Resting-State Functional Connectivity Magnetic Resonance Imaging and Outcome After Acute Stroke

Differences in motor network reorganization between patients with good and poor upper extremity impairment outcomes after stroke

Changes in cortical excitability after stroke are closely associated with motor function recovery. This study aimed to clarify the motor network reorganization mechanisms corresponding to the different clinical outcomes of upper limb motor impairment in patients with subacute stroke. Motor function was assessed before rehabilitation (pre), after rehabilitation (post), and at the 1-year follow-up (follow-up) using the Fugl-Meyer assessment upper extremity scale. Further, resting-state functional magnetic resonance imaging (fMRI) data were collected in both pre- and post-conditions. Twenty patients with stroke were categorized into good and poor outcome groups based on motor impairments at the 1-year follow-up. Functional connections between motor-related regions of interest and the rest of the brain were subsequently calculated. Finally, the correlation between motor network reorganization and behavioral improvement at the 1-year follow-up was analyzed. The good outcome group exhibited a positive precondition motor function and continuous improvement, whereas the poor outcome group showed a weak precondition motor function and insignificant improvement. Contralesional hemisphere-related connections were found to be higher in the good outcome group pre-conditioning, with both groups showing minimal change post-conditioning, while no relationship with motor impairment was found. Long interhemispheric connections were decreased and increased in the good and poor outcome groups respectively, and were negatively correlated with motor impairment. Different motor network reorganizations during the subacute phase can influence the varying motor outcomes in the affected upper limb after stroke. These findings may serve as the theoretical basis for future neuromodulatory research.

Evaluating post-thrombectomy effective connectivity changes in anterior circulation stroke

OBJECTIVE

Granger causal analysis (GCA) and amplitude of low-frequency fluctuation (ALFF) are commonly used to evaluate functional alterations in brain disorders. By combining the GCA and ALFF, this study aimed to investigate the effective connectivity (EC) changes in patients with acute ischemic stroke (AIS) and anterior circulation occlusion after mechanical thrombectomy (MT).

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 43 AIS patients with anterior circulation occlusion within 1 week post-MT and 37 healthy controls. ALFF and GCA were calculated for each participant. Patients were further divided into groups based on prognosis and perfusion levels. The differences in ALFF and EC were compared between AIS patients and healthy controls and between subgroups of patients. Pearson correlations between EC, ALFF values, and clinical characteristics of patients were calculated.

RESULTS

Compared to healthy controls, post-MT, AIS patients exhibited significant ALFF increases in the left precuneus and decreases in the left fusiform gyrus and right caudate. Increased EC from the contralesional lingual gyrus, contralesional putamen, ipsilesional thalamus, and contralesional thalamus to the contralesional caudate was obsrved, while decrease in EC were found for contralesional caudate to the ipsilesional thalamus and medial superior frontal gyrus. EC differences were particularly notable between perfusion groups, with significantly lower EC in the poorly perfused group. EC values were also positively correlated with National Institutes of Health Stroke Scale (NIHSS) scores pre-MT.

INTERPRETATION

In AIS patients, the caudate nucleus was central to the observed EC changes post-MT, characterized by decreased outputs and increased inputs. These changes indicate functional remodeling within the cortico-basal ganglia-thalamic-cortical pathway.

Applications of Functional Magnetic Resonance Imaging to the Study of Functional Connectivity and Activation in Neurological Disease: A Scoping Review of the Literature

BACKGROUND

Functional magnetic resonance imaging (fMRI) has transformed our understanding of brain's functional architecture, providing critical insights into neurological diseases. This scoping review synthesizes the current landscape of fMRI applications across various neurological domains, elucidating the evolving role of both task-based and resting-state fMRI in different settings.

METHODS

We conducted a comprehensive scoping review following the Preferred Reporting Items for Systematic Review and Meta-Analyses Extension for Scoping Reviews guidelines. Extensive searches in Medline/PubMed, Embase, and Web of Science were performed, focusing on studies published between 2003 and 2023 that utilized fMRI to explore functional connectivity and regional activation in adult patients with neurological conditions. Studies were selected based on predefined inclusion and exclusion criteria, with data extracted.

RESULTS

We identified 211 studies, covering a broad spectrum of neurological disorders including mental health, movement disorders, epilepsy, neurodegeneration, traumatic brain injury, cerebrovascular accidents, vascular abnormalities, neurorehabilitation, neuro-critical care, and brain tumors. The majority of studies utilized resting-state fMRI, underscoring its prominence in identifying disease-specific connectivity patterns. Results highlight the potential of fMRI to reveal the underlying pathophysiological mechanisms of various neurological conditions, facilitate diagnostic processes, and potentially guide therapeutic interventions.

CONCLUSIONS

fMRI serves as a powerful tool for elucidating complex neural dynamics and pathologies associated with neurological diseases. Despite the breadth of applications, further research is required to standardize fMRI protocols, improve interpretative methodologies, and enhance the translation of imaging findings to clinical practice. Advances in fMRI technology and analytics hold promise for improving the precision of neurological assessments and interventions.

Disruption of Resting-State Functional Connectivity in Acute Ischemic Stroke: Comparisons Between Right and Left Hemispheric Insults

Few resting-state functional magnetic resonance imaging (RS-fMRI) studies evaluated the impact of acute ischemic changes on cerebral functional connectivity (FC) and its relationship with functional outcomes after acute ischemic stroke (AIS), considering the side of lesions. To characterize alterations of FC of patients with AIS by analyzing 12 large-scale brain networks (NWs) with RS-fMRI. Additionally, we evaluated the impact of the side (right (RH) or left (LH) hemisphere) of insult on the disruption of brain NWs. 38 patients diagnosed with AIS (17 RH and 21 LH) who performed 3T MRI scans up to 72 h after stroke were compared to 44 healthy controls. Images were processed and analyzed with the software toolbox UF2C with SPM12. For the first level, we generated individual matrices based on the time series extraction from 70 regions of interest (ROIs) from 12 functional NWs, constructing Pearson's cross-correlation; the second-level analysis included an analysis of covariance (ANCOVA) to investigate differences between groups. The statistical significance was determined with p < 0.05, after correction for multiple comparisons with false discovery rate (FDR) correction. Overall, individuals with LH insults developed poorer clinical outcomes after six months. A widespread pattern of lower FC was observed in the presence of LH insults, while a contralateral pattern of increased FC was identified in the group with RH insults. Our findings suggest that LH stroke causes a severe and widespread pattern of reduction of brain networks' FC, presumably related to the impairment in their long-term recovery.

From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke

Stroke is a severe medical condition which may lead to permanent disability conditions. The initial 8 weeks following a stroke are crucial for rehabilitation, as most recovery occurs during this period. Personalized approaches and predictive biomarkers are needed for tailored rehabilitation. In this context, EEG brain connectivity and Artificial Intelligence (AI) can play a crucial role in diagnosing and predicting stroke outcomes efficiently. In the present study, 127 patients with subacute ischemic lesions and 90 age- and gender-matched healthy controls were enrolled. EEG recordings were obtained from each participant within 15 days of stroke onset. Clinical evaluations were performed at baseline and at 40-days follow-up using the National Institutes of Health Stroke Scale (NIHSS). Functional connectivity analysis was conducted using Total Coherence (TotCoh) and Small Word (SW). Quadratic support vector machines (SVM) algorithms were implemented to classify healthy subjects compared to stroke patients (Healthy vs Stroke), determine the affected hemisphere (Left vs Right Hemisphere), and predict functional recovery (Functional Recovery Prediction). In the classification for Functional Recovery Prediction, an accuracy of 94.75%, sensitivity of 96.27% specificity of 92.33%, and AUC of 0.95 were achieved; for Healthy vs Stroke, an accuracy of 99.09%, sensitivity of 100%, specificity of 98.46%, and AUC of 0.99 were achieved. For Left vs Right Hemisphere classification, accuracy was 86.77%, sensitivity was 91.44%, specificity was 80.33%, and AUC was 0.87. These findings highlight the potential of utilizing functional connectivity measures based on EEG in combination with AI algorithms to improve patient outcomes by targeted rehabilitation interventions.

From convergence insufficiency to functional reorganization: A longitudinal randomized controlled trial of treatment-induced connectivity plasticity

INTRODUCTION

Convergence Insufficiency (CI) is the most prevalent oculomotor dysfunction of binocular vision that negatively impacts quality of life when performing visual near tasks. Decreased resting-state functional connectivity (RSFC) is reported in the CI participants compared to binocularly normal control participants. Studies report that therapeutic interventions such as office-based vergence and accommodative therapy (OBVAT) can improve CI participants' clinical signs, visual symptoms, and task-related functional activity. However, longitudinal studies investigating the RSFC changes after such treatments in participants with CI have not been conducted. This study aimed to investigate the neural basis of OBVAT using RSFC in CI participants compared to the placebo treatment to understand how OBVAT improves visual function and symptoms.

METHODS

A total of 51 CI participants between 18 and 35 years of age were included in the study and randomly allocated to receive either 12 one-hour sessions of OBVAT or placebo treatment for 6 to 8 weeks (1 to 2 sessions per week). Resting-state functional magnetic resonance imaging and clinical assessments were evaluated at baseline and outcome for each treatment group. Region of interest (ROI) analysis was conducted in nine ROIs of the oculomotor vergence network, including the following: cerebellar vermis (CV), frontal eye fields (FEF), supplementary eye fields (SEF), parietal eye fields (PEF), and primary visual cortices (V1). Paired t-tests assessed RSFC changes in each group. A linear regression analysis was conducted for significant ROI pairs in the group-level analysis for correlations with clinical measures.

RESULTS

Paired t-test results showed increased RSFC in 10 ROI pairs after the OBVAT but not placebo treatment (p < 0.05, false discovery rate corrected). These ROI pairs included the following: Left (L)-SEF-Right (R)-V1, L-SEF-CV, R-SEF-R-PEF, R-SEF-L-V1, R-SEF-R-V1, R-SEF-CV, R-PEF-CV, L-V1-CV, R-V1-CV, and L-V1-R-V1. Significant correlations were observed between the RSFC strength of the R-SEF-R-PEF ROI pair and the following clinical visual function parameters: positive fusional vergence and near point of convergence (p < 0.05).

CONCLUSION

OBVAT, but not placebo treatment, increased the RSFC in the ROIs of the oculomotor vergence network, which was correlated with the improvements in the clinical measures of the CI participants.

Functional network reorganization after endovascular thrombectomy in patients with anterior circulation stroke

BACKGROUND

Endovascular thrombectomy has been confirmed to be an effective therapy for acute ischemic stroke (AIS). However, how functional brain networks reorganize after restoration of blood supply in AIS patients, and whether the degree of reperfusion associates with functional network changes remains unclear.

METHODS

Resting-state fMRI data were collected from 43 AIS patients with anterior circulation occlusion after thrombectomy and 37 healthy controls (HCs). Both static and dynamic functional connectivity (FC) within four advanced functional networks including dorsal attention network (DAN), ventral attention network (VAN), executive control network (ECN) and default mode network (DMN), were calculated and compared between post-thrombectomy patients and HCs, and between two subgroups of post-thrombectomy patients with different reperfusion conditions.

RESULTS

As compared to HCs, patients showed significant differences in static FC of four functional networks, and in dynamic FC of DAN, ECN and DMN. Furthermore, patients with better reperfusion conditions exhibited increased static FC with precuneus, and altered dynamic FC within precuneus. Moreover, these alterations were associated with clinical assessments of stroke severity and functional recovery in post-thrombectomy patients.

CONCLUSIONS

Collectively, these findings may provide the potential imaging markers for assessment of thrombectomy efficacy and help establish the specific rehabilitation treatments for post-thrombectomy patients.

Time synchronization between parietal-frontocentral connectivity with MRCP and gait in post-stroke bipedal tasks

BACKGROUND

In post-stroke rehabilitation, functional connectivity (FC), motor-related cortical potential (MRCP), and gait activities are common measures related to recovery outcomes. However, the interrelationship between FC, MRCP, gait activities, and bipedal distinguishability have yet to be investigated.

METHODS

Ten participants were equipped with EEG devices and inertial measurement units (IMUs) while performing lower limb motor preparation (MP) and motor execution (ME) tasks. MRCP, FCs, and bipedal distinguishability were extracted from the EEG signals, while the change in knee degree during the ME phase was calculated from the gait data. FCs were analyzed with pairwise Pearson's correlation, and the brain-wide FC was fed into support vector machine (SVM) for bipedal classification.

RESULTS

Parietal-frontocentral connectivity (PFCC) dysconnection and MRCP desynchronization were related to the MP and ME phases, respectively. Hemiplegic limb movement exhibited higher PFCC strength than nonhemiplegic limb movement. Bipedal classification had a short-lived peak of 75.1% in the pre-movement phase. These results contribute to a better understanding of the neurophysiological functions during motor tasks, with respect to localized MRCP and nonlocalized FC activities. The difference in PFCCs between both limbs could be a marker to understand the motor function of the brain of post-stroke patients.

CONCLUSIONS

In this study, we discovered that PFCCs are temporally dependent on lower limb gait movement and MRCP. The PFCCs are also related to the lower limb motor performance of post-stroke patients. The detection of motor intentions allows the development of bipedal brain-controlled exoskeletons for lower limb active rehabilitation.

Fractional amplitude of low-frequency fluctuation alterations in patients with cervical spondylotic myelopathy: a resting-state fMRI study

PURPOSE

We sought to use the fractional amplitude of low-frequency fluctuation (fALFF) method to investigate the changes in spontaneous brain activity in CSM patients and their relationships with clinical features.

METHODS

We recruited 20 patients with CSM, and 20 healthy controls (HCs) matched for age, sex, and education status. The fALFF method was used to evaluate the altered spontaneous brain activities. The Pearson correlation analysis of fALFF and the clinical features were carried out.

RESULTS

Compared with HC, CSM group showed increased fALFF values in the left middle frontal gyrus, inferior parietal lobule, and right angular gyrus. Decreased fALFF values were found in the right lingual gyrus, cuneus (P < 0.05). Pearson correlation analysis shows that the fALFF values of all CSM were positively correlated with JOA score in the right angular gyrus (r = 0.518, P < 0.05).

CONCLUSION

CSM patients have abnormal fALFF distribution in multiple brain regions and might be an appealing alternative approach for further exploration of the pathological and neuropsychological states in CSM.

The emergence of multiscale connectomics-based approaches in stroke recovery

Stroke is a leading cause of adult disability. Understanding stroke damage and recovery requires deciphering changes in complex brain networks across different spatiotemporal scales. While recent developments in brain readout technologies and progress in complex network modeling have revolutionized current understanding of the effects of stroke on brain networks at a macroscale, reorganization of smaller scale brain networks remains incompletely understood. In this review, we use a conceptual framework of graph theory to define brain networks from nano- to macroscales. Highlighting stroke-related brain connectivity studies at multiple scales, we argue that multiscale connectomics-based approaches may provide new routes to better evaluate brain structural and functional remapping after stroke and during recovery.

The role of intimate partner violence perpetrators' resting state functional connectivity in treatment compliance and recidivism

To expand the scientific literature on how resting state functional connectivity (rsFC) magnetic resonance imaging (MRI) (or the measurement of the strength of the coactivation of two brain regions over a sustained period of time) can be used to explain treatment compliance and recidivism among intimate partner violence (IPV) perpetrators. Therefore, our first aim was to assess whether men convicted of IPV (n = 53) presented different rsFC patterns from a control group of non-violent (n = 47) men. We also analyzed if the rsFC of IPV perpetrators before staring the intervention program could explain treatment compliance and recidivism one year after the intervention ended. The rsFC was measured by applying a whole brain analysis during a resting period, which lasted 45 min. IPV perpetrators showed higher rsFC in the occipital brain areas compared to controls. Furthermore, there was a positive association between the occipital pole (OP) and temporal lobes (ITG) and a negative association between the occipital (e.g., occipital fusiform gyrus, visual network) and both the parietal lobe regions (e.g., supramarginal gyrus, parietal operculum cortex, lingual gyrus) and the putamen in IPV perpetrators. This pattern was the opposite in the control group. The positive association between many of these occipital regions and the parietal, frontal, and temporal regions explained treatment compliance. Conversely, treatment compliance was also explained by a reduced rsFC between the rostral prefrontal cortex and the frontal gyrus and both the occipital and temporal gyrus, and between the temporal and the occipital and cerebellum areas and the sensorimotor superior networks. Last, the enhanced rsFC between the occipital regions and both the cerebellum and temporal gyrus predicted recidivism. Our results highlight that there are specific rsFC patterns that can distinguish IPV perpetrators from controls. These rsFC patterns could be useful to explain treatment compliance and recidivism among IPV perpetrators.

Intracerebral Transplantation of Autologous Mesenchymal Stem Cells Improves Functional Recovery in a Rat Model of Chronic Ischemic Stroke

While treatments exist for the acute phase of stroke, there are limited options for patients with chronic infarcts and long-term disability. Allogenic mesenchymal stem cells (alloMSCs) show promise for the treatment of stroke soon after ischemic injury. There is, however, no information on the use of autologous MSCs (autoMSCs), delivered intracerebrally in rats with a chronic infarct. In this study, rats underwent middle cerebral artery occlusion (MCAO) to induce stroke followed by bone marrow aspiration and MSC expansion in a closed bioreactor. Four weeks later, brain MRI was obtained and autoMSCs (1 × 106, 2.5 × 106 or 5 × 106; n = 6 each) were stereotactically injected into the peri-infarct and compared to controls (MCAO only; MCAO + PBS; n = 6-9). Behavior was assessed using the modified neurological severity score (mNSS). For comparison, an additional cohort of MCAO rats were implanted with 2.5 × 106 alloMSCs generated from a healthy rat. All doses of autoMSCs produced significant improvement (54-70%) in sensorimotor function 60 days later. In contrast, alloMSCs improved only 31.7%, similar to that in PBS controls 30%. Quantum dot-labeled auto/alloMSCs were found exclusively at the implantation site throughout the post-transplantation period with no tumor formation on MRI or Ki67 staining of engrafted MSCs. Small differences in stroke volume and no differences in corpus callosum width were observed after MSC treatment. Stroke-induced glial reactivity in the peri-infarct was long-lasting and unabated by auto/alloMSC transplantation. These studies suggest that intracerebral transplantation of autoMSCs as compared to alloMSCs may be a promising treatment in chronic stroke.

Functional and structural brain reorganization in patients with ischemic stroke: a multimodality MRI fusion study

Understanding how structural and functional reorganization occurs is crucial for stroke diagnosis and prognosis. Previous magnetic resonance imaging (MRI) studies focused on the analyses of a single modality and demonstrated abnormalities in both lesion regions and their associated distal regions. However, the relationships of multimodality alterations and their associations with poststroke motor deficits are still unclear. In this study, 71 hemiplegia patients and 41 matched healthy controls (HCs) were recruited and underwent MRI examination at baseline and at 2-week follow-up sessions. A multimodal fusion approach (multimodal canonical correlation analysis + joint independent component analysis), with amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV) as features, was used to extract the co-altered patterns of brain structure and function. Then compared the changes in patients' brain structure and function between baseline and follow-up sessions. Compared with HCs, the brain structure and function of stroke patients decreased synchronously in the local lesions and their associated distal regions. Damage to structure and function in the local lesion regions was associated with motor function. After 2 weeks, ALFF in the local lesion regions was increased, while GMV did not improve. Taken together, the brain structure and function in the local lesions and their associated distal regions were damaged synchronously after ischemic stroke, while during motor recovery, the 2 modalities were changed separately.

Integrative Approaches in Acute Ischemic Stroke: From Symptom Recognition to Future Innovations

Among the high prevalence of cerebrovascular diseases nowadays, acute ischemic stroke stands out, representing a significant worldwide health issue with important socio-economic implications. Prompt diagnosis and intervention are important milestones for the management of this multifaceted pathology, making understanding the various stroke-onset symptoms crucial. A key role in acute ischemic stroke management is emphasizing the essential role of a multi-disciplinary team, therefore, increasing the efficiency of recognition and treatment. Neuroimaging and neuroradiology have evolved dramatically over the years, with multiple approaches that provide a higher understanding of the morphological aspects as well as timely recognition of cerebral artery occlusions for effective therapy planning. Regarding the treatment matter, the pharmacological approach, particularly fibrinolytic therapy, has its merits and challenges. Endovascular thrombectomy, a game-changer in stroke management, has witnessed significant advances, with technologies like stent retrievers and aspiration catheters playing pivotal roles. For select patients, combining pharmacological and endovascular strategies offers evidence-backed benefits. The aim of our comprehensive study on acute ischemic stroke is to efficiently compare the current therapies, recognize novel possibilities from the literature, and describe the state of the art in the interdisciplinary approach to acute ischemic stroke. As we aspire for holistic patient management, the emphasis is not just on medical intervention but also on physical therapy, mental health, and community engagement. The future holds promising innovations, with artificial intelligence poised to reshape stroke diagnostics and treatments. Bridging the gap between groundbreaking research and clinical practice remains a challenge, urging continuous collaboration and research.

Lesion Network Mapping for Neurological Deficit in Acute Ischemic Stroke

OBJECTIVE

To create a comprehensive map of strategic lesion network localizations for neurological deficits, and identify prognostic neuroimaging biomarkers to facilitate the early detection of patients with a high risk of poor functional outcomes in acute ischemic stroke (AIS).

METHODS

In a large-scale multicenter study of 7,807 patients with AIS, we performed voxel-based lesion-symptom mapping, functional disconnection mapping (FDC), and structural disconnection mapping (SDC) to identify distinct lesion and network localizations for National Institutes of Health Stroke Scale (NIHSS) score. Impact scores were calculated based on the odds ratios or t-values of voxels from voxel-based lesion-symptom mapping, FDC, and SDC results. Ordinal regression models were used to investigate the predictive value of the impact scores on functional outcome (defined as the modified Rankin score at 3 months).

RESULTS

We constructed lesion, FDC, and SDC maps for each item of the NIHSS score, which provided insights into the neuroanatomical substrate and network localization of neurological function deficits after AIS. The lesion impact score of limb ataxia, the SDC impact score of limb deficit, and FDC impact score of sensation and dysarthria were significantly associated with modified Rankin Scale at 3 months. Adding the SDC impact score, FDC impact score, and lesion impact score to the NIHSS total score improved the performance in predicting functional outcomes, as compared with using the NIHSS score alone.

INTERPRETATION

We constructed comprehensive maps of strategic lesion network localizations for neurological deficits that were predictive of functional outcomes in AIS. These results may provide specifically localized targets for future neuromodulation therapies. ANN NEUROL 2023;94:572-584.

A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted Rehabilitation

It is essential for neuroscience and clinic to estimate the influence of neuro-intervention after brain damage. Most related studies have used Mirrored Contralesional-Ipsilesional hemispheres (MCI) methods flipping the axial neuroimaging on the x-axis in prognosis prediction. But left-right hemispheric asymmetry in the brain has become a consensus. MCI confounds the intrinsic brain asymmetry with the asymmetry caused by unilateral damage, leading to questions about the reliability of the results and difficulties in physiological explanations. We proposed the Separated Left-Right hemiplegia (SLR) method to model left and right hemiplegia separately. Two pipelines have been designed in contradistinction to demonstrate the validity of the SLR method, including MCI and removing intrinsic asymmetry (RIA) pipelines. A patient dataset with 18 left-hemiplegic and 22 right-hemiplegic stroke patients and a healthy dataset with 40 subjects, age- and sex-matched with the patients, were selected in the experiment. Blood-Oxygen Level-Dependent MRI and Diffusion Tensor Imaging were used to build brain networks whose nodes were defined by the Automated Anatomical Labeling atlas. We applied the same statistical and machine learning framework for all pipelines, logistic regression, artificial neural network, and support vector machine for classifying the patients who are significant or non-significant responders to brain-computer interfaces assisted training and optimal subset regression, support vector regression for predicting post-intervention outcomes. The SLR pipeline showed 5-15% improvement in accuracy and at least 0.1 upgrades in [Formula: see text], revealing common and unique recovery mechanisms after left and right strokes and helping clinicians make rehabilitation plans.

Longitudinal evaluation of the functional connectivity changes in the secondary somatosensory cortex (S2) of the monkey brain during acute stroke

Background

Somatosensory deficits are frequently seen in acute stroke patients and may recover over time and affect functional outcome. However, the underlying mechanism of function recovery remains poorly understood. In the present study, progressive function alteration of the secondary somatosensory cortex (S2) and its relationship with regional perfusion and neurological outcome were examined using a monkey model of stroke.

Methods and materials

Rhesus monkeys (n = 4) were induced with permanent middle cerebral artery occlusion (pMCAo). Resting-state functional MRI, dynamic susceptibility contrast perfusion MRI, diffusion-weighted, T₁ and T₂ weighted images were collected before surgery and at 4-6, 48, and 96 h post stroke on a 3T scanner. Progressive changes of relative functional connectivity (FC), cerebral blood flow (CBF), and CBF/Tmax (Time to Maximum) of affected S2 regions were evaluated. Neurological deficits were assessed using the Spetzler approach.

Results

Ischemic lesion was evidently seen in the MCA territory including S2 in each monkey. Relative FC of injured S2 regions decreased substantially following stroke. Spetzler scores dropped substantially at 24 h post stroke but slightly recovered from Day 2 to Day 4. Relative FC progressively increased from 6 to 48 and 96 h post stroke and correlated significantly with relative CBFand CBF/Tmax changes.

Conclusion

The present study revealed the progressive alteration of function connectivity in S2 during acute stroke. The preliminary results suggested the function recovery might start couple days post occlusion and collateral circulation might play a key role in the recovery of somatosensory function after stroke insult. The relative function connectivity in S2 may provide additional information for prediction of functional outcome in stroke patients.

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.

Connectome-based predictive modeling for functional recovery of acute ischemic stroke

Patients of acute ischemic stroke possess considerable chance of recovery of various levels in the first several weeks after stroke onset. Prognosis of functional recovery is important for decision-making in poststroke patient care and placement. Poststroke functional recovery has conventionally been based on demographic and clinical variables such as age, gender, and severity of stroke impairment. On the other hand, the concept of connectome has become a basis of interpreting the functional impairment and recovery of stroke patients. In this research, the connectome-based predictive modeling was used to provide predictive models for prognosing poststroke functional recovery. Predictive models were developed to use the brain connectivity at stroke onset to predict functional assessment scores at one or three months later, or to use the brain connectivity one-month poststroke to predict functional assessment scores at three months after stroke onset. The brain connectivity was computed from the resting-state fMRI signals. The functional assessment scores used in this research included modified Rankin Scale (mRS) and Barthel Index (BI). This research found significant models that used the brain connectivity at onset to predict the mRS one-month poststroke and to predict the BI three-month poststroke for patients with supratentorial infarction, as well as predictive models that used the brain connectivity one-month poststroke to predict the mRS three-month poststroke for patients with supratentorial infarction in the right hemisphere. The connectome-based predictive modeling could provide clinical value in prognosis of acute ischemic stroke.

Preoperative Brain Functional Connectivity Improve Predictive Accuracy of Outcomes After Revascularization in Moyamoya Disease

BACKGROUND

In patients with moyamoya disease (MMD), focal impairments in cerebral hemodynamics are often inconsistent with patients' clinical prognoses. Evaluation of entire brain functional networks may enable predicting MMD outcomes after revascularization.

OBJECTIVE

To investigate whether preoperative brain functional connectivity could predict outcomes after revascularization in MMD.

METHODS

We included 34 patients with MMD who underwent preoperative MRI scanning and combined revascularization surgery. We used region of interest analyses to explore the differences in functional connectivity for 90 paired brain regions between patients who had favorable outcomes 1 year after surgery (no recurrent stroke, with improved preoperative symptoms, or modified Rankin Scale [mRS]) and those who had unimproved outcomes (recurrent stroke, persistent symptoms, or declined mRS). Variables, including age, body mass index, mRS at admission, Suzuki stage, posterior cerebral artery involvement, and functional connectivity with significant differences between the groups, were included in the discriminant function analysis to predict patient outcomes.

RESULTS

Functional connectivity between posterior cingulate cortex and paracentral lobule within the right hemisphere, and interhemispheric connection between superior parietal gyrus and middle frontal gyrus, precuneus and middle cingulate cortex, cuneus and precuneus, differed significantly between the groups (P < .001, false discovery rate corrected) and had the greatest discriminant function in the prediction model. Although clinical characteristics of patients with MMD showed great accuracy in predicting outcomes (64.7%), adding information on functional connections improved accuracy to 91.2%.

CONCLUSION

Preoperative functional connectivity derived from rs-fMRI may be an early hallmark for predicting patients' prognosis after revascularization surgery for MMD.

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.

Cortical thickness of contralesional cortices positively relates to future outcome after severe stroke

Imaging studies have evidenced that contralesional cortices are involved in recovery after motor stroke. Cortical thickness (CT) analysis has proven its potential to capture the changes of cortical anatomy, which have been related to recovery and treatment gains under therapy. An open question is whether CT obtained in the acute phase after stroke might inform correlational models to explain outcome variability. Data of 38 severely impaired (median NIH Stroke Scale 9, interquartile range: 6-13) acute stroke patients of 2 independent cohorts were reanalyzed. Structural imaging data were processed via the FreeSurfer pipeline to quantify regional CT of the contralesional hemisphere. Ordinal logistic regression models were fit to relate CT to modified Rankin Scale as an established measure of global disability after 3-6 months, adjusted for the initial deficit, lesion volume, and age. The data show that CT of contralesional cortices, such as the precentral gyrus, the superior frontal sulcus, and temporal and cingulate cortices, positively relates to the outcome after stroke. This work shows that the baseline cortical anatomy of selected contralesional cortices can explain the outcome variability after severe stroke, which further contributes to the concept of structural brain reserve with respect to contralesional cortices to promote recovery.

Personalized neurorehabilitative precision medicine: from data to therapies (MWKNeuroReha) - a multi-centre prospective observational clinical trial to predict long-term outcome of patients with acute motor stroke

BACKGROUND

Stroke is one of the most frequent diseases, and half of the stroke survivors are left with permanent impairment. Prediction of individual outcome is still difficult. Many but not all patients with stroke improve by approximately 1.7 times the initial impairment, that has been termed proportional recovery rule. The present study aims at identifying factors predicting motor outcome after stroke more accurately than before, and observe associations of rehabilitation treatment with outcome.

METHODS

The study is designed as a multi-centre prospective clinical observational trial. An extensive primary data set of clinical, neuroimaging, electrophysiological, and laboratory data will be collected within 96 h of stroke onset from patients with relevant upper extremity deficit, as indexed by a Fugl-Meyer-Upper Extremity (FM-UE) score ≤ 50. At least 200 patients will be recruited. Clinical scores will include the FM-UE score (range 0-66, unimpaired function is indicated by a score of 66), Action Research Arm Test, modified Rankin Scale, Barthel Index and Stroke-Specific Quality of Life Scale. Follow-up clinical scores and applied types and amount of rehabilitation treatment will be documented in the rehabilitation hospitals. Final follow-up clinical scoring will be performed 90 days after the stroke event. The primary endpoint is the change in FM-UE defined as 90 days FM-UE minus initial FM-UE, divided by initial FM-UE impairment. Changes in the other clinical scores serve as secondary endpoints. Machine learning methods will be employed to analyze the data and predict primary and secondary endpoints based on the primary data set and the different rehabilitation treatments.

DISCUSSION

If successful, outcome and relation to rehabilitation treatment in patients with acute motor stroke will be predictable more reliably than currently possible, leading to personalized neurorehabilitation. An important regulatory aspect of this trial is the first-time implementation of systematic patient data transfer between emergency and rehabilitation hospitals, which are divided institutions in Germany.

TRIAL REGISTRATION

This study was registered at ClinicalTrials.gov ( NCT04688970 ) on 30 December 2020.

Functional connectivity drives stroke recovery: shifting the paradigm from correlation to causation

Stroke is a leading cause of disability, with deficits encompassing multiple functional domains. The heterogeneity underlying stroke poses significant challenges in the prediction of post-stroke recovery, prompting the development of neuroimaging-based biomarkers. Structural neuroimaging measurements, particularly those reflecting corticospinal tract injury, are well-documented in the literature as potential biomarker candidates of post-stroke motor recovery. Consistent with the view of stroke as a 'circuitopathy', functional neuroimaging measures probing functional connectivity may also prove informative in post-stroke recovery. An important step in the development of biomarkers based on functional neural network connectivity is the establishment of causality between connectivity and post-stroke recovery. Current evidence predominantly involves statistical correlations between connectivity measures and post-stroke behavioural status, either cross-sectionally or serially over time. However, the advancement of functional connectivity application in stroke depends on devising experiments that infer causality. In 1965, Sir Austin Bradford Hill introduced nine viewpoints to consider when determining the causality of an association: (i) strength; (ii) consistency; (iii) specificity; (iv) temporality; (v) biological gradient; (vi) plausibility; (vii) coherence; (viii) experiment; and (ix) analogy. Collectively referred to as the Bradford Hill Criteria, these points have been widely adopted in epidemiology. In this review, we assert the value of implementing Bradford Hill's framework to stroke rehabilitation and neuroimaging. We focus on the role of neural network connectivity measurements acquired from task-oriented and resting-state functional MRI, EEG, magnetoencephalography and functional near-infrared spectroscopy in describing and predicting post-stroke behavioural status and recovery. We also identify research opportunities within each Bradford Hill tenet to shift the experimental paradigm from correlation to causation.

Abnormal brain functional and structural connectivity between the left supplementary motor area and inferior frontal gyrus in moyamoya disease

BACKGROUND

Disruption of brain functional connectivity has been detected after stroke, but whether it also occurs in moyamoya disease (MMD) is unknown. Impaired functional connectivity is always correlated with abnormal white matter fibers. Herein, we used multimodal imaging techniques to explore the changes in brain functional and structural connectivity in MMD patients.

METHODS

We collected structural images, resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging for each subject. Cognitive functions of MMD patients were evaluated using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Trail Making Test parts A and B (TMT-A/-B). We calculated the functional connectivity for every paired region using 90 regions of interest from the Anatomical Automatic Labeling Atlas and then determined the differences between MMD patients and HCs. We extracted the functional connectivity of paired brain regions with significant differences between the two groups. Correlation analyses were then performed between the functional connectivity and variable cognitive functions. To explore whether the impaired functional connectivity and cognitive performances were attributed to the destruction of white matter fibers, we further analyzed fiber integrity using tractography between paired regions that were correlated with cognition.

RESULTS

There was lower functional connectivity in MMD patients as compared to HCs between the bilateral inferior frontal gyrus, between the bilateral supramarginal gyrus, between the left supplementary motor area (SMA) and the left orbital part of the inferior frontal gyrus (IFGorb), and between the left SMA and the left middle temporal gyrus (P < 0.01, FDR corrected). The decreased functional connectivity between the left SMA and the left IFGorb was significantly correlated with the MMSE (r = 0.52, P = 0.024), MoCA (r = 0.60, P = 0.006), and TMT-B (r = -0.54, P = 0.048) in MMD patients. White matter fibers were also injured between the SMA and IFGorb in the left hemisphere and were positively correlated with reduced functional connectivity.

CONCLUSIONS

Brain functional and structural connectivity between the supplementary motor area and inferior frontal gyrus in the left hemisphere are damaged in MMD. These findings could be useful in the evaluation of disease progression and prognosis of MMD.

Neuroimaging Techniques as Potential Tools for Assessment of Angiogenesis and Neuroplasticity Processes after Stroke and Their Clinical Implications for Rehabilitation and Stroke Recovery Prognosis

Stroke as the most frequent cause of disability is a challenge for the healthcare system as well as an important socio-economic issue. Therefore, there are currently a lot of studies dedicated to stroke recovery. Stroke recovery processes include angiogenesis and neuroplasticity and advances in neuroimaging techniques may provide indirect description of this action and become quantifiable indicators of these processes as well as responses to the therapeutical interventions. This means that neuroimaging and neurophysiological methods can be used as biomarkers—to make a prognosis of the course of stroke recovery and define patients with great potential of improvement after treatment. This approach is most likely to lead to novel rehabilitation strategies based on categorizing individuals for personalized treatment. In this review article, we introduce neuroimaging techniques dedicated to stroke recovery analysis with reference to angiogenesis and neuroplasticity processes. The most beneficial for personalized rehabilitation are multimodal panels of stroke recovery biomarkers, including neuroimaging and neurophysiological, genetic-molecular and clinical scales.

The Prognostic Utility of Electroencephalography in Stroke Recovery: A Systematic Review and Meta-Analysis

BACKGROUND

Improved ability to predict patient recovery would guide post-stroke care by helping clinicians personalize treatment and maximize outcomes. Electroencephalography (EEG) provides a direct measure of the functional neuroelectric activity in the brain that forms the basis for neuroplasticity and recovery, and thus may increase prognostic ability.

OBJECTIVE

To examine evidence for the prognostic utility of EEG in stroke recovery via systematic review/meta-analysis.

METHODS

Peer-reviewed journal articles that examined the relationship between EEG and subsequent clinical outcome(s) in stroke were searched using electronic databases. Two independent researchers extracted data for synthesis. Linear meta-regressions were performed across subsets of papers with common outcome measures to quantify the association between EEG and outcome.

RESULTS

75 papers were included. Association between EEG and clinical outcomes was seen not only early post-stroke, but more than 6 months post-stroke. The most studied prognostic potential of EEG was in predicting independence and stroke severity in the standard acute stroke care setting. The meta-analysis showed that EEG was associated with subsequent clinical outcomes measured by the Modified Rankin Scale, National Institutes of Health Stroke Scale, and Fugl-Meyer Upper Extremity Assessment (r = .72, .70, and .53 from 8, 13, and 12 papers, respectively). EEG improved prognostic abilities beyond prediction afforded by standard clinical assessments. However, the EEG variables examined were highly variable across studies and did not converge.

CONCLUSIONS

EEG shows potential to predict post-stroke recovery outcomes. However, evidence is largely explorative, primarily due to the lack of a definitive set of EEG measures to be used for prognosis.

Functional brain networks assessed with surface electroencephalography for predicting motor recovery in a neural guided intervention for chronic stroke

Predicting whether a chronic stroke patient is likely to benefit from a specific intervention can help patients establish reasonable expectations. It also provides the basis for candidates selecting for the intervention. Recent convergent evidence supports the value of network-based approach for understanding the relationship between dysfunctional neural activity and motor deficits after stroke. In this study, we applied resting-state brain connectivity networks to investigate intervention-specific predictive biomarkers of motor improvement in 22 chronic stroke participants who received either combined action observation with EEG-guided robot-hand training (Neural Guided-Action Observation Group, n = 12, age: 34–68 years) or robot-hand training without action observation and EEG guidance (non-Neural Guided-text group, n = 10, age: 42–57 years). The robot hand in Neural Guided-Action Observation training was activated only when significant mu suppression (8–12 Hz) was detected from participant’s EEG signals in ipsilesional hemisphere while it was randomly activated in non-Neural Guided-text training. Only the Neural Guided-Action Observation group showed a significant long-term improvement in their upper-limb motor functions (P < 0.5). In contrast, no significant training effect on the paretic motor functions was found in the non-Neural Guided-text group (P > 0.5). The results of brain connectivity estimated via EEG coherence showed that the pre-training interhemispheric connectivity of delta, theta, alpha and contralesional connectivity of beta were motor improvement related in the Neural Guided-Action Observation group. They can not only differentiate participants with good and poor recovery (interhemispheric delta: P = 0.047, Hedges’ g = 1.409; interhemispheric theta: P = 0.046, Hedges’ g = 1.333; interhemispheric alpha: P = 0.038, Hedges’ g = 1.536; contralesional beta: P = 0.027, Hedges’ g = 1.613) but also significantly correlated with post-training intervention gains (interhemispheric delta: r = −0.901, P < 0.05; interhemispheric theta: r = −0.702, P < 0.05; interhemispheric alpha: r = −0.641, P < 0.05; contralesional beta: r = −0.729, P < 0.05). In contrast, no EEG coherence was significantly correlated with intervention gains in the non-Neural Guided-text group (all Ps>0.05). Partial least square regression showed that the combination of pre-training interhemispheric and contralesional local connectivity could precisely predict intervention gains in the Neural Guided-Action Observation group with a strong correlation between predicted and observed intervention gains (r = 0.82r=0.82) and between predicted and observed intervention outcomes (r = 0.90r=0.90). In summary, EEG-based resting-state brain connectivity networks may serve clinical decision-making by offering an approach to predicting Neural Guided-Action Observation training-induced motor improvement.

Functional Connectivity Hypointensity of Middle Cingulate Gyrus and Thalamus in Age-Related Macular Degeneration Patients: A Resting-State Functional Magnetic Resonance Imaging Study

Objective

Age-related macular degeneration (AMD) causes visual damage and blindness globally. The purpose of this study was to investigate changes in functional connectivity (FC) in AMD patients using resting-state functional magnetic resonance imaging (rs-fMRI).

Subjects and Methods

A total of 23 patients (12 male, 11 female) with AMD were enrolled to the AMD patients group (AMDs), and 17 healthy age-, sex-, and education-matched controls (9 male, 8 female) to the healthy controls group (HCs). All participants underwent rs-fMRI and mean FC values were compared between the two groups.

Results

Significantly higher FC values were found in the inferior frontal gyrus (IFG), superior frontal gyrus (SFG), inferior parietal lobule (IPL), rectal gyrus (RTG), and superior parietal lobule (SPL) in AMDs compared with HCs. Conversely, FC values in the cerebellum posterior lobe (CPL), middle cingulate gyrus (MCG), medulla (MDL), cerebellum anterior lobe (CAL), and thalamus (TLM) were significantly lower in AMDs than in HCs.

Conclusion

This study demonstrated FC abnormalities in many specific cerebral regions in AMD patients, and may provide new insights for exploration of potential pathophysiological mechanism of AMD-induced functional cerebral changes.

Decreased Functional Connectivities of Low-Degree Level Rich Club Organization and Caudate in Post-stroke Cognitive Impairment Based on Resting-State fMRI and Radiomics Features

Background

Stroke is an important cause of cognitive impairment. Rich club organization, a highly interconnected network brain core region, is closely related to cognition. We hypothesized that the disturbance of rich club organization exists in patients with post-stroke cognitive impairment (PSCI).

Methods

We collected data on resting-state functional magnetic resonance imaging (rs-fMRI) with 21 healthy controls (HC), 16 hemorrhagic stroke (hPSCI), and 21 infarct stroke (iPSCI). 3D shape features and first-order statistics of stroke lesions were extracted using 3D slicer software. Additionally, we assessed cognitive function using the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE).

Results

Normalized rich club coefficients were higher in hPSCI and iPSCI than HC at low-degree k-levels (k = 1–8 in iPSCI, k = 2–8 in hPSCI). Feeder and local connections were significantly decreased in PSCI patients versus HC, mainly distributed in salience network (SN), default-mode network (DMN), cerebellum network (CN), and orbitofrontal cortex (ORB), especially involving the right and left caudate with changed nodal efficiency. The feeder and local connections of significantly between-group difference were positively related to MMSE and MoCA scores, primarily distributed in the sensorimotor network (SMN) and visual network (VN) in hPSCI, SN, and DMN in iPSCI. Additionally, decreased local connections and low-degree ϕnorm(k) were correlated to 3D shape features and first-order statistics of stroke lesions.

Conclusion

This study reveals the disrupted low-degree level rich club organization and relatively preserved functional core network in PSCI patients. Decreased feeder and local connections in cognition-related networks (DMN, SN, CN, and ORB), particularly involving the caudate nucleus, may offer insight into pathological mechanism of PSCI patients. The shape and signal features of stroke lesions may provide an essential clue for the damage of functional connectivity and the whole brain networks.

Reduced Severity of Tissue Injury Within the Infarct May Partially Mediate the Benefit of Reperfusion in Ischemic Stroke

BACKGROUND

Emerging data suggest tissue within the infarct lesion is not homogenously damaged following ischemic stroke but has a gradient of injury. Using blood-brain-barrier (BBB) disruption as a marker of tissue injury, we tested whether therapeutic reperfusion improves clinical outcome by reducing the severity of tissue injury within the infarct in patients with ischemic stroke.

METHODS

In a pooled analysis of patients treated for anterior circulation large vessel occlusion in the EXTEND-IA TNK (Tenecteplase Versus Alteplase Before Endovascular Therapy for Ischemic Stroke) and EXTEND-IA part-2 (Determining the Optimal Dose of Tenecteplase Before Endovascular Therapy for Ischaemic Stroke) trials, post-treatment BBB permeability at 24 hours was calculated based on the extent of T1-brightening by extravascular gadolinium on T2* perfusion-weighted imaging and measured within the diffusion-weighted-imaging lesion. First, to determine the clinical significance of BBB disruption as a marker of severity of tissue injury, we examined the association between post-treatment BBB permeability and functional outcome. Second, we performed an exploratory (reperfusion, BBB permeability, functional outcome) mediation analysis to estimate the proportion of the reperfusion-outcome relationship that is mediated by change in BBB permeability.

RESULTS

In the 238 patients analyzed, an increased BBB permeability measured within the infarct at 24 hours was associated with a reduced likelihood of favorable outcome (90-day modified Rankin Scale score of ≤2) after adjusting for age, baseline National Institutes of Health Stroke Scale, premorbid modified Rankin Scale, infarct topography, laterality, thrombolytic agent, sex, parenchymal hematoma, and follow-up infarct volume (adjusted odds ratio, 0.86 [95% CI, 0.75-0.98], P=0.023). Mediation analysis suggested reducing the severity of tissue injury (as estimated by BBB permeability) accounts for 18.2% of the association between reperfusion and favorable outcome, as indicated by a reduction in the regression coefficient of reperfusion after addition of BBB permeability as a covariate.

CONCLUSIONS

In patients with ischemic stroke, reduced severity of tissue injury within the infarct, as determined by assessing the integrity of the BBB, is independently associated with improved functional outcome. In addition to reducing diffusion-weighted imaging-defined infarct volume, reperfusion may also improve clinical outcome by reducing tissue injury severity within the infarct.

Biomarkers of plasticity for stroke recovery

Stroke is the commonest cause of physical disability in the world. Our understanding of the biologic mechanisms involved in recovery and repair has advanced to the point that therapeutic opportunities to promote recovery through manipulation of post-stroke plasticity have never been greater. This work has almost exclusively been carried out in rodent models of stroke with little translation into human studies. The challenge ahead is to develop a mechanistic understanding of recovery from stroke in humans. Advances in neuroimaging techniques can now provide the appropriate intermediate level of description to bridge the gap between a molecular and cellular account of recovery and a behavioral one. Clinical trials can then be designed in a stratified manner taking into account when an intervention should be delivered and who is most likely to benefit. This approach is most likely to lead to the step-change in how restorative therapeutic strategies are delivered in human stroke patients.

Longitudinal Changes of Sensorimotor Resting-State Functional Connectivity Differentiate between Patients with Thalamic Infarction and Pontine Infarction

Purpose. We investigated the disparate influence of lesion location on functional damage and reorganization of the sensorimotor brain network in patients with thalamic infarction and pontine infarction. Methods. Fourteen patients with unilateral infarction of the thalamus and 14 patients with unilateral infarction of the pons underwent longitudinal fMRI measurements and motor functional assessment five times during a 6-month period (<7 days, at 2 weeks, 1 month, 3 months, and 6 months after stroke onset). Twenty-five age- and sex-matched controls underwent MRI examination across five consecutive time points in 6 months. Functional images from patients with left hemisphere lesions were first flipped from the left to the right side. The voxel-wise connectivity analyses between the reference time course of each ROI (the contralateral dorsal lateral putamen (dl-putamen), pons, ventral anterior (VA), and ventral lateral (VL) nuclei of the thalamus) and the time course of each voxel in the sensorimotor area were performed for all five measurements. One-way ANOVA was used to identify between-group differences in functional connectivity (FC) at baseline stage (<7 days after stroke onset), with infarction volume included as a nuisance variable. The family-wise error (FWE) method was used to account for multiple comparison issues using SPM software. Post hoc repeated-measure ANOVA was applied to examine longitudinal FC reorganization. Results. At baseline stage, significant differences were detected between the contralateral VA and ipsilateral postcentral gyrus (cl_VA-ip_postcentral), contralateral VL and ipsilateral precentral gyrus (cl_VL-ip_precentral). Repeated measures ANOVA revealed that the FC change of cl_VA-ip_postcentral differ significantly among the three groups over time. The significant changes of FC between cl_VA and ip_postcentral at different time points in the thalamic infarction group showed that compared with 7 days after stroke onset, there was significantly increased FC of cl_VA-ip_postcentral at 1 month, 3 months, and 6 months after stroke onset. Conclusions. The different patterns of sensorimotor functional damage and reorganization in patients with pontine infarction and thalamic infarction may provide insights into the neural mechanisms underlying functional recovery after stroke.

Combined lesion-deficit and fMRI approaches in single-case studies: Unique contributions to cognitive neuroscience

Although lesion-deficit case studies are foundational in cognitive neuroscience, published papers presenting single lesion cases are declining. In this review, we argue that there is a valuable place for single-case lesion-deficit research, especially when combined with functional neuroimaging methods, such as functional magnetic resonance imaging (fMRI). To support this, we present a summary of notable findings from single-case combined lesion-deficit and fMRI studies published in recent years (2017-2020). These studies show the unique value that this combined approach brings to the understanding of complex functions, brain-level connectivity, and plasticity and recovery. We encourage researchers to consider combining lesion-deficit and functional imaging methods in the analysis of single cases, as this approach affords unique opportunities to address challenging unanswered questions about brain-behavior relationships.

Translating Functional Connectivity After Stroke: Functional Magnetic Resonance Imaging Detects Comparable Network Changes in Mice and Humans

[Figure: see text].

Brain Functional Changes in Stroke Following Rehabilitation Using Brain-Computer Interface-Assisted Motor Imagery With and Without tDCS: A Pilot Study

Brain-computer interface-assisted motor imagery (MI-BCI) or transcranial direct current stimulation (tDCS) has been proven effective in post-stroke motor function enhancement, yet whether the combination of MI-BCI and tDCS may further benefit the rehabilitation of motor functions remains unknown. This study investigated brain functional activity and connectivity changes after a 2 week MI-BCI and tDCS combined intervention in 19 chronic subcortical stroke patients. Patients were randomized into MI-BCI with tDCS group and MI-BCI only group who underwent 10 sessions of 20 min real or sham tDCS followed by 1 h MI-BCI training with robotic feedback. We derived amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) from resting-state functional magnetic resonance imaging (fMRI) data pre- and post-intervention. At baseline, stroke patients had lower ALFF in the ipsilesional somatomotor network (SMN), lower ReHo in the contralesional insula, and higher ALFF/Reho in the bilateral posterior default mode network (DMN) compared to age-matched healthy controls. After the intervention, the MI-BCI only group showed increased ALFF in contralesional SMN and decreased ALFF/Reho in the posterior DMN. In contrast, no post-intervention changes were detected in the MI-BCI + tDCS group. Furthermore, higher increases in ALFF/ReHo/FC measures were related to better motor function recovery (measured by the Fugl-Meyer Assessment scores) in the MI-BCI group while the opposite association was detected in the MI-BCI + tDCS group. Taken together, our findings suggest that brain functional re-normalization and network-specific compensation were found in the MI-BCI only group but not in the MI-BCI + tDCS group although both groups gained significant motor function improvement post-intervention with no group difference. MI-BCI and tDCS may exert differential or even opposing impact on brain functional reorganization during post-stroke motor rehabilitation; therefore, the integration of the two strategies requires further refinement to improve efficacy and effectiveness.

Altered hippocampal functional connectivity patterns in patients with cognitive impairments following ischaemic stroke: A resting-state fMRI study

BACKGROUND

Ischemic stroke with cognitive impairment is a considerable risk factor for developing dementia. Identifying imaging markers of cognitive impairment following ischemic stroke will help to develop prevention strategies against post-stroke dementia.

METHODS

We investigated the hippocampal functional connectivity (FC) pattern following ischemic stroke, using resting-state fMRI (rs-fMRI). Thirty-three cognitively impaired patients after ischemic stroke and sixteen age-matched controls with no known history of neurological disorder were recruited for the study. No patient had a direct ischaemic insult to hippocampus on the examination of brain imaging. Seven subfields of hippocampus were used as seeds region for FC analyses.

RESULTS

Across all hippocampal subfields, FC with the inferior parietal lobule was reduced in stroke patients as compared with healthy controls. This decreased FC included both supramarginal gyrus and angular gyrus. The FC of hippocampal subfields with cerebellum was increased. Importantly, the degree of the altered FC between hippocampal subfields and inferior parietal lobule was associated with their impaired memory function.

CONCLUSION

Our results demonstrated that decreased hippocampal-inferior parietal lobule connectivity was associated with cognitive impairment in patients with ischemic stroke. These findings provide novel insights into the role of hippocampus in cognitive impairment following ischemic stroke.

A Clinical-Radiomics Nomogram for Functional Outcome Predictions in Ischemic Stroke

INTRODUCTION

Stroke remains a leading cause of death and disability worldwide. Effective and prompt prognostic evaluation is vital for determining the appropriate management strategy. Radiomics is an emerging noninvasive method used to identify the quantitative imaging indicators for predicting important clinical outcomes. This study was conducted to investigate and validate a radiomics nomogram for predicting ischemic stroke prognosis using the modified Rankin scale (mRS).

METHODS

A total of 598 consecutive patients with subacute infarction confirmed by diffusion-weighted imaging (DWI), from January 2018 to December 2019, were retrospectively assessed. They were assigned to the good (mRS ≤ 2) and poor (mRS > 2) functional outcome groups, respectively. Then, 399 patients examined by MR scanner 1 and 199 patients scanned by MR scanner 2 were assigned to the training and validation cohorts, respectively. Infarction lesions underwent manual segmentation on DWI, extracting 402 radiomic features. A radiomics nomogram encompassing patient characteristics and the radiomics signature was built using a multivariate logistic regression model. The performance of the nomogram was evaluated in the training and validation cohorts. Ultimately, decision curve analysis was implemented to assess the clinical value of the nomogram. The performance of infarction lesion volume was also evaluated using univariate analysis.

RESULTS

Stroke lesion volume showed moderate performance, with an area under the curve (AUC) of 0.678. The radiomics signature, including 11 radiomics features, exhibited good prediction performance. The radiomics nomogram, encompassing clinical characteristics (age, hemorrhage, and 24 h National Institutes of Health Stroke Scale score) and the radiomics signature, presented good discriminatory potential in the training cohort [AUC = 0.80; 95% confidence interval (CI) 0.75-0.86], which was validated in the validation cohort (AUC = 0.73; 95% CI 0.63-0.82). In addition, it demonstrated good calibration in the training (p = 0.55) and validation (p = 0.21) cohorts. Decision curve analysis confirmed the clinical value of this nomogram.

CONCLUSION

This novel noninvasive clinical-radiomics nomogram shows good performance in predicting ischemic stroke prognosis.

High-resolution in vivo imaging of rhesus cerebral cortex with ultrafast portable photoacoustic microscopy

Revealing the structural and functional change of microvasculature is essential to match vascular response with neuronal activities in the investigation of neurovascular coupling. The increasing use of rhesus models in fundamental and clinical studies of neurovascular coupling presents an emerging need for a new imaging modality. Here we report a structural and functional cerebral vascular study of rhesus monkeys using an ultrafast, portable, and high resolution photoacoustic microscopic system with a long working distance and a special scanning mechanism to eliminate the relative displacement between the imaging interface and samples. We derived the structural and functional response of the cerebral vasculature to the alternating normoxic and hypoxic conditions by calculating the vascular diameter and functional connectivity. Both vasodilatation and vasoconstriction were observed in hypoxia. In addition to the change of vascular diameter, the decrease of functional connectivity is also an important phenomenon induced by the reduction of oxygen ventilatory. These results suggest that photoacoustic microscopy is a promising method to study the neurovascular coupling and cerebral vascular diseases due to the advanced features of high spatiotemporal resolution, excellent sensitivity to hemoglobin, and label-free imaging capability of observing hemodynamics.

Calhoun V, Grefkes C, Rost NS. Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke

The aim of the current study was to explore the whole-brain dynamic functional connectivity patterns in acute ischemic stroke (AIS) patients and their relation to short and long-term stroke severity. We investigated resting-state functional MRI-based dynamic functional connectivity of 41 AIS patients two to five days after symptom onset. Re-occurring dynamic connectivity configurations were obtained using a sliding window approach and k-means clustering. We evaluated differences in dynamic patterns between three NIHSS-stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we built Bayesian hierarchical models to evaluate the predictive capacity of dynamic connectivity and examine the interrelation with clinical measures, such as white matter hyperintensity lesions. Finally, we established correlation analyses between dynamic connectivity and AIS severity as well as 90-day neurological recovery (ΔNIHSS). We identified three distinct dynamic connectivity configurations acutely post-stroke. More severely affected patients spent significantly more time in a configuration that was characterized by particularly strong connectivity and isolated processing of functional brain domains (three-level ANOVA: p < .05, post hoc t tests: p < .05, FDR-corrected). Configuration-specific time estimates possessed predictive capacity of stroke severity in addition to the one of clinical measures. Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson's r = -.68, p = .003, FDR-corrected). Our findings demonstrate transiently increased isolated information processing in multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first 3 months poststroke.

Resting State Connectivity Is Modulated by Motor Learning in Individuals After Stroke

Objective

Activity patterns across brain regions that can be characterized at rest (ie, resting-state functional connectivity [rsFC]) are disrupted after stroke and linked to impairments in motor function. While changes in rsFC are associated with motor recovery, it is not clear how rsFC is modulated by skilled motor practice used to promote recovery. The current study examined how rsFC is modulated by skilled motor practice after stroke and how changes in rsFC are linked to motor learning.

Methods

Two groups of participants (individuals with stroke and age-matched controls) engaged in 4 weeks of skilled motor practice of a complex, gamified reaching task. Clinical assessments of motor function and impairment, and brain activity (via functional magnetic resonance imaging) were obtained before and after training.

Results

While no differences in rsFC were observed in the control group, increased connectivity was observed in the sensorimotor network, linked to learning in the stroke group. Relative to healthy controls, a decrease in network efficiency was observed in the stroke group following training.

Conclusions

Findings indicate that rsFC patterns related to learning observed after stroke reflect a shift toward a compensatory network configuration characterized by decreased network efficiency.

Whole-Brain Dynamics in Aging: Disruptions in Functional Connectivity and the Role of the Rich Club

Normal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state functional magnetic resonance imaging studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here, we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent signals to analyze resting-state fMRI data from 620 subjects divided into two groups (middle-age group (n = 310); age range, 50-64 years versus older group (n = 310); age range, 65-91 years). Applying the intrinsic-ignition framework to assess the effect of spontaneous local activation events on local-global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.

Gut microbiota from mice with cerebral ischemia-reperfusion injury affects the brain in healthy mice

Gut microorganisms can profoundly influence brain function in the host and their behavior. Since altered brain functional connectivity (FC) has been implicated in various cerebrovascular disorders, including cerebral ischemia-reperfusion (I/R) injury, we hypothesized that gut microbiota in mice with cerebral I/R injury would affect brain FC when transplanted into germ-free mice. Metagenomic analysis of germ-free male C57BL/6J mice colonized with microbiota from mice with and without cerebral I/R injury showed a clear distinction in microbiota composition between mice colonized with control and I/R microbiota. The I/R microbiota-colonized mice showed decreased FC in the cingulate cortex, hippocampus, and thalamus, and exhibited increased anxiety as well as diminished spatial learning and memory and short-term object recognition memory. I/R microbiota-colonized mice also had significantly reduced dendritic spine density and synaptic protein levels and exhibited increased hippocampal inflammation. These results indicate that gut microbiota components from mice with cerebral I/R injury can alter animal behavior, brain functional connectivity, hippocampal neuronal plasticity, and neuroinflammation. Moreover, they increase our understanding of the mechanisms through which the gut microbiome contributes to the pathobiology of cerebrovascular diseases.

Prediction of Long-term Cognitive Function After Minor Stroke Using Functional Connectivity

OBJECTIVE

To determine whether functional MRI connectivity can predict long-term cognitive function 36 months after minor stroke.

METHODS

Seventy-two participants with first-ever stroke were included at baseline and followed up for 36 months. A ridge regression machine learning algorithm was developed and used to predict cognitive scores 36 months poststroke on the basis of the functional networks measured using MRI at 6 months (referred to here as the poststroke cognitive impairment [PSCI] network). The prediction accuracy was evaluated in 4 domains (memory, attention/executive, language, and visuospatial functions) and compared with clinical data and other functional networks. The models' statistical significance was probed with permutation tests. The potential involvement of cortical atrophy was assessed 6 months poststroke. A second, independent dataset (n = 40) was used to validate the results and assess their generalizability.

RESULTS

Based on the PSCI network, a machine learning model was able to predict memory, attention, visuospatial functions, and language functions 36 months poststroke (r 2: 0.67, 0.73, 0.55, and 0.48, respectively). The PSCI-based model was at least as accurate as models based on other functional networks or clinical data. Specific patterns were demonstrated for the 4 cognitive domains, with involvement of the left superior frontal cortex for memory, attention, and visuospatial functions. The cortical thickness 6 months poststroke was not correlated with cognitive function 36 months poststroke. The independent validation dataset gave similar results.

CONCLUSIONS

A machine learning model based on the PSCI network can predict long-term cognitive outcome after stroke.

Subacute functional connectivity correlates with cognitive recovery six months after stroke

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Stroke disrupts ipsilesional and inter-hemispheric functional connectivity of DMN.

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Subacute cognition correlated to inter-hemispheric and ipsilesional DMN connectivity.

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Subacute cognition correlated to weaker contralesional SN connectivity.

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Functional connectivity remapping was not observed after six months.

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Cognitive recovery correlated to DMN and SN connectivity from the subacute phase.

Background and purpose

Cognitive impairment is a common consequence of stroke, and the rewiring of the surviving brain circuits might contribute to cognitive recovery. Studies investigating how the functional connectivity of networks change across time and whether their remapping relates to cognitive recovery in stroke patients are scarce. We aimed to investigate whether resting-state functional connectivity was associated with cognitive performance in stroke patients and if any alterations in these networks were correlated with cognitive recovery.

Methods

Using an fMRI ROI-ROI approach, we compared the ipsilesional, contralesional and interhemispheric functional connectivity of three resting-state networks involved in cognition – the Default Mode (DMN), Salience (SN) and Central Executive Networks (CEN), in subacute ischemic stroke patients (time 1, n = 37, stroke onset: 24.32 ± 7.44 days, NIHSS: 2.66 ± 3.45) with cognitively healthy controls (n = 20). Patients were reassessed six months after the stroke event (time 2, n = 20, stroke onset: 182.05 ± 8.17 days) to verify the subsequent reorganization of functional connections and whether such reorganization was associated with cognitive recovery.

Results

At time 1, patients had weaker interhemispheric connectivity in the DMN than controls; better cognitive performance at time 1 was associated with stronger interhemispheric and ipsilesional DMN connectivity, and weaker contralesional SN connectivity. At time 2, there were no changes in functional connectivity in stroke patients, compared to time 1. Better cognitive recovery measured at time 2 (time 2 – time 1) was associated with stronger functional connectivity in the DMN, and weaker interhemispheric subacute connectivity in the SN, both from time 1.

Conclusions

Stroke disrupts the functional connectivity of the DMN, not only at the lesioned hemisphere but also between hemispheres. Six months after the stroke event, we could not detect the remapping of networks. Cognitive recovery was associated with the connectivity of both the DMN and SN of time 1. Our findings may be helpful for facilitating further understanding of the potential mechanisms underlying post-stroke cognitive performance.

Functional Connectivity Analysis on Resting-State Electroencephalography Signals Following Chiropractic Spinal Manipulation in Stroke Patients

Stroke impairments often present as cognitive and motor deficits, leading to a decline in quality of life. Recovery strategy and mechanisms, such as neuroplasticity, are important factors, as these can help improve the effectiveness of rehabilitation. The present study investigated chiropractic spinal manipulation (SM) and its effects on resting-state functional connectivity in 24 subacute to chronic stroke patients monitored by electroencephalography (EEG). Functional connectivity of both linear and non-linear coupling was estimated by coherence and phase lag index (PLI), respectively. Non-parametric cluster-based permutation tests were used to assess the statistical significance of the changes in functional connectivity following SM. Results showed a significant increase in functional connectivity from the PLI metric in the alpha band within the default mode network (DMN). The functional connectivity between the posterior cingulate cortex and parahippocampal regions increased following SM, t (23) = 10.45, p = 0.005. No significant changes occurred following the sham control procedure. These findings suggest that SM may alter functional connectivity in the brain of stroke patients and highlights the potential of EEG for monitoring neuroplastic changes following SM. Furthermore, the altered connectivity was observed between areas which may be affected by factors such as decreased pain perception, episodic memory, navigation, and space representation in the brain. However, these factors were not directly monitored in this study. Therefore, further research is needed to elucidate the underlying mechanisms and clinical significance of the observed changes.

Resting-State Functional Magnetic Resonance Imaging Connectivity Between Semantic and Phonological Regions of Interest May Inform Language Targets in Aphasia

Purpose Brain imaging has provided puzzle pieces in the understanding of language. In neurologically healthy populations, the structure of certain brain regions is associated with particular language functions (e.g., semantics, phonology). In studies on focal brain damage, certain brain regions or connections are considered sufficient or necessary for a given language function. However, few of these account for the effects of lesioned tissue on the "functional" dynamics of the brain for language processing. Here, functional connectivity (FC) among semantic-phonological regions of interest (ROIs) is assessed to fill a gap in our understanding about the neural substrates of impaired language and whether connectivity strength can predict language performance on a clinical tool in individuals with aphasia. Method Clinical assessment of language, using the Western Aphasia Battery-Revised, and resting-state functional magnetic resonance imaging data were obtained for 30 individuals with chronic aphasia secondary to left-hemisphere stroke and 18 age-matched healthy controls. FC between bilateral ROIs was contrasted by group and used to predict Western Aphasia Battery-Revised scores. Results Network coherence was observed in healthy controls and participants with stroke. The left-right premotor cortex connection was stronger in healthy controls, as reported by New et al. (2015) in the same data set. FC of (a) connections between temporal regions, in the left hemisphere and bilaterally, predicted lexical-semantic processing for auditory comprehension and (b) ipsilateral connections between temporal and frontal regions in both hemispheres predicted access to semantic-phonological representations and processing for verbal production. Conclusions Network connectivity of brain regions associated with semantic-phonological processing is predictive of language performance in poststroke aphasia. The most predictive connections involved right-hemisphere ROIs-particularly those for which structural adaptions are known to associate with recovered word retrieval performance. Predictions may be made, based on these findings, about which connections have potential as targets for neuroplastic functional changes with intervention in aphasia. Supplemental Material https://doi.org/10.23641/asha.12735785.

Establishing an acquisition and processing protocol for resting state networks with a 1.5 T scanner: A case series in a middle-income country

OBJECTIVE

The aim of this study was to characterize the capability of detection of the resting state networks (RSNs) with functional magnetic resonance imaging (fMRI) in healthy subjects using a 1.5T scanner in a middle-income country.

MATERIALS AND METHODS

Ten subjects underwent a complete blood-oxygen-level dependent imaging (BOLD) acquisition on a 1.5T scanner. For the imaging analysis, we used the spatial independent component analysis (sICA). We designed a computer tool for 1.5 T (or above) scanners for imaging processing. We used it to separate and delineate the different components of the RSNs of the BOLD signal. The sICA was also used to differentiate the RSNs from noise artifact generated by breathing and cardiac cycles.

RESULTS

For each subject, 20 independent components (IC) were computed from the sICA (a total of 200 ICs). From these ICs, a spatial pattern consistent with RSNs was identified in 161 (80.5%). From the 161, 131 (65.5%) were fit for study. The networks that were found in all subjects were: the default mode network, the right executive control network, the medial visual network, and the cerebellar network. In 90% of the subjects, the left executive control network and the sensory/motor network were observed. The occipital visual network was present in 80% of the subjects. In 39 (19.5%) of the images, no any neural network was identified.

CONCLUSIONS

Reproduction and differentiation of the most representative RSNs was achieved using a 1.5T scanner acquisitions and sICA processing of BOLD imaging in healthy subjects.