Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. Publicly sharing these datasets can aid in the development of machine learning algorithms, particularly for lesion identification, brain health quantification, and prognosis. These algorithms thrive on large amounts of information, but require diverse datasets to avoid overfitting to specific populations or acquisitions. While there are many large public MRI datasets, few of these include acute stroke. We describe clinical MRI using diffusion-weighted, fluid-attenuated and T1-weighted modalities for 1715 individuals admitted in the upstate of South Carolina, of whom 1461 have acute ischemic stroke. Demographic and impairment data are provided for 1106 of the stroke survivors from this cohort. Our validation demonstrates that machine learning can leverage the imaging data to predict stroke severity as measured by the NIH Stroke Scale/Score (NIHSS). We share not only the raw data, but also the scripts for replicating our findings. These tools can aid in education, and provide a benchmark for validating improved methods.

The novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2) is the cause of the COVID-19 pandemic [5]. SARS-Cov-2 demonstrates partial resemblance to SARS-CoV and MERS-CoV in phylogenetic analysis, clinical manifestations, and pathological findings [6, 7]. Reports emerging from China have described ataxia as a neurological symptom of the SARS-CoV-2 infection [5]. Opsoclonus consists of back-to-back multidirectional conjugate saccades without an inter-saccadic interval [8]. Myoclonus is defined as a sudden, brief, “shock-like”, nonepileptic involuntary movement [9], which has been described as a symptom of SARS-CoV-2 infection [10]. Opsoclonus-Myoclonus-Ataxia syndrome (OMAS) associated COVID-19 infection has been reported recently [1112].

Background: Recent research suggests that damage to right hemisphere regions homotopic to the left hemisphere language network affects language abilities to a greater extent than previously thought. However, few studies have investigated acute disruption of language after lesion to the right hemisphere. Here, we examined lesion correlates of acute speech deficits following left and right hemisphere ischemic stroke to clarify the neural architecture underlying early language dysfunction. Methods: We retrospectively analyzed 410 patients (225 left, 185 right hemisphere lesions) from the Stroke Outcome Optimization Project dataset. Presence and severity of speech deficits was measured using the National Institute of Health Stroke Scale Best Language subscore within 48 h of onset. Manual lesion masks were derived from clinical MRI scans and normalized to MNI space. Lesion-symptom mapping was conducted using voxelwise and region-of-interest analyses with permutation correction (5000 iterations; p < 0.05), controlling for total lesion volume. Results: Speech deficits were observed in 53.7% of the cohort (58.2% left, 48.1% right hemisphere lesions). In the full sample, the presence of speech deficits was associated with bilateral subcortical and perisylvian damage, including the external and internal capsules, insula, putamen, and superior fronto-occipital fasciculus. Severity of speech deficits localized predominantly to left hemisphere structures, with peak associations in the external capsule (Z = 6.39), posterior insula (Z = 5.64), and inferior fronto-occipital fasciculus (Z = 5.43). In the right hemisphere cohort, the presence and severity of speech deficits were linked to homologous regions, including the posterior insula (Z = 3.70) and external capsule (Z = 3.63), although with smaller effect sizes relative to the left hemisphere cohort. Right hemisphere lesions resulted in milder deficits despite larger lesion volumes compared with left hemisphere lesions. Conclusions: Acute speech impairment following right hemisphere stroke is associated with damage to a homotopic network encompassing perisylvian cortical and subcortical regions analogous to the dominant left hemisphere language network. These findings demonstrate that damage to the right hemisphere consistently results in acute speech deficits, challenging the traditional left-centric view of post-stroke speech impairment. These results have important implications for models of bilateral language representation and the neuroplastic mechanisms supporting language recovery.

Parkinson's Disease (PD) is the second most prevalent neurodegenerative disease worldwide due to loss of dopaminergic neurons projecting from the basal ganglia (BG). It is associated with various motor symptoms that are grouped into subtypes, each with different clinical presentations and disease progressions. Neuroimaging biomarkers focusing on regions a part of motor circuits projecting from the BG can distinguish and improve overall subtyping. The supplementary motor cortex (SMC) is well established in PD neuropathology and associated with freezing of gait and bradykinesia, but has not been thoroughly evaluated across subtypes. This study aims to identify volumetric differences of the SMC based on PD subtypes of tremor dominant (TD), postural instability with gait difficulty (PIGD), and akinetic rigid (AR) using data from Parkinson's Progression Markers Initiative. To segment grey matter volume and extract region of interest values, voxel-based processing was used. Multi-factor ANCOVAs, Tukey Honest Significance Test, and Kruskal-Wallis were utilized for volumetric analyses (α < 0.05). Subjects were classified and evaluated using TD, PIGD, and AR subtypes from the MDS-UPDRS rating scales. Inter-subtype differences in SMC GMV between TD and PIGD were significant in the right hemisphere for females (p = 0.01). No significant inter-subtype differences were found in the TD/AR system. These results support the use of broader motor networks, specifically the SMC in further understanding the neuropathological heterogeneity of PD. Furthermore, it reveals SMC differences across sexes, subtypes, and subtyping systems, calling for further evaluation of subtyping schemas, specifically regarding sex differences.

Objective To determine alternative neural pathways for restitution of piano playing after right hemispheric infarction causing left arm and hand paralysis. Design Case report testing coordinated bimanual skills using structured motor skills tests and neuroimaging. Setting A professional pianist sustained a lacunar infarction in the posterior limb of his right internal capsule, which resulted in left hemiparesis with immobilized left-hand and -finger movements persisting for 13 weeks. After 6 months, he had recovered bimanual coordinated piano skills by “ignoring” his left hand while concentrating or discussing subjects other than music while playing. Patient A 63-year-old, male professional pianist. Intervention Detailed neurological examination including computed cranial tomography, functional magnetic resonance imaging, and positron emission tomography. Results Functional magnetic resonance imaging activation patterns correlated with rapid movements of fingers in each hand separately and together demonstrating that subcortical and cerebellar pathways were activated during skilled motor function of his left hand. Contralateral cerebral and cerebellar activation occurred with both left- and right-hand movements. During tapping of the left fingers, there was bilateral cerebellar, parietal, and left premotor strip and left thalamic activation. Conclusion Patterns of activation relate to task performance and they are not similar to subjects engaged in simpler tasks such as finger opposition.