Erica L. Busch
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View article: Latent Representation Learning for Multimodal Brain Activity Translation
Latent Representation Learning for Multimodal Brain Activity Translation Open
Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which…
View article: Looking through the mind's eye via multimodal encoder-decoder networks
Looking through the mind's eye via multimodal encoder-decoder networks Open
In this work, we explore the decoding of mental imagery from subjects using their fMRI measurements. In order to achieve this decoding, we first created a mapping between a subject's fMRI signals elicited by the videos the subjects watched…
View article: Manifold learning uncovers nonlinear interactions between the adolescent brain and environment that predict emotional and behavioral problems
Manifold learning uncovers nonlinear interactions between the adolescent brain and environment that predict emotional and behavioral problems Open
Background To progress adolescent mental health research beyond our present achievements - a complex account of brain and environmental risk factors without understanding neurobiological embedding in the environment - we need methods to un…
View article: Dissociation of Reliability, Heritability, and Predictivity in Coarse- and Fine-Scale Functional Connectomes during Development
Dissociation of Reliability, Heritability, and Predictivity in Coarse- and Fine-Scale Functional Connectomes during Development Open
The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mec…
View article: Multi-view manifold learning of human brain-state trajectories
Multi-view manifold learning of human brain-state trajectories Open
View article: Tasks collapse the intrinsic dimensionality of activity in non-selective cortex
Tasks collapse the intrinsic dimensionality of activity in non-selective cortex Open
View article: Dissociation of reliability, heritability, and predictivity in coarse- and fine-scale functional connectomes during development
Dissociation of reliability, heritability, and predictivity in coarse- and fine-scale functional connectomes during development Open
The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer–scale, vertex–wise connections that underlie specific information processing mec…
View article: Multi-view manifold learning of human brain state trajectories
Multi-view manifold learning of human brain state trajectories Open
The complexity and intelligence of the brain give the illusion that measurements of brain activity will have intractably high dimensionality, rifewith collection and biological noise. Nonlinear dimensionality reduction methods like UMAP an…
View article: Dissociation of Reliability, Predictability, and Heritability in Fine- and Coarse-Scale Functional Connectomes During Development
Dissociation of Reliability, Predictability, and Heritability in Fine- and Coarse-Scale Functional Connectomes During Development Open
View article: Learning shared neural manifolds from multi-subject FMRI data
Learning shared neural manifolds from multi-subject FMRI data Open
Functional magnetic resonance imaging (fMRI) is a notoriously noisy measurement of brain activity because of the large variations between individuals, signals marred by environmental differences during collection, and spatiotemporal averag…
View article: Hybrid hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity
Hybrid hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity Open
View article: Hybrid Hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity
Hybrid Hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity Open
Shared information content is represented across brains in idiosyncratic functional topographies. Hyperalignment addresses these idiosyncrasies by using neural responses to project individuals’ brain data into a common model space while ma…
View article: Application of Deep Neural Networks to Model Omnidirectional Gaze Behavior in Immersive VR
Application of Deep Neural Networks to Model Omnidirectional Gaze Behavior in Immersive VR Open
Convolutional neural networks (CNNs) are powerful computational tools for understanding visual cognition, including human gaze behavior (O’Connell et al., 2018). Traditionally, CNNs are designed to model visual images that typically repres…