Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning

Author:

Tchetchenian Ari1ORCID,Zekelman Leo23,Chen Yuqian2,Rushmore Jarrett4567,Zhang Fan8ORCID,Yeterian Edward H.9,Makris Nikos45610,Rathi Yogesh210ORCID,Meijering Erik1,Song Yang1,O'Donnell Lauren J.2ORCID

Affiliation:

1. Biomedical Image Computing Group, School of Computer Science and Engineering University of New South Wales (UNSW) Sydney New South Wales Australia

2. Department of Radiology, Brigham and Women's Hospital Harvard Medical School Boston Massachusetts USA

3. Harvard University Cambridge Massachusetts USA

4. Department of Psychiatry Massachusetts General Hospital, Harvard Medical School Boston Massachusetts USA

5. Department of Neurology Massachusetts General Hospital, Harvard Medical School Boston Massachusetts USA

6. Department of Radiology Massachusetts General Hospital, Harvard Medical School Boston Massachusetts USA

7. Department of Anatomy and Neurobiology Boston University School of Medicine Boston Massachusetts USA

8. School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

9. Department of Psychology Colby College Waterville Maine USA

10. Department of Psychiatry, Brigham and Women's Hospital Harvard Medical School Boston Massachusetts USA

Abstract

AbstractParcellation 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.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

National Institutes of Health

Publisher

Wiley

Reference125 articles.

1. Adebayo J. Gilmer J. Muelly M. Goodfellow I. Hardt M. &Kim B.(2018).Sanity checks for saliency maps. Advances in Neural Information Processing Systems 31. Retrieved fromhttps://proceedings.neurips.cc/paper/8160-sanity-checks-for-saliency-maps

2. Cerebellar Modules and Their Role as Operational Cerebellar Processing Units

3. Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex

4. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain

5. In vivo fiber tractography using DT-MRI data

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3