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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\u2010driven 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\u2013function saliency values that are clustered to parcellate cerebellar pathways. We refer to our method as <jats:italic>Deep Multimodal Saliency Parcellation<\/jats:italic> (<jats:italic>DeepMSP<\/jats:italic>), 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\u2010scale dataset from the Human Connectome Project Young Adult study (<jats:italic>n<\/jats:italic>\u2009=\u20091065), we found that it was feasible to identify multiple cerebellar pathway parcels with unique structure\u2013function saliency patterns that were stable across training folds. We thoroughly experimented with all stages of the DeepMSP pipeline, including network selection, structure\u2013function 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\u2010dimensional 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 <jats:italic>k<\/jats:italic>\u2009=\u20094 parcellation revealed that the highest\u2010saliency 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\u2010based parcellation framework, DeepMSP, enables multimodal, data\u2010driven tractography parcellation. Through utilising both structural features and functional performance measures, this parcellation strategy may have the potential to enhance the study of structure\u2013function relationships of the cerebellar pathways.<\/jats:p>","DOI":"10.1002\/hbm.70008","type":"journal-article","created":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T09:35:28Z","timestamp":1724664928000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8682-7516","authenticated-orcid":false,"given":"Ari","family":"Tchetchenian","sequence":"first","affiliation":[{"name":"Biomedical Image Computing Group, School of Computer Science and Engineering University of New South Wales (UNSW)  Sydney New South Wales Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leo","family":"Zekelman","sequence":"additional","affiliation":[{"name":"Department of Radiology, Brigham and Women's Hospital Harvard Medical School  Boston Massachusetts USA"},{"name":"Harvard University  Cambridge Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqian","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Brigham and Women's Hospital Harvard Medical School  Boston Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jarrett","family":"Rushmore","sequence":"additional","affiliation":[{"name":"Department of Psychiatry Massachusetts General Hospital, Harvard Medical School  Boston Massachusetts USA"},{"name":"Department of Neurology Massachusetts General Hospital, Harvard Medical School  Boston Massachusetts USA"},{"name":"Department of Radiology Massachusetts General Hospital, Harvard Medical School  Boston Massachusetts USA"},{"name":"Department of Anatomy and Neurobiology Boston University School of Medicine  Boston Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5032-6039","authenticated-orcid":false,"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering University of Electronic Science and Technology of China  Chengdu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edward H.","family":"Yeterian","sequence":"additional","affiliation":[{"name":"Department of Psychology Colby College  Waterville Maine USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikos","family":"Makris","sequence":"additional","affiliation":[{"name":"Department of Psychiatry Massachusetts General Hospital, Harvard Medical School  Boston Massachusetts USA"},{"name":"Department of Neurology Massachusetts General Hospital, Harvard Medical School  Boston Massachusetts USA"},{"name":"Department of Radiology Massachusetts General Hospital, Harvard Medical School  Boston Massachusetts USA"},{"name":"Department of Psychiatry, Brigham and Women's Hospital Harvard Medical School  Boston Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9946-2314","authenticated-orcid":false,"given":"Yogesh","family":"Rathi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Brigham and Women's Hospital Harvard Medical School  Boston Massachusetts USA"},{"name":"Department of Psychiatry, Brigham and Women's Hospital Harvard Medical School  Boston Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erik","family":"Meijering","sequence":"additional","affiliation":[{"name":"Biomedical Image Computing Group, School of Computer Science and Engineering University of New South Wales (UNSW)  Sydney New South Wales Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Song","sequence":"additional","affiliation":[{"name":"Biomedical Image Computing Group, School of Computer Science and Engineering University of New South Wales (UNSW)  Sydney New South Wales Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0197-7801","authenticated-orcid":false,"given":"Lauren J.","family":"O'Donnell","sequence":"additional","affiliation":[{"name":"Department of Radiology, Brigham and Women's Hospital Harvard Medical School  Boston Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,8,26]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"Adebayo J. 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