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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 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.

Stroke 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 and the development of associated markers is critical for tailoring treatments to each individual patient with the ultimate goal of maximizing therapeutic outcomes. Here, we used a novel and powerful method consisting of combined transcranial magnetic stimulation (TMS) and multichannel electroencephalography (EEG) to analyze TMS-induced brain oscillations in a large cohort of 60 stroke patients from the acute to the early-chronic phase after a stroke. A data-driven parallel factor analysis (PARAFAC) approach to tensor decomposition allowed to detect brain oscillatory modes specifically centered on the θ, α and β frequency bands. In the acute stage, patients presented a general slowdown of these oscillatory modes, highlighting stroke-induced perturbations within thalamocortical processing. Furthermore, low-frequency modes evolved across stroke stages, according to the extent of motor recovery, associated with changes in GABAergic intracortical inhibition. Overall, these longitudinal changes provide novel insights into the ongoing functional reorganization of brain networks after a stroke and the underlying mechanisms. Notably, we propose that the observed α-mode decrease corresponds to a beneficial disinhibition phase between the subacute and early-chronic stages that fosters structural and functional plasticity and facilitates recovery. Monitoring this phenomenon at the individual patient level will provide critical information for phenotyping patients, developing electrophysiological biomarkers and refining therapies based on personalized excitatory/inhibitory neuromodulation using noninvasive or invasive brain stimulation techniques.

The neuronal processes sustaining motor recovery after stroke are still largely unknown. Cortical excitation/inhibition dynamics have been suggested previously as a key mechanism occurring after a stroke. Their supportive or maladaptive role immediately after a stroke and during the process of recovery are still not completely understood; it is hypothesized that similar mechanisms (e.g., disinhibition) might yield differential functional roles depending on the stage after the stroke (e.g., acute vs. subacute vs. chronic) and the degree of deficit. Here, we used TMS-EEG to study brain reactivity, motor cortical excitability as well as intracortical inhibition and their impact on residual motor function and recovery longitudinally in a large cohort of stroke patients.EEG responses evoked by TMS applied to the ipsilesional motor cortex (iMC) were acquired in 66 stroke patients in the acute (1-week), sub-acute (3-weeks) and chronic stage (3-months). Readouts of ipsilesional cortical reactivity, excitability and intracortical inhibition were drawn from TMS-evoked potentials and derived metrics. Residual function of the upper limb was quantified through a detailed motor evaluation.A large proportion of patients, especially the most affected ones, exhibited large, simple TMS-evoked neuronal responses. Bayesian correlations revealed a link between higher excitability in iMC in the acute and stronger reduction of impairment determined by the upper extremity Fugl-Meyer (FM-UE) score in the early chronic stage. Furthermore, a decrease of this abnormally large response in the following months was related to better motor recovery. When investigating the underlying mechanisms with a focus on the intracortical GABAergic system, the present results revealed changes in intracortical inhibition in the first week after stroke that were associated with better recovery. Additionally, restoration of intracortical inhibition was present in patients, who recovered the most. Furthermore, the large component observed in a relevant part of the patients masks underlying mechanisms reflecting the importance of changes in intracortical inhibition for successful recovery.The present results strongly support the view of a beneficial role of cortical disinhibition in the first week after a stroke that promotes neuronal plasticity and recovery. However, to sustain long-term motor recovery, cortical disinhibition needs to be transient with crucial restoration of normal levels of intracortical inhibition. TMS-EEG has the exciting potential to provide proxies to better understand underlying mechanisms of stroke revovery, to determine outcome and to help to tailor interventional treatment strategies to each patient based on the brain reactivity status.

BackgroundMost 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.Methods50 subacute stroke patients (7±3days post-stroke) 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. Overlaying individual lesion masks onto the tractograms enabled us to split the connectomes into their affected and unaffected parts and associate them to impairment.ResultsTo 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. 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. Network weights associated with the rich-club were more strongly correlated to efficiency than non-rich-club weights.ConclusionsAttentional impairment is more sensitive to disruption of coordinated network activity between brain regions than motor impairment, which is sensitive to disruption of localized network activity. 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.

Nociceptive stimuli disrupt sleep, but may, or may not, entail an arousal. While arousal reactions go along with the activation of a widespread cortical network, the factors enabling such activation remain unknown. Here we used intracranial EEG in humans to test the relation between the cortical activity immediately preceding a noxious stimulus and the capacity of such a stimulus to trigger arousal. Intracranial EEG signals were analyzed during all-night sleep in 14 epileptic patients (4 women), who received laser stimuli slightly above their individual pain threshold. During 5 s preceding each stimulus, the functional correlation (spectral phase-coherence) between the main spinothalamic sensory area (posterior insula) and 12 other brain regions, grouped in four networks, as well as their spectral contents, were contrasted according to the presence of a stimulus-induced arousal, and then fed into a logistic regression model to assess their predictive value. Enhanced prestimulus phase-coherence between the sensory posterior insula and neocortical and limbic areas increased significantly the probability of arousal to nociceptive stimuli, in both slow-wave (N2) and rapid eye movements/paradoxical sleep. Furthermore, during N2 sleep, arousal was facilitated by stimulus delivery in periods of attenuated slow-wave activity. Together, these data indicate that sleep micro-states with enhanced interareal communication facilitate information transfer from sensory to higher-order cortical areas, and hence physiological arousal. SIGNIFICANCE STATEMENT Sleep is commonly subdivided into stages based on specific electrophysiological characteristics; however, within each single sleep stage, the functional state of the brain is continuously changing. Here we show that the probability for a phasic noxious stimulus to entail an arousal is modulated by the prestimulus interareal phase-coherence between sensory and higher-level cortical areas. Fluctuations in interareal communication immediately before the noxious stimulus may determine the responsiveness to incoming input by facilitating or preventing the transfer of noxious information from sensory to multiple higher-level cortical networks.

A key feature of a successful game is its ability to provide the player with an adequate level of challenge. However, the objective of difficulty adaptation in serious games is not only to maintain the player’s motivation by challenging, but also to ensure the completion of training objectives. This paper describes our proposed upper-limb rehabilitation game with tangible robots and investigates the effect of game elements and gameplay on the amount of the performed motion in several planes and percentage of failure by using the data from 33 unimpaired subjects who played 53 games within two consecutive days. In order to provide a more generic adaptation strategy in the future, we discretize the game area to circular zones. We then show the effect of changing these zones during gameplay on the activation of different muscles through EMG data in a pilot study. The study shows that it is possible to increase the challenge level by adding more active agents chasing the player and increasing the speed of these agents. However, only the increase in number of agents significantly increases the users’ motion on both planes. Analysis of player behaviors leads us to suggest that by adapting the behaviour of these active agents in specific zones, it is possible to change the trajectory of the user, and to provide a focus on the activation of specific muscles.

Sleep disruption by nociceptive stimuli follows the apparition of a u2018cognitiveu2019 wave, supposed to reflect the activation of a widespread cortical network (Bastuji et al 2008). The factors allowing the activation of this network are unknown and remain to be identified. The aim of the present study was to test if the characteristics of baseline cortical activity can predict reactivity to these nociceptive stimuli. For this purpose, intra-cerebral electrophysiological signal before nociceptive stimuli during sleep were analysed in the time-frequency domain.Intracerebral recordings were obtained in 14 epileptic patients receiving thermo-nociceptive stimulations, calibrated slightly above the individual pain threshold, during whole night sleep. Spectral content of 5 seconds pre-stimulus EEG signal and phase coherence between posterior insula and 14 other brain areas were compared according to presence or absence of arousal post-stimulus, during sleep N2 and paradoxical sleep (PS). Spectral power of delta activity pre-stimulus was significantly lower before arousal reaction as compared to that with no arousal in N2 (p

Objectif Des stimulations nociceptives delivrees au cours du sommeil provoquent dans 1/3 des cas une reaction d’eveil qui suit une reponse cerebrale de type cognitif (Bastuji et al., 08) [1] . L’hypothese de ce travail est que la presence ou l’absence de reaction d’eveil serait modulee par le profil d’activite cerebrale avant le stimulus nociceptif. Methodes Enregistrements intracrâniens nocturnes chez 14 patients epileptiques recevant des stimulations laser delivrees au seuil douloureux, sur le territoire cutane du nerf radial de la main controlaterale a l’hemisphere implante. Le contenu spectral des 5 secondes pre-stimulus et la coherence de phase entre l’activite de l’insula posterieure et celle des 14 autres structures implantees ont ete compares en fonction de la presence ou non de reaction d’eveil post-stimulus, en sommeil lent leger (N2) et en sommeil paradoxal (SP). Resultats La puissance de l’activite delta etait significativement plus faible avant une reaction d’eveil en N2 et plus elevee en SP. La coherence de phase pre-stimulus entre l’insula posterieure et les autres aires cerebrales etait significativement superieure avant une reaction d’eveil qu’en son absence, aussi bien en N2 qu’en SP. Conclusion La faible puissance delta observee avant une reaction d’eveil en sommeil lent suggere qu’une moindre profondeur du sommeil favorise cette reactivite. L’augmentation de la connectivite fonctionnelle, presente lors des deux stades de sommeil, faciliterait le traitement de l’information sensorielle a venir et l’emergence de la reaction d’eveil par sa propagation rapide et etendue aux structures superieures.

Task-specific training constitutes a core element for evidence-based rehabilitation strategies targeted at improving upper extremity activity after stroke. Its combination with additional treatment strategies and neurotechnology-based solutions could further improve patients' outcomes. Here, we studied the effect of gamified robot-assisted upper limb motor training on motor performance, skill learning, and transfer with respect to a non-gamified control condition with a group of chronic stroke survivors. The results suggest that a gamified training strategy results in more controlled motor performance during the training phase, which is characterized by a higher accuracy (lower deviance), higher smoothness (lower jerk), but slower speed. The responder analyses indicated that mildly impaired patients benefited most from the gamification approach. In conclusion, gamified robot-assisted motor training, which is personalized to the individual capabilities of a patient, constitutes a promising investigational strategy for further improving motor performance after a stroke.

Stroke is the main source of long-lasting disability, affecting dominantly motor functions. The extent and course of recovery are highly heterogeneous between patients, with a minority of patients fully recovering from their initial impairments, leaving 85% persisting deficits. The pathophysiological mechanisms underlying inter-patients heterogeneity are still not fully understood. Most motor recovery is taking place during the first months after a stroke, with limited improvement after that time, emphasizing the importance of this early period. These first months after a stroke are characterized by dynamic modulations of excitatory and inhibitory processes in the brain. Most notably, modulation of intracortical inhibition is thought to promote both neuronal protection from further damage in the hyperacute phase and functional reorganization to compensate for the lesioned brain regions in the following phases. Previous research in animal models and stroke patients has highlighted specifically the importance of the GABAergic system, the main actor of inhibition in the brain. However, the specific functional role and the time course of changes of GABAergic inhibition in the course of recovery are only partially understood. To better characterize the spatial and temporal properties of the inhibiting mechanisms occurring after a stroke and their association with motor recovery, we investigated the neurophysiological changes of 66 stroke patients longitudinally from the first week to 3 months post stroke. Cortical excitability and inhibition were determined by transcranial magnetic stimulation (TMS) coupled with electroencephalography (EEG). The present results revealed two disinhibition phases with distinct regionality and timing patterns. In Study I, a local ipsilesional disinhibition, expressed by larger evoked activity, in the acute phase was related with better motor recovery at 3 months post stroke. Patients recovering the most showed a return to normal excitatory/inhibitory balance between the acute and early chronic stage. In Study II, global excitatory and inhibitory activity were evaluated through a data-driven analysis of TMS-induced braion oscillatory modes. The late alpha-oscillations, a proxy of GABAergic activity, displayed a small increase in the acute stage followed by a large decrease between the subacute and early chronic stage. This global disinhibition was correlated with greater recovery of fine upper-limb motor function. This thesis underlines the importance of GABAergic disinhibition, both locally and globally, for motor recovery after a stroke and determined its specific time courses. The acquired knowledge will provide the basis to pave the way to electrophysiological biomarkers for individual phenotyping of patients. Personalized interventional strategies targeting changes in cortical excitability have to potential to maximize functional recovery in each individual patient.

Despite recent improvements, complete motor recovery occurs in less than 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 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 towards individually tailored treatment concepts using neuro-technologies, thus paving the way towards personalized precision medicine approaches in stroke rehabilitation.

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).