Harmonizing Multi-Modality Biases in Infant Development Analysis with an Integrated MRI Data Processing Pipeline

Motivation: Understanding infant neurodevelopment is pivotal for unraveling the anatomical underpinnings of psychomotor and cognitive functions, as well as pinpointing the origins of various disorders. Goal(s): Introduce an integrated multi-modality MRI data processing pipeline tailored for infant development studies, with the goal of reliablly discerning relationship across brain anatomy and cognitive functions. Approach: Incorporating precise deep learning tools specifically designed for infant brain, structural, functional, diffusion MRI data can be accurately analyzed, w.r.t. surface attributes for group-level study and network attributes for individual-level study. Results: We introduce an integrated multi-modal infant MRI data processing pipeline toolkit with dedicated processing results. Impact: We introduce the first infant multi-modal atlas and parcellation map

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/0238
Primary Topic
Gaussian Processes and Bayesian Inference
Type
article
Field-Weighted Citation Impact
0.00
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article

Harmonizing Multi-Modality Biases in Infant Development Analysis with an Integrated MRI Data Processing Pipeline

Dinggang G. Shen, Zehong Cao, Jiameng Liu, Luoyu Wang et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Gaussian Processes and Bayesian Inference
article

Harmonizing Multi-Modality Biases in Infant Development Analysis with an Integrated MRI Data Processing Pipeline

Dinggang G. Shen, Zehong Cao, Jiameng Liu, Luoyu Wang, Zhongrui Huang, Feihong Liu, Lianghu Guo, Tianli Tao, Feng Shi, Xuyun Wen, Yichu He, Chunfeng Lian, Haifeng Tang, Han Zhang, Geng Chen, Jiawei Huang, Rui Hua, Qian Wang, Yajuan Zhang, Jun Feng, Fan Wang, Xinyi Cai, Jinchen Gu
article en

Abstract

Motivation: Understanding infant neurodevelopment is pivotal for unraveling the anatomical underpinnings of psychomotor and cognitive functions, as well as pinpointing the origins of various disorders. Goal(s): Introduce an integrated multi-modality MRI data processing pipeline tailored for infant development studies, with the goal of reliablly discerning relationship across brain anatomy and cognitive functions. Approach: Incorporating precise deep learning tools specifically designed for infant brain, structural, functional, diffusion MRI data can be accurately analyzed, w.r.t. surface attributes for group-level study and network attributes for individual-level study. Results: We introduce an integrated multi-modal infant MRI data processing pipeline toolkit with dedicated processing results. Impact: We introduce the first infant multi-modal atlas and parcellation map

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 81%
Gaussian Processes and Bayesian Inference
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