The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke.

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.

A cross-sectional study to test equivalence of low- versus intermediate-flip angle dynamic susceptibility contrast MRI measures of relative cerebral blood volume in patients with high-grade gliomas at 1.5 Tesla field strength.

Introduction: 1.5 Tesla (1.5T) remain a significant field strength for brain imaging worldwide. Recent computer simulations and clinical studies at 3T MRI have suggested that dynamic susceptibility contrast (DSC) MRI using a 30° flip angle ("low-FA") with model-based leakage correction and no gadolinium-based contrast agent (GBCA) preload provides equivalent relative cerebral blood volume (rCBV) measurements to the reference-standard acquisition using a single-dose GBCA preload with a 60° flip angle ("intermediate-FA") and model-based leakage correction. However, it remains unclear whether this holds true at 1.5T. The purpose of this study was to test this at 1.5T in human high-grade glioma (HGG) patients. Methods: This was a single-institution cross-sectional study of patients who had undergone 1.5T MRI for HGG. DSC-MRI consisted of gradient-echo echo-planar imaging (GRE-EPI) with a low-FA without preload (30°/P-); this then subsequently served as a preload for the standard intermediate-FA acquisition (60°/P+). Both normalized (nrCBV) and standardized relative cerebral blood volumes (srCBV) were calculated using model-based leakage correction (C+) with IBNeuro™ software. Whole-enhancing lesion mean and median nrCBV and srCBV from the low- and intermediate-FA methods were compared using the Pearson's, Spearman's and intraclass correlation coefficients (ICC). Results: Twenty-three HGG patients composing a total of 31 scans were analyzed. The Pearson and Spearman correlations and ICCs between the 30°/P-/C+ and 60°/P+/C+ acquisitions demonstrated high correlations for both mean and median nrCBV and srCBV. Conclusion: Our study provides preliminary evidence that for HGG patients at 1.5T MRI, a low FA, no preload DSC-MRI acquisition can be an appealing alternative to the reference standard higher FA acquisition that utilizes a preload.

Smaller spared subcortical nuclei are associated with worse post-stroke sensorimotor outcomes in 28 cohorts worldwide.

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 T1-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.

Brain cortical structural differences between non-central nervous system cancer patients treated with and without chemotherapy compared to non-cancer controls: a cross-sectional pilot MRI study using clinically-indicated scans.

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.