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Brain connectivity measures improve modeling of functional outcome after acute ischemic stroke
Open accessPreprint — published as 10.1161/strokeaha.119.025738 · Paper page
Abstract
Background: The ability to model long-term functional outcomes after acute ischemic stroke (AIS) represents a major clinical challenge. One approach to potentially improve prediction modeling involves the analysis of connectomics. The field of connectomics represents the brain’s connectivity as a graph, whose topological properties have helped uncover underlying mechanisms of brain function in health and disease. Specifically, we assessed the impact of stroke lesions on rich club (RC) organization, a high capacity backbone system of brain function. Methods: In a hospital-based cohort of 41 AIS patients, we investigated the effect of acute infarcts on the brain’s pre-stroke RC backbone and post-stroke functional connectomes with respect to post-stroke outcome. Functional connectomes were created utilizing three anatomical atlases and characteristic path-length ( L ) was calculated for each connectome. The number of RC regions (N RC ) affected were manually determined using each patient’s diffusion weighted image (DWI). We investigated differences in L with respect to outcome (modified Rankin Scale score (mRS); 90-days; poor: mRS>2) and the National Institutes of Health Stroke Scale (NIHSS; early: 2-5 days; late: 90-day follow-up). Furthermore, we assessed the effect of including N RC and L in ‘outcome’ models, using linear regression and assessing the explained variance (R 2 ). Results: Of 41 patients (mean age (range): 70 (45-89) years), 61% were male. There were differences in L between patients with good and poor outcome (mRS). Including NRC in the backward selection models of outcome, R 2 increased between 1.3- and 2.6-fold beyond that of traditional markers (age and acute lesion volume) for NIHSS and mRS. Conclusion: In this proof-of-concept study, we showed that information on network topology can be leveraged to improve modeling of post-stroke functional outcome. Future studies are warranted to validate this approach in larger prospective studies of outcome prediction in stroke.
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Metadata
- Authors
- Ktena, S. I., Schirmer, M. D., Etherton, M. R., Giese, A.-K., Tuozzo, C., Mills, B. B., Rueckert, D., Wu, O., & Rost, N. S.
- Affiliations
- Harvard University (US)
- Imperial College London (GB)
- Massachusetts General Hospital (US)
- Corresponding author
- Schirmer, Markus D.
- ORCID
- Venue
- Stroke
- Publisher
- openRxiv
- Published
- 2019-03-28
- Volume
- 50
- Issue
- 10
- Pages
- 2761-2767
- ISSN
- 0039-2499 (ISSN-L)
- PMC
- PMC6756947
- OpenAlex
- W2972711230
- Cited by
- 32
- References
- 1
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 4 · 2023: 3 · 2022: 7 · 2021: 7 · 2020: 1 · 2019: 4
- FWCI
- 2.76
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- Locations
- Stroke (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DZNE Pub (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS086905, R01NS082285)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Clinical Neurology › Acute Ischemic Stroke Management
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- acute ischemic stroke, functional outcome prediction, connectomics, rich club, functional connectome, network topology, average path length, stroke lesion location, modified Rankin Scale, NIHSS, diffusion-weighted imaging
- MeSH terms
- Aged, Aged, 80 and over, Brain Ischemia, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Models, Neurological, Neural Pathways, Recovery of Function, Stroke, Connectome
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