Modeling FC in AD using The Virtual Brain

2015  -  Irvine, CA, USA


University of Toronto; University of California, Irvine

Project description

In this study, Diffusion Spectrum Imaging (DSI) and simultaneous fMRI-EEG will be obtained from ADs, amnesic MCIs and age-matched healthy  controls. Structural connectivity (ie density  of white matter  fibers between regions  pairs)  will be inferred from DSI for 96 regions of interest  (Bezgin  et al., 2011).   Functional connectivity will be derived  from temporal correlations of the EEG and fMRI time series  between  brain regions. Structural connectivity matrices of individual subjects {the input) will be processed  through The Virtual Brain. Here, we are able to adjust physiological  parameters (such as strength of structural connections, inhibitory-excitatory balance, etc) in an iterative manner. The Virtual Brain then simulates functional connectivity {the output). We will conduct a systematic exploration of physiological parameters until a biologically realistic simulation is obtained. The goal is to find a set of physiological  parameters that create a simulated functional connectivity matrix that matches the empirical functional connectivity matrix. We plan to computationally approximate how both large-scale (ie connectivity deterioration), as well as local (ie/ inhibitory-excitatory imbalance) patterns of functional brain activity change from health to MCI and then conversion to AD. We hypothesize that specific model parameters determining brain dynamic changes will be associated with pathological transitions, and can hence be used as biomarkers for disease severity in the context of AD.

Final abstract

The ongoing project conducted at UC Irvine, California, generously funded by the Weston Brain Institute International Fellowship in Neuroscience 2016, has led to incredible developments in our understanding of the neural markers underlying Alzheimer’s disease (AD) using The Virtual Brain (TVB) simulator of brain dynamics ( We built a brain network model that uses individual patient’s diffusion imaging data to identify biophysical markers that actually predict disease severity for that particular person. We were able to, based on the unique blueprint of white-matter pathways in each patient’s brain, show how a particular marker in the personalized Virtual Brain is linked to the individual’s ability to maintain independent functioning. To provide an example: we show that there is a particular, patient-specific balance in excitation and inhibition that predicts how well that person maintains activities of daily living (i.e. self-grooming, independent travel), as well as cognitive capacity in the realms of attention, memory, executive function etc. The present findings provide us with reasonable expectations that our Virtual Brain models can be used as an individualized substitute for drug treatment, surgery, or other intrusive procedures.