Early post-stroke predictors of cognitive performance evolution and long-term impairment
Poster No:
134
Submission Type:
Abstract Submission
Authors:
Mohamed Salah Khlif1, Emilio Werden2, Laura Bird3, Stanley Hung4, Amy Brodtmann1
Institutions:
1Cognitive Health Initiative, School of Translational Medicine (STM), Monash University, Melbourne, VIC, 2The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC, 3Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, 4University of British Columbia, Department of Physical Therapy, Vancouver, British Columbia
First Author:
Mohamed Salah Khlif
Cognitive Health Initiative, School of Translational Medicine (STM), Monash University
Melbourne, VIC
Cognitive Health Initiative, School of Translational Medicine (STM), Monash University
Melbourne, VIC
Co-Author(s):
Emilio Werden
The Florey Institute of Neuroscience and Mental Health, University of Melbourne
Parkville, VIC
The Florey Institute of Neuroscience and Mental Health, University of Melbourne
Parkville, VIC
Stanley Hung
University of British Columbia, Department of Physical Therapy
Vancouver, British Columbia
University of British Columbia, Department of Physical Therapy
Vancouver, British Columbia
Amy Brodtmann
Cognitive Health Initiative, School of Translational Medicine (STM), Monash University
Melbourne, VIC
Cognitive Health Initiative, School of Translational Medicine (STM), Monash University
Melbourne, VIC
Introduction:
Cognitive impairment is common after stroke (Rost et al., 2022) with up to 60% of survivors experiencing cognitive impairment within the first year (El Husseini et al., 2023). Though many survivors show cognitive recovery over this period (Tang et al., 2018), many still develop cognitive impairment or dementia in the ensuing years (Levine et al., 2015).
Cognitive performance in stroke is often studied at the group-level and at fixed timepoints. Less studies have longitudinally investigated the determinants of short/long-term cognitive impairment. Though crucial for prognosis, more individualized study of cognitive progression post-stroke has seldom been done. Here, we aimed to explore the early post-stroke predictors of cognitive performance following ischemic stroke.
Cognitive performance in stroke is often studied at the group-level and at fixed timepoints. Less studies have longitudinally investigated the determinants of short/long-term cognitive impairment. Though crucial for prognosis, more individualized study of cognitive progression post-stroke has seldom been done. Here, we aimed to explore the early post-stroke predictors of cognitive performance following ischemic stroke.
Methods:
We assessed cognition in stroke survivors 3- and 36-months post-stroke and concurrently in age-matched healthy controls. We sampled 93 stroke and 35 healthy participants from the CANVAS study (Brodtmann et al., 2014) with demographic, clinical, neuroimaging, mood, physical activity, and sleep variables available at both timepoints. Stroke participants were classified as cognitively normal/impaired (CN/CI) using z-score=-1.5SD as threshold in any of five domains: attention/processing speed, memory, language, visuospatial, and executive. We identified four distinctive cognitive trajectories between assessments: people remained normal or impaired at both timepoints, recovered, or declined.
Brain volumes were estimated from manually-corrected FreeSurfer/7.3.2 segmentation of T1w MRI including dedicated pipelines for estimating hippocampal/thalamic volumes (Iglesias et al., 2015; Iglesias et al., 2018). White matter hyperintensities (WMHs) were delineated manually from FLAIR MRI. Comparisons between groups were made via mixed-effect models with age, sex, and education as covariates. Findings are reported using bias-corrected Hedges' g effect sizes.
Brain volumes were estimated from manually-corrected FreeSurfer/7.3.2 segmentation of T1w MRI including dedicated pipelines for estimating hippocampal/thalamic volumes (Iglesias et al., 2015; Iglesias et al., 2018). White matter hyperintensities (WMHs) were delineated manually from FLAIR MRI. Comparisons between groups were made via mixed-effect models with age, sex, and education as covariates. Findings are reported using bias-corrected Hedges' g effect sizes.
Results:
At 36 months, 76.3% of stroke survivors were classified as CN (60.2% unchanged+16.1% recovery) and 23.7% as CI (12.9% unchanged+10.8% decline). We found widespread deficits in early post-stroke variables worsening over time (Fig. 1). The stroke cohort showed improvements in executive and attention but stable status in other domains. However, there were substantial variations among stroke subgroups. The attention and executive improvements were found in CN survivors only. The CI survivors experienced significant decline in memory (-0.43SD), driven exclusively by large memory reduction (-1.12SD) accompanied by executive reduction (-0.61SD) in the decline group. The recovery group showed improvements in memory (0.52SD), visuospatial (1.2SD), and executive (0.93SD). Stroke survivors impaired at both timepoints exhibited improvement in executive function (0.52SD), perhaps linked to the large reductions in anxiety (-3.2) and depression (-2.5) scores. The sustained high baseline anxiety and depression could equivalently relate to the decline group's large reductions in memory and executive function.
Baseline factors that distinctively predicted cognitive impairment at 3-years include age, comorbidities, physical activity, vascular risk factors (VRFs), global and ipsiregional brain atrophy particularly hippocampal and thalamic, lesion characteristics, and cognitive deficits (Fig. 2-A). Excluding VRFs, the variables above also predicted cognitive evolution, in addition to education, depression, WMHs, enlargements of ventricles and contralesional pallidum, atrophy in contralesional cortex and accumbens, and exclusively deficits in language and processing speed (Fig. 2-B).
Baseline factors that distinctively predicted cognitive impairment at 3-years include age, comorbidities, physical activity, vascular risk factors (VRFs), global and ipsiregional brain atrophy particularly hippocampal and thalamic, lesion characteristics, and cognitive deficits (Fig. 2-A). Excluding VRFs, the variables above also predicted cognitive evolution, in addition to education, depression, WMHs, enlargements of ventricles and contralesional pallidum, atrophy in contralesional cortex and accumbens, and exclusively deficits in language and processing speed (Fig. 2-B).
Conclusions:
We showed that the conglomerate analysis of cognition in stroke obscures the heterogeneous cognitive evolution among survivors and that several early post-stroke risk factors contribute to long-term cognitive impairment. Our findings may guide the development of risk scores for cognitive impairment post-stroke and provide basis for accurate machine-learning approaches.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Segmentation and Parcellation
Other Methods
Keywords:
Anxiety
Cognition
Cortex
Degenerative Disease
Machine Learning
Memory
MRI
Multivariate
Thalamus
White Matter
1|2Indicates the priority used for review
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