Fast Bayesian estimation of brain activation with cortical surface fMRI data using EM

Task functional magnetic resonance imaging (fMRI) is a type of neuroimaging data used to identify areas of the brain that activate during specific tasks or stimuli. These data are conventionally modeled using a massive univariate approach across all data locations, which ignores spatial dependence at the cost of model power. We previously developed and validated a spatial Bayesian model leveraging dependencies along the cortical surface of the brain in order to improve accuracy and power. This model utilizes stochastic partial differential equation spatial priors with sparse precision matrices to allow for appropriate modeling of spatially-dependent activations seen in the neuroimaging literature, resulting in substantial increases in model power. Our original implementation relies on the computational efficiencies of the integrated nested Laplace approximation (INLA) to overcome the computational challenges of analyzing high-dimensional fMRI data while avoiding issues associated with variational Bayes implementations. However, this requires significant memory resources, extra software, and software licenses to run. In this article, we develop an exact Bayesian analysis method for the general linear model, employing an efficient expectation-maximization algorithm to find maximum a posteriori estimates of task-based regressors on cortical surface fMRI data. Through an extensive simulation study of cortical surface-based fMRI data, we compare our proposed method to the existing INLA implementation, as well as a conventional massive univariate approach employing ad-hoc spatial smoothing. We also apply the method to task fMRI data from the Human Connectome Project and show that our proposed implementation produces similar results to the validated INLA implementation. Both the INLA and EM-based implementations are available through our open-source BayesfMRI R package.Comment: 29 pages, 10 figures. arXiv admin note: text overlap with arXiv:2203.0005

Spatiotemporal brain imaging and modeling

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2004.Includes bibliographical references.This thesis integrates hardware development, data analysis, and mathematical modeling to facilitate our understanding of brain cognition. Exploration of these brain mechanisms requires both structural and functional knowledge to (i) reconstruct the spatial distribution of the activity, (ii) to estimate when these areas are activated and what is the temporal sequence of activations, and (iii)to determine how the information flows in the large-scale neural network during the execution of cognitive and/or behavioral tasks. Advanced noninvasive medical imaging modalities are able to locate brain activities at high spatial and temporal resolutions. Quantitative modeling of these data is needed to understand how large-scale distributed neuronal interactions underlying perceptual, cognitive, and behavioral functions emerge and change over time. This thesis explores hardware enhancement and novel analytical approaches to improve the spatiotemporal resolution of single (MRI) or combined (MRI/fMRI and MEG/EEG) imaging modalities. In addition, mathematical approaches for identifying large-scale neural networks and their correlation to behavioral measurements are investigated. Part I of the thesis investigates parallel MRI. New hardware and image reconstruction techniques are introduced to improve spatiotemporal resolution and to reduce image distortion in structural and functional MRI. Part II discusses the localization of MEG/EEG signals on the cortical surface using anatomical information from AMTRI, and takes advantage of the high temporal resolution of MEG/EEG measurements to study cortical oscillations in the human auditory system. Part III introduces a multivariate modeling technique to identify "nodes" and "connectivity" in a(cont.) large-scale neural network and its correlation to behavior measurements in the human motor system.by Fa-Hsuan Lin.Ph.D

Distinct higher-order representations of natural sounds in human and ferret auditory cortex

Little is known about how neural representations of natural sounds differ across species. For example, speech and music play a unique role in human hearing, yet it is unclear how auditory representations of speech and music differ between humans and other animals. Using functional ultrasound imaging, we measured responses in ferrets to a set of natural and spectrotemporally matched synthetic sounds previously tested in humans. Ferrets showed similar lower-level frequency and modulation tuning to that observed in humans. But while humans showed substantially larger responses to natural vs. synthetic speech and music in non-primary regions, ferret responses to natural and synthetic sounds were closely matched throughout primary and non-primary auditory cortex, even when tested with ferret vocalizations. This finding reveals that auditory representations in humans and ferrets diverge sharply at late stages of cortical processing, potentially driven by higher-order processing demands in speech and music

Physics of Brain Folding

The human brain is characterized by its folded structure, being the most folded brain among all primates. The process by which these folds emerge, called gyrogenesis, is still not fully understood. The brain is divided into an outer region, called gray matter, which grows at a faster rate than the inner region, called white matter. It is hypothesized that this imbalance in growth -- and the mechanical stress thereby generated -- drives gyrogenesis, which is the focus of this thesis. Finite element simulations are performed where the brain is modeled as a non-linear elastic and growth is introduced via a multiplicative decomposition. A small section of the brain, represented by a rectangular slab, is analyzed. This slab is divided into a thin hard upper layer mimicking the gray matter, and a soft substrate, mimicking the white matter. The top layer is then grown tangentially, while the underlying substrate does not grow. JuFold, the software developed to perform these simulations, is introduced, and its design is explained. An overview of its capabilities, and examples of simulation possibilities are shown. Additionally, one patent-leading application of JuFold in the realm of material science showcases its flexibility. Simulations are first performed by minimizing the elastic energy, corresponding to the slow growth regime. Systems with homogeneous cortices are studied, where growth initially compresses, and then buckles the cortical region, which generates wavy patterns with wavelength proportional to cortical thickness. After buckling, the sulcal regions (i.e. the valleys of the system) are thinner than the gyral regions (i.e. the hills). Introducing thickness inhomogeneities along the cortex lead to new and localized configurations, which are strongly dependent not only on the thickness of the region, but also on its gradient. Furthermore, cortical landmarks appear sequentially, consistent with the hierarchical folding observed during gestation. A linear stability theory is developed based on thin plate theory and is compared with homogeneous and inhomogeneous systems. Next, we turn to more physically stringent dynamic simulations. For slow growth rate and time-constant thickness, the results obtained through energy minimization are recovered, justifying previous literature. For faster growth, an overshoot of the wavenumber and a broad wavenumber spectrum are observed immediately after buckling. After a relaxation period, where the average wavenumber decreases and the wavenumber spectrum narrows, it is observed that the system stabilizes into a finite spectrum, whose average wavelength is smaller than that expected from energy minimization arguments. Cortical inhomogeneities are further explored in this new regime. Systems with inhomogeneous cortical thickness are revisited, with effects similar to the homogeneous cortex (i.e., results are consistent between the slow growth and the quasistatic regimes, and overshoot is observed in the fast growth regimes). Systems with inhomogeneous cortical growth are simulated, with this new type of inhomogeneity inducing fissuration and localized folding. The interplay between these two inhomogeneities is studied, and their interaction is shown to be nonlinear, with each inhomogeneity type inhibiting the folding effects of the other. That is, the folding profile of each individual region emerges as a result of the local inhomogeneity, and the system does not display an intermediate behavior. Finally, these results are compared with an extended linear stability theory. Taken together, our simulations and analytical theory expose new phenomena predicted by an incremented buckling hypothesis for folding and show a series of new avenues which could give rise to the important cortical features in the mammalian brain, especially those related to higher-order folding

Exploring the Neural Mechanisms of Physics Learning

This dissertation presents a series of neuroimaging investigations and achievements that strive to deepen and broaden our understanding of human problem solving and physics learning. Neuroscience conceives of dynamic relationships between behavior, experience, and brain structure and function, but how neural changes enable human learning across classroom instruction remains an open question. At the same time, physics is a challenging area of study in which introductory students regularly struggle to achieve success across university instruction. Research and initiatives in neuroeducation promise a new understanding into the interactions between biology and education, including the neural mechanisms of learning and development. These insights may be particularly useful in understanding how students learn, which is crucial for helping them succeed. Towards this end, we utilize methods in functional magnetic resonance imaging (fMRI), as informed by education theory, research, and practice, to investigate the neural mechanisms of problem solving and learning in students across semester-long University-level introductory physics learning environments. In the first study, we review and synthesize the neuroimaging problem solving literature and perform quantitative coordinate-based meta-analysis on 280 problem solving experiments to characterize the common and dissociable brain networks that underlie human problem solving across different representational contexts. Then, we describe the Understanding the Neural Mechanisms of Physics Learning project, which was designed to study functional brain changes associated with learning and problem solving in undergraduate physics students before and after a semester of introductory physics instruction. We present the development, facilitation, and data acquisition for this longitudinal data collection project. We then perform a sequence of fMRI analyses of these data and characterize the first-time observations of brain networks underlying physics problem solving in students after university physics instruction. We measure sustained and sequential brain activity and functional connectivity during physics problem solving, test brain-behavior relationships between accuracy, difficulty, strategy, and conceptualization of physics ideas, and describe differences in student physics-related brain function linked with dissociations in conceptual approach. The implications of these results to inform effective instructional practices are discussed. Then, we consider how classroom learning impacts the development of student brain function by examining changes in physics problem solving-related brain activity in students before and after they completed a semester-long Modeling Instruction physics course. Our results provide the first neurobiological evidence that physics learning environments drive the functional reorganization of large-scale brain networks in physics students. Through this collection of work, we demonstrate how neuroscience studies of learning can be grounded in educational theory and pedagogy, and provide deep insights into the neural mechanisms by which students learn physics