Prediction of Long-term Cognitive Function After Minor Stroke Using Functional Connectivity

Lopes, Renaud; Bournonville, Clement; Kuchcinski, Gregory; Dondaine, Thibaut; Mendyk, Anne-Marie; Viard, Romain; Pruvo, Jean-Pierre; Henon, Hilde; Georgakis, Marios K.; Duering, Marco; Dichgans, Martin; Cordonnier, Charlotte; Leclerc, Xavier und Bordet, Regis (2021): Prediction of Long-term Cognitive Function After Minor Stroke Using Functional Connectivity. In: Neurology, Bd. 96, Nr. 8, E1167-E1179

Abstract

Objective To determine whether functional MRI connectivity can predict long-term cognitive function 36 months after minor stroke. Methods Seventy-two participants with first-ever stroke were included at baseline and followed up for 36 months. A ridge regression machine learning algorithm was developed and used to predict cognitive scores 36 months poststroke on the basis of the functional networks measured using MRI at 6 months (referred to here as the poststroke cognitive impairment [PSCI] network). The prediction accuracy was evaluated in 4 domains (memory, attention/executive, language, and visuospatial functions) and compared with clinical data and other functional networks. The models' statistical significance was probed with permutation tests. The potential involvement of cortical atrophy was assessed 6 months poststroke. A second, independent dataset (n = 40) was used to validate the results and assess their generalizability. Results Based on the PSCI network, a machine learning model was able to predict memory, attention, visuospatial functions, and language functions 36 months poststroke (r(2): 0.67, 0.73, 0.55, and 0.48, respectively). The PSCI-based model was at least as accurate as models based on other functional networks or clinical data. Specific patterns were demonstrated for the 4 cognitive domains, with involvement of the left superior frontal cortex for memory, attention, and visuospatial functions. The cortical thickness 6 months poststroke was not correlated with cognitive function 36 months poststroke. The independent validation dataset gave similar results. Conclusions A machine learning model based on the PSCI network can predict long-term cognitive outcome after stroke.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 0028-3878
• Sprache: Englisch