Speaker
Description
Stroke can lead to a wide range of symptoms including acute motor impairment, post-stroke depression or cognitive impairment. Anticipating these outcomes early on and understanding their underlying causes would enable clinicians to initiate appropriate targeted treatments, which could not only help reduce the severity of symptoms but also improve long-term recovery. Convolutional neural networks (CNNs) are often used for these types of tasks and commonly rely on either computed tomography (CT) or magnetic resonance imaging (MRI) images as data for training. However, it's increasingly understood that complex neurological symptoms often arise from diaschisis, that is, from disconnections between brain regions, rather than being attributable to a single focal neuroanatomical substrate. Lesion-network mapping (LNM) uses normative rs-MRI data to derive so-called connectivity maps indicating functional connections from brain lesions. In this project we investigated 1) whether CNNs trained on connectivity maps (LNM-CNN) outperform those trained on lesion masks (Lesion-CNN) in predicting post-stroke motor-impairment, and assessed 2) whether layer-wise relevance propagation (LRP) can be employed to extract valuable insights into model decisions. Finally, 3) we compared regional importance extracted from the LNM-CNN to those of a current standard procedure (permutation-based LNM).
Both models performed similarly, though the LNM-CNN showed higher recall and F1-Score indicating a higher sensitivity in identifying affected patients as well as better balance between precision and recall. LRP showed that importance often clustered near the lesion area, even in connectivity-based models, providing valuable context for the interpretation of the prediction results. The LNM-CNN revealed broader importance across cortical, subcortical, and white matter regions, while the permutation-based analysis predominantly identified cortical regions as important.
Overall our study showed that in predicting acute motor impairments after a stroke, connectivity-based features slightly improved predictive performance compared to lesion masks in CNN models. Additionally, our results show that connectivity-based CNN models may offer complementary insights into the functional impact of brain lesions and highlight the need for further investigation of their clinical relevance
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