Prediction of Long-term Cognitive Function After Minor Stroke Using Functional Connectivity. (23rd February 2021)

• Record Type: Journal Article
• Authors: Lopes, Renaud; Bournonville, Clément; Kuchcinski, Grégory; Dondaine, Thibaut; Mendyk, Anne-Marie; Viard, Romain; Pruvo, Jean-Pierre; Hénon, Hilde; Georgakis, Marios K.; Duering, Marco; Dichgans, Martin; Cordonnier, Charlotte; Leclerc, Xavier; Bordet, Régis
• Is Part Of: Neurology. Volume 96:Number 8(2021)
• Publication Date: 2021-02-23
• Subjects: Neurology -- Periodicals; Neurology -- Periodicals; Neurologie -- Périodiques; 616.8
• DOI: 10.1212/WNL.0000000000011452
• Languages: English
• ISSNs: 0028-3878

Abstract

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.