Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning.
cerebellar pathways
deep learning
diffusion MRI
explainable AI
multitask learning
tractography
white matter parcellation
Journal
Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065
Informations de publication
Date de publication:
15 Aug 2024
15 Aug 2024
Historique:
revised:
18
07
2024
received:
29
01
2024
accepted:
10
08
2024
medline:
26
8
2024
pubmed:
26
8
2024
entrez:
26
8
2024
Statut:
ppublish
Résumé
Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely on fibre tract structure. However, each fibre tract may relay information related to multiple cognitive and motor functions of the cerebellum. Hence, it may be beneficial for parcellation to consider the potential importance of the fibre tracts for individual motor and cognitive functional performance measures. In this work, we propose a multimodal data-driven method for cerebellar pathway parcellation, which incorporates both measures of microstructure and connectivity, and measures of individual functional performance. Our method involves first training a multitask deep network to predict various cognitive and motor measures from a set of fibre tract structural features. The importance of each structural feature for predicting each functional measure is then computed, resulting in a set of structure-function saliency values that are clustered to parcellate cerebellar pathways. We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation. Applying DeepMSP to a large-scale dataset from the Human Connectome Project Young Adult study (n = 1065), we found that it was feasible to identify multiple cerebellar pathway parcels with unique structure-function saliency patterns that were stable across training folds. We thoroughly experimented with all stages of the DeepMSP pipeline, including network selection, structure-function saliency representation, clustering algorithm, and cluster count. We found that a 1D convolutional neural network architecture and a transformer network architecture both performed comparably for the multitask prediction of endurance, strength, reading decoding, and vocabulary comprehension, with both architectures outperforming a fully connected network architecture. Quantitative experiments demonstrated that a proposed low-dimensional saliency representation with an explicit measure of motor versus cognitive category bias achieved the best parcellation results, while a parcel count of four was most successful according to standard cluster quality metrics. Our results suggested that motor and cognitive saliencies are distributed across the cerebellar white matter pathways. Inspection of the final k = 4 parcellation revealed that the highest-saliency parcel was most salient for the prediction of both motor and cognitive performance scores and included parts of the middle and superior cerebellar peduncles. Our proposed saliency-based parcellation framework, DeepMSP, enables multimodal, data-driven tractography parcellation. Through utilising both structural features and functional performance measures, this parcellation strategy may have the potential to enhance the study of structure-function relationships of the cerebellar pathways.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e70008Subventions
Organisme : National Key Research and Development Program of China
ID : 2023YFE0118600
Organisme : National Natural Science Foundation of China
ID : 62371107
Organisme : UNSW-USA Networks of Excellence Grant
Organisme : NIH HHS
ID : K24MH116366
Pays : United States
Organisme : NIH HHS
ID : P41EB015902
Pays : United States
Organisme : NIH HHS
ID : R01AG042512
Pays : United States
Organisme : NIH HHS
ID : R01MH111917
Pays : United States
Organisme : NIH HHS
ID : R01MH112748
Pays : United States
Organisme : NIH HHS
ID : R01MH119222
Pays : United States
Organisme : NIH HHS
ID : R01MH125860
Pays : United States
Organisme : NIH HHS
ID : R01MH132610
Pays : United States
Organisme : NIH HHS
ID : R01NS125307
Pays : United States
Organisme : NIH HHS
ID : R01NS125781
Pays : United States
Organisme : NIH HHS
ID : R21DA042271
Pays : United States
Organisme : NIH HHS
ID : 1U54MH091657
Pays : United States
Informations de copyright
© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.
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