Neural mass modelling was developed in the 1970s, first by Wilson and Cowan and then by Freeman, and summarized the behavior of interconnected masses of two classes of neurons: excitatory and inhibitory neurons. The alpha rhythm provided further insight to this modeling, including an additional mass of excitatory neurons that has become popularly known as the Jansen and Rit model. These were all generative models whose properties were explored by simulations. The first estimation of model parameters from data within an inferential framework in the late 1990s was closely followed by work with dynamical causal modelling (DCM). In parallel, the neural field models were developed.
Neural mass and field models have achieved maturity to the point that they are able to describe generic properties of brain dynamics as reflected in the EEG, MEG and fMRI. The current focus of interests is to describe individualized brain models adapted to specific subjects, as exemplified in The Virtual Brain (TVB) platform. These will allow the analysis of inter-individual differences, as well as the analysis of brain disorders, and thus pave the way to develop disease progression models grounded on theory-based disease progression models.
This Research Topic is motivated by the 2018 BrainModes meeting, dedicated to “Computational Modeling of neurodegeneration and the aging brain”, hosted by the Cuban Neuroscience Center in Havana, and aims to solicit research which explores innovative means of understanding complex brain activity and multimodal neuroscience data sets. Therefore, we call for contributions to this collection that cover development of theoretical methods for modelling and analysis of neuroscience data to research on application of these theoretical methods to the study of neurodegenerative diseases and/or other aging-related brain disorders.
BrainModes is an annual meeting which seeks to explore innovative means of understanding complex brain activity and multimodal neuroscience data sets. The objective is to foster informal discussion of brain modelling and multivariate data analysis (EEG, MEG, fMRI, etc). The central motif is that of “modes” – that is, understanding how complex brain activity is organized around low dimensional manifolds. Previous meetings can be found in
www.brainmodes.org.
Some of the main areas of research we would like to receive contributions on include, but are not limited to:
1) Theoretical methods for modelling and analysis of neuroscience data
a. Linking scales, the transition from micro, to meso, to macro-scale neural mass and neural field modeling.
b. Modelling fMRI signal and effective connectivity
c. Physical brain connectomics and dynamics
d. Joint estimation of effective brain wave activation modes using EEG sensor
arrays and multimodal MRI volumes
e. Statistical estimation of EEG brain connectivity
f. Time delayed neuronal interactions and synchronization in the brain
g. The electrophysiological basis of the Default Mode network
h. Accurate estimation of long-range temporal correlations in EEG and MEG
data
2) Research on application of these theoretical methods to the study of
neurodegenerative diseases and/or other aging-related brain disorders
a. qEEG biomarkers for disease progression models
b. Neuronal signaling imbalances underlie early sensory deficit in dementia
c. Aging under the Free Energy Principle
d. Dynamic Brain State Allocation in Health and Neurodegeneration
e. Modelling healthy brain aging using neuroimaging
f. Mapping the spatio-temporal dynamics of hippocampal dialogue in health and
Alzheimer’s disease
g. Virtual brain. Platform to uncovering cellular mechanisms of Alzheimer’s
Disease
3) Experimental confirmation
a. Consciousness in human and primates is supported by dynamic complex
patterns of brain signal coordination
b. Gut-feelings: brainmodes in control of bodily states
c. Computational multiple control of absence seizures in the brain
d. Using semantic networks to semantically enrich high resolution diffusion
tensor imaging
