Automated volume estimate of the brain's lateral ventricles reveals a persistent association with cognitive performance among stroke survivors.
Authors
Affiliations (15)
Affiliations (15)
- Computational Radiology & Artificial Intelligence Unit, Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway.
- Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Canada.
- Hurvitz Brain Sciences, Sandra Black Centre for Brain Resilience & Recovery, Physical Sciences Platform, Sunnybrook Research Institute, Canada.
- Norwegian Air Ambulance Foundation, Department of Research and Development, Oslo, Norway.
- Dept. of Radiology, Vestfold Hospital Trust, Tønsberg, Norway.
- ECMO Center Karolinska, Intensive Care and Transport, Pediatric Perioperative Medicine and Intensive Care, Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden.
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
- Department of Neurology, Oslo University Hospital, Oslo, Norway.
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
- Department of Medical Research, Bærum Hospital, Vestre Viken Hospital Trust, Norway.
- Center for Medical Ethics, University of Oslo, Norway.
- Department of Acute Medicine, Oslo University Hospital, Norway.
- Department of Geriatric Medicine, Clinic of Medicine, St. Olavs Hospital, Trondheim, Norway.
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Abstract
Post-stroke cognitive impairment (PSCI) is a significant barrier to recovery. While computed tomography (CT) is the dominant modality for acute stroke diagnosis, it can also quantify brain anatomy, such as lateral ventricular volume (LVV). This study investigates whether initial LVV is associated with Montreal Cognitive Assessment (MoCA) scores over long-term follow-up. Initial head CT, longitudinal MoCA, and functional assessments were collected at sites from the Norwegian COgnitive impairment After STroke (Nor-COAST) study. LVV was estimated using an established deep learning segmentation tool. Associations between initial LVV and repeat MoCA were tested across four timepoints (up to three years) using a linear mixed effects model. To provide context, we explored the independent influence of LVV in relation to National Institutes of Health Stroke Scale (NIHSS), modified Rankin Scale (mRS), and Global Deterioration Scale (GDS). The sample of N = 547 participants were 74 ± 12 years (range: 35-97 years) and 54% female. Log-transformed LVV was inversely associated with longitudinal MoCA scores (t=-3.75, p < 0.001). This relationship was consistent across cross-sectional analyses at individual time points. Secondary analyses revealed significant LVV associations with NIHSS, mRS, and GDS scores (all p < 0.05). LVV was independently associated with lower MoCA scores across the three-year follow-up after stroke. Specifically, a patient with twice the initial LVV scored 0.7 points lower on the MoCA across three years of follow-up. These findings reveal that CT obtained at stroke admission may yield prognostic information about long-term cognitive outcomes, supporting the case for automated LVV estimation.