The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta-and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Up to two-thirds of stroke survivors experience persistent sensorimotor impairments. Recovery relies on the integrity of spared brain areas to compensate for damaged tissue. Deep grey matter structures play a critical role in the control and regulation of sensorimotor circuits. The goal of this work is to identify associations between volumes of spared subcortical nuclei and sensorimotor behaviour at different timepoints after stroke. We pooled high-resolution T 1 -weighted MRI brain scans and behavioural data in 828 individuals with unilateral stroke from 28 cohorts worldwide. Cross-sectional analyses using linear mixed-effects models related post-stroke sensorimotor behaviour to non-lesioned subcortical volumes (Bonferroni-corrected, P < 0.004). We tested subacute (≤90 days) and chronic (≥180 days) stroke subgroups separately, with exploratory analyses in early stroke (≤21 days) and across all time. Sub-analyses in chronic stroke were also performed based on class of sensorimotor deficits (impairment, activity limitations) and side of lesioned hemisphere. Worse sensorimotor behaviour was associated with a smaller ipsilesional thalamic volume in both early ( n = 179; d = 0.68) and subacute ( n = 274, d = 0.46) stroke. In chronic stroke ( n = 404), worse sensorimotor behaviour was associated with smaller ipsilesional putamen ( d = 0.52) and nucleus accumbens ( d = 0.39) volumes, and a larger ipsilesional lateral ventricle ( d = −0.42). Worse chronic sensorimotor impairment specifically (measured by the Fugl-Meyer Assessment; n = 256) was associated with smaller ipsilesional putamen ( d = 0.72) and larger lateral ventricle ( d = −0.41) volumes, while several measures of activity limitations ( n = 116) showed no significant relationships. In the full cohort across all time ( n = 828), sensorimotor behaviour was associated with the volumes of the ipsilesional nucleus accumbens ( d = 0.23), putamen ( d = 0.33), thalamus ( d = 0.33) and lateral ventricle ( d = −0.23). We demonstrate significant relationships between post-stroke sensorimotor behaviour and reduced volumes of deep grey matter structures that were spared by stroke, which differ by time and class of sensorimotor measure. These findings provide additional insight into how different cortico-thalamo-striatal circuits support post-stroke sensorimotor outcomes.

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 1,800 stroke patients collected across 32 research sites and 10 countries around the world, comprising the largest multi-site retrospective stroke data collaboration to date. This paper outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multi-site stroke brain magnetic resonance imaging (MRI), behavioral and demographics data. Specifically, the processes for scalable data intake and pre-processing, multi-site data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Background Cognitive deficit associated with cancer and its treatment is called cancer-related cognitive impairment (CRCI). Increases in cancer survival have made understanding the basis of CRCI more important. CRCI neuroimaging studies have traditionally used dedicated research brain MRIs in breast cancer survivors after chemotherapy with small sample sizes; little is known about other non-central nervous system (CNS) cancers after chemotherapy as well as those not exposed to chemotherapy. However, there may be a wealth of unused data from clinically-indicated MRIs that could be used to study CRCI. Objective Evaluate brain cortical structural differences in those with various non-CNS cancers using clinically-indicated MRIs. Design Cross-sectional Patients Adult non-CNS cancer and non-cancer control (C) patients who underwent clinically-indicated MRIs. Methods Brain cortical surface area and thickness were measured using 3D T1-weighted images. An age-adjusted linear regression model was used and the Benjamini and Hochberg false discovery rate (FDR) corrected for multiple comparisons. Group comparisons were: cancer cases with chemotherapy (Ch+), cancer cases without chemotherapy (Ch−) and subgroup of lung cancer cases with and without chemotherapy vs C. Results Sixty-four subjects were analyzed: 22 Ch+, 23 Ch− and 19 C patients. Subgroup analysis of 16 lung cancer (LCa) patients was also performed. Statistically significant decreases in either cortical surface area or thickness were found in multiple regions of interest (ROIs) primarily within the frontal and temporal lobes for all comparisons. Effect sizes were variable with the greatest seen in the left middle temporal surface area ROI (Cohen’s d −0.690) in the Ch− vs C group comparison. Limitations Several limitations were apparent including a small sample size that precluded adjustment for other covariates. Conclusions Our preliminary results suggest that, in addition to breast cancer, other types of non-CNS cancers treated with chemotherapy may result in brain structural abnormalities. Similar findings also appear to occur in those not exposed to chemotherapy. These results also suggest that there is potentially a wealth of untapped clinical MRIs that could be used for future CRCI studies.

Background: Up to two-thirds of stroke survivors experience persistent sensorimotor impairments. Recovery relies on the integrity of spared brain areas to compensate for damaged tissue. Subcortical regions play critical roles in the control and regulation of sensorimotor circuits. Identifying relationships between sensorimotor behavior and non-lesioned subcortical volumes will reveal new neural targets for improving outcomes. Methods: We pooled high-resolution T1-weighted MRI brain scans and behavioral data in 828 individuals with unilateral stroke from 28 cohorts worldwide (age: median 63, interquartile range 19 years; 516 males, 312 females). Cross-sectional analyses using linear mixed-effects models related post-stroke sensorimotor behavior to non-lesioned subcortical volumes. We analyzed subacute (≤90 days) and chronic (≥180 days) stroke; sub-analyses in chronic stroke were performed on class of sensorimotor deficit (impairment, activity limitations) and side of lesioned hemisphere, with exploratory analyses in early stroke (≤21 days) and across time (Bonferroni-corrected, p<0.004). Results: Worse sensorimotor behavior was associated with a smaller ipsilesional thalamic volume in both subacute (n=274, d=0.46) and early stroke (n=179; d=0.68). In chronic stroke (n=404), worse sensorimotor behavior was associated with smaller ipsilesional putamen (d=0.52) and nucleus accumbens (d=0.39) volumes, and a larger ipsilesional lateral ventricle volume (d=-0.42), representing atrophy. In chronic stroke, worse sensorimotor impairment specifically (measured by the Fugl-Meyer Assessment; n=256) was associated with a smaller ipsilesional putamen (d=0.72), and larger lateral ventricle (d=-0.41), while several measures of activity limitations (n=116) showed no significant relationships. Side of lesion (left=214, right=190) had no impact. The full cohort (n=828) revealed associations of sensorimotor behavior with the ipsilesional nucleus accumbens (d=0.23), putamen (d=0.33), thalamus (d=0.33), and lateral ventricle (d=-0.23). Discussion: This analysis identified significant relationships between sensorimotor behavior and key subcortical regions at different times post-stroke. While further longitudinal studies are needed, these findings may represent brain imaging markers of resilience and reserve and provide putative neuroanatomical targets for improving sensorimotor outcomes post-stroke.