Dataset Information

Reclassifying stroke lesion anatomy.

ABSTRACT: Cognitive and behavioural outcomes in stroke reflect the interaction between two complex anatomically-distributed patterns: the functional organization of the brain and the structural distribution of ischaemic injury. Conventional outcome models-for individual prediction or population-level inference-commonly ignore this complexity, discarding anatomical variation beyond simple characteristics such as lesion volume. This sets a hard limit on the maximum fidelity such models can achieve. High-dimensional methods can overcome this problem, but only at prohibitively large data scales. Drawing on one of the largest published collections of anatomically-registered imaging of acute stroke-N = 1333-here we use non-linear dimensionality reduction to derive a succinct latent representation of the anatomical patterns of ischaemic injury, agglomerated into 21 distinct intuitive categories. We compare the maximal predictive performance it enables against both simpler low-dimensional and more complex high-dimensional representations, employing multiple empirically-informed ground truth models of distributed structure-outcome relationships. We show our representation sets a substantially higher ceiling on predictive fidelity than conventional low-dimensional approaches, but lower than that achievable within a high-dimensional framework. Where descriptive simplicity is a necessity, such as within clinical care or research trials of modest size, the representation we propose arguably offers a favourable compromise of compactness and fidelity.

SUBMITTER: Bonkhoff AK

PROVIDER: S-EPMC8650827 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Reclassifying stroke lesion anatomy.

Bonkhoff Anna K AK Xu Tianbo T Nelson Amy A Gray Robert R Jha Ashwani A Cardoso Jorge J Ourselin Sebastien S Rees Geraint G Jäger Hans Rolf HR Nachev Parashkev P

Cortex; a journal devoted to the study of the nervous system and behavior 20211002

Cognitive and behavioural outcomes in stroke reflect the interaction between two complex anatomically-distributed patterns: the functional organization of the brain and the structural distribution of ischaemic injury. Conventional outcome models-for individual prediction or population-level inference-commonly ignore this complexity, discarding anatomical variation beyond simple characteristics such as lesion volume. This sets a hard limit on the maximum fidelity such models can achieve. High-dim ...[more]

PMID: 34673291

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Project description:Clinicians and scientists alike have long sought to predict the course and severity of chronic post-stroke cognitive and motor outcomes, as the ability to do so would inform treatment and rehabilitation strategies. However, it remains difficult to make accurate predictions about chronic post-stroke outcomes due, in large part, to high inter-individual variability in recovery and a reliance on clinical heuristics rather than empirical methods. The neuroanatomical location of a stroke is a key variable associated with long-term outcomes, and because lesion location can be derived from routinely collected clinical neuroimaging data there is an opportunity to use this information to make empirically based predictions about post-stroke deficits. For example, lesion location can be compared to statistically weighted multivariate lesion-behaviour maps of neuroanatomical regions that, when damaged, are associated with specific deficits based on aggregated outcome data from large cohorts. Here, our goal was to evaluate whether we can leverage lesion-behaviour maps based on data from two large cohorts of individuals with focal brain lesions to make predictions of 12-month cognitive and motor outcomes in an independent sample of stroke patients. Further, we evaluated whether we could augment these predictions by estimating the structural and functional networks disrupted in association with each lesion-behaviour map through the use of structural and functional lesion network mapping, which use normative structural and functional connectivity data from neurologically healthy individuals to elucidate lesion-associated networks. We derived these brain network maps using the anatomical regions with the strongest association with impairment for each cognitive and motor outcome based on lesion-behaviour map results. These peak regional findings became the 'seeds' to generate networks, an approach that offers potentially greater precision compared to previously used single-lesion approaches. Next, in an independent sample, we quantified the overlap of each lesion location with the lesion-behaviour maps and structural and functional lesion network mapping and evaluated how much variance each could explain in 12-month behavioural outcomes using a latent growth curve statistical model. We found that each lesion-deficit mapping modality was able to predict a statistically significant amount of variance in cognitive and motor outcomes. Both structural and functional lesion network maps were able to predict variance in 12-month outcomes beyond lesion-behaviour mapping. Functional lesion network mapping performed best for the prediction of language deficits, and structural lesion network mapping performed best for the prediction of motor deficits. Altogether, these results support the notion that lesion location and lesion network mapping can be combined to improve the prediction of post-stroke deficits at 12-months.

Project description:The impact of stroke on motor functioning is analyzed at different levels. 'Impairment' denotes the loss of basic characteristics of voluntary movement. 'Activity limitation' denotes the loss of normal capacity for independent execution of daily activities. Recovery from impairment is accomplished by 'restitution' and recovery from activity limitation is accomplished by the combined effect of 'restitution' and 'compensation.' We aimed to unravel the long-term effects of variation in lesion topography on motor impairment of the hemiparetic lower limb (HLL), and gait capacity as a measure of related activity limitation. Gait was assessed by the 3 m walk test (3MWT) in 67 first-event chronic stroke patients, at their homes. Enduring impairment of the HLL was assessed by the Fugl-Meyer Lower Extremity (FMA-LE) test. The impact of variation in lesion topography on HLL impairment and on walking was analyzed separately for left and right hemispheric damage (LHD, RHD) by voxel-based lesion-symptom mapping (VLSM). In the LHD group, HLL impairment tended to be affected by damage to the posterior limb of the internal capsule (PLIC). Walking capacity tended to be affected by a larger array of structures: PLIC and corona radiata, external capsule and caudate nucleus. In the RHD group, both HLL impairment and walking capacity were sensitive to damage in a much larger number of brain voxels. HLL impairment was affected by damage to the corona radiata, superior longitudinal fasciculus and insula. Walking was affected by damage to the same areas, plus the internal and external capsules, putamen, thalamus and parts of the perisylvian cortex. In both groups, voxel clusters have been found where damage affected FMA-LE and also 3MWT, along with voxels where damage affected only one of the measures (mainly 3MWT). In stroke, enduring 'activity limitation' is affected by damage to a much larger array of brain structures and voxels within specific structures, compared to enduring 'impairment.' Differences between the effects of left and right hemisphere damage are likely to reflect variation in motor-network organization and post-stroke re-organization related to hemispheric dominance. Further studies with larger sample size are required for the validation of these results.

Dataset Information

Reclassifying stroke lesion anatomy.

Publications

Reclassifying stroke lesion anatomy.

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