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Objective Methods Results Interpretation To systematically evaluate which lesion‐based imaging features and methods allow for the best statistical prediction of poststroke deficits across independent datasets.We utilized imaging and clinical data from three independent datasets of patients experiencing acute stroke (N1 = 109, N2 = 638, N3 = 794) to statistically predict acute stroke severity (NIHSS) based on lesion volume, lesion location, and structural and functional disconnection with the lesion location using normative connectomes.We found that prediction models trained on small single‐center datasets could perform well using within‐dataset cross‐validation, but results did not generalize to independent datasets (median R2N1 = 0.2%). Performance across independent datasets improved using large single‐center training data (R2N2 = 15.8%) and improved further using multicenter training data (R2N3 = 24.4%). These results were consistent across lesion attributes and prediction models. Including either structural or functional disconnection in the models outperformed prediction based on volume or location alone (P < 0.001, FDR‐corrected).We conclude that (1) prediction performance in independent datasets of patients with acute stroke cannot be inferred from cross‐validated results within a dataset, as performance results obtained via these two methods differed consistently, (2) prediction performance can be improved by training on large and, importantly, multicenter datasets, and (3) structural and functional disconnection allow for improved prediction of acute stroke severity. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich‐club) brain regions on functional outcome post‐stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3‐months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI‐GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas‐defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non‐rich club regions. In spatial specificity analyses, we randomized the split into "rich club" and "non‐rich club" regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex‐specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female‐specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club‐combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long‐term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women. [ABSTRACT FROM AUTHOR]

Background and Purpose: Ischemic diffusion‐weighted imaging‐fluid‐attenuated inversion recovery (DWI‐FLAIR) mismatch may be useful in guiding acute stroke treatment decisions given its relationship to onset time and parenchymal viability; however, it relies on subjective grading. Radiomics is an emerging image quantification methodology that may objectively represent continuous image characteristics. We propose a novel radiomics approach to characterize DWI‐FLAIR mismatch. Methods: Ischemic lesions were visually graded for FLAIR positivity (absent, subtle, obvious) among consecutive large vessel occlusion stroke patients who underwent hyperacute MRI. Radiomic features were extracted from within the lesions on DWI and FLAIR. The DWI‐FLAIR mismatch radiomics signature was built with features systematically selected by a cross‐validated ElasticNet linear regression model of mismatch. Results: We identified 103 patients with mean age 68 ± 16 years; 63% were female. FLAIR hyperintensity was absent in 25%, subtle in 55%, and obvious in 20%. Inter‐rater agreement for visual grading was moderate (Κ =.58). The radiomics signature of DWI‐FLAIR mismatch included native FLAIR histogram kurtosis and local binary pattern‐filtered FLAIR gray‐level cluster shade; both correlated with visual grading (ρ = –.42, p <.001 and ρ =.40, p <.001, respectively). Conclusions: Radiomics can describe DWI‐FLAIR mismatch and may provide objective, continuous biomarkers for infarct evolution using clinical‐grade images. These novel biomarkers may prove useful for treatment decisions and future research. [ABSTRACT FROM AUTHOR]

The aim of the current study was to explore the whole‐brain dynamic functional connectivity patterns in acute ischemic stroke (AIS) patients and their relation to short and long‐term stroke severity. We investigated resting‐state functional MRI‐based dynamic functional connectivity of 41 AIS patients two to five days after symptom onset. Re‐occurring dynamic connectivity configurations were obtained using a sliding window approach and k‐means clustering. We evaluated differences in dynamic patterns between three NIHSS‐stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we built Bayesian hierarchical models to evaluate the predictive capacity of dynamic connectivity and examine the interrelation with clinical measures, such as white matter hyperintensity lesions. Finally, we established correlation analyses between dynamic connectivity and AIS severity as well as 90‐day neurological recovery (ΔNIHSS). We identified three distinct dynamic connectivity configurations acutely post‐stroke. More severely affected patients spent significantly more time in a configuration that was characterized by particularly strong connectivity and isolated processing of functional brain domains (three‐level ANOVA: p <.05, post hoc t tests: p <.05, FDR‐corrected). Configuration‐specific time estimates possessed predictive capacity of stroke severity in addition to the one of clinical measures. Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson's r = −.68, p =.003, FDR‐corrected). Our findings demonstrate transiently increased isolated information processing in multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first 3 months poststroke. [ABSTRACT FROM AUTHOR]