The Neuroimaging Brain Chart Software Suite

神经影像脑图软件套件

• 批准号: 10581015
• 负责人: Christos Davatzikos
• 金额: $ 97.73万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10581015
• 关键词: Adopted; Adult; Aging; Artificial Intelligence; Atlases; Back; Brain; Clinical; Communities; Complex; Computer software; Data; Data Analyses; Detection; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Ecosystem; Educational workshop; Functional Magnetic Resonance Imaging; Genomics; Goals; Heterogeneity; High Performance Computing; Image; Individual; Infrastructure; Internet; Knowledge Discovery; Libraries; Machine Learning; Maps; Methods; Modeling; Nerve Degeneration; Neurodegenerative Disorders; Online Systems; Pattern; Persons; Phenotype; Process; Pythons; Research; Research Personnel; Software Tools; Source; Standardization; Structure; System; Testing; Training; Variant; Visual; Visualization; Work; aging brain; analysis pipeline; brain magnetic resonance imaging; cloud based; cluster computing; combat; data sharing; deep learning; deep learning model; machine learning method; machine learning model; meetings; multimodality; nervous system disorder; neuroimaging; neuropsychiatric disorder; novel; programs; structural determinants; tool; web interface; web portal

This study proposes to refine, integrate and disseminate the NeuroImaging Brain Chart (NIBCh) software toolbox and machine learning (ML) model library, an ecosystem of software components enabling constructive integration, statistical harmonization, and ML-centric data analyses across studies. NIBCh enables large-scale analyses of multi-modal brain MRI data by mapping such data into a compact coordinate system of informative neuroimaging signatures implemented by our library of ML models. The axes of this coordinate system represent two types of information: 1) a variety of structural (sMRI and dMRI) and functional connectomic (rsfMRI) imaging derived phenotypes (IDPs), such as multi-scale brain parcelations and brain networks; 2) complex ML-based imaging signatures (ML-IDPs), which capture multi-variate imaging patterns that reflect the heterogeneity of brain aging, neurodegeneration, as well as of neuropsychiatic disorders and have been previously derived from carefully processed and curated data of over 65,000 individuals. Using our software toolboxes (Tbx), researchers will be able to map new data into NIBCh, and hence to use ML-IDP models trained in NIBCh, as well as perform statistical tests against NIBCh normative ranges and compare their results with those of other studies using the same Tbx. The software suite will include a set of containerized pre-processing and analysis pipelines, as well as statistical harmonization and ML inference toolboxes, which will be accessible via a standalone python front-end visualization, as cloud-based containers, and via a web-interface supported by our high-performance computing cluster. Several dissemination plans are discussed, including a github user community, tutorials at major technical and clinical meetings, and support of both standalone pipelines locally or on the cloud, and web-based access of harmonization and ML inference modules. The over-arching primary goal of our program is to provide the software tools that will allow users to contribute to an actively growing community-based dimensional neuroimaging system that will utilize machine learning models to provide rich, yet precise, compact, concise, and informative representations of brain structure, function and connectivity.

本研究提出对NIBCh （NeuroImaging Brain Chart）软件进行完善、整合和推广