[Comp-neuro] New Paper on Network Dynamics of the Human Brain

Xerxes D. Arsiwalla x.d.arsiwalla at gmail.com
Thu Feb 26 19:08:01 CET 2015


Dear Colleagues,

For your interest, our paper on dynamics of the human connectome is now
available open access.

http://journal.frontiersin.org/article/10.3389/fninf.2015.00002/abstract

Network Dynamics with BrainX3: A Large-Scale Simulation of the Human Brain
Network with Real-Time Interaction

Arsiwalla XD, Zucca R, Betella A, Martinez E, Dalmazzo D, Omedas P, Deco G
and Verschure PFMJ (2015)  Front. Neuroinform. 9:02. doi:
10.3389/fninf.2015.00002

Abstract
BrainX3 is a large-scale simulation of human brain activity with real-time
interaction, rendered in 3D in a virtual reality environment, which
combines computational power with human intuition for the exploration and
analysis of complex dynamical networks. We ground this simulation on
structural connectivity obtained from diffusion spectrum imaging data and
model it on neuronal population dynamics. Users can interact with BrainX3
in real-time by perturbing brain regions with transient stimulations to
observe reverberating network activity, simulate lesion dynamics or
implement network analysis functions from a library of graph theoretic
measures. BrainX3 can thus be used as a novel immersive platform for
exploration and analysis of dynamical activity patterns in brain networks,
both at rest or in a task-related state, for discovery of signaling
pathways associated to brain function and/or dysfunction and as a tool for
virtual neurosurgery. Our results demonstrate these functionalities and
shed insight on the dynamics of the resting-state attractor. Specifically,
we found that a noisy network seems to favor a low firing attractor state.
We also found that the dynamics of a noisy network is less resilient to
lesions. Our simulations on TMS perturbations show that even though TMS
inhibits most of the network, it also sparsely excites a few regions. This
is presumably due to anti-correlations in the dynamics and suggests that
even a lesioned network can show sparsely distributed increased activity
compared to healthy resting-state, over specific brain areas.
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