Sleep spindles mediate hippocampal-neocortical coupling during long-duration ripples
Sleep is pivotal for memory consolidation. According to two-stage accounts, memory traces
are gradually translocated from hippocampus to neocortex during non-rapid-eye-movement …
are gradually translocated from hippocampus to neocortex during non-rapid-eye-movement …
On nonregularized estimation of psychological networks
An important goal for psychological science is developing methods to characterize
relationships between variables. Customary approaches use structural equation models to …
relationships between variables. Customary approaches use structural equation models to …
Data analytics on graphs part III: Machine learning on graphs, from graph topology to applications
Modern data analytics applications on graphs often operate on domains where graph
topology is not known a priori, and hence its determination becomes part of the problem …
topology is not known a priori, and hence its determination becomes part of the problem …
Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning
In the literature, there are substantial machine learning attempts to classify schizophrenia
based on alterations in resting-state (RS) brain patterns using functional magnetic …
based on alterations in resting-state (RS) brain patterns using functional magnetic …
Introduction to graph signal processing
Graph signal processing deals with signals whose domain, defined by a graph, is irregular.
An overview of basic graph forms and definitions is presented first. Spectral analysis of …
An overview of basic graph forms and definitions is presented first. Spectral analysis of …
Bayesian estimation for Gaussian graphical models: Structure learning, predictability, and network comparisons
DR Williams - Multivariate Behavioral Research, 2021 - Taylor & Francis
Gaussian graphical models (GGM;“networks”) allow for estimating conditional dependence
structures that are encoded by partial correlations. This is accomplished by identifying non …
structures that are encoded by partial correlations. This is accomplished by identifying non …
Distributionally robust inverse covariance estimation: The Wasserstein shrinkage estimator
We introduce a distributionally robust maximum likelihood estimation model with a
Wasserstein ambiguity set to infer the inverse covariance matrix of ap-dimensional Gaussian …
Wasserstein ambiguity set to infer the inverse covariance matrix of ap-dimensional Gaussian …
[HTML][HTML] Reliability and subject specificity of personalized whole-brain dynamical models
JWM Domhof, SB Eickhoff, OV Popovych - Neuroimage, 2022 - Elsevier
Dynamical whole-brain models were developed to link structural (SC) and functional
connectivity (FC) together into one framework. Nowadays, they are used to investigate the …
connectivity (FC) together into one framework. Nowadays, they are used to investigate the …
Capturing the forest but missing the trees: microstates inadequate for characterizing shorter-scale EEG dynamics
The brain is known to be active even when not performing any overt cognitive tasks, and
often it engages in involuntary mind wandering. This resting state has been extensively …
often it engages in involuntary mind wandering. This resting state has been extensively …
Graph Signal Processing--Part III: Machine Learning on Graphs, from Graph Topology to Applications
Many modern data analytics applications on graphs operate on domains where graph
topology is not known a priori, and hence its determination becomes part of the problem …
topology is not known a priori, and hence its determination becomes part of the problem …