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#9196. Reclassifying stroke lesion anatomy
| November 2026 | publication date |
| Proposal available till | 14-05-2025 |
| 4 total number of authors per manuscript | 6020 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Experimental and Cognitive Psychology; Neuropsychology and Physiological Psychology; Cognitive Neuroscience; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2510 $ | 1340 $ | 1170 $ | 1000 $ |
Contract №9196.1   | Contract №9196.2 | Contract №9196.3 | Contract №9196.4 |
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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 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.
Keywords:
Brain imaging; Dimensionality reduction; Lesion anatomy; Lesion–deficit prediction; Stroke
Contacts :
2 place - free (for sale)
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4 place - free (for sale)
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 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.
Keywords:
Brain imaging; Dimensionality reduction; Lesion anatomy; Lesion–deficit prediction; Stroke
Contacts :
