BrainIAK: the brain imaging analysis kit
Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the
neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an …
neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an …
[HTML][HTML] Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis
With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive
neuroscience researchers, large volumes of brain imaging data have been accumulated in …
neuroscience researchers, large volumes of brain imaging data have been accumulated in …
Gaussian process linking functions for mind, brain, and behavior
The link between mind, brain, and behavior has mystified philosophers and scientists for
millennia. Recent progress has been made by forming statistical associations between …
millennia. Recent progress has been made by forming statistical associations between …
Enhanced hyperalignment via spatial prior information
Functional alignment between subjects is an important assumption of functional magnetic
resonance imaging (fMRI) group‐level analysis. However, it is often violated in practice …
resonance imaging (fMRI) group‐level analysis. However, it is often violated in practice …
Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging
Several problems in neuroimaging and beyond require inference on the parameters of multi-
task sparse hierarchical regression models. Examples include M/EEG inverse problems …
task sparse hierarchical regression models. Examples include M/EEG inverse problems …
Differential network knockoff filter with application to brain connectivity analysis
J Ji, Z Hou, Y He, L Liu, F Xue, H Chen… - Statistics in …, 2024 - Wiley Online Library
The brain functional connectivity can typically be represented as a brain functional network,
where nodes represent regions of interest (ROIs) and edges symbolize their connections …
where nodes represent regions of interest (ROIs) and edges symbolize their connections …
Information Geometry and Asymptotics for Kronecker Covariances
A McCormack, P Hoff - arXiv preprint arXiv:2308.02260, 2023 - arxiv.org
We explore the information geometry and asymptotic behaviour of estimators for Kronecker-
structured covariances, in both growing-$ n $ and growing-$ p $ scenarios, with a focus …
structured covariances, in both growing-$ n $ and growing-$ p $ scenarios, with a focus …
Joint learning of full-structure noise in hierarchical Bayesian regression models
We consider the reconstruction of brain activity from electroencephalography (EEG). This
inverse problem can be formulated as a linear regression with independent Gaussian scale …
inverse problem can be formulated as a linear regression with independent Gaussian scale …
Toward Generalizing Visual Brain Decoding to Unseen Subjects
Visual brain decoding aims to decode visual information from human brain activities.
Despite the great progress, one critical limitation of current brain decoding research lies in …
Despite the great progress, one critical limitation of current brain decoding research lies in …
Scalable multi-task Gaussian process tensor regression for normative modeling of structured variation in neuroimaging data
Most brain disorders are very heterogeneous in terms of their underlying biology and
developing analysis methods to model such heterogeneity is a major challenge. A promising …
developing analysis methods to model such heterogeneity is a major challenge. A promising …