Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.

Background Falls are a common and serious public health issue among older adults, contributing to the loss of independence, psychological distress, and incapability to engage in meaningful occupations, etc. However, there is a lack of abundant information about the fall risk self-evaluation scale for community-dwelling older people. Therefore, this study aimed to evaluate the preliminary reliability and validity of the fall risk self-assessment scale (FRSAS) among community-dwelling older adults. Methods A cross-sectional study was conducted. A total of 230 individuals aged 65 years and over were recruited by a convenience sampling between October and December 2020 from three communities in Haidian district, Beijing. Eligible participants were required to fill in the general condition questionnaire and the fall risk self-assessment scale. The reliability and validity were analyzed by using SPSS 20.0. Results Two hundred twenty-two participants completed the assessment as required (the completion rate was 96.52%). The most items of FRSAS were understood by older adults, which was completed in 10 min. Cronbach’s α and intraclass correlation coefficient ICC (2,1) of the scale were 0.757 and 0.967 respectively, suggesting good internal consistency and test-retest reliability. Exploratory factor analysis yielded 14 factors that explained 61.744% of the variance. Five items failed to be categorized into any factors because the factor loading of these items was less than 0.4. A future large-sample study needs to be conducted to explore its construct validity. The total scores and dimensional scores except for C-dimension showed significant differences between participants who had experienced a fall in the previous 6 months and those who had not (P < 0.05), indicating good discriminant validity. Conclusions The fall risk self-assessment scale including 41 items demonstrated relatively high feasibility as well as satisfactory results in the internal consistency, test-retest reliability, and discriminant validity. Trial registration Registration number: ChiCTR2000038856; Date of registration: 7 Oct 2020.

Background and objectives:Functional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. Here, we examined the impact of brain age, a measure of neurobiological aging derived from whole brain structural neuroimaging, on post-stroke outcomes, with a focus on sensorimotor performance. We hypothesized that more lesion damage would result in older brain age, which would in turn be associated with poorer outcomes. Related, we expected that brain age would mediate the relationship between lesion damage and outcomes. Finally, we hypothesized that structural brain resilience, which we define in the context of stroke as younger brain age given matched lesion damage, would differentiate people with good versus poor outcomes.Methods:We conducted a cross-sectional observational study using a multi-site dataset of 3D brain structural MRIs and clinical measures from ENIGMA Stroke Recovery. Brain age was calculated from 77 neuroanatomical features using a ridge regression model trained and validated on 4,314 healthy controls. We performed a three-step mediation analysis with robust mixed-effects linear regression models to examine relationships between brain age, lesion damage, and stroke outcomes. We used propensity score matching and logistic regression to examine whether brain resilience predicts good versus poor outcomes in patients with matched lesion damage.Results:We examined 963 patients across 38 cohorts. Greater lesion damage was associated with older brain age (β=0.21; 95% CI 0.04,0.38,P=0.015), which in turn was associated with poorer outcomes, both in the sensorimotor domain (β=-0.28; 95% CI: -0.41,-0.15,P<0.001) and across multiple domains of function (β=-0.14; 95% CI: -0.22,-0.06,P<0.001). Brain age mediated 15% of the impact of lesion damage on sensorimotor performance (95% CI: 3%,58%,P=0.01). Greater brain resilience explained why people have better outcomes, given matched lesion damage (OR=1.04, 95% CI: 1.01,1.08,P=0.004).Conclusions:We provide evidence that younger brain age is associated with superior post-stroke outcomes and modifies the impact of focal damage. The inclusion of imaging-based assessments of brain age and brain resilience may improve the prediction of post-stroke outcomes compared to focal injury measures alone, opening new possibilities for potential therapeutic targets.

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