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Stroke is the second leading cause of death and the third leading cause of disability worldwide. Though the burden of stroke worldwide seems to have declined in the past three decades, much of this effect reflects decreases in high-income countries (HICs). By contrast, the burden of stroke has grown rapidly in low-income and middle-income countries (LMICs), where epidemiological, socioeconomic and demographic shifts have increased the incidence of stroke and other non-communicable diseases. Furthermore, even in HICs, disparities in stroke epidemiology exist along racial, ethnic, socioeconomic and geographical lines. In this Review, we highlight the under-acknowledged disparities in the burden of stroke. We emphasize the shifting global landscape of stroke risk factors, critical gaps in stroke service delivery, and the need for a more granular analysis of the burden of stroke within and between LMICs and HICs to guide context-appropriate capacity-building. Finally, we review strategies for addressing key inequalities in stroke epidemiology, including improvements in epidemiological surveillance and context-specific research efforts in under-resourced regions, development of the global workforce of stroke care providers, expansion of access to preventive and treatment services through mobile and telehealth platforms, and scaling up of evidence-based strategies and policies that target local, national, regional and global stroke disparities.Though the burden of stroke has declined, it has grown rapidly in low-income and middle-income countries, and disparities still exist within high-income countries. In this Review, the authors highlight under-acknowledged disparities in the burden of stroke and review strategies for addressing key inequalities.

Speech neuroprostheses have the potential to restore communication to people living with paralysis, but naturalistic speed and expressivity are elusive 1 . Here we use high-density surface recordings of the speech cortex in a clinical-trial participant with severe limb and vocal paralysis to achieve high-performance real-time decoding across three complementary speech-related output modalities: text, speech audio and facial-avatar animation. We trained and evaluated deep-learning models using neural data collected as the participant attempted to silently speak sentences. For text, we demonstrate accurate and rapid large-vocabulary decoding with a median rate of 78 words per minute and median word error rate of 25%. For speech audio, we demonstrate intelligible and rapid speech synthesis and personalization to the participant’s pre-injury voice. For facial-avatar animation, we demonstrate the control of virtual orofacial movements for speech and non-speech communicative gestures. The decoders reached high performance with less than two weeks of training. Our findings introduce a multimodal speech-neuroprosthetic approach that has substantial promise to restore full, embodied communication to people living with severe paralysis. A study using high-density surface recordings of the speech cortex in a person with limb and vocal paralysis demonstrates real-time decoding of brain activity into text, speech sounds and orofacial movements.

Neuroprostheses have the potential to restore communication to people who cannot speak or type due to paralysis. However, it is unclear if silent attempts to speak can be used to control a communication neuroprosthesis. Here, we translated direct cortical signals in a clinical-trial participant (ClinicalTrials.gov; NCT03698149) with severe limb and vocal-tract paralysis into single letters to spell out full sentences in real time. We used deep-learning and language-modeling techniques to decode letter sequences as the participant attempted to silently spell using code words that represented the 26 English letters (e.g. “alpha” for “a”). We leveraged broad electrode coverage beyond speech-motor cortex to include supplemental control signals from hand cortex and complementary information from low- and high-frequency signal components to improve decoding accuracy. We decoded sentences using words from a 1,152-word vocabulary at a median character error rate of 6.13% and speed of 29.4 characters per minute. In offline simulations, we showed that our approach generalized to large vocabularies containing over 9,000 words (median character error rate of 8.23%). These results illustrate the clinical viability of a silently controlled speech neuroprosthesis to generate sentences from a large vocabulary through a spelling-based approach, complementing previous demonstrations of direct full-word decoding. Previous work has described a neuroprosthesis to directly decode full words in real time during attempts to speak. Here the authors demonstrate that a patient with anarthria can control this neuroprosthesis to spell out intended messages in real time using attempts to silently speak.

The nervous and immune systems control whole-body homeostasis and respond to various types of tissue injury, including stroke, in a coordinated manner. Cerebral ischaemia and subsequent neuronal cell death activate resident or infiltrating immune cells, which trigger neuroinflammation that affects functional prognosis after stroke. Inflammatory immune cells exacerbate ischaemic neuronal injury after the onset of brain ischaemia; however, some of the immune cells thereafter change their function to neural repair. The recovery processes after ischaemic brain injury require additional and close interactions between the nervous and immune systems through various mechanisms. Thus, the brain controls its own inflammation and repair processes after injury via the immune system, which provides a promising therapeutic opportunity for stroke recovery.Clarification of mechanisms underlying inflammation and neural repair after ischaemic stroke could lead to improved prognosis. In this Review, Shichita et al. discuss the biphasic nature of the post-stroke inflammatory response and the key molecules and cells involved.

Upper-extremity impairment after stroke remains a major therapeutic challenge and a target of neuromodulation treatment efforts. In this open-label, non-randomized phase I trial, we applied deep brain stimulation to the cerebellar dentate nucleus combined with renewed physical rehabilitation to promote functional reorganization of ipsilesional cortex in 12 individuals with persistent (1–3 years), moderate-to-severe upper-extremity impairment. No serious perioperative or stimulation-related adverse events were encountered, with participants demonstrating a seven-point median improvement on the Upper-Extremity Fugl-Meyer Assessment. All individuals who enrolled with partial preservation of distal motor function exceeded minimal clinically important difference regardless of time since stroke, with a median improvement of 15 Upper-Extremity Fugl-Meyer Assessment points. These robust functional gains were directly correlated with cortical reorganization evidenced by increased ipsilesional metabolism. Our findings support the safety and feasibility of deep brain stimulation to the cerebellar dentate nucleus as a promising tool for modulation of late-stage neuroplasticity for functional recovery and the need for larger clinical trials. ClinicalTrials.gov registration: NCT02835443 . A phase I trial of cerebellar deep brain stimulation to enhance chronic post-stroke motor rehabilitation supports the safety and feasibility of the approach while providing encouraging evidence of motor improvements.

Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions ( https://doi.org/10.5281/zenodo.7153326 ). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge ( https://www.isles-challenge.org/ ) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke.

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry 1 , 2 . Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated ( P  < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis 3 , and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN ) and variants (such as at GRK5 and NOS3 ). Using a three-pronged approach 4 , we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry 5 . Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries. A cross-ancestry meta-analysis of genome-wide association studies identifies association signals for stroke and its subtypes at 89 (61 new) independent loci, reveals putative causal genes, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as potential drug targets, and provides cross-ancestry integrative risk prediction.

Despite many years of research, no biomarkers for stroke are available to use in clinical practice. Progress in high-throughput technologies has provided new opportunities to understand the pathophysiology of this complex disease, and these studies have generated large amounts of data and information at different molecular levels. The integration of these multi-omics data means that thousands of proteins (proteomics), genes (genomics), RNAs (transcriptomics) and metabolites (metabolomics) can be studied simultaneously, revealing interaction networks between the molecular levels. Integrated analysis of multi-omics data will provide useful insight into stroke pathogenesis, identification of therapeutic targets and biomarker discovery. In this Review, we detail current knowledge on the pathology of stroke and the current status of biomarker research in stroke. We summarize how proteomics, metabolomics, transcriptomics and genomics are all contributing to the identification of new candidate biomarkers that could be developed and used in clinical stroke management.In this Review, Montaner and colleagues summarize how proteomics, genomics, transcriptomics and metabolomics are contributing to the discovery and development of biomarkers in stroke, and how bringing them together with integromics could provide new biomarkers and therapeutic opportunities.

Radiological examination of the brain is a critical determinant of stroke care pathways. Accessible neuroimaging is essential to detect the presence of intracerebral hemorrhage (ICH). Conventional magnetic resonance imaging (MRI) operates at high magnetic field strength (1.5–3 T), which requires an access-controlled environment, rendering MRI often inaccessible. We demonstrate the use of a low-field MRI (0.064 T) for ICH evaluation. Patients were imaged using conventional neuroimaging (non-contrast computerized tomography (CT) or 1.5/3 T MRI) and portable MRI (pMRI) at Yale New Haven Hospital from July 2018 to November 2020. Two board-certified neuroradiologists evaluated a total of 144 pMRI examinations (56 ICH, 48 acute ischemic stroke, 40 healthy controls) and one ICH imaging core lab researcher reviewed the cases of disagreement. Raters correctly detected ICH in 45 of 56 cases (80.4% sensitivity, 95%CI: [0.68–0.90]). Blood-negative cases were correctly identified in 85 of 88 cases (96.6% specificity, 95%CI: [0.90–0.99]). Manually segmented hematoma volumes and ABC/2 estimated volumes on pMRI correlate with conventional imaging volumes (ICC = 0.955, p  = 1.69e-30 and ICC = 0.875, p  = 1.66e-8, respectively). Hematoma volumes measured on pMRI correlate with NIH stroke scale (NIHSS) and clinical outcome (mRS) at discharge for manual and ABC/2 volumes. Low-field pMRI may be useful in bringing advanced MRI technology to resource-limited settings. Conventional magnetic resonance imaging (MRI) operates at a high magnetic field strength and requires a strict access-controlled environment, making MRI often inaccessible. Here, the authors present a portable low-field MRI device that detects intracerebral hemorrhage with high accuracy.

Stroke is a leading cause of disability, dementia and death worldwide. Approximately 70% of deaths from stroke and 87% of stroke-related disability occur in low-income and middle-income countries. At the turn of the century, the most common diseases in Africa were communicable diseases, whereas non-communicable diseases, including stroke, were considered rare, particularly in sub-Saharan Africa. However, evidence indicates that, today, Africa could have up to 2–3-fold greater rates of stroke incidence and higher stroke prevalence than western Europe and the USA. In Africa, data published within the past decade show that stroke has an annual incidence rate of up to 316 per 100,000, a prevalence of up to 1,460 per 100,000 and a 3-year fatality rate greater than 80%. Moreover, many Africans have a stroke within the fourth to sixth decades of life, with serious implications for the individual, their family and society. This age profile is particularly important as strokes in younger people tend to result in a greater loss of self-worth and socioeconomic productivity than in older individuals. Emerging insights from research into stroke epidemiology, genetics, prevention, care and outcomes offer great prospects for tackling the growing burden of stroke on the continent. In this article, we review the unique profile of stroke in Africa and summarize current knowledge on stroke epidemiology, genetics, prevention, acute care, rehabilitation, outcomes, cost of care and awareness. We also discuss knowledge gaps, emerging priorities and future directions of stroke medicine for the more than 1 billion people who live in Africa.In this Review, Akinyemi and colleagues provide an overview of stroke in Africa, including epidemiology, risk factors, genetics and available stroke services. The authors also discuss the future of stroke care in Africa, highlighting the promise of biobanking and novel leadership initiatives.