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.

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.

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.

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.

BACKGROUND:Noninvasive brain stimulation has been successfully applied to improve stroke-related impairments in different behavioral domains. Yet, clinical translation is limited by heterogenous outcomes within and across studies. It has been proposed to develop and apply noninvasive brain stimulation in a patient-tailored, precision medicine-guided fashion to maximize response rates and effect magnitude. An important prerequisite for this task is the ability to accurately predict the expected response of the individual patient.OBJECTIVE:This review aims to discuss current approaches studying noninvasive brain stimulation in stroke and challenges associated with the development of predictive models of responsiveness to noninvasive brain stimulation.METHODS:Narrative review.RESULTS:Currently, the field largely relies on in-sample associational studies to assess the impact of different influencing factors. However, the associational approach is not valid for making claims of prediction, which generalize out-of-sample. We will discuss crucial requirements for valid predictive modeling in particular the presence of sufficiently large sample sizes.CONCLUSION:Modern predictive models are powerful tools that must be wielded with great care. Open science, including data sharing across research units to obtain sufficiently large and unbiased samples, could provide a solid framework for addressing the task of building robust predictive models for noninvasive brain stimulation responsiveness.

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 metric to be the sum of the entries in the disconnectome and, more importantly, the 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 . 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.

Abstract 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.

At the prime 2, Behrens, Hill, Hopkins and Mahowald showed that M2(1,4) admits a 32-periodic v2-self-map. More recently, in joint work with Mahowald, we showed that A1 also admits a 32-periodic v2-self-map. This leads to the question of whether there exists a finite 2-local complex with periodicity less than 32. We answer this question in the affirmative by producing a class of finite 2-local spectra Z˜ all of which admit a 1-periodic v2-self-map.

Recently we introduced a class $\widetilde{\mathcal{Z}}$ of $2$-local finite spectra and showed that all spectra $Z\in \widetilde{\mathcal{Z}}$ admit a $v_2$-self-map of periodicity $1$. The aim of this article is to compute the $K(2)$-local homotopy groups $\pi_*L_{K(2)}Z$ of all spectra $Z \in \widetilde{\mathcal{Z}}$ using a homotopy fixed point spectral sequence, and we give an almost complete computation. The incompleteness lies in the fact that we are unable to eliminate one family of $d_3$-differentials and a few potential hidden extensions, though we conjecture that all these differentials and hidden extensions are trivial.

We prove that the minimal $v_2$ self-map of the 2-local spectrum $A_1$ is 32 periodic.

L'ARTICLE TRAITE ESSENTIELLEMENT LA QUESTION DE SAVOIR DANS QUELLE MESURE INVESTIGATIONS ET AUSCULTATIONS PEUVENT CONTRIBUER A UNE CONSTRUCTION SOUTERRAINE SURE ET ECONOMIQUE. L'ATTENTION EST ATTIREE SUR LA NECESSITE D'UN CHOIX JUDICIEUX DES PARAMETRES DU MASSIF ROCHEUX A DETERMINER ET SUR LES DIMENSIONS NECESSAIRES DES ZONES DE ROCHE RESP. DES ECHANTILLONS ETUDIES; UN CRITERE DEFINISSANT LES DIMENSIONS MINIMALES D'UN ECHANTILLON REPRESENTATIF EST INDIQUE, LE CHOIX DE LA METHODE D'ESSAI LA MIEUX APPROPRIEE Y ETANT INTIMEMENT LIE. A TITRE D'ILLUSTATION, ON CITE UN EXEMPLE POUR LA DEFINITION D'UN PROGRAMME D'ESSAIS COMPRENANT LA DETERMINATION DES CONTRAINTES INITIALES QUI A D'AILLEURS PERMIS DES ECONOMIES NOTABLES POUR LE SOUTENEMENT. LA DEUXIEME PARTIE DE LA CONTRIBUTION DISCUTE L'OPPORTUNITE DE GALERIES PILOTES ET DE PLOTS D'ESSAIS; ON MONTRE LEUR INTERET POUR SE PROCURER UNE IMPRESSION REALISTE, EXEMPTE D'EFFETS D'ECHELLE, DU CONTEXTE GEOTECHNIQUE ET DE L'INTERACTION ENTRE TUNNEL ET MILIEU ENCAISSANT. L'IMPORTANCE DES AUSCULTATIONS DE L'OUVRAGE, POUR LE CONTROLE DE LA SECURITE, PENDANT LES TRAVAUX ET A L'ETAT DEFINITIF EST SOULIGNEE. LA NOTION DE SECURITE DANS LA CONSTRUCTION SOUTERRAINE EST DISCUTEE : SI LES MESURES DES CONTRAINTES NE PERMETTENT DE CONCLURE SUR LA STABILITE QU'AVEC CERTAINES RESERVES , LES MESURES DES DEFORMATIONS MONTRENT TRES CLAIREMENT L'APPROCHE A UN ETAT CRITIQUE, ILLUSTREE A L'EXEMPLE DE MESURES EFFECTUEES AVANT L'EFFONDREMENT D'UN TUNNEL.

Let k ≥ 2 be an integer. We show that the space Ck of ordered cacti with k lobes is homotopy-equivalent to the k-th ordered configuration space in the complex plane up to translation, denoted by Fk . We then give an explicit homotopy-equivalence between Fk and Ck .