AIMC Topic: Connectome
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Design of Deep Learning Model for Task-Evoked fMRI Data Classification.
Computational intelligence and neuroscience
Aug 12, 2021
Machine learning methods have been successfully applied to neuroimaging signals, one of which is to decode specific task states from functional magnetic resonance imaging (fMRI) data. In this paper, we propose a model that simultaneously utilizes cha...
Representation learning of resting state fMRI with variational autoencoder.
NeuroImage
Jul 23, 2021
Resting state functional magnetic resonance imaging (rsfMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rsfMRI data. Here we establish a variational auto-encoder, as a generative model ...
Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations.
NeuroImage
Jul 14, 2021
We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of b...
Delineating between-subject heterogeneity in alpha networks with Spatio-Spectral Eigenmodes.
NeuroImage
Jul 6, 2021
Between subject variability in the spatial and spectral structure of oscillatory networks can be highly informative but poses a considerable analytic challenge. Here, we describe a data-driven modal decomposition of a multivariate autoregressive mode...
Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain.
NeuroImage
Jul 2, 2021
Interest in understanding the organization of the brain has led to the application of graph theory methods across a wide array of functional connectivity studies. The fundamental basis of a graph is the node. Recent work has shown that functional nod...
Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics.
Human brain mapping
Jun 29, 2021
Large-scale brain dynamics are believed to lie in a latent, low-dimensional space. Typically, the embeddings of brain scans are derived independently from different cognitive tasks or resting-state data, ignoring a potentially large-and shared-portio...
Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method.
NeuroImage
Jun 9, 2021
Resting-state functional connectivity (RSFC) can be used for mapping large-scale human brain networks during rest. There is considerable interest in distinguishing the individual-shared and individual-specific components in RSFC for the better identi...
Integrative learning for population of dynamic networks with covariates.
NeuroImage
May 20, 2021
Although there is a rapidly growing literature on dynamic connectivity methods, the primary focus has been on separate network estimation for each individual, which fails to leverage common patterns of information. We propose novel graph-theoretic ap...
Spatio-temporal graph convolutional network for diagnosis and treatment response prediction of major depressive disorder from functional connectivity.
Human brain mapping
May 10, 2021
The pathophysiology of major depressive disorder (MDD) has been explored to be highly associated with the dysfunctional integration of brain networks. It is therefore imperative to explore neuroimaging biomarkers to aid diagnosis and treatment. In th...
A unified framework for personalized regions selection and functional relation modeling for early MCI identification.
NeuroImage
Apr 18, 2021
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely adopted to investigate functional abnormalities in brain diseases. Rs-fMRI data is unsupervised in nature because the psychological and neurological labels are coarse-grain...
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