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3 results on '"Kate P. Revill"'

1. Chronic Stroke Sensorimotor Impairment Is Related to Smaller Hippocampal Volumes: An ENIGMA Analysis

Author

Artemis Zavaliangos‐Petropulu, Bethany Lo, Miranda R. Donnelly, Nicolas Schweighofer, Keith Lohse, Neda Jahanshad, Giuseppe Barisano, Nerisa Banaj, Michael R. Borich, Lara A. Boyd, Cathrin M. Buetefisch, Winston D. Byblow, Jessica M. Cassidy, Charalambos C. Charalambous, Adriana B. Conforto, Julie A. DiCarlo, Adrienne N. Dula, Natalia Egorova‐Brumley, Mark R. Etherton, Wuwei Feng, Kelene A. Fercho, Fatemeh Geranmayeh, Colleen A. Hanlon, Kathryn S. Hayward, Brenton Hordacre, Steven A. Kautz, Mohamed Salah Khlif, Hosung Kim, Amy Kuceyeski, David J. Lin, Jingchun Liu, Martin Lotze, Bradley J. MacIntosh, John L. Margetis, Feroze B. Mohamed, Fabrizio Piras, Ander Ramos‐Murguialday, Kate P. Revill, Pamela S. Roberts, Andrew D. Robertson, Heidi M. Schambra, Na Jin Seo, Mark S. Shiroishi, Cathy M. Stinear, Surjo R. Soekadar, Gianfranco Spalletta, Myriam Taga, Wai Kwong Tang, Gregory T. Thielman, Daniela Vecchio, Nick S. Ward, Lars T. Westlye, Emilio Werden, Carolee Winstein, George F. Wittenberg, Steven L. Wolf, Kristin A. Wong, Chunshui Yu, Amy Brodtmann, Steven C. Cramer, Paul M. Thompson, and Sook‐Lei Liew

Subjects
hippocampus, MRI, sensorimotor impairment, stroke, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Background Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippocampus is vulnerable to poststroke secondary degeneration and is involved in sensorimotor behavior but has not been widely studied within the context of poststroke upper‐limb sensorimotor impairment. We investigated associations between non‐lesioned hippocampal volume and upper limb sensorimotor impairment in people with chronic stroke, hypothesizing that smaller ipsilesional hippocampal volumes would be associated with greater sensorimotor impairment. Methods and Results Cross‐sectional T1‐weighted magnetic resonance images of the brain were pooled from 357 participants with chronic stroke from 18 research cohorts of the ENIGMA (Enhancing NeuoImaging Genetics through Meta‐Analysis) Stroke Recovery Working Group. Sensorimotor impairment was estimated from the FMA‐UE (Fugl‐Meyer Assessment of Upper Extremity). Robust mixed‐effects linear models were used to test associations between poststroke sensorimotor impairment and hippocampal volumes (ipsilesional and contralesional separately; Bonferroni‐corrected, P

Published
2022
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2. Association of brain age, lesion volume, and functional outcome in patients with stroke

Author

Sook-Lei Liew, Nicolas Schweighofer, James H. Cole, Artemis Zavaliangos-Petropulu, Bethany P. Lo, Laura K.M. Han, Tim Hahn, Lianne Schmaal, Miranda R. Donnelly, Jessica N. Jeong, Zhizhuo Wang, Aisha Abdullah, Jun H. Kim, Alexandre Hutton, Giuseppe Barisano, Michael R. Borich, Lara A. Boyd, Amy Brodtmann, Cathrin M. Buetefisch, Winston D. Byblow, Jessica M. Cassidy, Charalambos C. Charalambous, Valentina Ciullo, Adriana Bastos Conforto, Rosalia Dacosta-Aguayo, Julie A. DiCarlo, Martin Domin, Adrienne N. Dula, Natalia Egorova-Brumley, Wuwei Feng, Fatemeh Geranmayeh, Chris M. Gregory, Colleen A. Hanlon, Kathryn Hayward, Jess A. Holguin, Brenton Hordacre, Neda Jahanshad, Steven A. Kautz, Mohamed Salah Khlif, Hosung Kim, Amy Kuceyeski, David J. Lin, Jingchun Liu, Martin Lotze, Bradley J. MacIntosh, John L. Margetis, Maria Mataro, Feroze B. Mohamed, Emily R. Olafson, Gilsoon Park, Fabrizio Piras, Kate P. Revill, Pamela Roberts, Andrew D. Robertson, Nerses Sanossian, Heidi M. Schambra, Na Jin Seo, Surjo R. Soekadar, Gianfranco Spalletta, Cathy M. Stinear, Myriam Taga, Wai Kwong Tang, Greg T. Thielman, Daniela Vecchio, Nick S. Ward, Lars T. Westlye, Carolee J. Winstein, George F. Wittenberg, Steven L. Wolf, Kristin A. Wong, Chunshui Yu, Steven C. Cramer, Paul M. Thompson, Liew, Sook Lei, Schweighofer, Nicolas, Cole, James H, Zavaliangos-Petropulu, Artemis, Hordacre, Brenton, and Thompson, Paul M

Subjects
brain health, Neurology (clinical), stroke, functional outcome, brain age
Abstract

Background and ObjectivesFunctional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. In this study, we examined the impact of brain age, a measure of neurobiological aging derived from whole-brain structural neuroimaging, on poststroke 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 vs poor outcomes.MethodsWe conducted a cross-sectional observational study using a multisite dataset of 3-dimensional brain structural MRIs and clinical measures from the 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 3-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 vs poor outcomes in patients with matched lesion damage.ResultsWe 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 to −0.15,p< 0.001) and across multiple domains of function (β = −0.14; 95% CI −0.22 to −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 (odds ratio 1.04, 95% CI 1.01–1.08,p= 0.004).DiscussionWe provide evidence that younger brain age is associated with superior poststroke 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 poststroke outcomes compared with focal injury measures alone, opening new possibilities for potential therapeutic targets.

Published
2023

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

Author

Sook-Lei Liew, Bethany P. Lo, Miranda R. Donnelly, Artemis Zavaliangos-Petropulu, Jessica N. Jeong, Giuseppe Barisano, Alexandre Hutton, Julia P. Simon, Julia M. Juliano, Anisha Suri, Zhizhuo Wang, Aisha Abdullah, Jun Kim, Tyler Ard, Nerisa Banaj, Michael R. Borich, Lara A. Boyd, Amy Brodtmann, Cathrin M. Buetefisch, Lei Cao, Jessica M. Cassidy, Valentina Ciullo, Adriana B. Conforto, Steven C. Cramer, Rosalia Dacosta-Aguayo, Ezequiel de la Rosa, Martin Domin, Adrienne N. Dula, Wuwei Feng, Alexandre R. Franco, Fatemeh Geranmayeh, Alexandre Gramfort, Chris M. Gregory, Colleen A. Hanlon, Brenton G. Hordacre, Steven A. Kautz, Mohamed Salah Khlif, Hosung Kim, Jan S. Kirschke, Jingchun Liu, Martin Lotze, Bradley J. MacIntosh, Maria Mataró, Feroze B. Mohamed, Jan E. Nordvik, Gilsoon Park, Amy Pienta, Fabrizio Piras, Shane M. Redman, Kate P. Revill, Mauricio Reyes, Andrew D. Robertson, Na Jin Seo, Surjo R. Soekadar, Gianfranco Spalletta, Alison Sweet, Maria Telenczuk, Gregory Thielman, Lars T. Westlye, Carolee J. Winstein, George F. Wittenberg, Kristin A. Wong, Chunshui Yu, University of Southern California (USC), University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), Clinical and Behavioral Neurology - Neuroscienze e riabilitazione, IRCCS Fondazione Santa Lucia [Roma], Emory University School of Medicine, Emory University [Atlanta, GA], University of British Columbia [Vancouver], University of Melbourne, Child Mind Institute, Department Biostatistics University of North Carolina, University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC)-University of North Carolina System (UNC), Universidade de São Paulo = University of São Paulo (USP), University of California (UC), University of Barcelona, Technische Universität München = Technical University of Munich (TUM), Universität Greifswald - University of Greifswald, University of Texas at Austin [Austin], Duke University [Durham], Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, New York University School of Medicine (NYU Grossman School of Medicine), Imperial College London, Modèles et inférence pour les données de Neuroimagerie (MIND), IFR49 - Neurospin - CEA, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Medical University of South Carolina [Charleston] (MUSC), Wake Forest School of Medicine [Winston-Salem], Wake Forest Baptist Medical Center, University of South Australia [Adelaide], The Florey Institute of Neuroscience and Mental Health, Tianjin University (TJU), University of Toronto, Universitat de Barcelona (UB), Oslo Metropolitan University (OsloMet), University of Michigan [Ann Arbor], University of Michigan System, University of Bern, University of Waterloo [Waterloo], Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Oslo University Hospital [Oslo], Supported by The European Research Council under the European Union’s Horizon 2020 research and Innovation program (ERC StG, Grant 802998)., Liew, Sook-Lei, Lo, Bethany P, Donnelly, Miranda R, Zavaliangos-Petropulu, Artemis, Hordacre, Brenton G, and Winstein, Carolee J

Subjects
accurate image processing, Statistics and Probability, Image Processing, 610 Medicine & health, Neuroimaging, Bioengineering, [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], Library and Information Sciences, Education, Computer-Assisted, Image Processing, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, stroke rehabilitation, [STAT.AP]Statistics [stat]/Applications [stat.AP], segmented lesion, Neurosciences, Brain, ATLAS, Magnetic Resonance Imaging, Computer Science Applications, Stroke, Networking and Information Technology R&D (NITRD), 570 Life sciences, biology, Statistics, Probability and Uncertainty, Algorithms, Information Systems
Abstract

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.

Published
2022
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