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Infrastructure for hyperaligning fMRI data and estimating functional topographies

用于超对齐功能磁共振成像数据和估计功能拓扑的基础设施

基本信息

批准号：
10689268
负责人：
Maria I Gobbini
金额：
$ 64.84万
依托单位：
DARTMOUTH COLLEGE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-23 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10689268
关键词：
Aging Algorithms Alzheimer&apos s Disease Anatomy Architecture Atlases Brain Brain imaging Clinical Communities Computer software Consumption Data Data Aggregation Data Set Databases Functional Magnetic Resonance Imaging Generations Grain Hour Human Individual Individual Differences Infrastructure Inosine Dialdehyde Maps Modeling Molecular Conformation Neurosciences Participant Personality Tests Population Population Analysis Principal Investigator Research Research Infrastructure Research Personnel Research Project Grants Research Support Resources Rest Sampling Scanning Software Tools Space Models Standardization Structure Surface System Time Work affective neuroscience aging brain behavior prediction cognitive neuroscience cognitive testing connectome cortex mapping cost effective data sharing experimental analysis flexibility high dimensionality information model movie neuroimaging open source preservation response sharing platform tool

项目摘要

PROJECT SUMMARY Shared information in cortical functional architecture is embedded in topographies that are idiosyncratic, posing a major impediment for functional brain imaging research. Hyperalignment resolves this problem by projecting information from individual brains into a common model information space. The proposed research project will create HyperBase – research infrastructure that will enable the brain imaging research community to leverage hyperalignment to greatly enrich their data, enable analyses of shared information and individual differences embedded in idiosyncratic fine-scale cortical topographies, and create a data sharing platform for data in the hyperaligned common model information space. The infrastructure will be an optimized, standardized template common model space based on a normative database, turnkey software tools for hyperaligning new brains and estimating individual functional topographies, and a framework for sharing hyperaligned data. These data and tools will provide community infrastructural support for research on a broad range of topics in clinical neuroscience, brain aging, and basic cognitive neuroscience. The proposed database will consist of fMRI data in 60 participants collected during movie viewing, story listening, at rest, and during a large set of functional localizers, augmented with demographic information and cognitive and personality test scores. Specific aims 1. Produce an optimized, standardized template for hyperalignment based on a normative database with open-source software that will allow mapping numerous functional topographies, based on standard localizer data in the normative sample, into new participant brains using only fMRI data collected while the new participants watch a movie, listen to a story, or are at rest. 2. Adapt hyperalignment algorithms to work with a standard template and estimate functional topographies via the template and normative localizer data. Develop new hyperalignment algorithms that increase power, precision, and flexibility. 3. Create a system for sharing functional brain imaging data that are projected into the common information space model, allowing accumulation of data in a framework that affords at a fine-grained level of detail. Hyperalign existing public datasets.

项目总结