The VAE is trained on voxelwise rs-fMRI data and performs non-linear dimensionality reduction that retains meaningful information.
In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting.
For instance, how spatial networks of functional connectivity in the brain evolve during a task is not well-understood.
We propose the use of a multimodal multi-layer perceptron model to enhance the predictive power of structural and functional magnetic resonance imaging (sMRI and fMRI) combined.
Complexity of the task increases even further when segmenting structural MRI of the brain into an atlas with more than 50 regions.
In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel method for detecting higher order interactions among biologically relevant mulit-view data.
Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes.
We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI).
Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data.
That is, these algorithms all learn causal structure without assuming any particular relation between the measurement and system timescales; they are thus rate-agnostic.
Variational methods that rely on a recognition network to approximate the posterior of directed graphical models offer better inference and learning than previous methods.