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 large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms.

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

Towards Understanding the Relation Between Gait, Built-environment, and Real-life Mobility of Older Adults via Accelerometry and Global Positioning System-based Wearable Technology

Mobility limitations affect approximately 30 million older adults in United States. One in three older adults experience a fall annually. Mobility related disability is known to reduce participation in the community and leads to a reduced quality of life. These alarming trends require understanding of mobility, which is a multi-factorial concept. Beyond an individual’s physical capacity, their ability to walk efficiently can impact mobility behaviors in real-world. Besides, growing evidence suggests cognition and psycho-social factors also act as facilitators and barriers to mobility. Walking or gait is a highly complex daily-activity, most instrumental in driving our day-to-day active mobility. Quantifying ’how we walk’ via laboratory-measured gait patterns is of interest to clinicians and researchers. The relation of gait performance measures to outcomes such as real-life mobility, cognition fear of falling, and neighborhood walkability characteristics could help in identifying individuals at greater risk of developing gait, cognitive, and psychosocial disability, and further inform intervention strategies. In this research, we utilize advanced signal processing techniques with sensor technology in quantifying ’quality of walking’ and ’daily-life mobility’. In addition to statistical methods, we use supervised and unsupervised machine learning approaches to identify patterns in gait response when individuals are exposed to walking tasks. Spatio-temporal mobility in natural environments is quantified using actigraphy and global positioning system. Like never before, utilizing signal processing and machine learning in understanding gait and mobility can help in identifying risk-factors, ultimately delaying disability, for an independent healthy aging

Bacterial extracellular vesicle applications in cancer immunotherapy

Cancer therapy is undergoing a paradigm shift toward immunotherapy focusing on various approaches to activate the host immune system. As research to identify appropriate immune cells and activate anti-tumor immunity continues to expand, scientists are looking at microbial sources given their inherent ability to elicit an immune response. Bacterial extracellular vesicles (BEVs) are actively studied to control systemic humoral and cellular immune responses instead of using whole microorganisms or other types of extracellular vesicles (EVs). BEVs also provide the opportunity as versatile drug delivery carriers. Unlike mammalian EVs, BEVs have already made it to the clinic with the meningococcal vaccine (Bexsero®). However, there are still many unanswered questions in the use of BEVs, especially for chronic systemically administered immunotherapies. In this review, we address the opportunities and challenges in the use of BEVs for cancer immunotherapy and provide an outlook towards development of BEV products that can ultimately translate to the clinic

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