Modeling functional resting-state brain networks through neural message passing on the human connectome

  title={Modeling functional resting-state brain networks through neural message passing on the human connectome},
  author={Julio A. Peraza-Goicolea and Eduardo Mart'inez-Montes and Eduardo Aubert and Pedro Antonio Valdes-Hernandez and Roberto Mulet},
  journal={Neural networks : the official journal of the International Neural Network Society},

CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis

This work proposes a granger causality-inspired graph neural network (CI-GNN), a built-in interpretable model that is able to identify the most influen-tial subgraph that is causally related to the decision (e.g., functional connectivity within brain regions) and theoretically justifies the validity of the CMI regula-tion in capturing the causal relationship.

Modeling Neurodegeneration in silico With Deep Learning

A paradigm for modeling neural diseases in silico with deep learning is proposed and its use in modeling posterior cortical atrophy (PCA), an atypical form of Alzheimer’s disease affecting the visual cortex is demonstrated.

On the accuracy of message-passing approaches to percolation in complex networks

It is found that the closer a non-tree network is to a tree, the worse the MPA accuracy becomes, and the fact that the M PA is exact on trees does not imply that it is nearly exact on tree-like networks.

The 2-D Cluster Variation Method: Topography Illustrations and Their Enthalpy Parameter Correlations

One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much image patterns themselves, but the nature

The 2-D Cluster Variation Method: Initial Findings and Topography Illustrations

The 2-D CVM can potentially function as a secondary free energy minimization within the hidden layer of a neural network, providing a basis for extending node activations over time and allowing temporal correlation of patterns.



A graph-theoretical approach in brain functional networks. Possible implications in EEG studies

Results suggest that spinal cord injuries affect the functional architecture of the cortical network sub-serving the volition of motor acts mainly in its local feature property and methodological aspects of cortical activity from scalp EEG signals are emphasized.

Exploring the brain network: a review on resting-state fMRI functional connectivity.

Highlighting the Structure-Function Relationship of the Brain with the Ising Model and Graph Theory

Graphing the functional and structural brain organization supports the model and can also highlight the strategy used to process and organize large amount of information traveling between the different modules, to better understand how global or integrated behavior can emerge from local and modular interactions.

Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors

This approach offers a realistic mechanistic model at the level of each single brain area based on spiking neurons and realistic AMPA, NMDA, and GABA synapses and fits quantitatively best the experimentally observed functional connectivity in humans when the brain network operates at the edge of instability.

Brain organization into resting state networks emerges at criticality on a model of the human connectome.

The results show that critical dynamics unfolding on the structural connectivity of the human brain allow the recovery of many key experimental findings obtained from functional magnetic resonance imaging, such as divergence of the correlation length, the anomalous scaling of correlation fluctuations, and the emergence of large-scale resting state networks.

The graphical brain: Belief propagation and active inference

This paper formulate neuronal processing as belief propagation under deep generative models that can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture.


Current empirical efforts toward generating a network map of the human brain, the human connectome, are reviewed, and how the connectome can provide new insights into the organization of the brain's structural connections and their role in shaping functional dynamics are explored.

Spin-glass model predicts metastable brain states that diminish in anesthesia

Computer simulation was used to explore the repertoire of global brain states as a function of cortical activation level to describe UP/DOWN state transitions of neuronal populations at a mesoscopic scale based on resting state BOLD fMRI data.