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Neuroimaging and behavioural biomarkers of post-stroke cognitive recovery outcomes

Poster No:

140

Submission Type:

Abstract Submission

Authors:

Margaret Moore¹, Jason Mattingley², Nele Demeyere³

Institutions:

¹The University of Queensland, Brisbane, Queensland, ²University of Queensland, St Lucia, Queensland, ³University of Oxford, Oxford, Oxfordshire

First Author:

Margaret Moore, PhD
The University of Queensland
Brisbane, Queensland

Co-Author(s):

Jason Mattingley, PhD
University of Queensland
St Lucia, Queensland

Nele Demeyere
University of Oxford
Oxford, Oxfordshire

Introduction:

Following stroke, some patients experience positive cognitive recovery outcomes, while others remain stable or decline. Previous work has identified neuroimaging biomarkers that are correlated with post-stroke cognitive recovery outcomes. However, it is not clear whether these measures provide additive prognostic utility relative to established risk factors. In this study we identified lesion-location based predictors of persistent, domain specific cognitive impairment and evaluated whether these measures can act as clinically useful prognostic indicators.

Methods:

Stroke survivors (n=408) completed the Oxford Cognitive Screen (OCS) during acute hospitalisation and at 6 months post-stroke. The OCS is a short, domain-specific cognitive screen that assesses language, memory, attention, numerical cognition, and praxis. ROI-level, network-level, and multivariate voxel-level lesion mapping was used to identify brain areas associated with persistent impairment on each OCS subtest. These were then used to generate scores summarising the extent to which critical regions were impacted in each individual. The prognostic utility of these neuroimaging-derived impact scores was then evaluated relative to the information provided by acute cognitive scores and visual ratings of brain health (e.g., global cortical atrophy, white matter integrity). Specifically, multivariate regression and ROC analyses were conducted to determine whether lesion-derived biomarkers provide additive prognostic information and whether these metrics can be used to accurately predict chronic cognitive status.

Results:

Correlates of persistent cognitive impairment were identified for 12/15 OCS subtasks. Within multivariate regression analyses, ROI-level and network-level impact scores significantly improved these models' ability to predict chronic cognitive status for 9/12 and 11/12 OCS tasks, respectively. However, lesion impact scores were classed as being either of poor or no diagnostic value (AUC mean = 0.59, range= 0.46 – 0.66) in ROC analyses. Acute cognitive status was the single best predictor of chronic cognitive status (AUC mean = 0.66, range = 0.41 – 0.95). In the 5 OCS subtests where acute behaviour was not the best predictor of chronic performance, the best predictors were cortical atrophy severity (n = 2), white matter integrity (n = 1), lesion side (n =1), and ROI-level lesion impact score (n=1). Combining lesion impact scores and acute cognitive scores improved AUC in 5/12 cases, but this improvement was small (mean AUC improvement = 0.03).

Conclusions:

Our findings suggest that the prognostic utility of lesion-location metrics is limited relative to more readily available prognostic indicators. While considering lesion location can significantly improve the ability of models to predict outcomes, the improvement is inconsistent and small. In line with past work, we suggest that acute cognitive status is a better predictor than lesion metrics for chronic cognitive status post-stroke.

Disorders of the Nervous System:

Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) ¹

Higher Cognitive Functions:

Higher Cognitive Functions Other ²

Modeling and Analysis Methods:

Other Methods

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Neuroanatomy Other

Keywords:

Cerebrovascular Disease

Cognition

DISORDERS

Other - Lesion Mapping

^1|2Indicates the priority used for review

·Figure 1. ROI-level lesion-mapping results. Significant correlates of persistent impairment on each OCS subtest are shown in red. R = right, L = left

·Figure 2: Diagnostic accuracy of predictors across OCS tests. Line colour represents predictor type. The best performing predictive metric is reported in each plot.

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