BackgroundStroke is the leading cause of long-term disability, making the search for successful rehabilitation treatment one of the most important public health issues. A better understanding of the neural mechanisms underlying impairment and recovery is critical for optimizing treatments. Objective: We studied the longitudinal changes in brain oscillatory modes, linked to GABAergic system activity, and determined their importance for residual upper-limb motor functions and recovery.MethodsTranscranial Magnetic Stimulation (TMS) was combined with scalp Electroencephalography (EEG) to analyze TMS-induced brain oscillations in a cohort of 66 stroke patients in the acute (N = 60), early (N = 48), and late subacute stages (N = 37).ResultsA data-driven parallel factor analysis (PARAFAC) approach to tensor decomposition extracted brain oscillatory modes, which significantly evolved longitudinally across stroke stages (permutation tests, pBonf < 0.05). Notably, the observed decrease of the α-mode, known to be linked with GABAergic system activity, was mainly driven by the recovering patients and was supportive of stroke recovery at the group level (Bayesian Kendall correlation, moderate to strong statistical evidence).ConclusionsOverall, longitudinal evaluation of brain modes provides novel insights into functional reorganization of brain networks after a stroke. Notably, we propose that the observed α-mode decrease could correspond to a beneficial disinhibition toward the late subacute stage that fosters plasticity and facilitates recovery. These results confirm the relevance of future individual and direct monitoring of post-stroke modulations in inhibitory system activity, with the ultimate goal of designing electrophysiological biomarkers and refining therapies based on personalized neuromodulation.

White matter hyperintensities of presumed vascular origin (WMH) are associated with various clinical sequelae. In stroke patients, the total WMH burden is linked to recurrent cerebrovascular events and worse clinical outcomes. As WMH also affect the integrity of large-scale structural brain networks, we hypothesize that the extent of WMH-related network damage carries relevant information to explain outcome variability in addition to global WMH volume. Clinical and structural brain imaging data of 33 severely affected acute stroke patients were analyzed from two independent cohorts. Imaging data were acquired within the first two weeks after stroke. WMH-related localized and global network damage was derived. WMH network effects were differentially assessed for total, periventricular (pWMH), and deep WMH (dWMH). Using ordinal logistic regression analyses, network damage was associated with functional outcome at follow-up after three to six months. WMH were linked to a significant disconnection of multiple cortical and subcortical brain regions. Global and localized pWMH-related network damage affecting distinct brain regions of both hemispheres were independently associated with a worse outcome after adjustment for baseline symptom burden, age, brain infarct volume, and total WMH volume. Total and dWMH-related network disturbances did not show similar associations. This study indicates that pWMH-related network damage affecting specific brain regions is linked to functional outcome in acute stroke patients. It underscores the potential significance of pre-existing WMH-related network damage as a crucial factor in comprehending outcome variability after severe stroke.

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.

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.

Structural disconnectome analyses have provided valuable insights into how a stroke lesion results in widespread network disturbances and how these relate to deficits, recovery patterns, and outcome. Previous analyses have primarily focused on patients with relatively mild to moderate deficits. However, outcomes vary among survivors of severe strokes, and the mechanisms of recovery remain poorly understood. This study assesses the association between lesion-induced network disconnection and outcome after severe stroke. Thirty-eight ischaemic stroke patients underwent MRI brain imaging early after stroke and longitudinal clinical follow-up. Lesion information was integrated with normative connectome data to infer individual disconnectome profiles on a localized regional and region-to-region pathway level. Ordinal logistic regressions were computed to link disconnectome information to the modified Rankin Scale after 3-6 months. Disconnections of ipsilesional frontal, parietal and temporal cortical brain areas were significantly associated with a worse motor outcome after a severe stroke, adjusted for the initial deficit, lesion volume, and age. The analysis of the underlying pathways mediating this association revealed location specific results: For frontal, prefrontal and temporal brain areas, the association was primarily driven by relatively sparse intrahemispheric disconnections. In contrast, the ipsilesional primary motor cortex, the dorsal premotor cortex, and various parietal brain regions showed a remarkable involvement of either frontoparietal intrahemispheric or additionally interhemispheric disconnections. These results indicate that localized disconnection of multiple regions embedded in the structural frontoparietal network correlates with worse outcome after severe stroke. Specifically, primary motor and parietal cortices might gain a particular importance as they structurally link frontoparietal networks of both hemispheres. These data shed novel light on the significance of distinct brain networks for recovery after severe stroke.

IntroductionDespite 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 methodsIn 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.ResultsASPECTS 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 conclusionPerfusion 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.

HomeStrokeVol. 54, No. 2Border Zone Infarcts in the Setting of Eosinophilia No AccessCase ReportRequest AccessFull TextAboutView Full TextView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toNo AccessCase ReportRequest AccessFull TextBorder Zone Infarcts in the Setting of Eosinophilia Ulf Jensen-Kondering, Philipp Johannes Koch and Victoria Hagelstein Ulf Jensen-KonderingUlf Jensen-Kondering Correspondence to: Ulf Jensen-Kondering, MD, Department of Neuroradiology, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, Haus A, 23538 Lübeck, Germany. Email: E-mail Address: [email protected] https://orcid.org/0000-0002-0860-4410 Department of Neuroradiology (U.J.-K.), University Medical Center Schleswig-Holstein, Lübeck, Germany. , Philipp Johannes KochPhilipp Johannes Koch Department of Neurology (P.J.K.), University Medical Center Schleswig-Holstein, Lübeck, Germany. and Victoria HagelsteinVictoria Hagelstein Department of Dermatology (V.H.), University Medical Center Schleswig-Holstein, Lübeck, Germany. Originally published12 Oct 2022https://doi.org/10.1161/STROKEAHA.122.040754Stroke. 2023;54:e36–e37"Border Zone Infarcts in the Setting of Eosinophilia." Stroke, 54(2), pp. e36–e37FootnotesFor Sources of Funding and Disclosures, see page e36.Correspondence to: Ulf Jensen-Kondering, MD, Department of Neuroradiology, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, Haus A, 23538 Lübeck, Germany. Email: Ulf.[email protected]deReferences1. Mangla R, Kolar B, Almast J, Ekholm SE. Border zone infarcts: pathophysiologic and imaging characteristics.Radiographics. 2011; 31:1201–1214. doi: 10.1148/rg.315105014CrossrefMedlineGoogle Scholar2. Ono R, Iwahana T, Kato H, Okada S, Kobayashi Y. Literature reviews of stroke with hypereosinophilic syndrome.Int J Cardiol Heart Vasc. 2021; 37:100915. doi: 10.1016/j.ijcha.2021.100915CrossrefMedlineGoogle Scholar3. Sethi HS, Schmidley JW. Cerebral infarcts in the setting of eosinophilia: three cases and a discussion.Arch Neurol. 2010; 67:1275–1277. doi: 10.1001/archneurol.2010.256CrossrefGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetails February 2023Vol 54, Issue 2 Advertisement Article InformationMetrics © 2022 American Heart Association, Inc.https://doi.org/10.1161/STROKEAHA.122.040754PMID: 36252114 Originally publishedOctober 12, 2022 PDF download Advertisement SubjectsMagnetic Resonance Imaging (MRI)

Background: Around 25% of patients who have had a stroke suffer from severe upper-limb impairment and lack effective rehabilitation strategies. The AVANCER proof-of-concept clinical trial (NCT04448483) tackles this issue through an intensive and personalized-dosage cumulative inter-vention that combines multiple non-invasive neurotechnologies. Methods: The therapy consists of two sequential interventions, lasting until the patient shows no further motor improvement, for a minimum of 11 sessions each. The first phase involves a brain-computer interface governing an exoskeleton and multi-channel functional electrical stim-ulation enabling full upper-limb movements. The second phase adds anodal transcranial direct current stimulation of the motor cortex of the lesioned hemisphere. Clinical, electrophysiological, and neuroi-maging examinations are performed before, between, and after the two interventions (T0, T1, and T2). This case report presents the results from the first patient of the study. Findings: The primary outcome (i.e., 4-point improvement in the Fugl-Meyer assessment of the upper extremity) was met in the first patient, with an increase from 6 to 11 points between T0 and T2. This improvement was paralleled by changes in motor-network structure and function. Resting-state and transcranial magnetic stimulation -evoked electroencephalography revealed brain functional changes, and magnetic resonance imaging (MRI) measures detected structural and task-related functional changes. Conclusions: These first results are promising, pointing to feasibility, safety, and potential efficacy of this personalized approach acting syn-ergistically on the nervous and musculoskeletal systems. Integrating multi-modal data may provide valuable insights into underlying mech-anisms driving the improvements and providing predictive information regarding treatment response and outcomes.

Impairment of hand motor function is a frequent consequence after a stroke and strongly determines the ability to regain a self-determined life. An influential research strategy for improving motor deficits is the combined application of behavioral training and non-invasive brain stimulation of the motor cortex (M1). However, a convincing clinical translation of the present stimulation strategies has not been achieved yet. One alternative and innovative approach is to target the functionally relevant brain network-based architecture, e.g., the dynamic interactions within the cortico-cerebellar system during learning. Here, we tested a sequential multifocal stimulation strategy targeting the cortico-cerebellar loop. Anodal transcranial direct current stimulation (tDCS) was applied simultaneously to a hand-based motor training in N = 11 chronic stroke survivors during four training sessions on two consecutive days. The tested conditions were: sequential multifocal (M1-cerebellum (CB)-M1-CB) vs. monofocal control stimulation (M1-sham-M1-sham). Additionally, skill retention was assessed 1 and 10 days after the training phase. Paired-pulse transcranial magnetic stimulation data were recorded to characterize stimulation response determining features. The application of CB-tDCS boosted motor behavior in the early training phase in comparison to the control condition. No faciliatory effects on the late training phase or skill retention were detected. Stimulation response variability was related to the magnitude of baseline motor ability and short intracortical inhibition (SICI). The present findings suggest a learning phase-specific role of the cerebellar cortex during the acquisition of a motor skill in stroke and that personalized stimulation strategies encompassing several nodes of the underlying brain network should be considered.

Stroke is the leading cause of motor disability in the adult population, making the understanding of the exact neural mechanisms underlying motor impairment and recovery one of the most important public health issues. Such knowledge is needed to tailor treatments to each individual patient with the ultimate goal to improve therapeutic outcomes. Using TMS-EEG coupling, we focused on the analysis of brain oscillations induced by transcranial magnetic stimulation (TMS) in a large cohort of 60 patients longitudinally, from the acute to the early chronic stage. A data-driven approach (PARAFAC tensor decomposition) allowed to unveil brain oscillatory modes that were specifically centered on the theta, alpha and beta frequency bands. Our results showed that the decomposition weight, i.e., the importance, associated to the alpha mode significantly decreased across the different stages after stroke. Furthermore, this change in alpha activity was correlated with the degree of motor recovery at the individual patient level. Overall, our results indicate that motor recovery follows changes in inhibitory activity represented by the alpha mode. We propose that the observed decrease of alpha activity represents beneficial disinhibition occurring between the subacute and early chronic stage which might foster structural and functional plasticity. Monitoring this phenomenon at the individual patient level could be critical information for programming therapies based on excitatory or inhibitory neuromodulation using non-invasive brain stimulation techniques. Research Category and Technology and Methods Translational Research: 10. Transcranial Magnetic Stimulation (TMS) Keywords: TMS-EEG, induced oscillations, stroke, desinhibition

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.

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.

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).

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.

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.

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.

Disruption of resting-state functional connectivity (RSFC) between core regions of the dorsal attention network (DAN), including the bilateral superior parietal lobule (SPL), and structural damage of the right-lateralized ventral attention network (VAN), including the temporo-parietal junction (TPJ), have been described as neural basis for hemispatial neglect. Pursuing a virtual lesion model, we aimed to perturbate the attention networks of 22 healthy subjects by applying continuous theta burst stimulation (cTBS) to the right SPL or TPJ. We first created network masks of the DAN and VAN based on RSFC analyses from a RS-fMRI baseline session and determined the SPL and TPJ stimulation site within the respective mask. We then performed RS-fMRI immediately after cTBS of the SPL, TPJ (active sites) or vertex (control site). RSFC between SPL/TPJ and whole brain as well as between predefined regions of interest (ROI) in the attention networks was analyzed in a within-subject design. Contrary to our hypothesis, seed-based RSFC did not differ between the four experimental conditions. The individual change in ROI-to-ROI RSFC from baseline to post-stimulation did also not differ between active (SPL, TPJ) and control (vertex) cTBS. In our study, a single session offline cTBS over the right SPL or TPJ could not alter RSFC in the attention networks as compared to a control stimulation, maybe because effects wore off too early. Future studies should consider a modified cTBS protocol, concurrent TMS-fMRI or transcranial direct current stimulation.