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Post Stroke Cognitive Impairment: more than a lesion-symptom model

Huygelier, H.; Moore, M. J.; Odom, A.; Tuts, N.; Thielen, H.; Mancuso, M.; Gillebert, C. R.; Demeyere, N.

2025-02-21 neurology
10.1101/2025.02.19.25322521 medRxiv
Show abstract

Post-stroke cognitive impairment (PSCI) is highly heterogeneous, reflecting both domain-specific deficits associated with focal lesions and broader impairments linked to premorbid health and demographic factors. In the current study we investigated whether PSCI can be distinguished into distinct across-domain cognitive profiles and how such PSCI profiles are associated with lesion neuroanatomy, demographic and premorbid health factors. This cross-sectional study employed data-driven analyses (i.e., Latent Class Analysis) on 2172 stroke survivors who completed a domain-specific cognitive screen (i.e., Oxford Cognitive Screen) within 6 months after stroke (M = 19; Mdn = 7, SD = 35.7 days). In addition, the association of the PSCI profiles with lesion and demographic characteristics was investigated. We identified two viable cognitive class solutions: a 5-class model capturing classical and novel PSCI profiles and a more detailed 13-class model reflecting a broader set of distinct cognitive profiles that commonly occur after stroke. The 5-class solution distinguished classical lateralized deficits (e.g., aphasia, neglect) alongside a minimal impairment and non-lateralized global impairment profile. In contrast, the 13-class solution provided finer-grained differentiation, particularly for non-lateralized cognitive profiles which were more strongly associated with premorbid health and education level. Importantly, lesion anatomy alone could not fully account for class distinctions. While lesion location was predictive, particularly, in hyper-acute stages, profiles for patients tested 2 weeks post-stroke revealed less influence of lesion location and more of lesion volume. These findings underscore the importance of considering both anatomical and premorbid factors in understanding PSCI. By providing a nuanced classification of PSCI profiles, this study establishes a foundation for future translational research aimed at improving clinical care and predicting cognitive trajectories.

Published in Imaging Neuroscience (predicted rank #22) · training set

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