Tracing Neural Circuits by Dynamically Simulating Whole-Brain Activity Patterns in the Human Connectome
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1
Universitat Pompeu Fabra, Dept of Technology, Spain
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2
Institució Catalana de Recerca i Estudis Avançats (ICREA), Spain
How does one unravel neural circuits from whole-brain connectivity data? To answer that we build a large-scale dynamic simulation of the human connectome in virtual reality, that reconstructs whole-brain activity in real-time. Using DTI structural connectivity data from [1] we built an interactive 3D visualization of the human connectome network in an immersive virtual reality environment (Fig. 1) using the Unity 3D gaming engine. Further, the virtual reality brain network in Unity is coupled to a real-time neuronal simulator, iqr [2]. As we see, coupling structural connectivity data with detailed enough neuronal population dynamics is sufficient in predicting functional correlations and large-scale activity patterns. We model neuronal dynamics by a linear-threshold filter (as work in progress, we are currently implementing population dynamics from mean-field models [3]). Each population module is stochastic, having Gaussian noise. The user can stimulate any region or simultaneous regions of the network with external input currents. The simulation then reconstructs reverberating neural activity propagating throughout the network in real-time. As an explicit example, we stimulate the superior parietal areas and watch causal activity propagating in the parietal lobe, indicative of visuo-motor integration (Fig. 2). This is a first step to simulating and mapping large-scale brain activity in real-time.
As quantitative analysis methods and data-recording technology in neuroscience make improvements, it is becoming evident large-scale dynamics and whole-brain quantitative measures play an important role. For instance, oscillations across large brain regions are precursors to several cognitive functions. Moreover, the causal map in these interactions is crucial. Compared to functional correlations, large-scale temporal activity maps across directionally connected brain structures serve as a more powerful tools to unravel mechanisms of large-scale neural circuits. Our results show that stimulating brain areas triggers a sequence of causal activations in associated network loops that represent cognitively related functions.
Acknowledgements
The work is supported by the CEEDS project (258749 FP7-ICT-2009-5)
References
1. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O: Mapping the Structural Core of Human Cerebral Cortex. PLoS Biol (2008) 6(7): e159. doi:10.1371/journal.pbio.0060159.
2. Bernardet U, Verschure P: iqr: A Tool for the Construction of Multi-level Simulations of Brain and Behaviour. Neuroinformatics (2010), 113-134, Vol. 8, issue 2.
3. Wong KF, Wang XJ: A Recurrent Network Mechanism of Time Integration in Perceptual Decisions. Journal of Neuroscience (2006), 26(4):1314 –1328.
4. Arsiwalla, XD et al.: What a dynamical connectome informs us about large scale neural circuits and whole brain activity. IJCNN '13 abstract nr 1366. (2013).
Keywords:
Large scale modeling,
human connectome,
neural circuits,
Whole Brain dynamics,
virtual environments
Conference:
Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.
Presentation Type:
Poster
Topic:
Large scale modeling
Citation:
Zucca
R,
Arsiwalla
XD,
Betella
A,
Martinez
E,
Omedas
P and
Verschure
PF
(2013). Tracing Neural Circuits by Dynamically Simulating Whole-Brain Activity Patterns in the Human Connectome.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2013.
doi: 10.3389/conf.fninf.2013.09.00097
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Received:
08 Apr 2013;
Published Online:
11 Jul 2013.
*
Correspondence:
Mr. Riccardo Zucca, Universitat Pompeu Fabra, Dept of Technology, Barcelona, Spain, riccardo.zucca@upf.edu