A novel stroke lesion network mapping approach: improved accuracy yet still low deficit prediction
| Autor: | Michele De Filippo De Grazia, Alessandro Salvalaggio, Lorenzo Pini, Maurizio Corbetta, Michel Thiebaut de Schotten, Marco Zorzi |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
computer.software_genre Lesion Correlation Voxel medicine Functional disconnection Stroke medicine.diagnostic_test business.industry AcademicSubjects/SCI01870 functional connectivity General Engineering Network mapping resting-state networks Pattern recognition medicine.disease stroke behaviour Principal component analysis Original Article AcademicSubjects/MED00310 Artificial intelligence medicine.symptom business Functional magnetic resonance imaging computer |
| Zdroj: | Brain Communications |
| ISSN: | 2632-1297 |
| DOI: | 10.1093/braincomms/fcab259 |
| Popis: | Lesion network mapping estimates functional network abnormalities caused by a focal brain lesion. The method requires embedding the volume of the lesion into a normative functional connectome and using the average functional magnetic resonance imaging signal from that volume to compute the temporal correlation with all other brain locations. Lesion network mapping yields a map of potentially functionally disconnected regions. Although promising, this approach does not predict behavioural deficits well. We modified lesion network mapping by using the first principal component of the functional magnetic resonance imaging signal computed from the voxels within the lesioned area for temporal correlation. We measured potential improvements in connectivity strength, anatomical specificity of the lesioned network and behavioural prediction in a large cohort of first-time stroke patients at 2-weeks post-injury (n = 123). This principal component functional disconnection approach localized mainly cortical voxels of high signal-to-noise; and it yielded networks with higher anatomical specificity, and stronger behavioural correlation than the standard method. However, when examined with a rigorous leave-one-out machine learning approach, principal component functional disconnection approach did not perform better than the standard lesion network mapping in predicting neurological deficits. In summary, even though our novel method improves the specificity of disconnected networks and correlates with behavioural deficits post-stroke, it does not improve clinical prediction. Further work is needed to capture the complex adjustment of functional networks produced by focal damage in relation to behaviour. Pini et al. developed a new methodology for ‘lesion network mapping’ in stroke. This new methodology enabled a more accurate estimation of functional brain network disconnected in stroke. However, the method did not predict well neurological impairment limiting its application to clinical questions. Graphical Abstract Graphical Abstract |
| Databáze: | OpenAIRE |
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