontal gyrus and anterior cingulate cortex. No significant associations were found between WM connectivity and VAT or liver fat. Our findings suggest that altered neural communication between these brain regions may affect cognitive processes, emotional regulation, and reward perception in individuals with obesity, potentially contributing to weight gain. While our study did not identify a link between WM connectivity and VAT or liver fat, further investigation of the role of various fat depots and metabolic factors in brain networks is required to advance obesity prevention and treatment approaches.

Preserved Corticospinal Tract Revealed by Acute Perfusion Imaging Relates to Better Outcome After Thrombectomy in Stroke.

BACKGROUND: The indication for mechanical thrombectomy (MT) in stroke patients with large vessel occlusion has been constantly expanded over the past years. Despite remarkable treatment effects at the group level in clinical trials, many patients remain severely disabled even after successful recanalization. A better understanding of this outcome variability will help to improve clinical decision-making on MT in the acute stage. Here, we test whether current outcome models can be refined by integrating information on the preservation of the corticospinal tract as a functionally crucial white matter tract derived from acute perfusion imaging. METHODS: We retrospectively analyzed 162 patients with stroke and large vessel occlusion of the anterior circulation who were admitted to the University Medical Center Lübeck between 2014 and 2020 and underwent MT. The ischemic core was defined as fully automatized based on the acute computed tomography perfusion with cerebral blood volume data using outlier detection and clustering algorithms. Normative whole-brain structural connectivity data were used to infer whether the corticospinal tract was affected by the ischemic core or preserved. Ordinal logistic regression models were used to correlate this information with the modified Rankin Scale after 90 days. RESULTS: The preservation of the corticospinal tract was associated with a reduced risk of a worse functional outcome in large vessel occlusion-stroke patients undergoing MT, with an odds ratio of 0.28 (95% CI, 0.15-0.53). This association was still significant after adjusting for multiple confounding covariables, such as age, lesion load, initial symptom severity, sex, stroke side, and recanalization status. CONCLUSIONS: A preinterventional computed tomography perfusion-based surrogate of corticospinal tract preservation or disconnectivity is strongly associated with functional outcomes after MT. If validated in independent samples this concept could serve as a novel tool to improve current outcome models to better understand intersubject variability after MT in large vessel occlusion stroke.

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.

Evaluating reproducibility and subject-specificity of microstructure-informed connectivity.

Tractography enables identifying and evaluating the healthy and diseased brain's white matter pathways from diffusion-weighted magnetic resonance imaging data. As previous evaluation studies have reported significant false-positive estimation biases, recent microstructure-informed tractography algorithms have been introduced to improve the trade-off between specificity and sensitivity. However, a major limitation for characterizing the performance of these techniques is the lack of ground truth brain data. In this study, we compared the performance of two relevant microstructure-informed tractography methods, SIFT2 and COMMIT, by assessing the subject specificity and reproducibility of their derived white matter pathways. Specifically, twenty healthy young subjects were scanned at eight different time points at two different sites. Subject specificity and reproducibility were evaluated using the whole-brain connectomes and a subset of 29 white matter bundles. Our results indicate that although the raw tractograms are more vulnerable to the presence of false-positive connections, they are highly reproducible, suggesting that the estimation bias is subject-specific. This high reproducibility was preserved when microstructure-informed tractography algorithms were used to filter the raw tractograms. Moreover, the resulting track-density images depicted a more uniform coverage of streamlines throughout the white matter, suggesting that these techniques could increase the biological meaning of the estimated fascicles. Notably, we observed an increased subject specificity by employing connectivity pre-processing techniques to reduce the underlaying noise and the data dimensionality (using principal component analysis), highlighting the importance of these tools for future studies. Finally, no strong bias from the scanner site or time between measurements was found. The largest intraindividual variance originated from the sole repetition of data measurements (inter-run).

The structural connectome and motor recovery after stroke: predicting natural recovery.

Stroke patients vary considerably in terms of outcomes: some patients present 'natural' recovery proportional to their initial impairment (fitters), while others do not (non-fitters). Thus, a key challenge in stroke rehabilitation is to identify individual recovery potential to make personalized decisions for neuro-rehabilitation, obviating the 'one-size-fits-all' approach. This goal requires (i) the prediction of individual courses of recovery in the acute stage; and (ii) an understanding of underlying neuronal network mechanisms. 'Natural' recovery is especially variable in severely impaired patients, underscoring the special clinical importance of prediction for this subgroup. Fractional anisotropy connectomes based on individual tractography of 92 patients were analysed 2 weeks after stroke (TA) and their changes to 3 months after stroke (TC - TA). Motor impairment was assessed using the Fugl-Meyer Upper Extremity (FMUE) scale. Support vector machine classifiers were trained to separate patients with natural recovery from patients without natural recovery based on their whole-brain structural connectomes and to define their respective underlying network patterns, focusing on severely impaired patients (FMUE < 20). Prediction accuracies were cross-validated internally, in one independent dataset and generalized in two independent datasets. The initial connectome 2 weeks after stroke was capable of segregating fitters from non-fitters, most importantly among severely impaired patients (TA: accuracy = 0.92, precision = 0.93). Secondary analyses studying recovery-relevant network characteristics based on the selected features revealed (i) relevant differences between networks contributing to recovery at 2 weeks and network changes over time (TC - TA); and (ii) network properties specific to severely impaired patients. Important features included the parietofrontal motor network including the intraparietal sulcus, premotor and primary motor cortices and beyond them also attentional, somatosensory or multimodal areas (e.g. the insula), strongly underscoring the importance of whole-brain connectome analyses for better predicting and understanding recovery from stroke. Computational approaches based on structural connectomes allowed the individual prediction of natural recovery 2 weeks after stroke onset, especially in the difficult to predict group of severely impaired patients, and identified the relevant underlying neuronal networks. This information will permit patients to be stratified into different recovery groups in clinical settings and will pave the way towards personalized precision neurorehabilitative treatment.

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 (MLPRLLPR) 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.

Toward precision medicine: tailoring interventional strategies based on noninvasive brain stimulation for motor recovery after stroke.

PURPOSE OF REVIEW: To support the recovery of disability and the reduced functional capacity influencing the independence of daily life after focal brain lesions like stroke, the application of noninvasive brain stimulation (NIBS) by repetitive transcranial magnetic stimulation or transcranial electric stimulation has been found useful in the last decades. Still, a positive influence on the recovery seems to be restricted to specific subgroups of patients. Therefore, a closer look on individual parameters influencing the recovery course and the effect of NIBS is needed. RECENT FINDINGS: Neuroimaging studies investigated alterations in neuronal network settings during the recovery process from stroke and can explain a relevant amount of variance in residual motor function. In this regard for instance, the microstructural integrity of the corticospinal tract and its influence on cortical and subcortical functional and structural connectivity alterations shows a relevant impact on individual recovery from the acute to the chronic state. SUMMARY: Based on this understanding, a 'one-suits-all' NIBS strategy for clinical application appears insufficient and understanding of therapeutic susceptibility to NIBS gained from structural and functional imaging studies will help to develop patient-tailored NIBS-based interventional strategies towards precision medicine, as a promising future prospective within this field.

The lesion core extent modulates the impact of early perfusion mismatch imaging on outcome variability after thrombectomy in stroke.

Introduction: Despite profitable group effects on functional outcomes after mechanical thrombectomy (MT) in large vessel occlusion (LVO), many patients with successful reperfusion show a non-favorable long-term outcome, highlighting the necessity to identify potential biomarkers predicting outcome variability. In this regard, the role of perfusion mismatch imaging for outcome variability in the early time window within 6 h after symptom onset is a matter of debate. We attempted to investigate under which conditions early perfusion mismatch imaging accounts for variability in functional outcomes after mechanical thrombectomy. Patients and methods: In a retrospective single-center study, we examined 190 consecutive patients with LVO who were admitted to the Medical Center Lübeck within 6 h after symptom onset, all of whom underwent MT. Perfusion mismatch was quantified by applying the Alberta Stroke Program Early CT score (ASPECTS) on CT-measured cerebral blood flow (CBF-ASPECTS) and subtracting it from an ASPECTS application on cerebral blood volume (CBV-ASPECTS), i.e., ASPECTS mismatch. Using multivariate ordinal regression models, associations between ASPECTS mismatch and modified Rankin Scale (mRS) after 90 days were assessed. Furthermore, the interaction between ASPECTS mismatch and the core lesion volume was calculated to evaluate conditional associations. Results: ASPECTS mismatch did not correlate with functional outcomes when corrected for multiple influencing covariables. However, interactions between ASPECTS mismatch and CBV-ASPECTS [OR: 1.12 (1.06-1.18), p-value < 0.001], as well as NCCT-ASPECTS [OR: 1.15 (1.06-1.25), p-value < 0.001], did show a significant association with functional outcomes. Model comparisons revealed that, profoundly, in patients with large core lesion volumes (CBV-ASPECTS < 6 or NCCT-ASPECTS < 6), perfusion mismatch showed a negative correlation with the mRS. Discussion and conclusion: Perfusion mismatch imaging within the first 6 h of symptom onset provides valuable insights into the outcome variability of LVO stroke patients receiving thrombectomy but only in patients with large ischemic core lesions.

Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions.

Quantitative magnetic resonance imaging (qMRI) can increase the specificity and sensitivity of conventional weighted MRI to underlying pathology by comparing meaningful physical or chemical parameters, measured in physical units, with normative values acquired in a healthy population. This study focuses on multi-echo T2 relaxometry, a qMRI technique that probes the complex tissue microstructure by differentiating compartment-specific T2 relaxation times. However, estimation methods are still limited by their sensitivity to the underlying noise. Moreover, estimating the model's parameters is challenging because the resulting inverse problem is ill-posed, requiring advanced numerical regularization techniques. As a result, the estimates from distinct regularization strategies are different. In this work, we aimed to investigate the variability and reproducibility of different techniques for estimating the transverse relaxation time of the intra- and extra-cellular space (T2IE) in gray (GM) and white matter (WM) tissue in a clinical setting, using a multi-site, multi-session, and multi-run T2 relaxometry dataset. To this end, we evaluated three different techniques for estimating the T2 spectra (two regularized non-negative least squares methods and a machine learning approach). Two independent analyses were performed to study the effect of using raw and denoised data. For both the GM and WM regions, and the raw and denoised data, our results suggest that the principal source of variance is the inter-subject variability, showing a higher coefficient of variation (CoV) than those estimated for the inter-site, inter-session, and inter-run, respectively. For all reconstruction methods studied, the CoV ranged between 0.32 and 1.64%. Interestingly, the inter-session variability was close to the inter-scanner variability with no statistical differences, suggesting that T2IE is a robust parameter that could be employed in multi-site neuroimaging studies. Furthermore, the three tested methods showed consistent results and similar intra-class correlation (ICC), with values superior to 0.7 for most regions. Results from raw data were slightly more reproducible than those from denoised data. The regularized non-negative least squares method based on the L-curve technique produced the best results, with ICC values ranging from 0.72 to 0.92.

Toward individualized medicine in stroke-The TiMeS project: Protocol of longitudinal, multi-modal, multi-domain study in stroke.

Despite recent improvements, complete motor recovery occurs in <15% of stroke patients. To improve the therapeutic outcomes, there is a strong need to tailor treatments to each individual patient. However, there is a lack of knowledge concerning the precise neuronal mechanisms underlying the degree and course of motor recovery and its individual differences, especially in the view of brain network properties despite the fact that it became more and more clear that stroke is a network disorder. The TiMeS project is a longitudinal exploratory study aiming at characterizing stroke phenotypes of a large, representative stroke cohort through an extensive, multi-modal and multi-domain evaluation. The ultimate goal of the study is to identify prognostic biomarkers allowing to predict the individual degree and course of motor recovery and its underlying neuronal mechanisms paving the way for novel interventions and treatment stratification for the individual patients. A total of up to 100 patients will be assessed at 4 timepoints over the first year after the stroke: during the first (T1) and third (T2) week, then three (T3) and twelve (T4) months after stroke onset. To assess underlying mechanisms of recovery with a focus on network analyses and brain connectivity, we will apply synergistic state-of-the-art systems neuroscience methods including functional, diffusion, and structural magnetic resonance imaging (MRI), and electrophysiological evaluation based on transcranial magnetic stimulation (TMS) coupled with electroencephalography (EEG) and electromyography (EMG). In addition, an extensive, multi-domain neuropsychological evaluation will be performed at each timepoint, covering all sensorimotor and cognitive domains. This project will significantly add to the understanding of underlying mechanisms of motor recovery with a strong focus on the interactions between the motor and other cognitive domains and multimodal network analyses. The population-based, multi-dimensional dataset will serve as a basis to develop biomarkers to predict outcome and promote personalized stratification toward individually tailored treatment concepts using neuro-technologies, thus paving the way toward personalized precision medicine approaches in stroke rehabilitation.

Resting-State Functional Connectivity in the Dorsal Attention Network Relates to Behavioral Performance in Spatial Attention Tasks and May Show Task-Related Adaptation.

Between-subject variability in cognitive performance has been related to inter-individual differences in functional brain networks. Targeting the dorsal attention network (DAN) we questioned (i) whether resting-state functional connectivity (FC) within the DAN can predict individual performance in spatial attention tasks and (ii) whether there is short-term adaptation of DAN-FC in response to task engagement. Twenty-seven participants first underwent resting-state fMRI (PRE run), they subsequently performed different tasks of spatial attention [including visual search (VS)] and immediately afterwards received another rs-fMRI (POST run). Intra- and inter-hemispheric FC between core hubs of the DAN, bilateral intraparietal sulcus (IPS) and frontal eye field (FEF), was analyzed and compared between PRE and POST. Furthermore, we investigated rs-fMRI-behavior correlations between the DAN-FC in PRE/POST and task performance parameters. The absolute DAN-FC did not change from PRE to POST. However, different significant rs-fMRI-behavior correlations were revealed for intra-/inter-hemispheric connections in the PRE and POST run. The stronger the FC between left FEF and IPS before task engagement, the better was the learning effect (improvement of reaction times) in VS (r = 0.521, p = 0.024). And the faster the VS (mean RT), the stronger was the FC between right FEF and IPS after task engagement (r = -0.502, p = 0.032). To conclude, DAN-FC relates to the individual performance in spatial attention tasks supporting the view of functional brain networks as priors for cognitive ability. Despite a high inter- and intra-individual stability of DAN-FC, the change of FC-behavior correlations after task performance possibly indicates task-related adaptation of the DAN, underlining that behavioral experiences may shape intrinsic brain activity. However, spontaneous state fluctuations of the DAN-FC over time cannot be fully ruled out as an alternative explanation.

Multifocal stimulation of the cerebro-cerebellar loop during the acquisition of a novel motor skill.

Transcranial direct current stimulation (tDCS)-based interventions for augmenting motor learning are gaining interest in systems neuroscience and clinical research. Current approaches focus largely on monofocal motorcortical stimulation. Innovative stimulation protocols, accounting for motor learning related brain network interactions also, may further enhance effect sizes. Here, we tested different stimulation approaches targeting the cerebro-cerebellar loop. Forty young, healthy participants trained a fine motor skill with concurrent tDCS in four sessions over two days, testing the following conditions: (1) monofocal motorcortical, (2) sham, (3) monofocal cerebellar, or (4) sequential multifocal motorcortico-cerebellar stimulation in a double-blind, parallel design. Skill retention was assessed after circa 10 and 20 days. Furthermore, potential underlying mechanisms were studied, applying paired-pulse transcranial magnetic stimulation and multimodal magnetic resonance imaging-based techniques. Multisession motorcortical stimulation facilitated skill acquisition, when compared with sham. The data failed to reveal beneficial effects of monofocal cerebellar or additive effects of sequential multifocal motorcortico-cerebellar stimulation. Multimodal multiple linear regression modelling identified baseline task performance and structural integrity of the bilateral superior cerebellar peduncle as the most influential predictors for training success. Multisession application of motorcortical tDCS in several daily sessions may further boost motor training efficiency. This has potential implications for future rehabilitation trials.

Cerebellar transcranial alternating current stimulation in the gamma range applied during the acquisition of a novel motor skill.

The development of novel strategies to augment motor training success is of great interest for healthy persons and neurological patients. A promising approach is the combination of training with transcranial electric stimulation. However, limited reproducibility and varying effect sizes make further protocol optimization necessary. We tested the effects of a novel cerebellar transcranial alternating current stimulation protocol (tACS) on motor skill learning. Furthermore, we studied underlying mechanisms by means of transcranial magnetic stimulation and analysis of fMRI-based resting-state connectivity. N = 15 young, healthy participants were recruited. 50 Hz tACS was applied to the left cerebellum in a double-blind, sham-controlled, cross-over design concurrently to the acquisition of a novel motor skill. Potential underlying mechanisms were assessed by studying short intracortical inhibition at rest (SICIrest) and in the premovement phase (SICImove), intracortical facilitation at rest (ICFrest), and seed-based resting-state fMRI-based functional connectivity (FC) in a hypothesis-driven motor learning network. Active stimulation did not enhance skill acquisition or retention. Minor effects on striato-parietal FC were present. Linear mixed effects modelling identified SICImove modulation and baseline task performance as the most influential determining factors for predicting training success. Accounting for the identified factors may allow to stratify participants for future training-based interventions.

Brain networks and their relevance for stroke rehabilitation.

Stroke has long been regarded as focal disease with circumscribed damage leading to neurological deficits. However, advances in methods for assessing the human brain and in statistics have enabled new tools for the examination of the consequences of stroke on brain structure and function. Thereby, it has become evident that stroke has impact on the entire brain and its network properties and can therefore be considered as a network disease. The present review first gives an overview of current methodological opportunities and pitfalls for assessing stroke-induced changes and reorganization in the human brain. We then summarize principles of plasticity after stroke that have emerged from the assessment of networks. Thereby, it is shown that neurological deficits do not only arise from focal tissue damage but also from local and remote changes in white-matter tracts and in neural interactions among wide-spread networks. Similarly, plasticity and clinical improvements are associated with specific compensatory structural and functional patterns of neural network interactions. Innovative treatment approaches have started to target such network patterns to enhance recovery. Network assessments to predict treatment response and to individualize rehabilitation is a promising way to enhance specific treatment effects and overall outcome after stroke.