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Echo state network models for nonlinear Granger causality.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
While Granger causality (GC) has been often employed in network neuroscience, most GC applications are based on linear multivariate autoregressive (MVAR) models. However, real-life systems like biological networks exhibit notable nonlinear behaviour,...

Brain functional and effective connectivity based on electroencephalography recordings: A review.

Human brain mapping
Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional...

Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics.

Human brain mapping
Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic lateral sclerosis (ALS). While most studies focus on structural data, recent studies assessing functional ...

MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning.

Nature communications
Understanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires...

Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity.

Brain structure & function
Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the unde...

The information theory of developmental pruning: Optimizing global network architectures using local synaptic rules.

PLoS computational biology
During development, biological neural networks produce more synapses and neurons than needed. Many of these synapses and neurons are later removed in a process known as neural pruning. Why networks should initially be over-populated, and the processe...

Effective gene expression prediction from sequence by integrating long-range interactions.

Nature methods
How noncoding DNA determines gene expression in different cell types is a major unsolved problem, and critical downstream applications in human genetics depend on improved solutions. Here, we report substantially improved gene expression prediction a...

A deep learning based approach identifies regions more relevant than resting-state networks to the prediction of general intelligence from resting-state fMRI.

Human brain mapping
Prediction of cognitive ability latent factors such as general intelligence from neuroimaging has elucidated questions pertaining to their neural origins. However, predicting general intelligence from functional connectivity limit hypotheses to that ...

Random walks on B distributed resting-state functional connectivity to identify Alzheimer's disease and Mild Cognitive Impairment.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Resting-state functional connectivity reveals a promising way for the early detection of dementia. This study proposes a novel method to accurately classify Healthy Controls, Early Mild Cognitive Impairment, Late Mild Cognitive Impairment,...

A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex.

PLoS computational biology
A fundamental challenge for the theoretical study of neuronal networks is to make the link between complex biophysical models based directly on experimental data, to progressively simpler mathematical models that allow the derivation of general opera...