Corticospinal Tract Lesion Load Originating From Both Ventral Premotor and Primary Motor Cortices Are Associated With Post-stroke Motor Severity

Lesion load of the corticospinal tract (CST-LL), a measure of overlap between a stroke lesion and the CST, is one of the strongest predictors of motor outcomes following stroke. CST-LL is typically calculated by using a probabilistic map of the CST originating from the primary motor cortex (M1). However, higher order motor areas also have projections that contribute to the CST and motor control. In this retrospective study, we examined whether evaluating CST-LL from additional motor origins is more strongly associated with post-stroke motor severity than using CST-LL originating from M1 only. We found that lesion load to both the ventral premotor (PMv) cortex and M1 were more strongly related to stroke motor severity indexed by Fugl-Meyer Assessment cut-off scores than CST-LL of M1 alone, suggesting that higher order motor regions add clinical relevance to motor impairment.

Lesions in putative language and attention regions are linked to more severe strokes in patients with higher white matter hyperintensity burden

Objective To examine whether high white matter hyperintensity (WMH) burden is associated with greater stroke severity and worse functional outcomes in lesion pattern-specific ways. Methods MR neuroimaging and National Institutes of Health Stroke Scale data at index stroke, as well as modified Rankin Scale (mRS) at 3-6 months post-stroke were obtained from MRI-GENIE study of acute ischemic stroke (AIS) patients. Individual WMH volume was automatically derived from FLAIR-images. Stroke lesions were automatically segmented from DWI-images, spatially normalized and parcellated into atlas-defined brain regions. Stroke lesion effects on AIS severity and unfavorable outcomes (mRS>2) were modeled within a purpose-built machine learning and Bayesian regression framework. In particular, interaction effects between stroke lesions and a high versus low WMH burden were integrated via hierarchical model structures. Models were adjusted for the covariates age, age2, sex, total DWI-lesion and WMH volumes, and comorbidities. Data were split into derivation and validation cohorts. Results A total of 928 AIS patients contributed to stroke severity analyses (mean age: 64.8(14.5), 40% women), 698 patients to functional outcome analyses (mean age: 65.9(14.7), 41% women). Individual stroke lesions were represented in five anatomically distinct left-hemispheric and five right-hemispheric lesion patterns. Across all patients, acute stroke severity was substantially explained by three of these patterns, that were particularly focused on bilateral subcortical and left-hemispherically pronounced cortical regions. In high WMH burden patients, two lesion patterns consistently emerged as more pronounced in case of stroke severity: the first pattern was centered on left-hemispheric insular, opercular and inferior frontal regions, while the second pattern combined right-hemispheric temporo-parietal regions. Bilateral subcortical regions were most relevant in explaining long term unfavorable outcome. No WMH-specific lesion patterns of functional outcomes were substantiated. However, a higher overall WMH burden was associated with higher odds of unfavorable outcomes. Conclusions Higher WMH burden increases stroke severity in case of stroke lesions involving left-hemispheric insular, opercular and inferior frontal regions (potentially linked to language functions) and right-hemispheric temporo-parietal regions (potentially linked to attention). These findings may contribute to augment stroke outcome predictions and motivate a WMH burden and stroke lesion pattern-specific clinical management of AIS patients.

Deep Learning Framework to Detect Ischemic Stroke Lesion in Brain MRI Slices of Flair/DW/T1 Modalities

Ischemic stroke lesion (ISL) is a brain abnormality. Studies proved that early detection and treatment could reduce the disease impact. This research aimed to develop a deep learning (DL) framework to detect the ISL in multi-modality magnetic resonance image (MRI) slices. It proposed a convolutional neural network (CNN)-supported segmentation and classification to execute a consistent disease detection framework. The developed framework consisted of the following phases; (i) visual geometry group (VGG) developed VGG16 scheme supported SegNet (VGG-SegNet)-based ISL mining, (ii) handcrafted feature extraction, (iii) deep feature extraction using the chosen DL scheme, (iv) feature ranking and serial feature concatenation, and (v) classification using binary classifiers. Fivefold cross-validation was employed in this work, and the best feature was selected as the final result. The attained results were separately examined for (i) segmentation; (ii) deep-feature-based classification, and (iii) concatenated feature-based classification. The experimental investigation is presented using the Ischemic Stroke Lesion Segmentation (ISLES2015) database. The attained result confirms that the proposed ISL detection framework gives better segmentation and classification results. The VGG16 scheme helped to obtain a better result with deep features (accuracy > 97%) and concatenated features (accuracy > 98%).

Effects of prismatic adaptation on balance and postural disorders in patients with chronic right stroke: protocol for a multicentre double-blind randomised sham-controlled trial

IntroductionPatients with right stroke lesion have postural and balance disorders, including weight-bearing asymmetry, more pronounced than patients with left stroke lesion. Spatial cognition disorders post-stroke, such as misperceptions of subjective straight-ahead and subjective longitudinal body axis, are suspected to be involved in these postural and balance disorders. Prismatic adaptation has showed beneficial effects to reduce visuomotor disorders but also an expansion of effects on cognitive functions, including spatial cognition. Preliminary studies with a low level of evidence have suggested positive effects of prismatic adaptation on weight-bearing asymmetry and balance after stroke. The objective is to investigate the effects of this intervention on balance but also on postural disorders, subjective straight-ahead, longitudinal body axis and autonomy in patients with chronic right stroke lesion.Methods and analysisIn this multicentre randomised double-blind sham-controlled trial, we will include 28 patients aged from 18 to 80 years, with a first right supratentorial stroke lesion at chronic stage (≥12 months) and having a bearing ≥60% of body weight on the right lower limb. Participants will be randomly assigned to the experimental group (performing pointing tasks while wearing glasses shifting optical axis of 10 degrees towards the right side) or to the control group (performing the same procedure while wearing neutral glasses without optical deviation). All participants will receive a 20 min daily session for 2 weeks in addition to conventional rehabilitation. The primary outcome will be the balance measured using the Berg Balance Scale. Secondary outcomes will include weight-bearing asymmetry and parameters of body sway during static posturographic assessments, as well as lateropulsion (measured using the Scale for Contraversive Pushing), subjective straight-ahead, longitudinal body axis and autonomy (measured using the Barthel Index).Ethics and disseminationThe study has been approved by the ethical review board in France. Findings will be submitted to peer-reviewed journals relative to rehabilitation or stroke.Trial registration numberNCT03154138.

A Retrospective Study on Statins and Post-stroke Patients: What About Functional Outcome and Follow-Up in a Stroke Rehabilitation Cohort?

Objective: Statins exert pleiotropic effects by influencing several mechanisms, including synaptogenesis, neurogenesis, cerebral flow regulation, and angiogenesis. Results from in vitro and animal models suggest that statins could have beneficial effect on functional recovery and outcome after stroke events. However, results in human studies are still controversial. The aim of our study was to evaluate the role of statin in influencing functional outcome and subsequent clinical follow-up in a large cohort of post-stroke rehabilitation patients.Methods: This retrospective study consecutively enrolled 413 adult patients with stroke event, admitted to the division of Neurorehabilitation of the IRCCS ICS Maugeri, Veruno (Italy), for an individual rehabilitation program between 2015 and 2017. Follow-up lasted 3–5 years after discharge. Demographic data, etiology, classification, and anatomical site of stroke lesion, functional assessment, use and duration of statin therapy, and death during hospitalization were collected at baseline and on discharge. Clinical data on subsequent follow-up were also evaluated, considering these as variables: stroke recurrence, bone fractures, cardiovascular complications, and death.Results: In our cohort, 177 patients (42.9%) were prescribed statin therapy, of whom 50 (28.2%) before the stroke event and 127 (71.8%) at the beginning of the rehabilitation process. The use and type of statin therapy as well as the duration of treatment were not associated with recovery and functional outcome, regardless of confounders including sex, age, etiology, and site of stroke lesion, and initial functional level. For what concern post-discharge clinical follow-up, the use of statin therapy was significantly associated with a lower risk of bone fractures (OR = 0.095, CI 95%: 0.012–0.743, p = 0.01) independently from age, sex, initial and final functional level, and comorbidities.Conclusions: The use of statins does not seem to influence the functional outcome in post-stroke patients. However, they could exert a protective role against bone fractures during post-discharge follow-up, suggesting further evaluation on this topic.

A Method to Experimentally Estimate the Conductivity of Chronic Stroke Lesions: A Tool to Individualize Transcranial Electric Stimulation

The inconsistent response to transcranial electric stimulation in the stroke population is attributed to, among other factors, unknown effects of stroke lesion conductivity on stimulation strength at the targeted brain areas. Volume conduction models are promising tools to determine optimal stimulation settings. However, stroke lesion conductivity is often not considered in these models as a source of inter-subject variability. The goal of this study is to propose a method that combines MRI, EEG, and transcranial stimulation to estimate the conductivity of cortical stroke lesions experimentally. In this simulation study, lesion conductivity was estimated from scalp potentials during transcranial electric stimulation in 12 chronic stroke patients. To do so, first, we determined the stimulation configuration where scalp potentials are maximally affected by the lesion. Then, we calculated scalp potentials in a model with a fixed lesion conductivity and a model with a randomly assigned conductivity. To estimate the lesion conductivity, we minimized the error between the two models by varying the conductivity in the second model. Finally, to reflect realistic experimental conditions, we test the effect rotation of measurement electrode orientation and the effect of the number of electrodes used. We found that the algorithm converged to the correct lesion conductivity value when noise on the electrode positions was absent for all lesions. Conductivity estimation error was below 5% with realistic electrode coregistration errors of 0.1° for lesions larger than 50 ml. Higher lesion conductivities and lesion volumes were associated with smaller estimation errors. In conclusion, this method can experimentally estimate stroke lesion conductivity, improving the accuracy of volume conductor models of stroke patients and potentially leading to more effective transcranial electric stimulation configurations for this population.