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Personalized spatiotemporal hemodynamic response models for functional magnetic resonance imaging

用于功能磁共振成像的个性化时空血流动力学响应模型

基本信息

批准号：
10705163
负责人：
Martin Lindquist
金额：
$ 76.51万
依托单位：
JOHNS HOPKINS UNIVERSITY
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：
2022-09-15 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10705163
关键词：
Accounting Address Affect Affective Age Aging Anxiety Area Atlases Biophysics Blood Vessels Body mass index Brain Brain region Characteristics Clinical Clinical Research Code Cognition Cognitive Communities Complex Computer software Coupling Data Data Set Development Disease Ecosystem Estimation Techniques Functional Magnetic Resonance Imaging Future Grant Human Image Individual Knowledge Link Longevity Maps Measurable Measures Mental Depression Mental Health Mental Processes Mental disorders Methods Modeling Moods Morphologic artifacts Neurosciences Paper Parameter Estimation Participant Patients Persons Phenotype Population Post-Traumatic Stress Disorders Process Proxy Reproducibility Research Rest Sampling Series Shapes Signal Transduction Socioeconomic Status Statistical Models Structure Substance abuse problem Symptoms Techniques Testing Time Variant Work age related aged cognitive function cognitive neuroscience connectome connectome data depressed patient healthy aging hemodynamics high body mass index human data individual variation interest low socioeconomic status memory retrieval mental function neural neuroimaging neurovascular neurovascular coupling next generation novel pathological aging population stratification regional difference response secondary analysis sex spatiotemporal statistics substance misuse substance use virtual

项目摘要

Functional Magnetic Resonance Imaging (fMRI) shows great promise in characterizing brain circuits and networks related to human mental function and identifying pathophysiological changes underlying mental health disorders, healthy and pathological aging, substance misuse, and beyond. Great strides are being made in many areas, but the vast majority of fMRI research relies on the simplifying assumption of a canonical (or highly constrained) hemodynamic response function (HRF) that is substantially inaccurate. The HRF varies across brain regions, individuals, and age, but estimating it with sufficient accuracy and precision is problematic in small to medium-sized studies. As a result, over 95% of fMRI studies use a canonical HRF of fixed form. This results in substantial bias, power loss, and confounding. These problems apply to both task-based and connectivity studies, which rely implicitly on the assumption of a constant HRF across regions and individuals. In response to the “Notice of Special Interest (NOSI) regarding the Use of Human Connectome [HCP] Data for Secondary Analysis”, propose to use the Lifespan aged 5-100) combined with advanced statistical modeling t we HCP data (n=~3,600 high-quality datasets from people o address this issue. In Aim 1, we will contrast commonly used HRF models across the lifespan based on reliability and ability to ‘decode’ task state and phenotypic variables (e.g., cognitive function and mood). We develop novel methods for extracting meaningful phenotypic information from HRF shape and population inference, and develop robust software for best-in-class models. In Aim 2, we integrate best-in-class HRF models into a novel Gaussian process model and use it derive a demographic-specific, spatiotemporal HRF atlas, providing customized HRFs based on readily measurable characteristics (age, sex, and body- mass index) and brain region. In Aim 3, we use the HRF atlas to deconvolve rs-fMRI data and construct an HRF-corrected connectome map. We validate the HRF models, atlas, and connectome on two independent HCP Disease Connectomes and the CAM- CAN dataset (n=~700), and share the atlas, connectome, and software integrations with the research community. The development of these large-sample models will provide more accurate and precise estimates of task-related fMRI activity and connectivity in basic and clinical studies of mental health, aging, substance use, and beyond.

功能磁共振成像（fMRI）在表征方面显示出巨大的希望