Reliable longitudinal brain age prediction in stroke patients: Associations with cognitive function and response to cognitive training

Abstract

Cognitive deficits are important predictors for outcome, independence and quality of life after stroke, but often remain unnoticed and unattended because other impairments are more evident. Computerized cognitive training (CCT) is among the candidate interventions that may alleviate cognitive difficulties, but the evidence supporting its feasibility and effectiveness is scarce, partly due to the lack of tools for outcome prediction and monitoring.Magnetic resonance imaging (MRI) provides candidate markers for disease monitoring and outcome prediction. By integrating information not only about lesion extent and localization, but also regarding the integrity of the unaffected parts of the brain, advanced MRI provides relevant information for developing better prediction models in order to tailor cognitive intervention for patients, especially in a chronic phase.Using brain age prediction based on MRI based brain morphometry and machine learning, we tested the hypotheses that stroke patients with a younger-appearing brain relative to their chronological age perform better on cognitive tests and benefit more from cognitive training compared to patients with an older-appearing brain. In this randomized double-blind study, 54 patients who suffered mild stroke (>6 months since hospital admission, NIHSS<7 at hospital discharge) underwent 3-weeks CCT and MRI before and after the intervention. In addition, patients were randomized to one of two groups receiving either active or sham transcranial direct current stimulation (tDCS). We tested for main effects of brain age gap (estimated age -chronological age) on cognitive performance, and associations between brain age gap and task improvement. Finally, we tested if longitudinal changes in brain age gap during the intervention were sensitive to treatment response. Briefly, our results suggest that longitudinal brain age prediction based on automated brain morphometry is feasible and reliable in stroke patients. However, no significant association between brain age and both performance and response to cognitive training were found.

Reliability, sensitivity and predictive value of fMRI during multiple object tracking as a marker of cognitive training gain in combination with tDCS in stroke survivors

“…To explore potential association between initial cognitive capacity and Cogmed change score, we correlated performance on the cognitive performance at baseline assessed with the Cabpad battery (https://www.cognisoft.info) at TP1, described in detail previously (Richard et al, 2019) with the Cogmed change score. To test for associations between initial cognitive performance and response to tDCS we estimated linear models using the same baseline cognitive performance from Cabpad as the dependent variable, and Cogmed change score and tDCS group as independent variables.…”

Interação entre o acido fólico e acidente vascular encefálico: uma revisão integrativa da literatura

“…O Acidente Vascular Encefálico (AVE) representa a principal causa de morte por doenças cardiovasculares, sendo a segunda causa de óbito no mundo e a primeira no Brasil (Meschia et al, 2014). Além disso, está entre as causas mais comuns de deficiências cognitivas adquiridas durante a idade adulta, com um considerável aumento na prevalência nas próximas décadas diante do envelhecimento da população geral (Richard et al, 2020).…”