Kathryn Hayward1, Jennifer K Ferris2, Keith R Lohse3, Michael R Borich4, Alexandra Borstad5, Jessica M Cassidy6, Steven C Cramer7, Sean P Dukelow8, Sonja E Findlater8, Rachel L Hawe9, Sook-Lei Liew10, Jason L Neva11, Jill C Stewart12, Lara A Boyd2. 1. Departments of Physiotherapy, Medicine and Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Heidelberg VIC 3084 Australia kate.hayward@unimelb.edu.au. 2. Rehabilitation Sciences Graduate Research Program, University of British Columbia, Vancouver BC V6T1B2 Canada. 3. Physical Therapy and Neurology. Washington University School of Medicine in Saint Louis, 4444 Forest Park, Saint Louis, MO, 63110, USA. 4. Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA USA. 5. School of Health Sciences, Department of Physical Therapy, College of St. Scholastica, Duluth, MN USA, 55812. 6. Department of Allied Health Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC USA, 27514. 7. Department of Neurology, University of California, Los Angeles; California Rehabilitation Institute; Los Angeles, CA USA. 8. Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N2T9 Canada. 9. School of Kinesiology, University of Minnesota, Minneapolis, MN, 55455, USA. 10. Chan Division of Occupational Science and Occupational Therapy, Biokinesiology and Physical Therapy, Biomedical Engineering, and Neurology, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA 90089-9003. 11. Université de Montréal, École de kinésiologie et des sciences de l'activité physique, Faculté de médecine, and Centre de recherche de l'institut universitaire de gériatrie de Montréal, Montréal, QC, Canada. 12. Physical Therapy Program, Department of Exercise Science, University of South Carolina, Columbia, SC USA, 29208.
Abstract
BACKGROUND AND OBJECTIVES: It is difficult to predict post-stroke outcome for people with severe motor impairment, as both clinical tests and corticospinal tract (CST) microstructure may not reliably indicate severe motor impairment. Here, we test whether imaging biomarkers beyond the CST relate to severe upper limb impairment post-stroke by evaluating white matter microstructure in the corpus callosum (CC). In an international, multisite hypothesis-generating observational study we determined if: a) CST asymmetry index can differentiate between individuals with mild-moderate and severe upper limb impairment; and b) CC biomarkers relate to upper limb impairment within individuals with severe impairment post-stroke. We hypothesised that CST asymmetry index would differentiate between mild-moderate and severe impairment, but CC microstructure would relate to motor outcome for individuals with severe upper limb impairment. METHODS: Seven cohorts with individual diffusion imaging and motor impairment (Fugl Meyer-Upper Limb) data were pooled. Hand-drawn regions-of-interest were used to seed probabilistic tractography for CST (ipsilesional/contralesional) and CC (prefrontal/premotor/motor/sensory/posterior) tracts. Our main imaging measure was mean fractional anisotropy. Linear mixed-effect regression explored relationships between candidate biomarkers and motor impairment, controlling for observations nested within cohorts, as well as age, sex, time post-stroke and lesion volume. RESULTS: Data from 110 individuals (30 mild-moderate, 80 with severe motor impairment) were included. In the full sample, greater CST asymmetry index (i.e., lower fractional anisotropy in the ipsilesional hemisphere, p<.001) and larger lesion volume (p=.139) were negatively related to impairment. In the severe subgroup, CST asymmetry index was not reliably associated with impairment across models. Instead, lesion volume and CC microstructure explained impairment in the severe group beyond CST asymmetry index (p's<.010). CONCLUSIONS: Within a large cohort of individuals with severe upper limb impairment, CC microstructure related to motor outcome post-stroke. Our findings demonstrate that CST microstructure does relate to upper limb outcome across the full range of motor impairment but was not reliably associated within the severe subgroup. Therefore, CC microstructure may provide a promising biomarker for severe upper limb outcome post-stroke, which may advance our ability to predict recovery in people with severe motor impairment after stroke.
BACKGROUND AND OBJECTIVES: It is difficult to predict post-stroke outcome for people with severe motor impairment, as both clinical tests and corticospinal tract (CST) microstructure may not reliably indicate severe motor impairment. Here, we test whether imaging biomarkers beyond the CST relate to severe upper limb impairment post-stroke by evaluating white matter microstructure in the corpus callosum (CC). In an international, multisite hypothesis-generating observational study we determined if: a) CST asymmetry index can differentiate between individuals with mild-moderate and severe upper limb impairment; and b) CC biomarkers relate to upper limb impairment within individuals with severe impairment post-stroke. We hypothesised that CST asymmetry index would differentiate between mild-moderate and severe impairment, but CC microstructure would relate to motor outcome for individuals with severe upper limb impairment. METHODS: Seven cohorts with individual diffusion imaging and motor impairment (Fugl Meyer-Upper Limb) data were pooled. Hand-drawn regions-of-interest were used to seed probabilistic tractography for CST (ipsilesional/contralesional) and CC (prefrontal/premotor/motor/sensory/posterior) tracts. Our main imaging measure was mean fractional anisotropy. Linear mixed-effect regression explored relationships between candidate biomarkers and motor impairment, controlling for observations nested within cohorts, as well as age, sex, time post-stroke and lesion volume. RESULTS: Data from 110 individuals (30 mild-moderate, 80 with severe motor impairment) were included. In the full sample, greater CST asymmetry index (i.e., lower fractional anisotropy in the ipsilesional hemisphere, p<.001) and larger lesion volume (p=.139) were negatively related to impairment. In the severe subgroup, CST asymmetry index was not reliably associated with impairment across models. Instead, lesion volume and CC microstructure explained impairment in the severe group beyond CST asymmetry index (p's<.010). CONCLUSIONS: Within a large cohort of individuals with severe upper limb impairment, CC microstructure related to motor outcome post-stroke. Our findings demonstrate that CST microstructure does relate to upper limb outcome across the full range of motor impairment but was not reliably associated within the severe subgroup. Therefore, CC microstructure may provide a promising biomarker for severe upper limb outcome post-stroke, which may advance our ability to predict recovery in people with severe motor impairment after stroke.
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