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Because of the depth of the hippocampal-entorhinal complex (HC-EC) in the brain, understanding of its role in spatial navigation via neuromodulation was limited in humans. Here, we aimed to better elucidate this relationship in healthy volunteers, using transcranial temporal interference electric stimulation (tTIS), a noninvasive technique allowing to selectively neuromodulate deep brain structures. We applied tTIS to the right HC-EC in either continuous or intermittent theta-burst stimulation patterns (cTBS or iTBS), compared to a control condition, during a virtual reality-based spatial navigation task and concomitant functional magnetic resonance imaging. iTBS improved spatial navigation performance, correlated with hippocampal activity modulation, and decreased grid cell-like activity in EC. Collectively, these data provide the evidence that human HC-EC activity can be directly and noninvasively modulated leading to changes of spatial navigation behavior. These findings suggest promising perspectives for patients suffering from cognitive impairment such as following traumatic brain injury or dementia.

Reinforcement feedback can improve motor learning, but the underlying brain mechanisms remain underexplored. In particular, the causal contribution of specific patterns of oscillatory activity within the human striatum is unknown. To address this question, we exploited a recently developed non-invasive deep brain stimulation technique called transcranial temporal interference stimulation (tTIS) during reinforcement motor learning with concurrent neuroimaging, in a randomized, sham-controlled, double-blind study. Striatal tTIS applied at 80 Hz, but not at 20 Hz, abolished the benefits of reinforcement on motor learning. This effect was related to a selective modulation of neural activity within the striatum. Moreover, 80 Hz, but not 20 Hz, tTIS increased the neuromodulatory influence of the striatum on frontal areas involved in reinforcement motor learning. These results show that tTIS can non-invasively and selectively modulate a striatal mechanism involved in reinforcement learning, expanding our tools for the study of causal relationships between deep brain structures and human behaviour., (© 2024. The Author(s).)

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 ( P cluster <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., Competing Interests: Disclosures Dr Hummel serves as a board member for Novartis Foundation for Medical-Biological Research. Dr Blanke is a cofounder and a shareholder of Metaphysiks Engineering Société Anonyme, a company that develops immersive technologies, including applications of the robotic induction of presence hallucinations that are not related to the diagnosis, prognosis, or treatment in medicine. Dr Blanke is a member of the board and a shareholder of Mindmaze Société Anonyme.

Objective . Selective neuromodulation of deep brain regions has for a long time only been possible through invasive approaches, because of the steep depth-focality trade-off of conventional non-invasive brain stimulation (NIBS) techniques. Approach . An approach that has recently emerged for deep NIBS in humans is transcranial Temporal Interference Stimulation (tTIS). However, a crucial aspect for its potential wide use is to ensure that it is tolerable, compatible with efficient blinding and safe. Main results . Here, we show the favorable tolerability and safety profiles and the robust blinding efficiency of deep tTIS targeting the striatum or hippocampus by leveraging a large dataset (119 participants, 257 sessions), including young and older adults and patients with traumatic brain injury. tTIS-evoked sensations were generally rated as 'mild', were equivalent in active and placebo tTIS conditions and did not enable participants to discern stimulation type. Significance . Overall, tTIS emerges as a promising tool for deep NIBS for robust double-blind, placebo-controlled designs., (Creative Commons Attribution license.)

The stimulation of deep brain structures has thus far only been possible with invasive methods. Transcranial electrical temporal interference stimulation (tTIS) is a novel, noninvasive technology that might overcome this limitation. The initial proof-of-concept was obtained through modeling, physics experiments and rodent models. Here we show successful noninvasive neuromodulation of the striatum via tTIS in humans using computational modeling, functional magnetic resonance imaging studies and behavioral evaluations. Theta-burst patterned striatal tTIS increased activity in the striatum and associated motor network. Furthermore, striatal tTIS enhanced motor performance, especially in healthy older participants as they have lower natural learning skills than younger subjects. These findings place tTIS as an exciting new method to target deep brain structures in humans noninvasively, thus enhancing our understanding of their functional role. Moreover, our results lay the groundwork for innovative, noninvasive treatment strategies for brain disorders in which deep striatal structures play key pathophysiological roles., (© 2023. The Author(s).)

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 intervention 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 stimulation 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 neuroimaging 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 synergistically on the nervous and musculoskeletal systems. Integrating multi-modal data may provide valuable insights into underlying mechanisms driving the improvements and providing predictive information regarding treatment response and outcomes., Funding: This work was funded by the Wyss-Center for Bio and Neuro Engineering (WCP-030), the Defitech Foundation, PHRT-#2017-205, ERA-NET-NEURON (Discover), and SNSF (320030L&#95;197899, NiBS-iCog)., Competing Interests: Declaration of interests A.E. works for the Wyss Center for Bio and Neuroengineering, sponsor of the clinical trial., (Copyright © 2023 Elsevier Inc. All rights reserved.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Fleury, Koch, Wessel, Bonvin, San Millan, Constantin, Vuadens, Adolphsen, Cadic Melchior, Brügger, Beanato, Ceroni, Menoud, De Leon Rodriguez, Zufferey, Meyer, Egger, Harquel, Popa, Raffin, Girard, Thiran, Vaney, Alvarez, Turlan, Mühl, Léger, Morishita, Micera, Blanke, Van De Ville and Hummel.)

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)., Competing Interests: Conflict of interest G.F.P., T.H., and T.K. are employees of Siemens Healthcare AG, Switzerland., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

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 T 2 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 ( T 2 I E ) in gray (GM) and white matter (WM) tissue in a clinical setting, using a multi-site, multi-session, and multi-run T 2 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 T 2 I E 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.T 2 relaxometry dataset. To this end, we evaluated three different techniques for estimating the T 2 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 T 2 I E 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., Competing Interests: Authors GP, TH, TY, and TK are employees of Siemens Healthineers International AG, Switzerland. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Fischi-Gomez, Girard, Koch, Yu, Pizzolato, Brügger, Piredda, Hilbert, Cadic-Melchior, Beanato, Park, Morishita, Wessel, Schiavi, Daducci, Kober, Canales-Rodríguez, Hummel and Thiran.)

Effective, patient-tailored rehabilitation to restore upper-limb motor function in severely impaired stroke patients is still missing. If suitably combined and administered in a personalized fashion, neurotechnologies offer a large potential to assist rehabilitative therapies to enhance individual treatment effects. AVANCER (clinicaltrials.gov NCT04448483) is a two-center proof-of-concept trial with an individual based cumulative longitudinal intervention design aiming at reducing upper-limb motor impairment in severely affected stroke patients with the help of multiple neurotechnologies. AVANCER will determine feasibility, safety, and effectivity of this innovative intervention. Thirty chronic stroke patients with a Fugl-Meyer assessment of the upper limb (FM-UE) <20 will be recruited at two centers. All patients will undergo the cumulative personalized intervention within two phases: the first uses an EEG-based brain-computer interface to trigger a variety of patient-tailored movements supported by multi-channel functional electrical stimulation in combination with a hand exoskeleton. This phase will be continued until patients do not improve anymore according to a quantitative threshold based on the FM-UE. The second interventional phase will add non-invasive brain stimulation by means of anodal transcranial direct current stimulation to the motor cortex to the initial approach. Each phase will last for a minimum of 11 sessions. Clinical and multimodal assessments are longitudinally acquired, before the first interventional phase, at the switch to the second interventional phase and at the end of the second interventional phase. The primary outcome measure is the 66-point FM-UE, a significant improvement of at least four points is hypothesized and considered clinically relevant. Several clinical and system neuroscience secondary outcome measures are additionally evaluated. AVANCER aims to provide evidence for a safe, effective, personalized, adjuvant treatment for patients with severe upper-extremity impairment for whom to date there is no efficient treatment available., Competing Interests: MC and AE is employed by the Wyss Center acting as the sponsor of this study. MC is employed by confinis AG. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Bigoni, Zandvliet, Beanato, Crema, Coscia, Espinosa, Henneken, Hervé, Oflar, Evangelista, Morishita, Wessel, Bonvin, Turlan, Birbaumer and Hummel.)

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., (© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain.)

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.

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.