The <scp>ENIGMA</scp> Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

Liew, Sook‐Lei; Zavaliangos‐Petropulu, Artemis; Jahanshad, Neda; Lang, Catherine E.; Hayward, Kathryn S; Lohse, Keith R.; Juliano, Julia M.; Assogna, Francesca; Baugh, Lee A.; Bhattacharya, A. K.; Bigjahan, Bavrina; Borich, Michael R.; Boyd, Lara A.; Brodtmann, Amy; Buetefisch, Cathrin M.; Byblow, Winston D.; Cassidy, Jessica M.; Conforto, Adriana Bastos; Craddock, R. Cameron; Dimyan, Michael A.; Dula, Adrienne N.; Ermer, Elsa; Etherton, Mark R.; Fercho, Kelene A.; Gregory, Chris; Hadidchi, Shahram; Holguin, Jess A.; Hwang, Darryl; Jung, Simon; Kautz, Steven A.; Khlif, Mohamed Salah; Khoshab, Nima; Kim, Bokkyu; Kim, Ho‐Sung; Kuceyeski, Amy; Lotze, Martín; MacIntosh, Bradley J.; Margetis, John L.; Mohamed, Feroze B.; Piras, Fabrizio; Ramos‐Murguialday, Ander; Richard, Geneviève; Roberts, Pamela; Robertson, Andrew D.; Rondina, Jane Maryam; Rost, Natalia S.; Sanossian, Nerses; Schweighofer, Nicolas; Seo, Na Jin; Shiroishi, Mark S.; Soekadar, Surjo R.; Spalletta, Gianfranco; Stinear, Cathy M.; Suri, Anisha; Tang, Wai Kwong; Thielman, Gregory; Vecchio, Daniela; Villringer, Arno; Ward, Nick; Werden, Emilio; Westlye, Lars T.; Winstein, Carolee J.; Wittenberg, George F.; Wong, Kristin A; Yu, Chunshui; Cramer, Steven C.; Thompson, Paul M.

doi:10.1002/hbm.25015

Cited by 70 publications

(68 citation statements)

References 118 publications

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“…31 The relationship between chronic sensorimotor behavioral deficits and atrophy of the ipsilesional putamen after stroke, however, has not previously been reported. As atrophy of the putamen has been associated with a wide variety of neuropsychiatric and neurodegenerative disorders, 32 including Alzheimer’s disease, 33 multiple sclerosis, attention deficit disorder, 12 and Huntington’s disease, 10 it is possible that the integrity of the putamen is required not only for specifically sensorimotor behavior but also, more generally, for overall brain health.…”

Section: Discussionmentioning

confidence: 99%

“…The ENIGMA Stroke Recovery Working Group recommends following consensus guidelines for greater harmonization of prospectively-collected data to facilitate more precise pooled analyses across all times after stroke. 10, 47…”

Section: Discussionmentioning

confidence: 99%

“…A detailed overview of ENIGMA Stroke Recovery procedures and methods are reported elsewhere. 10 The retrospective data were collected across 28 different research studies (i.e., cohorts) at 16 different research institutes in 10 countries. Data were collected in accordance with the Declaration of Helsinki and in compliance with local ethics review boards at each institute (see Supplementary Table 1 for details).…”

Section: Methodsmentioning

confidence: 99%

“…The current cross-sectional pooled analysis used data from the ENIGMA Stroke Recovery Working Group, which was frozen for this analysis on May 22, 2020. A detailed overview of ENIGMA Stroke Recovery procedures and methods are reported elsewhere (Liew et al ., 2020). The data were collected across 28 different research studies (i.e., cohorts) at 16 different research institutes in 10 countries, in accordance with the Declaration of Helsinki and in compliance with local ethics review boards at each institute (see Supplementary Table 1 for details).…”

Section: Methodsmentioning

confidence: 99%

“…Although acute stroke research has successfully utilized pooled approaches with individual patient data to examine acute treatment outcomes (Goyal et al ., 2016; Campbell et al ., 2019), stroke rehabilitation research has been slower to adopt this type of approach due to the complexity of combining elaborate rehabilitation research protocols, differences in the site and size of infarcts, diversity of the patient populations recruited, and variety of the stroke neuroimaging and behavioral measures collected. To address these challenges, we formed the international ENIGMA Stroke Recovery Working Group to harmonize and combine diverse individual patient data, including high-resolution structural brain MRIs and behavioral outcome measures, across multiple research centers (Liew et al ., 2020). This combined analysis pools individual patient data across research sites using a harmonized analytical pipeline and includes both published and unpublished data.…”

Section: Introductionmentioning

confidence: 99%

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

Liew¹,

Zavaliangos‐Petropulu²,

Schweighofer³

et al. 2020

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Liew¹,

Zavaliangos‐Petropulu²,

Schweighofer³

et al. 2020

Preprint

Self Cite

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

Liew

Zavaliangos‐Petropulu

Jahanshad

et al. 2020

Human Brain Mapping

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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.

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Testing a convolutional neural network‐based hippocampal segmentation method in a stroke population

Zavaliangos‐Petropulu

Tubi²,

Haddad³

et al. 2020

Human Brain Mapping

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As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long-term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in poststroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation.Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlasbased approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network-based hippocampal segmentation method, does not rely solely on a single atlas-based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well-accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric correlation with manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control.All three automated segmentation methods had good correlation with manual segmentations and no one method was significantly more correlated than the others.Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy.

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

Cited by 70 publications

References 118 publications

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

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

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

Testing a convolutional neural network‐based hippocampal segmentation method in a stroke population

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