Functional MRI Method Development

National Institute Of Mental Health

Investigators

Linked publications & trials

Abstract

Perception and Attention (Sharif Kronemer, Micah Holness, and Tori Gobo) Regarding perception and attention, the first project aims to understand neural mechanisms of afterimage perception and the second project aims to understand changes in conscious state associated with pupil size. For the first project, the entire dataset, including more than 40 behavioral and MRI study sessions, has been collected and is currently being analyzed. The goal is to compare brain activation associated with a visual afterimage with that associated with an artificially produced afterimage, thus identifying the unique neural correlates of internally-generated after images. We found that, although participants could not differentiate between real and mock afterimages, there were activation differences in Fusiform Gyrus and Anterior Cingulate. One other finding is that the degree to which participants perceived afterimages was correlated with measures of their ability to form mental images. This research so far has been presented in two conference abstracts presented at the annual meetings for the Association for the Scientific Study of Consciousness (ASSC; research talk) and Organization for Human Brain Mapping (OHBM; poster presentation). It is anticipated that at least one peer-reviewed publication will be achieved from this project in the coming year. The second project aims to study changes in conscious state using fluctuations in pupil size using innovative real-time monitoring methods. Two study paradigms were developed and piloted. Initial data collection has begun with magnetoencephalogram (MEG). The preliminary findings from this study indicate that performance and brain activation associated with tasks were correlated with moment-to-moment changes in pupil size. A larger pupil size indicated stronger arousal. This work has so far resulted in poster abstracts presented at ASSC and OHBM. A peer-reviewed publication is anticipated from this study within the next year. Visual face recognition in humans (Fernando Ramirez) Recognition of the identity of a face regardless of viewpoint is critical for social interactions. In primates, the representation of mirror-symmetric face-views is a key step leading from strictly view-tuned to viewpoint-invariant representations. Human neuroimaging studies have shown contradictory conclusions regarding the face viewpoint location's face-selective areas. We have shown that low-level stimulus confounds and data-analysis choices explain these discordant findings. We developed a network model that replicates view-tuning and mirror-symmetry, correspondingly, in early and late processing stages. The discordant literature findings of mirror-symmetry are explained by differences in processing brain activation pattern dissimilarity measures. This work was recently published as a preprint, currently under review in the Journal of Neuroscience, and selected to be orally presented in this years Annual Meeting of the Society for Neuroscience, in Washington DC (Nov, 2023). Characterization of functional network dynamics (Josh Faskowitz) Synchronous activity between pairs of regions during the scan is commonly called brain connectivity. Recently, we have developed methods to determine the time-varying nature of these synchronicities. We call these dynamic data edge time series, or ETS. We have characterized conditions that enable the brain to briefly enter these highly synchronized states. We have learned that the clustering of the brain, or the degree to which some brain areas are highly connected, is likely necessary for these phenomena. Additionally, we have explored ways to summarize ETS in order to best relate to phenotypic features. In this work, we explore different summary statistics of ETS using repetitions of multivariate machine learning, enabling us to determine which measures are most predictive. This work, performed in collaboration with post-bac IRTA, Megan Spurney, has been presented at the 2023 Organization for Human Brain Mapping meeting in Montreal. Dimensionality reduction of fMRI data ( Javier Gonzalez-Castillo) We have recently published work that was mostly performed in the previous year on this approach to dimensionality reduction of time series data. The problem addressed is how can fMRI data be collapsed and visualized while still retaining essential information? Which is the best way to collapse it? We find an effective way to estimate the true dimensionality of data and show how the use of manifolds (low dimensional shapes) can capture the essential features of high dimensional data. Results have been reported as a scientific publication in the journal Frontiers in Human Neuroscience titled: Manifold Learning for time-varying functional connectivity. Multi-echo fMRI processing (Dan Handwerker and Micah Holness) We have continued to contribute to multi-echo fMRI methods and software for removing noise from fMRI data. The basic concept is that true fMRI signal shows echo time (or TE) dependence while artifacts do not. Collection of images at different echo times, processed through our software tedana helps remove these artifacts. We generated a major update in tedana that makes it easier for users to understand their results and to improve and evaluate novel denoising methods. As part of this work, we examined how multi-echo denoising methods work on slow breathing fluctuations during a movie viewing and paced breathing task and tested how the tedana approach combined with measures of breathing fluctuations can better remove respiratory noise. Both the updated software and these new analyses were presented at OHBM 2023. Staff continue to be regularly contacted for advice on how to best acquire and use multi-echo fMRI data. Brain network dynamics during the wake-drowsy transition (Samika Kumar) Alertness fluctuations and cognitive processing both engage similar brain networks involving frontoparietal, thalamocortical, and sensory pathways. This project explored how alertness fluctuations and cognitive processing differentially engage these common functional brain networks. We analyzed two simultaneous EEG-fMRI datasets that were collected by collaborators from the University of Cambridge (UK). We found stronger long-range frontoparietal and auditory network synchronization with decreasing alertness when participants actively engaged and responded in an auditory task, but not when passively listening to auditory tones. With decreasing alertness, passive listening, but not active task engagement, was associated with increased synchronization in the parietal cortex and widespread increased thalamocortical synchronization. A preprint of this project titled has been published on bioRxiv and is currently being submitted to a journal. Decreased alertness changes brain network dynamics (Samika Kumar) Naturalistic movie-viewing continuously engages task-relevant brain networks, including those involved in low-level sensory processing and high-level integration, but it is unknown how this engagement changes across different levels of alertness. This project explored how brain functional organization and subcortico-cortical dynamics during passive movie-viewing change between canonical alert and drowsy arousal states. We found that participants who became drowsy had increased frontoparietal functional connectivity during movie-viewing. During drowsiness, participants also had increased functional connectivity between the supplementary eye field and other nodes of the visual attention network. This work was presented at the Organization for Human Brain Mapping conference (July, 2023) and also received a Merit Abstract Award and is being prepared as a manuscript.