Functional MRI Method Development

功能性 MRI 方法开发

• 批准号: 10266587
• 负责人: Peter Bandettini
• 金额: $ 240.96万
• 依托单位: NATIONAL INSTITUTE OF MENTAL HEALTH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10266587
• 关键词: 3-Dimensional; Address; Affective; Anatomy; Animal Model; Anterior; Area; Attention; Auditory; Auditory area; Behavioral; Blood; Brain; Brain Mapping; Brain region; COVID-19; Cognition; Collaborations; Communities; Complex; Computer software; Conscious; Data; Data Analyses; Data Collection; Development; Dimensions; Face; Functional Imaging; Functional Magnetic Resonance Imaging; Future; Goals; Healthcare; Human; Image; Individual; Inpatients; Intelligence; International; Laboratories; Letters; Light; Link; Liquid substance; Magnetic Resonance Imaging; Manuscripts; Mental Depression; Mental disorders; Methods; Modeling; Mood Disorders; Motion; Motor; Nature; Neurobiology; Neurons; Neurosciences; Newsletter; Noise; Nuts; Output; Paper; Pattern; Perfusion; Personality; Phenotype; Physiologic pulse; Physiology; Pial Veins; Prefrontal Cortex; Preparation; Protocols documentation; Publishing; Questionnaires; Research; Resolution; Rest; Safety; Sampling; Scanning; Sensory; Sequence Analysis; Series; Short-Term Memory; Signal Transduction; Sleep; Specific qualifier value; Specificity; Stimulus; Structure; Techniques; Testing; Time; Training; United States National Institutes of Health; Veins; Visual; Wakefulness; Work; base; behavioral phenotyping; behavioral study; brain behavior; cognitive ability; commune; data acquisition; design; hackathon; healthy volunteer; high resolution imaging; imaging modality; improved; insight; large scale data; method development; movie; multimodality; multisensory; neural patterning; neuroimaging; novel; novel strategies; open source; predictive modeling; relating to nervous system; response; social; tool; trait; ultra high resolution; vigilance; white matter

Protocol number 93-M-0170, NCT00001360 High resolution MRI In 2019, our Section published 5 papers that involved ultra-high resolution imaging. They were all carried out 7 Tesla (typical research is performed at 3 Tesla) which allows for higher resolution or smaller voxels because of the increased signal to noise ratio that comes with increased field strength. At 7T fMRI, spatial resolution has been pushed to the sub-millimeter domain, making it possible to resolve functional activity across cortical depths/layers. This approach faces technical constraints such as limited sensitivity, sensitivity to larger veins causing layer-specific microvasculature signal to be washed out by the dominant signal from ascending and pial veins, and misregistration between separately acquired anatomic reference images and functional images. To address these problems, we used a novel pulse sequence developed by Yuhui Chai, called VASO and PERfusion (VAPER) contrast acquired by combining the blood-suppression module of DANTE (Delay Alternating with Nutation for Tailored Excitation) pulse trains with 3D-EPI. This new sequence shows a highly specified functional layer profile and an improved sensitivity compared to current established laminar fMRI approach like VASO since it includes perfusion contrast along with volume contrast. To address the point of structural imaging, we introduced magnetization transfer (MT) weighted imaging which provides superior gray-white matter contrast so that all laminar fMRI analysis can be performed in the native EPI space, without the need for distortion correction and registration as the resulting anatomical data is completely matched for distortion with the VAPER. We demonstrated VAPER-fMRI using high resolution (0.8mm) to disentangle auditory and visual inputs in human planum temporale at submillimeter resolution over both dimensions of columnar distance and laminar depth. The integration of visual and auditory motion information is important to our ability to navigate the world. Planum temporale (PT) is part of the auditory cortex and it serves as a computational hub for processing multisensory information. We have found that PT area is topographically organized such that auditory and visual inputs consistently activate anterior and posterior subareas along the cortical ribbon respectively. Furthermore, along the laminar dimension, visual input led to response suppression to concurrent auditory input in anterior PT, most prominently in superficial layers. We also extended VASO imaging to allow for whole brain fMRI using sub-millimeter voxel dimensions, as previously, it was limited to single slabs in the brain. This allows us to address whole brain, layer specific connectivity questions. We openly shared our imaging sequence and analysis suite (open source) with anyone in the high-field community to be applied across various brain areas and the improved layer-specificity of our methods could be reproduced in more than 25 research labs worldwide. (https://layerfmri.page.link/VASO_worldwide). In the last year, we have continued our work on extending layer-specific fMRI methods to higher-order brain regions. Following our empirical paper published in 2019 in Nature Neuroscience, in which we demonstrated depth-dependent activity in human dorsolateral prefrontal cortex during a working memory task, we have developed a theoretical framework for adapting layer fMRI for the unique challenges and opportunities of association regions (whereas previously, applications had been limited to primary/unimodal motor and sensory areas). We have compiled best practices for data acquisition and analysis in these regions, as well as conceptual areas where layer fMRI can shed light on major outstanding questions in neuroscience (e.g., attention, consciousness, mental illness). These perspectives have been put forth in a manuscript that is pending at Progress in Neurobiology. Time Series Dynamics In the last year, we have developed a novel technique, called inter-subject representational similarity analysis (IS-RSA), to extract individual information from naturalistic fMRI data and shown that it can recover brain-behavior relationships while people watch complex, engaging videos. We have used this approach to demonstrate that individuals with higher fluid intelligence show more similar neural responses during movie-watching, while individuals with lower fluid intelligence display more variable patterns (i.e., less similar to one another and to high scorers). We have also used this approach to demonstrate that individuals with more similar responses to a personality questionnaire show more similar patterns of neural responses during movie-watching. Also along these lines, we have shown that functional connectivity calculated from data collected as subjects watch movies is more sensitive to trait-level behavioral differences than functional connectivity calculated during rest. In fully cross-validated models, both cognitive ability and affective traits could be predicted from as little as 2-3 minutes of functional connectivity data from movie-watching. Movies that were high in social content were particularly effective in generating accurate predictions. This result has implications for future large-scale data collection efforts aimed at brain-behavior predictive modeling, suggesting that using naturalistic paradigms in addition to or instead of resting-state acquisitions may hasten the development of translational tools based on fMRI functional connectivity. A manuscript on these results is in preparation. We have also designed, piloted, and begun data collection for a novel study that combines naturalistic tasks during fMRI with detailed behavioral and phenotypic assessment to test the hypothesis that patterns of brain activity during certain social videos selected based on extensive pilot behavioral studies to be particularly evocative but also ambiguous/open to different interpretations will stratify subjects into phenotypes relevant for depression and related mood disorders. Prior to the disruption of data collection due to COVID-19, we had acquired data from 40 healthy volunteers and 4 inpatients with severe depression in collaboration with Dr. Zarate's Section on the Neurobiology and Treatment of Mood Disorders. Analyses of these data are ongoing and we expect to resume data collection as soon as safety protocols allow. We have continued work to improve and better understand how multi-echo fMRI can be used to remove noise from fMRI data. Some of this work has focused on tedana.readthedocs.io software. In November 2019, SFIM organized and hosted a hackathon at NIH, which brought together the international collaborators of tedana for the first time. This work has greatly advanced this project and the communal accessibility and quality of multi-echo fMRI analysis methods. A brief summary of the accomplishments of the hackathon was shared with the tedana community at: https://tinyletter.com/tedana-devs/letters/tedana-hackathon-newsletter We have also been exploring methods for assessing ongoing vigilance state in the scanner to help reduce artifactual fluctuations in resting state fMRI. Recent work suggests that ultra-slow CSF fluctuations accompany descent into sleep. Here we evaluate how such fluctuations help track wakefulness in rest scans acquired on non sleep-deprived subjects using sequences not optimized for detecting such inflow-related fluctuations. We conclude that those fluctuations can be easily detected in other samples, and that they may provide valuable time-resolved information about fluctuations in wakefulness, as well as a means to segregate subjects according to their overall wakefulness levels.

协议号93-M-0170， NCT00001360