Brain connectivity and neurological disorders after stroke

Evidence that robot-assisted gait training modulates neuroplasticity after stroke: An fMRI pilot study based on graph theory analysis

OBJECTIVES

To investigate alterations of whole-brain network after stroke and therapeutic mechanisms of robot-assisted gait training (RAGT).

METHODS

21 stroke patients and 20 healthy subjects were enrolled, with the stroke patients randomized to either control group (n = 11) or robot group (n = 10), and resting-state functional magnetic resonance imaging data were collected. The global network metrics were obtained using graph theory analysis and compared between stroke patients and healthy subjects, and the effect of the RAGT on the whole-brain networks was explored.

RESULTS

Compared to healthy subjects, area under the curve (AUC) for small-worldness (σ), clustering coefficient (Cp), global efficiency (Eg) and mean local efficiency (Eloc) were significantly lower in stroke patients, whereas AUC for characteristic path length (Lp) were significantly higher. Compared with the control group, patients in robot group showed significant improvement in lower limb motor function, balance function and walking function after intervention, with a significant reduction in the AUC of Cp. Moreover, the improvement of walking function was positively correlated with the changes of AUC of σ and Eg, and negatively correlated with the changes of AUC of Cp.

CONCLUSIONS

Small-worldness and network efficiency were significantly reduced after stroke, whereas RAGT decreased characteristic path length and promoted normalization of the whole-brain network, and this change was associated with improvement in walking function. Our findings reveal the mechanism by which RAGT regulates network reorganization and neuroplasticity after stroke.

Sleep-like cortical dynamics during wakefulness and their network effects following brain injury

By connecting old and recent notions, different spatial scales, and research domains, we introduce a novel framework on the consequences of brain injury focusing on a key role of slow waves. We argue that the long-standing finding of EEG slow waves after brain injury reflects the intrusion of sleep-like cortical dynamics during wakefulness; we illustrate how these dynamics are generated and how they can lead to functional network disruption and behavioral impairment. Finally, we outline a scenario whereby post-injury slow waves can be modulated to reawaken parts of the brain that have fallen asleep to optimize rehabilitation strategies and promote recovery.

Cerebellar Atrophy and Language Processing in Chronic Left-Hemisphere Stroke

Chronic stroke results in significant downstream changes at connected cortical sites. However, less is known about the impact of cortical stroke on cerebellar structure. Here, we examined the relationship between chronic stroke, cerebellar volume, cerebellar symmetry, language impairment, and treatment trajectories in a large cohort (N = 249) of chronic left hemisphere (LH) stroke patients with aphasia, using a healthy aging cohort (N = 244) as control data. Cerebellar gray matter volume was significantly reduced in chronic LH stroke relative to healthy control brains. Within the chronic LH stroke group, we observed a robust relationship between cerebellar volume, lesion size, and days post-stroke. Notably, the extent of cerebellar atrophy in chronic LH patients, particularly in the contralesional (right) cerebellar gray matter, explained significant variability in post-stroke aphasia severity, as measured by the Western Aphasia Battery-Revised, above and beyond traditional considerations such as cortical lesion size, days post-stroke, and demographic measures (age, race, sex). In a subset of participants that took part in language treatment studies, greater cerebellar gray matter volume was associated with greater treatment gains. These data support the importance of considering both cerebellar volume and symmetry in models of post-stroke aphasia severity and recovery.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

Maternal deprivation causes CaMKII downregulation and modulates glutamate, norepinephrine and serotonin in limbic brain areas in a rat model of single prolonged stress

BACKGROUND

Early life stress is a major risk factor for later development of psychiatric disorders, including post-traumatic stress disorder (PTSD). An intricate relationship exists between various neurotransmitters (such as glutamate, norepinephrine or serotonin), calcium/calmodulin-dependent protein kinase II (CaMKII), as an important regulator of glutamatergic synaptic function, and PTSD. Here, we developed a double-hit model to investigate the interaction of maternal deprivation (MD) as an early life stress model and single prolonged stress (SPS) as a PTSD model at the behavioral and molecular levels.

METHODS

Male Wistar rats exposed to these stress paradigms were subjected to a comprehensive behavioral analysis. In hippocampal synaptosomes we investigated neurotransmitter release and glutamate concentration. The expression of CaMKII and the content of monoamines were determined in selected brain regions. Brain-derived neurotrophic factor (BDNF) mRNA was quantified by radioactive in situ hybridization.

RESULTS

We report a distinct behavioral phenotype in the double-hit group. Double-hit and SPS groups had decreased hippocampal presynaptic glutamatergic function. In hippocampus, double-hit stress caused a decrease in autophosphorylation of CaMKII. In prefrontal cortex, both SPS and double-hit stress had a similar effect on CaMKII autophosphorylation. Double-hit stress, rather than SPS, affected the norepinephrine and serotonin levels in prefrontal cortex, and suppressed BDNF gene expression in prefrontal cortex and hippocampus.

LIMITATIONS

The study was conducted in male rats only. The affected brain regions cannot be restricted to hippocampus, prefrontal cortex and amygdala.

CONCLUSION

Double-hit stress caused more pronounced and distinct behavioral, molecular and functional changes, compared to MD or SPS alone.

Influence of Transcutaneous Vagus Nerve Stimulation on Motor Planning: A Resting-State and Task-State EEG Study

Transcutaneous vagus nerve stimulation (tVNS) shows a potential regulatory role for motor planning. Still, existing research mainly focuses on behavioral studies, and the neural modulation mechanism needs to be clarified. Therefore, we designed a multi-condition (active or sham, pre or under, difficult or easy, left-hand or right-hand) motor planning experiment to explore the effect of online tVNS (i.e., tVNS and tasks synchronized). Twenty-eight subjects were recruited and randomly assigned to active and sham groups. Both groups performed the same tasks in the experiment and separately collected task-state EEG and 5-min eye-open resting-state EEG. The results showed that the changes in event-related potential (ERP) and movement-related cortical potential (MRCP) amplitudes were more significant for the left-hand difficult task (LD) under active-tVNS. According to the power spectrum results, active-tVNS significantly modulated the activities of the contralateral motor cortex at beta and gamma bands in the resting state. The functional connectivity based on partial directed coherence (PDC) showed significant changes in the parietal lobe after active-tVNS. These findings suggest that tVNS is a promising way to improve motor planning ability.

Increased interhemispheric functional connectivity after right anodal tDCS in chronic non-fluent aphasia: preliminary findings

Introduction

Anodal transcranial Direct Current Stimulation (tDCS) is a non-invasive, low-cost and environment-friendly brain neuromodulation technique that increases cortical excitability. In post-stroke aphasia, the role of the right hemisphere in language recovery remains debated. In this preliminary study, we aimed to investigate the efficacy of excitatory tDCS on the right hemisphere in chronic aphasic patients.

Methods

We applied anodal tDCS to the right homologous region of Broca's area in four chronic aphasic patients while performing a one-month naming rehabilitation treatment. Longitudinal data on language assessment and naming performance were collected. Resting-state fMRI images were acquired before and after treatment to measure changes in functional connectivity.

Results

Results showed enhanced positive functional connectivity of the right Broca homologous with the left middle frontal and middle temporal gyri. Every patient showed improvements in language functions, but no major changes in naming performance.

Conclusion

These preliminary findings suggest that tDCS applied over the unaffected hemisphere may result in longitudinal inter-hemispheric functional neuroplastic changes that could specifically improve language recovery and could potentially be included in therapeutic neurorehabilitative plans.

Neural oscillations after acute large artery atherosclerotic cerebral infarction during resting state and sleep spindles

Electroencephalogram-microstate analysis was conducted using low-resolution electromagnetic tomography (LORETA)-KEY to evaluate dynamic brain network changes in patients with acute large artery atherosclerotic cerebral infarction (LAACI) during the rest and sleep stages. This study included 35 age- and sex-matched healthy controls and 34 patients with acute LAACI. Each participant performed a 3-h, 19-channel video electroencephalogram test. Subsequently, 20 epochs of 2-s sleep spindles during stage N2 sleep and five epochs of 10-s electroencephalogram data in the resting state for each participant were obtained. In both the resting state and sleep spindles, patients with LAACI displayed altered neural oscillations. The parameters of microstate A (coverage, occurrence, and duration) increased during the resting state in the patients with LAACI compared with healthy controls. The coverage and occurrence of microstate B and D were reduced in the LAACI group compared with the healthy controls (p < 0.05). Moreover, during sleep spindles, the duration of microstate A and the transition probability from microstate A and B to C decreased, but the coverage of microstate B and the transition rate from microstate B to D increased (p < 0.05) in the LAACI group compared with the healthy controls. These results enable better understanding of how neural oscillations are modified in patients with LAACI during the resting state and sleep spindles. Following LAACI, the dynamic brain network undergoes changes during sleep spindles and the resting state. Continued long-term investigations are required to determine how well these changes in brain dynamics reflect the clinical characteristics of patients with LAACI.

Brain Topological Reorganization Associated with Visual Neglect After Stroke

Background/Purpose: To identify brain hubs that are behaviorally relevant for neglect after stroke as well as to characterize their functional architecture of communication. Methods: Twenty acute right hemisphere damaged patients underwent neuropsychological and resting-state functional magnetic resonance imaging sessions. Spatial neglect was assessed by means of the Center of Cancellation on the Bells Cancellation Test. For each patient, resting-state functional connectivity matrices were derived by adopting a brain parcellation scheme consisting of 153 nodes. For every node, we extracted its betweenness centrality (BC) defined as the portion of all shortest paths in the connectome involving such node. Then, neglect hubs were identified as those regions showing a high correlation between their BC and neglect scores. Results: A first set of neglect hubs was identified in multiple systems including dorsal attention and ventral attention, default mode, and frontoparietal executive-control networks within the damaged hemisphere as well as in the posterior and anterior cingulate cortex. Such cortical regions exhibited a loss of BC and increased (i.e., less efficient) weighted shortest path length (WSPL) related to severe neglect. Conversely, a second group of neglect hubs found in visual and motor networks, in the undamaged hemisphere, exhibited a pathological increase of BC and reduction of WSPL associated with severe neglect. Conclusion: The topological reorganization of the brain in neglect patients might reflect a maladaptive shift in processing spatial information from higher level associative-control systems to lower level visual and sensory-motor processing areas after a right hemisphere lesion.

Neuroimaging evidence supporting a dual-network architecture for the control of visuospatial attention in the human brain: a mini review

Neuroimaging studies conducted in the last three decades have distinguished two frontoparietal networks responsible for the control of visuospatial attention. The present review summarizes recent findings on the neurophysiological mechanisms implemented in both networks and describes the evolution from a model centered on the distinction between top-down and bottom-up attention to a model that emphasizes the dynamic interplay between the two networks based on attentional demands. The role of the dorsal attention network (DAN) in attentional orienting, by boosting behavioral performance, has been investigated with multiple experimental approaches. This research effort allowed us to trace a distinction between DAN regions involved in shifting vs. maintenance of attention, gather evidence for the modulatory influence exerted by the DAN over sensory cortices, and identify the electrophysiological correlates of the orienting function. Simultaneously, other studies have contributed to reframing our understanding of the functions of the ventral attention network (VAN) and its relevance for behavior. The VAN is not simply involved in bottom-up attentional capture but interacts with the DAN during reorienting to behaviorally relevant targets, exhibiting a general resetting function. Further studies have confirmed the selective rightward asymmetry of the VAN, proposed a functional dissociation along the anteroposterior axis, and suggested hypotheses about its emergence during the evolution of the primate brain. Finally, novel models of network interactions explain the expression of complex attentional functions and the emergence and restorations of symptoms characterizing unilateral spatial neglect. These latter studies emphasize the importance of considering patterns of network interactions for understanding the consequences of brain lesions.

Recent advances in treatment of spatial neglect: networks and neuropsychology

INTRODUCTION

Spatial neglect remains an underdiagnosed and undertreated consequence of stroke that imposes significant disability. A growing appreciation of brain networks involved in spatial cognition is helping us to develop a mechanistic understanding of different therapies under development.

AREAS COVERED

This review focuses on neuromodulation of brain networks for the treatment of spatial neglect after stroke, using evidence-based approaches including 1) Cognitive strategies that are more likely to impact frontal lobe executive function networks; 2) Visuomotor adaptation, which may depend on the integrity of parietal and parieto- and subcortical-frontal connections and the presence of a particular subtype of neglect labeled Aiming neglect; 3) Non-invasive brain stimulation that may modulate relative levels of activity of the two hemispheres and depend on corpus callosum connectivity; and 4) Pharmacological modulation that may exert its effect primarily via right-lateralized networks more closely involved in arousal.

EXPERT OPINION

Despite promising results from individual studies, significant methodological heterogeneity between trials weakened conclusions drawn from meta-analyses. Improved classification of spatial neglect subtypes will benefit research and clinical care. Understanding the brain network mechanisms of different treatments and different types of spatial neglect will make possible a precision medicine treatment approach.

Causal Associations between Functional/Structural Connectivity and Stroke: A Bidirectional Mendelian Randomization Study

Disruption of brain resting-state networks (RSNs) is known to be related to stroke exposure, but determining causality can be difficult in epidemiological studies. We used data on genetic variants associated with the levels of functional (FC) and structural connectivity (SC) within 7 RSNs identified from a genome-wide association study (GWAS) meta-analysis among 24,336 European ancestries. The data for stroke and its subtypes were obtained from the MEGASTROKE consortium, including up to 520,000 participants. We conducted a two-sample bidirectional Mendelian randomization (MR) study to investigate the causality relationship between FC and SC within 7 RSNs and stroke and its subtypes. The results showed that lower global mean FC and limbic network FC were associated with a higher risk of any ischemic stroke and small vessel stroke separately. Moreover, ventral attention network FC and default mode network SC have a positive causal relationship with the risk of small vessel stroke and large artery stroke, respectively. In the inverse MR analysis, any stroke and large artery stroke were causally related to dorsal attention network FC and somatomotor FC, respectively. The present study provides genetic support that levels of FC or SC within different RSNs have contrasting causal effects on stroke and its subtypes. Moreover, there is a combination of injury and compensatory physiological processes in brain RSNs following a stroke. Further studies are necessary to validate our results and explain the physiological mechanisms.

High-Speed Time-Domain Diffuse Optical Tomography with a Sensitivity Equation-based Neural Network

Steady progress in time-domain diffuse optical tomography (TD-DOT) technology is allowing for the first time the design of low-cost, compact, and high-performance systems, thus promising more widespread clinical TD-DOT use, such as for recording brain tissue hemodynamics. TD-DOT is known to provide more accurate values of optical properties and physiological parameters compared to its frequency-domain or steady-state counterparts. However, achieving high temporal resolution is still difficult, as solving the inverse problem is computationally demanding, leading to relatively long reconstruction times. The runtime is further compromised by processes that involve 'nontrivial' empirical tuning of reconstruction parameters, which increases complexity and inefficiency. To address these challenges, we present a new reconstruction algorithm that combines a deep-learning approach with our previously introduced sensitivity-equation-based, non-iterative sparse optical reconstruction (SENSOR) code. The new algorithm (called SENSOR-NET) unfolds the iterations of SENSOR into a deep neural network. In this way, we achieve high-resolution sparse reconstruction using only learned parameters, thus eliminating the need to tune parameters prior to reconstruction empirically. Furthermore, once trained, the reconstruction time is not dependent on the number of sources or wavelengths used. We validate our method with numerical and experimental data and show that accurate reconstructions with 1 mm spatial resolution can be obtained in under 20 milliseconds regardless of the number of sources used in the setup. This opens the door for real-time brain monitoring and other high-speed DOT applications.

The analysis of brain functional connectivity of post-stroke cognitive impairment patients: an fNIRS study

Background

Post-stroke cognitive impairment (PSCI) is a considerable risk factor for developing dementia and reoccurrence of stroke. Understanding the neural mechanisms of cognitive impairment after stroke can facilitate early identification and intervention.

Objectives

Using functional near-infrared spectroscopy (fNRIS), the present study aimed to examine whether resting-state functional connectivity (FC) of brain networks differs in patients with PSCI, patients with Non-PSCI (NPSCI), and healthy controls (HCs), and whether these features could be used for clinical diagnosis of PSCI.

Methods

The present study recruited 16 HCs and 32 post-stroke patients. Based on the diagnostic criteria of PSCI, post-stroke patients were divided to the PSCI or NPSCI group. All participants underwent a 6-min resting-state fNRIS test to measure the hemodynamic responses from regions of interests (ROIs) that were primarily distributed in the prefrontal, somatosensory, and motor cortices.

Results

The results showed that, when compared to the HC group, the PSCI group exhibited significantly decreased interhemispheric FC and intra-right hemispheric FC. ROI analyses showed significantly decreased FC among the regions of somatosensory cortex, dorsolateral prefrontal cortex, and medial prefrontal cortex for the PSCI group than for the HC group. However, no significant difference was found in the FC between the PSCI and the NPSCI groups.

Conclusion

Our findings provide evidence for compromised interhemispheric and intra-right hemispheric functional connectivity in patients with PSCI, suggesting that fNIRS is a promising approach to investigate the effects of stroke on functional connectivity of brain networks.

Motor and Cognitive Modulation of a Single Session of Transcutaneous Auricular Vagus Nerve Stimulation in Post Stroke Patients: A Pilot Study

Objective: The aim of the present study is to explore whether a single session of transcutaneous Vagus Nerve Stimulation (tVNS) can enhance the ipsilesional, and contralesional upper limb motor functions as well as cognitive functions in stroke patients. The effects of the stimulation were evaluated through two different tasks: the box and blocks test (BB), indexing manual dexterity, and the Go/No-go task, a visuomotor paradigm used to assess both motor readiness and response inhibition. Tests were administered without tVNS, during tVNS and during sham tVNS. Results: The BB showed a statistical difference for both contralesional side (p = 0.05) between Basal-Real condition (p = 0.042) and ipsilesional side (p = 0.001) between Basal-Real (p = 0.008) and for Real-Sham (p = 0.005). Any statistical difference was found for the mean latencies in the three conditions of the Go/No-go test. Conclusion: A single session of tVNS seems to improve upper limb motor functions but not cognitive functions in post-stroke patients, despite a positive trend was detected.

Brain Connectivity Signature Extractions from TMS Invoked EEGs

(1) Background: The correlations between brain connectivity abnormality and psychiatric disorders have been continuously investigated and progressively recognized. Brain connectivity signatures are becoming exceedingly useful for identifying patients, monitoring mental health disorders, and treatment. By using electroencephalography (EEG)-based cortical source localization along with energy landscape analysis techniques, we can statistically analyze transcranial magnetic stimulation (TMS)-invoked EEG signals, for obtaining connectivity among different brain regions at a high spatiotemporal resolution. (2) Methods: In this study, we analyze EEG-based source localized alpha wave activity in response to TMS administered to three locations, namely, the left motor cortex (49 subjects), left prefrontal cortex (27 subjects), and the posterior cerebellum, or vermis (27 subjects) by using energy landscape analysis techniques to uncover connectivity signatures. We then perform two sample t-tests and use the (5 × 10^-5) Bonferroni corrected p-valued cases for reporting six reliably stable signatures. (3) Results: Vermis stimulation invoked the highest number of connectivity signatures and the left motor cortex stimulation invoked a sensorimotor network state. In total, six out of 29 reliable, stable connectivity signatures are found and discussed. (4) Conclusions: We extend previous findings to localized cortical connectivity signatures for medical applications that serve as a baseline for future dense electrode studies.

Importance of Retesting for the Final Diagnosis of Post-Stroke Cognitive Impairment

Background and Objectives: Post-stroke cognitive impairment (PSCI) has been defined as all problems in cognitive function that occur following a stroke. Studies published thus far on the prevalence of PSCI and post-stroke dementia (PSD) have shown conflicting estimates. The aim of this study was screening for cognitive impairment (CogI) in patients with an ischaemic stroke and finding the relationship between CogI (and its changes) and cardiovascular risk factors and imaging procedures-CT/MRI. Materials and Methods: We prospectively included patients with an ischaemic stroke admitted in the period from October 2019 to May 2022. In this period, 1328 patients were admitted, 305 of whom met the established inclusion criteria and underwent an examination of cognitive functions using the Montreal Cognitive Assessment (MoCA). Of these, 50 patients appeared for the control examination after 6 months. Results: In the retested group, CogI at discharge was diagnosed in 37 patients (74%). In follow-up testing after 6 months, CogI was present in 30 patients (60%). Only arterial hypertension (OR: 15; 95% CI; Pearson r: 0.001), lower education level (less than 13 years) (OR: 9.7; 95% CI 2.0-48.5; Pearson r: 0.002), and higher age were significantly associated with CogI after stroke. Conclusions: We established the prevalence of CogI and its course after 6 months in a well-defined group of patients after a mild ischaemic stroke (mean NIHSS: 2 and mean mRS: 1 at the discharge). Our results show that the prevalence of CogI after an ischaemic stroke at discharge is relatively high (74%), and it tends to be a spontaneous reduction. Cognitive functions were changed in 35% of patients. The definition of PSCI was completed in only 24% of individuals. Only an examination several months after a stroke can give us more accurate information about the true prevalence of persistent CogI after a stroke.

Case report: Multiple disconnection patterns revealed by a multi-modal analysis explained behavior after a focal frontal damage

Introduction

There is overwhelming evidence that focal lesions cause structural, metabolic, functional, and electrical disconnection of regions directly and indirectly connected with the site of injury. Unfortunately, methods to study disconnection (positron emission tomography, structural and functional magnetic resonance imaging, electroencephalography) have been applied primarily in isolation without capturing their interaction. Moreover, multi-modal imaging studies applied to focal lesions are rare.

Case report

We analyzed with a multi-modal approach the case of a patient presenting with borderline cognitive deficits across multiple domains and recurrent delirium. A post-surgical focal frontal lesion was evident based on the brain anatomical MRI. However, we were able to acquire also simultaneous MRI (structural and functional) and [18F]FDG using a hybrid PET/MRI scan along with EEG recordings. Despite the focality of the primary anatomical lesion, structural disconnection in the white matter bundles extended far beyond the lesion and showed a topographical match with the cortical glucose hypometabolism seen both locally and remotely, in posterior cortices. Similarly, a right frontal delta activity near/at the region of structural damage was associated with alterations of distant occipital alpha power. Moreover, functional MRI revealed even more widespread local and distant synchronization, involving also regions not affected by the structural/metabolic/electrical impairment.

Conclusion

Overall, this exemplary multi-modal case study illustrates how a focal brain lesion causes a multiplicity of disconnection and functional impairments that extend beyond the borders of the anatomical irrecoverable damage. These effects were relevant to explain patient’s behavior and may be potential targets of neuro-modulation strategies.

Changes in Resting-State Neural Activity and Nerve Fibres in Ischaemic Stroke Patients with Hemiplegia

Many neuroimaging studies have reported that stroke induces abnormal brain activity. However, little is known about resting-state networks (RSNs) and the corresponding white matter changes in stroke patients with hemiplegia. Here, we utilized functional magnetic resonance imaging (fMRI) to measure neural activity and related fibre tracts in 14 ischaemic stroke patients with hemiplegia and 12 healthy controls. Fractional amplitude of low-frequency fluctuations (fALFF) calculation and correlation analyses were used to assess the relationship between regional neural activity and movement scores. Tractography was performed using diffusion tensor imaging (DTI) data to analyse the fibres passing through the regions of interest. Compared with controls, stroke patients showed abnormal functional connectivity (FC) between some brain regions in the RSNs. The fALFF was increased in the contralesional parietal lobe, with the regional fALFF being correlated with behavioural scores in stroke patients. Additionally, the passage of fibres across regions with reduced FC in the RSNs was increased in stroke patients. This study suggests that structural remodelling of functionally relevant white matter tracts is probably an adaptive response that compensates for injury to the brain.

Connectomics underlying motor functional outcomes in the acute period following stroke

Objective

Stroke remains the number one cause of morbidity in many developing countries, and while effective neurorehabilitation strategies exist, it remains difficult to predict the individual trajectories of patients in the acute period, making personalized therapies difficult. Sophisticated and data-driven methods are necessary to identify markers of functional outcomes.

Methods

Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 79 patients following stroke. Sixteen models were constructed to predict performance across six tests of motor impairment, spasticity, and activities of daily living, using either whole-brain structural or functional connectivity. Feature importance analysis was also performed to identify brain regions and networks associated with performance in each test.

Results

The area under the receiver operating characteristic curve ranged from 0.650 to 0.868. Models utilizing functional connectivity tended to have better performance than those utilizing structural connectivity. The Dorsal and Ventral Attention Networks were among the top three features in several structural and functional models, while the Language and Accessory Language Networks were most commonly implicated in structural models.

Conclusions

Our study highlights the potential of machine learning methods combined with connectivity analysis in predicting outcomes in neurorehabilitation and disentangling the neural correlates of functional impairments, though further longitudinal studies are necessary.

Dynamic brain states in spatial neglect after stroke

Previous studies indicated that spatial neglect is characterized by widespread alteration of resting-state functional connectivity and changes in the functional topology of large-scale brain systems. However, whether such network modulations exhibit temporal fluctuations related to spatial neglect is still largely unknown. This study investigated the association between brain states and spatial neglect after the onset of focal brain lesions. A cohort of right-hemisphere stroke patients (n = 20) underwent neuropsychological assessment of neglect as well as structural and resting-state functional MRI sessions within 2 weeks from stroke onset. Brain states were identified using dynamic functional connectivity as estimated by the sliding window approach followed by clustering of seven resting state networks. The networks included visual, dorsal attention, sensorimotor, cingulo-opercular, language, fronto-parietal, and default mode networks. The analyses on the whole cohort of patients, i.e., with and without neglect, identified two distinct brain states characterized by different degrees of brain modularity and system segregation. Compared to non-neglect patients, neglect subjects spent more time in less modular and segregated state characterized by weak intra-network coupling and sparse inter-network interactions. By contrast, patients without neglect dwelt mainly in more modular and segregated states, which displayed robust intra-network connectivity and anti-correlations among task-positive and task-negative systems. Notably, correlational analyses indicated that patients exhibiting more severe neglect spent more time and dwelt more often in the state featuring low brain modularity and system segregation and vice versa. Furthermore, separate analyses on neglect vs. non-neglect patients yielded two distinct brain states for each sub-cohort. A state featuring widespread strong connections within and between networks and low modularity and system segregation was detected only in the neglect group. Such a connectivity profile blurred the distinction among functional systems. Finally, a state exhibiting a clear separation among modules with strong positive intra-network and negative inter-network connectivity was found only in the non-neglect group. Overall, our results indicate that stroke yielding spatial attention deficits affects the time-varying properties of functional interactions among large-scale networks. These findings provide further insights into the pathophysiology of spatial neglect and its treatment.

Improvement of cognition across a decade after stroke correlates with the integrity of functional brain networks

BACKGROUND AND OBJECTIVE

We recently reported improvements of working memory across 10 years post stroke among middle-aged individuals. However, the mechanisms underlying working-memory recovery are largely unknown. This study investigated the associations between long-term improvement of working memory and resting-state functional connectivity in two frontoparietal networks: the frontoparietal network and the dorsal attention network.

METHODS

Working memory was repeatedly assessed by the Digit Span Backwards task in 21 persons, within 1 year after stroke onset and again 10 years post stroke onset. Brain functional connectivity was examined by resting state functional magnetic resonance imaging at the 10-year follow-up.

RESULTS

A significant improvement of working memory was found among 21 persons after stroke (median age = 64) at the 10-year follow-up compared to the within-one-year assessment. The magnitude of performance improvement on the Digit Span Backwards task was significantly positively correlated with stronger brain connectivity in the frontoparietal network (r = 0.51, p = 0.018) measured at the 10-year follow-up only. A similar association was observed in the dorsal attention network (r = 0.43, p = 0.052) but not in a visual network (r = -0.17, p = 0.46) that served as a control network. The association between functional connectivity within the above-mentioned networks and Digit Span Backwards scores at 10-year after stroke was in the same direction but did not reach significance.

CONCLUSIONS

The present work relate stronger long-term performance improvement on the Digit Span Backwards task with higher integrity of frontoparietal network connectivity.

Multimodal and multidomain lesion network mapping enhances prediction of sensorimotor behavior in stroke patients

Beyond the characteristics of a brain lesion, such as its etiology, size or location, lesion network mapping (LNM) has shown that similar symptoms after a lesion reflects similar dis-connectivity patterns, thereby linking symptoms to brain networks. Here, we extend LNM by using a multimodal strategy, combining functional and structural networks from 1000 healthy participants in the Human Connectome Project. We apply multimodal LNM to a cohort of 54 stroke patients with the aim of predicting sensorimotor behavior, as assessed through a combination of motor and sensory tests. Results are two-fold. First, multimodal LNM reveals that the functional modality contributes more than the structural one in the prediction of sensorimotor behavior. Second, when looking at each modality individually, the performance of the structural networks strongly depended on whether sensorimotor performance was corrected for lesion size, thereby eliminating the effect that larger lesions generally produce more severe sensorimotor impairment. In contrast, functional networks provided similar performance regardless of whether or not the effect of lesion size was removed. Overall, these results support the extension of LNM to its multimodal form, highlighting the synergistic and additive nature of different types of network modalities, and their corresponding influence on behavioral performance after brain injury.

Neuroimaging biomarkers of cognitive recovery after ischemic stroke

Post-stroke cognitive impairment affects more than one-third of patients after an ischemic stroke (IS). Identifying markers of potential cognitive recovery after ischemic stroke can guide patients' selection for treatments, enrollment in clinical trials, and cognitive rehabilitation methods to restore cognitive abilities in post-stroke patients. Despite the burden of post-stroke cognitive impairment, biomarkers of cognitive recovery are an understudied area of research. This narrative review summarizes and critically reviews the current literature on the use and utility of neuroimaging as a predictive biomarker of cognitive recovery after IS. Most studies included in this review utilized structural Magnetic Resonance Imaging (MRI) to predict cognitive recovery after IS; these studies highlighted baseline markers of cerebral small vessel disease and cortical atrophy as predictors of cognitive recovery. Functional Magnetic Resonance Imaging (fMRI) using resting-state functional connectivity and Diffusion Imaging are potential biomarkers of cognitive recovery after IS, although more precise predictive tools are needed. Comparison of these studies is limited by heterogeneity in cognitive assessments. For all modalities, current findings need replication in larger samples. Although no neuroimaging tool is ready for use as a biomarker at this stage, these studies suggest a clinically meaningful role for neuroimaging in predicting post-stroke cognitive recovery.

Low intensity repetitive transcranial magnetic stimulation modulates brain-wide functional connectivity to promote anti-correlated c-Fos expression

Repetitive transcranial magnetic stimulation (rTMS) induces action potentials to induce plastic changes in the brain with increasing evidence for the therapeutic importance of brain-wide functional network effects of rTMS; however, the influence of sub-action potential threshold (low-intensity; LI-) rTMS on neuronal activity is largely unknown. We investigated whether LI-rTMS modulates neuronal activity and functional connectivity and also specifically assessed modulation of parvalbumin interneuron activity. We conducted a brain-wide analysis of c-Fos, a marker for neuronal activity, in mice that received LI-rTMS to visual cortex. Mice received single or multiple sessions of excitatory 10 Hz LI-rTMS with custom rodent coils or were sham controls. We assessed changes to c-Fos positive cell densities and c-Fos/parvalbumin co-expression. Peak c-Fos expression corresponded with activity during rTMS. We also assessed functional connectivity changes using brain-wide c-Fos-based network analysis. LI-rTMS modulated c-Fos expression in cortical and subcortical regions. c-Fos density changes were most prevalent with acute stimulation, however chronic stimulation decreased parvalbumin interneuron activity, most prominently in the amygdala and striatum. LI-rTMS also increased anti-correlated functional connectivity, with the most prominent effects also in the amygdala and striatum following chronic stimulation. LI-rTMS induces changes in c-Fos expression that suggest modulation of neuronal activity and functional connectivity throughout the brain. Our results suggest that LI-rTMS promotes anticorrelated functional connectivity, possibly due to decreased parvalbumin interneuron activation induced by chronic stimulation. These changes may underpin therapeutic rTMS effects, therefore modulation of subcortical activity supports rTMS for treatment of disorders involving subcortical dysregulation.

Pathological changes of brain oscillations following ischemic stroke

Brain oscillations recorded in the extracellular space are among the most important aspects of neurophysiology data reflecting the activity and function of neurons in a population or a network. The signal strength and patterns of brain oscillations can be powerful biomarkers used for disease detection and prediction of the recovery of function. Electrophysiological signals can also serve as an index for many cutting-edge technologies aiming to interface between the nervous system and neuroprosthetic devices and to monitor the efficacy of boosting neural activity. In this review, we provided an overview of the basic knowledge regarding local field potential, electro- or magneto- encephalography signals, and their biological relevance, followed by a summary of the findings reported in various clinical and experimental stroke studies. We reviewed evidence of stroke-induced changes in hippocampal oscillations and disruption of communication between brain networks as potential mechanisms underlying post-stroke cognitive dysfunction. We also discussed the promise of brain stimulation in promoting post stroke functional recovery via restoring neural activity and enhancing brain plasticity.

Segregation over time in functional networks in prefrontal cortex for individuals suffering from pathological fatigue after traumatic brain injury

Pathological fatigue is present when fatigue is perceived to continually interfere with everyday life. Pathological fatigue has been linked with a dysfunction in the cortico-striatal-thalamic circuits. Previous studies have investigated measures of functional connectivity, such as modularity to quantify levels of segregation. However, previous results have shown both increases and decreases in segregation for pathological fatigue. There are multiple factors why previous studies might have differing results, including: (i) Does the functional connectivity of patients with pathological fatigue display more segregation or integration compared to healthy controls? (ii) Do network properties differ depending on whether patients with pathological fatigue perform a task compared to periods of rest? (iii) Are the brain networks of patients with pathological fatigue and healthy controls differently affected by prolonged cognitive activity? We recruited individuals suffering from pathological fatigue after mild traumatic brain injury (n = 20) and age-matched healthy controls (n = 20) to perform cognitive tasks for 2.5 h. We used functional near-infrared spectroscopy (fNIRS) to assess hemodynamic changes in the frontal cortex. The participants had a resting state session before and after the cognitive test session. Cognitive testing included the Digit Symbol Coding test at the beginning and the end of the procedure to measure processing speed. We conducted an exploratory network analysis on these resting state and Digit Symbol Coding sessions with no a priori hypothesis relating to how patients and controls differ in their functional networks since previous research has found results in both directions. Our result showed a Group vs. Time interaction (p = 0.026, η_p² = 0.137), with a post hoc test revealing that the TBI patients developed higher modularity toward the end of the cognitive test session. This work helps to identify how functional networks differ under pathological fatigue compared to healthy controls. Further, it shows how the functional networks dynamically change over time as the patient performs tasks over a time scale that affect their fatigue level.

Homotopic contralesional excitation suppresses spontaneous circuit repair and global network reconnections following ischemic stroke

Understanding circuit-level manipulations that affect the brain's capacity for plasticity will inform the design of targeted interventions that enhance recovery after stroke. Following stroke, increased contralesional activity (e.g. use of the unaffected limb) can negatively influence recovery, but it is unknown which specific neural connections exert this influence, and to what extent increased contralesional activity affects systems- and molecular-level biomarkers of recovery. Here, we combine optogenetic photostimulation with optical intrinsic signal imaging to examine how contralesional excitatory activity affects cortical remodeling after stroke in mice. Following photothrombosis of left primary somatosensory forepaw (S1FP) cortex, mice either recovered spontaneously or received chronic optogenetic excitation of right S1FP over the course of 4 weeks. Contralesional excitation suppressed perilesional S1FP remapping and was associated with abnormal patterns of stimulus-evoked activity in the unaffected limb. This maneuver also prevented the restoration of resting-state functional connectivity (RSFC) within the S1FP network, RSFC in several networks functionally distinct from somatomotor regions, and resulted in persistent limb-use asymmetry. In stimulated mice, perilesional tissue exhibited transcriptional changes in several genes relevant for recovery. Our results suggest that contralesional excitation impedes local and global circuit reconnection through suppression of cortical activity and several neuroplasticity-related genes after stroke, and highlight the importance of site selection for targeted therapeutic interventions after focal ischemia.

The Amnestic Syndrome of Posterior Cerebral Artery Infarction

OBJECTIVE

Little is known about the character and underlying lesions of ischemic amnesia. We therefore studied episodic memory functions and brain lesions in 84 patients with acute ischemic infarcts in the supply territory of the posterior cerebral artery (PCA). We also aimed to learn how the neural memory systems are organized.

METHODS

Standard neuropsychological tests were used to assess verbal and figural memory. Patients were split in memory-impaired and memory-intact. Lesions were demarcated, normalized and anatomically labeled, using standard mapping procedures.

RESULTS

Of the 84 patients more than 80% had an amnestic syndrome, mostly with combined, less often with figural or verbal memory impairment. Amnesia in subjects with left hemispheric lesions was more frequent and more severe, with significantly lower scores on the verbal memory test. Normal performance or figural amnesia were prevalent after right hemispheric lesions. However, no amnesia subtype was strictly tied to left- or right-sided brain damage. Hippocampal and thalamic lesions were common, but 30% of lesions were extrahippocampal located in the ventral occipito-temporal cortex and long occipital white matter tracts. Most amnestic-patients lacked awareness for their memory impairment.

CONCLUSIONS

Memory impairment is a key clinical manifestation of acute PCA stroke. Amnesia is more frequent and more severe after left stroke, suggesting a left hemisphere dominance of the two memory systems. Domain specific memory appears not strictly lateralized, since deficits in verbal and figural memory were found after lesions of both sides. Extrahippocampal lesions may also cause memory impairment.

Artificially-reconstructed brain images with stroke lesions from non-imaging data: modeling in categorized patients based on lesion occurrence and sparsity

Brain imaging is necessary for understanding disease symptoms, including stroke. However, frequent imaging procedures encounter practical limitations. Estimating the brain information (e.g., lesions) without imaging sessions is beneficial for this scenario. Prospective estimating variables are non-imaging data collected from standard tests. Therefore, the current study aims to examine the variable feasibility for modelling lesion locations. Heterogeneous variables were employed in the multivariate logistic regression. Furthermore, patients were categorized (i.e., unsupervised clustering through k-means method) by the charasteristics of lesion occurrence (i.e., ratio between the lesioned and total regions) and sparsity (i.e., density measure of lesion occurrences across regions). Considering those charasteristics in models improved estimation performances. Lesions (116 regions in Automated Anatomical Labeling) were adequately predicted (sensitivity: 80.0-87.5% in median). We confirmed that the usability of models was extendable to different resolution levels in the brain region of interest (e.g., lobes, hemispheres). Patients' charateristics (i.e., occurrence and sparsity) might also be explained by the non-imaging data as well. Advantages of the current approach can be experienced by any patients (i.e., with or without imaging sessions) in any clinical facilities (i.e., with or without imaging instrumentation).

Traumatic Brain Magnetic Resonance Imaging Feature Extraction Based on Variable Model Algorithm in Stroke Examination

The purpose of this study was to explore the diagnostic value of different sequence scanning of nonparametric variable model-based cranial magnetic resonance imaging (MRI) for ischemic stroke. A histogram analysis-based nonparametric variable model was proposed first, which was compared with the parametric deformation (PD) model and geometric deformation (GD) model. Then, 116 patients with acute ischemic stroke were selected as the research subjects. Routine MRI (T2WI, T1WI, FLAIR, DWI, SWI, and 3D TOF MRA) and MR SCALE-PWI were performed. The results showed that the nonparametric variable model algorithm was relatively complete in the actual segmentation results of MRI images, and the display clarity of lesions was better than PD and GD algorithms. The diagnostic sensitivity, specificity, and overall performance of the variable model algorithm were significantly higher than those of the other two algorithms (P < 0.05). According to ROC curve analysis, the AUC areas of DWI, SWI, 3D TOF MRA, and MR SCALE-PWI for the diagnosis of ischemic penumbra were 0.793, 0.825, 0.871, and 0.933, respectively. In summary, the segmentation results of MRI images by the nonparametric variable model based on histogram analysis were relatively complete, and the clarity of lesions was better than that of the traditional model. MRI images can effectively identify the occurrence of ischemic stroke. Moreover, MR SCALE-PWI had a good early identification effect on ischemic penumbra, which can reduce unnecessary treatment for patients.

Post-stroke outcomes predicted from multivariate lesion-behaviour and lesion network mapping

Clinicians and scientists alike have long sought to predict the course and severity of chronic post-stroke cognitive and motor outcomes, as the ability to do so would inform treatment and rehabilitation strategies. However, it remains difficult to make accurate predictions about chronic post-stroke outcomes due, in large part, to high inter-individual variability in recovery and a reliance on clinical heuristics rather than empirical methods. The neuroanatomical location of a stroke is a key variable associated with long-term outcomes, and because lesion location can be derived from routinely collected clinical neuroimaging data there is an opportunity to use this information to make empirically based predictions about post-stroke deficits. For example, lesion location can be compared to statistically weighted multivariate lesion-behaviour maps of neuroanatomical regions that, when damaged, are associated with specific deficits based on aggregated outcome data from large cohorts. Here, our goal was to evaluate whether we can leverage lesion-behaviour maps based on data from two large cohorts of individuals with focal brain lesions to make predictions of 12-month cognitive and motor outcomes in an independent sample of stroke patients. Further, we evaluated whether we could augment these predictions by estimating the structural and functional networks disrupted in association with each lesion-behaviour map through the use of structural and functional lesion network mapping, which use normative structural and functional connectivity data from neurologically healthy individuals to elucidate lesion-associated networks. We derived these brain network maps using the anatomical regions with the strongest association with impairment for each cognitive and motor outcome based on lesion-behaviour map results. These peak regional findings became the 'seeds' to generate networks, an approach that offers potentially greater precision compared to previously used single-lesion approaches. Next, in an independent sample, we quantified the overlap of each lesion location with the lesion-behaviour maps and structural and functional lesion network mapping and evaluated how much variance each could explain in 12-month behavioural outcomes using a latent growth curve statistical model. We found that each lesion-deficit mapping modality was able to predict a statistically significant amount of variance in cognitive and motor outcomes. Both structural and functional lesion network maps were able to predict variance in 12-month outcomes beyond lesion-behaviour mapping. Functional lesion network mapping performed best for the prediction of language deficits, and structural lesion network mapping performed best for the prediction of motor deficits. Altogether, these results support the notion that lesion location and lesion network mapping can be combined to improve the prediction of post-stroke deficits at 12-months.

Cognitive outcome is related to functional thalamo-cortical connectivity after pediatric stroke

The thalamus has complex connections with the cortex and is involved in various cognitive processes. Despite increasing interest in the thalamus and the underlying thalamo-cortical interaction, little is known about thalamo-cortical connections after pediatric arterial ischemic stroke. Therefore, the aim of this study was to investigate thalamo-cortical connections and their association with cognitive performance after arterial ischemic stroke.

Twenty patients in the chronic phase after pediatric arterial ischemic stroke (≥ 2 years after diagnosis, diagnosed &lt;16 years; aged 5–23 years, mean 15.1 years) and twenty healthy controls matched for age and sex were examined in a cross-sectional study design. Cognitive performance (selective attention, inhibition, working memory, and cognitive flexibility) was evaluated using standardized neuropsychological tests. Resting-state functional magnetic resonance imaging was used to examine functional thalamo-cortical connectivity. Lesion masks were integrated in the preprocessing pipeline to ensure that structurally damaged voxels did not influence functional connectivity analyses.

Cognitive performance (selective attention, inhibition and working memory) was significantly reduced in patients compared to controls. Network analyses revealed significantly lower thalamo-cortical connectivity for the motor, auditory, visual, default mode network, salience, left/right executive and dorsal attention network in patients compared to controls. Interestingly, analyses revealed as well higher thalamo-cortical connectivity in some subdivisions of the thalamus for the default mode network (medial nuclei), motor (lateral nuclei), dorsal attention (anterior nuclei), and the left executive network (posterior nuclei) in patients compared to controls. Increased and decreased thalamo-cortical connectivity strength within the same networks was, however, found in different thalamic sub-divisions. Thus, alterations in thalamo-cortical connectivity strength after pediatric stroke seem to point in both directions, with stronger as well as weaker thalamo-cortical connectivity in patients compared to controls. Multivariate linear regression, with lesion size and age as covariates, revealed significant correlations between cognitive performance (selective attention, inhibition, and working memory) and the strength of thalamo-cortical connectivity in the motor, auditory, visual, default mode network, posterior default mode network, salience, left/right executive, and dorsal attention network after childhood stroke.

Our data suggest that the interaction between different sub-nuclei of the thalamus and several cortical networks relates to post-stroke cognition. The variability in cognitive outcomes after pediatric stroke might partly be explained by functional thalamo-cortical connectivity strength.

Neuroimaging Parameters Are Not Associated With Chronic Post-stroke Fatigue in Young Stroke Patients

Introduction

Post-stroke fatigue is frequently present in young adults, but its underlying mechanism is still unclear. The aim of the study was to investigate the association between lesion location, network efficiency and chronic post-stroke fatigue based on voxel-based lesion-symptom mapping and structural network connectivity analysis.

Patients and Methods

One hundred and thirty five young patients, aged 18–50 years, with a first-ever transient ischemic attack or cerebral infarction from the Follow-Up of Transient ischemic attack and stroke patients and Unelucidated Risk factor Evaluation (FUTURE) study, underwent 1.5T MRI and were assessed for fatigue using the self-report Checklist Individual Strength. Stroke lesions were manually segmented, and structural network efficiency was calculated using the diffusion MRI-based brain networks and graph theory for each patient. Univariate and multivariate analyses was performed to study the associations between MRI parameters and chronic post-stroke fatigue. In addition, we used voxel-based lesion-symptom mapping to analyze the relationship between the lesion location and chronic post-stroke fatigue.

Results

Mean age at index event was 39.0 years (SD ± 8.2), and mean follow-up duration was 11.0 years (SD ± 8.0). 50 patients (37%) had post-stroke fatigue. Voxel-based lesion-symptom mapping showed no significant relation between stroke lesions and the presence of chronic post-stroke fatigue. Furthermore, there were no significant associations between the lesion size or network efficiency, and the presence of chronic post-stroke fatigue.

Discussion

We did not find any association between stroke characteristics (lesion location and size) and chronic post-stroke fatigue (CIS20-R), nor associations between structural brain network connectivity and post-stroke fatigue on the long term in young stroke patients.

Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence

Stroke ranks among the leading causes for morbidity and mortality worldwide. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience. Throughout the review we will highlight methodological aspects of novel machine-learning approaches as they are particularly crucial to realize precision medicine. We will finally provide an outlook on how artificial intelligence approaches might contribute to enhancing favourable outcomes after stroke.

Dysfunction in the Interaction of Information Between and Within the Bilateral Primary Sensory Cortex

Background

Interhemispheric and intrahemispheric long-range synchronization and information communication are crucial features of functional integration between the bilateral hemispheres. Previous studies have demonstrated that disrupted functional connectivity (FC) exists in the bilateral hemispheres of patients with carpal tunnel syndrome (CTS), but they did not clearly clarify the phenomenon of central dysfunctional connectivity. This study aimed to further investigate the potential mechanism of the weakened connectivity of primary somatosensory cortex (S1) based on a precise template.

Methods

Patients with CTS (n = 53) and healthy control subjects (HCs) (n = 23) participated and underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. We used FC to investigate the statistical dependency of the whole brain, effective connectivity (EC) to analyze time-dependent effects, and voxel-mirrored homotopic connectivity (VMHC) to examine the coordination of FC, all of which were adopted to explore the change in interhemispheric and intrahemispheric S1.

Results

Compared to the healthy controls, we significantly found a decreased strength of the two connectivities in the interhemispheric S1_hand, and the results of EC and VMHC were basically consistent with FC in the CTS. The EC revealed that the information output from the dominant hemisphere to the contralateral hemisphere was weakened.

Conclusion

This study found that maladjusted connections between and within the bilateral S1 revealed by these methods are present in patients with CTS. The dominant hemisphere with deafferentation weakens its effect on the contralateral hemisphere. The disturbance in the bilateral S1 provides reliable evidence to understand the neuropathophysiological mechanisms of decreased functional integration in the brains of patients with CTS.

Concomitant sensory stimulation during therapy to enhance hand functional recovery post stroke

Background

Post-stroke hand impairment is prevalent and persistent even after a full course of rehabilitation. Hand diminishes stroke survivors’ abilities for activities of daily living and independence. One way to improve treatment efficacy is to augment therapy with peripheral sensory stimulation. Recently, a novel sensory stimulation, TheraBracelet, has been developed in which imperceptible vibration is applied during task practice through a wrist-worn device. The objective of this trial is to determine if combining TheraBracelet with hand task practice is superior to hand task practice alone.

Methods

A double-blind randomized controlled trial will be used. Chronic stroke survivors will undergo a standardized hand task practice therapy program (3 days/week for 6 weeks) while wearing a device on the paretic wrist. The device will deliver TheraBracelet vibration for the treatment group and no vibration for the control group. The primary outcome is hand function measured by the Wolf Motor Function Test. Other outcomes include the Box and Block Test, Action Research Arm Test, upper extremity use in daily living, biomechanical measure of the sensorimotor grip control, and EEG-based neural communication.

Discussion

This research will determine clinical utility of TheraBracelet to guide future translation. The TheraBracelet stimulation is delivered via a wrist-worn device, does not interfere with hand motion, and can be easily integrated into clinical practice. Enhancing hand function should substantially increase stroke survivors' independence and quality of life and reduce caregiver burden.

Trial registration

NCT04569123. Registered on September 29, 2020

Alterations in Functional Network Topology Within Normal Hemispheres Contralateral to Anterior Circulation Steno-Occlusive Disease: A Resting-State BOLD Study

The purpose of this study was to assess spatially remote effects of hemodynamic impairment on functional network topology contralateral to unilateral anterior circulation steno-occlusive disease (SOD) using resting-state blood oxygen level-dependent (BOLD) imaging, and to investigate the relationships between network connectivity and cerebrovascular reactivity (CVR), a measure of hemodynamic stress. Twenty patients with unilateral, chronic anterior circulation SOD and 20 age-matched healthy controls underwent resting-state BOLD imaging. Five-minute standardized baseline BOLD acquisition was followed by acetazolamide infusion to measure CVR. The BOLD baseline was used to analyze network connectivity contralateral to the diseased hemispheres of SOD patients. Compared to healthy controls, reduced network degree (z-score = −1.158 ± 1.217, P < 0.001, false discovery rate (FDR) corrected), local efficiency (z-score = −1.213 ± 1.120, P < 0.001, FDR corrected), global efficiency (z-score = −1.346 ± 1.119, P < 0.001, FDR corrected), and enhanced modularity (z-score = 1.000 ± 1.205, P = 0.002, FDR corrected) were observed in the contralateral, normal hemispheres of SOD patients. Network degree (P = 0.089, FDR corrected; P = 0.027, uncorrected) and nodal efficiency (P = 0.089, FDR corrected; P = 0.045, uncorrected) showed a trend toward a positive association with CVR. The results indicate remote abnormalities in functional connectivity contralateral to the diseased hemispheres in patients with unilateral SOD, despite the absence of macrovascular disease or demonstrable hemodynamic impairment. The clinical impact of remote functional disruptions requires dedicated investigation but may portend far reaching consequence for even putatively unilateral cerebrovascular disease.

Temporal exponential random graph models of longitudinal brain networks after stroke

Plasticity after stroke is a complex phenomenon. Functional reorganization occurs not only in the perilesional tissue but throughout the brain. However, the local connection mechanisms generating such global network changes remain largely unknown. To address this question, time must be considered as a formal variable of the problem rather than a simple repeated observation. Here, we hypothesized that the presence of temporal connection motifs, such as the formation of temporal triangles (T) and edges (E) over time, would explain large-scale brain reorganization after stroke. To test our hypothesis, we adopted a statistical framework based on temporal exponential random graph models (tERGMs), where the aforementioned temporal motifs were implemented as parameters and adapted to capture global network changes after stroke. We first validated the performance on synthetic time-varying networks as compared to standard static approaches. Then, using real functional brain networks, we showed that estimates of tERGM parameters were sufficient to reproduce brain network changes from 2 weeks to 1 year after stroke. These temporal connection signatures, reflecting within-hemisphere segregation (T) and between hemisphere integration (E), were associated with patients' future behaviour. In particular, interhemispheric temporal edges significantly correlated with the chronic language and visual outcome in subcortical and cortical stroke, respectively. Our results indicate the importance of time-varying connection properties when modelling dynamic complex systems and provide fresh insights into modelling of brain network mechanisms after stroke.

Lost in Time: Temporal Monitoring Elicits Clinical Decrements in Sustained Attention Post-Stroke

OBJECTIVES

Mental fatigue, 'brain fog', and difficulties maintaining engagement are commonly reported issues in a range of neurological and psychiatric conditions. Traditional sustained attention tasks commonly measure this capacity as the ability to detect target stimuli based on sensory features in the auditory or visual domains. However, with this approach, discrete target stimuli may exogenously capture attention to aid detection, thereby masking deficits in the ability to endogenously sustain attention over time.

METHODS

To address this, we developed the Continuous Temporal Expectancy Task (CTET) where individuals continuously monitor a stream of patterned stimuli alternating at a fixed temporal interval (690 ms) and detect an infrequently occurring target stimulus defined by a prolonged temporal duration (1020 ms or longer). As such, sensory properties of target and non-target stimuli are perceptually identical and differ only in temporal duration. Using the CTET, we assessed stroke survivors with unilateral right hemisphere damage (N = 14), a cohort in which sustained attention deficits have been extensively reported.

RESULTS

Stroke survivors had overall lower target detection accuracy compared with neurologically healthy age-matched older controls (N = 18). Critically, stroke survivors performance was characterised by significantly steeper within-block performance decrements, which occurred within short temporal windows (˜3 ½ min), and were restored by the break periods between blocks.

CONCLUSIONS

These findings suggest that continuous temporal monitoring taxes sustained attention processes to capture clinical deficits in this capacity over time, and outline a precise measure of the endogenous processes hypothesised to underpin sustained attention deficits following right hemisphere stroke.

Resting-State Beta-Band Recovery Network Related to Cognitive Improvement After Stroke

Background

Stroke is the second leading cause of death worldwide and it causes important long-term cognitive and physical deficits that hamper patients' daily activity. Neuropsychological rehabilitation (NR) has increasingly become more important to recover from cognitive disability and to improve the functionality and quality of life of these patients. Since in most stroke cases, restoration of functional connectivity (FC) precedes or accompanies cognitive and behavioral recovery, understanding the electrophysiological signatures underlying stroke recovery mechanisms is a crucial scientific and clinical goal.

Methods

For this purpose, a longitudinal study was carried out with a sample of 10 stroke patients, who underwent two neuropsychological assessments and two resting-state magnetoencephalographic (MEG) recordings, before and after undergoing a NR program. Moreover, to understand the degree of cognitive and neurophysiological impairment after stroke and the mechanisms of recovery after cognitive rehabilitation, stroke patients were compared to 10 healthy controls matched for age, sex, and educational level.

Findings

After intra and inter group comparisons, we found the following results: (1) Within the stroke group who received cognitive rehabilitation, almost all cognitive domains improved relatively or totally; (2) They exhibit a pattern of widespread increased in FC within the beta band that was related to the recovery process (there were no significant differences between patients who underwent rehabilitation and controls); (3) These FC recovery changes were related with the enhanced of cognitive performance. Furthermore, we explored the capacity of the neuropsychological scores before rehabilitation, to predict the FC changes in the brain network. Significant correlations were found in global indexes from the WAIS-III: Performance IQ (PIQ) and Perceptual Organization index (POI) (i.e., Picture Completion, Matrix Reasoning, and Block Design).

Missing links: The functional unification of language and memory (L∪M)

The field of neurocognition is currently undergoing a significant change of perspective. Traditional neurocognitive models evolved into an integrative and dynamic vision of cognitive functioning. Dynamic integration assumes an interaction between cognitive domains traditionally considered to be distinct. Language and declarative memory are regarded as separate functions supported by different neural systems. However, they also share anatomical structures (notably, the inferior frontal gyrus, the supplementary motor area, the superior and middle temporal gyrus, and the hippocampal complex) and cognitive processes (such as semantic and working memory) that merge to endorse our quintessential daily lives. We propose a new model, "L∪M" (i.e., Language/union/Memory), that considers these two functions interactively. We fractionated language and declarative memory into three fundamental dimensions or systems ("Receiver-Transmitter", "Controller-Manager" and "Transformer-Associative" Systems), that communicate reciprocally. We formalized their interactions at the brain level with a connectivity-based approach. This new taxonomy overcomes the modular view of cognitive functioning and reconciles functional specialization with plasticity in neurological disorders.

Ultrafast and Ultrahigh-Resolution Diffuse Optical Tomography for Brain Imaging with Sensitivity Equation based Noniterative Sparse Optical Reconstruction (SENSOR)

We introduce a novel image reconstruction method for time-resolved diffuse optical tomography (DOT) that yields submillimeter resolution in less than a second. This opens the door to high-resolution real-time DOT in imaging of the brain activity. We call this approach the sensitivity equation based noniterative sparse optical reconstruction (SENSOR) method. The high spatial resolution is achieved by implementing an asymptotic l ₀-norm operator that guarantees to obtain sparsest representation of reconstructed targets. The high computational speed is achieved by employing the nontruncated sensitivity equation based noniterative inverse formulation combined with reduced sensing matrix and parallel computing. We tested the new method with numerical and experimental data. The results demonstrate that the SENSOR algorithm can achieve 1 mm³ spatial-resolution optical tomographic imaging at depth of ∼60 mean free paths (MFPs) in 20∼30 milliseconds on an Intel Core i9 processor.

Common and unique structural plasticity after left and right hemisphere stroke

Strokes to the left and right hemisphere lead to distinctive behavioral profiles. Are left and right hemisphere strokes (LHS and RHS) associated with distinct or common poststroke neuroplasticity patterns? Understanding this issue would reveal hemispheric neuroplasticity mechanisms in response to brain damage. To this end, we investigated poststroke structural changes (2 weeks to 3 months post-onset) using longitudinal MRI data from 69 LHS and 55 RHS patients and 31 demographic-matched healthy control participants. Both LHS and RHS groups showed statistically common plasticity independent of the lesioned hemisphere, including 1) gray matter (GM) expansion in the ipsilesional and contralesional precuneus, and contralesional superior frontal gyrus; 2) GM shrinkage in the ipsilesional medial orbital frontal gyrus and middle cingulate cortex. On the other hand, only RHS patients had significant GM expansion in the ipsilesional medial superior and orbital frontal cortex. Importantly, these common and unique GM changes post-stroke largely overlapped with highly-connected cortical hub regions in healthy individuals. Moreover, they correlated with behavioral recovery, indicating that post-stroke GM volumetric changes in cortical hubs reflect compensatory rather than maladaptive mechanisms. These results highlight the importance of structural neuroplasticity in hub regions of the cortex, along with the hemispheric specificity, for stroke recovery.

Independent contributions of structural and functional connectivity: Evidence from a stroke model

Altered functional connectivity is related to severity of language impairment in poststroke aphasia. However, it is not clear whether this finding specifically reflects loss of functional coherence, or more generally, is related to decreased structural connectivity due to cortical necrosis. The aim of the current study was to investigate this issue by factoring out structural connectivity from functional connectivity measures and then relating the residual data to language performance poststroke. Ninety-seven participants with a history of stroke were assessed using language impairment measures (Auditory Verbal Comprehension and Spontaneous Speech scores from the Western Aphasia Battery-Revised) and MRI (structural, diffusion tensor imaging, and resting-state functional connectivity). We analyzed the association between functional connectivity and language and controlled for multiple potential neuroanatomical confounders, namely structural connectivity. We identified functional connections within the left hemisphere ventral stream where decreased functional connectivity, independent of structural connectivity, was associated with speech comprehension impairment. These connections exist in frontotemporal and temporoparietal regions. Our results suggest poor speech comprehension in aphasia is at least partially caused by loss of cortical synchrony in a left hemisphere ventral stream network and is not only reflective of localized necrosis or structural connectivity.

Endothelin-1 induced global ischaemia in adult zebrafish: A model with novel entity of stroke research

BACKGROUND

Stroke is a leading cause of death in the general population, and it occurs three times more frequently in diabetic patients, necessitating extensive research into new therapeutics. The reproducibility, similarity, and technical limitations of current animal models are limited.

METHODS

We developed a stroke induction model using pink zebra-Danio-rerio. Diabetes was induced in zebrafish by giving them D-glucose (111 mM) for 14 days, and those with blood glucose levels higher than 100 mg/dl were included in the study. In Zebrafish, an experimental stroke was induced by a single oral administration of Endothelin-1 (ET-1, 3µl/gm). Swimming, behavioural patterns, and cognitive performance were all recorded and analysed using UMA Tracker. The brains were removed for histopathological analysis.

RESULTS

In both the normal and diabetic groups, ET-1 administration resulted in a statistically significant change in swimming pattern and movements. Furthermore, changes in swimming pattern and recovery time were statistically significant in the diabetic ET-1 treatment group. In the neurocognitive assessment paradigm, the behavioural study of ET-1 treated groups revealed a disturbed cognitive profile and locomotor coordination, with an increase in the number of errors and a decrease in total distance travelled. Histopathological analysis of ET-1 treated groups revealed cortical lesions, shrunken neuronal cells, and thrombocytes in spheroid form with disturbed normal architecture of brain tissue when compared to normal control groups in tectum opticum and telencephalon. In terms of stability, reproducibility, and genetic similarity to human stroke, the current experimental model outperforms other available rodent stroke models.

CONCLUSION

The ET-1 induced experimental zebrafish stroke model opens up new avenues for diabetes-related stroke research due to its novelty, reproducibility, and ability to overcome technical errors found in other recent models.

Migraine in Multiple Sclerosis Patients Affects Functional Connectivity of the Brain Circuitry Involved in Pain Processing

Migraine is particularly common in patients with multiple sclerosis (MS) and has been linked to the dysfunction of the brain circuitry modulating the peripheral nociceptive stimuli. Using MRI, we explored whether changes in the resting state-functional connectivity (RS-FC) may characterize the occurrence of migraine in patients with MS. The RS-FC characteristics in concerned brain regions were explored in 20 MS patients with migraine (MS+M) during the interictal phase, and compared with 19 MS patients without migraine (MS-M), which served as a control group. Functional differences were correlated to the frequency and severity of previous migraine attacks, and with the resulting impact on daily activities. In MS+M, the loss of periaqueductal gray matter (PAG) positive connectivity with the default mode network and the left posterior cranial pons was associated with an increase of migraine attacks frequency. In contrast, the loss of PAG negative connectivity with sensorimotor and visual network was linked to migraine symptom severity and related daily activities impact. Finally, a PAG negative connection was established with the prefrontal executive control network. Migraine in MS+M patients and its impact on daily activities, underlies RS-FC rearrangements between brain regions involved in pain perception and modulation.

Data augmentation using generative adversarial neural networks on brain structural connectivity in multiple sclerosis

BACKGROUND AND OBJECTIVE

Machine learning frameworks have demonstrated their potentials in dealing with complex data structures, achieving remarkable results in many areas, including brain imaging. However, a large collection of data is needed to train these models. This is particularly challenging in the biomedical domain since, due to acquisition accessibility, costs and pathology related variability, available datasets are limited and usually imbalanced. To overcome this challenge, generative models can be used to generate new data.

METHODS

In this study, a framework based on generative adversarial network is proposed to create synthetic structural brain networks in Multiple Sclerosis (MS). The dataset consists of 29 relapsing-remitting and 19 secondary-progressive MS patients. T1 and diffusion tensor imaging (DTI) acquisitions were used to obtain the structural brain network for each subject. Evaluation of the quality of newly generated brain networks is performed by (i) analysing their structural properties and (ii) studying their impact on classification performance.

RESULTS

We demonstrate that advanced generative models could be directly applied to the structural brain networks. We quantitatively and qualitatively show that newly generated data do not present significant differences compared to the real ones. In addition, augmenting the existing dataset with generated samples leads to an improvement of the classification performance (F1_score 81%) with respect to the baseline approach (F1_score 66%).

CONCLUSIONS

Our approach defines a new tool for biomedical application when connectome-based data augmentation is needed, providing a valid alternative to usual image-based data augmentation techniques.