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Network dynamics with BrainX(3): 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, Verschure PF - Front Neuroinform (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Synthetic Perceptive Emotive and Cognitive Systems Lab, Center of Autonomous Systems and Neurorobotics, Universitat Pompeu Fabra Barcelona, Spain.

ABSTRACT
BrainX(3) 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 BrainX(3) 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. BrainX(3) 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.

No MeSH data available.


Analysis of lesioned (focal) brain activity vs. noise. 2D plots for neural activity following a focal lesion in areas rCUN, rLOCC and rPCUN for the same four simulation runs shown in Figure 6. Subplots (A–D) respectively refer to noise amplitudes 0.01, 0.05, 0.07, and 0.1. Each subplot shows three graphics: 2D distribution of nodes with activity indicated by colors from the color bar (warmer colors refer to higher activation and lesioned nodes are shown in black), mean firing rate for all nodes over the last 2 s of simulation and time-series signals extracted for the three seed ROIs rCAC (node 193, shown in black), rISTC (node 205, shown in green) and lPCUN (node 830, shown in magenta).
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Figure 7: Analysis of lesioned (focal) brain activity vs. noise. 2D plots for neural activity following a focal lesion in areas rCUN, rLOCC and rPCUN for the same four simulation runs shown in Figure 6. Subplots (A–D) respectively refer to noise amplitudes 0.01, 0.05, 0.07, and 0.1. Each subplot shows three graphics: 2D distribution of nodes with activity indicated by colors from the color bar (warmer colors refer to higher activation and lesioned nodes are shown in black), mean firing rate for all nodes over the last 2 s of simulation and time-series signals extracted for the three seed ROIs rCAC (node 193, shown in black), rISTC (node 205, shown in green) and lPCUN (node 830, shown in magenta).

Mentions: Having looked at the healthy resting-state network above, we now show how lesions can be simulated in BrainX3. We consider two lesion types, (i) focal lesions, which occur in the case of stroke patients, and (ii) diffuse lesions, which typically occur in patients with multiple sclerosis. Figures 6, 7 shows results for the former lesion type with the same four levels of noise as above. Figures 8, 9 shows results with diffuse lesions. The focal lesion is constructed on the right hemisphere by severing all white matter fibers connections from all nodes in regions rCUN, rLOCC, and rPCUN. These are a total of 52 disconnected ROIs, amounting to 6.64% of the total connections. The diffuse lesions are constructed by randomly disconnecting individual ROIs distributed throughout the network. To compare with the focal case, we chose 50 scattered ROIs, which amount to 4.91% of the total connections.


Network dynamics with BrainX(3): 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, Verschure PF - Front Neuroinform (2015)

Analysis of lesioned (focal) brain activity vs. noise. 2D plots for neural activity following a focal lesion in areas rCUN, rLOCC and rPCUN for the same four simulation runs shown in Figure 6. Subplots (A–D) respectively refer to noise amplitudes 0.01, 0.05, 0.07, and 0.1. Each subplot shows three graphics: 2D distribution of nodes with activity indicated by colors from the color bar (warmer colors refer to higher activation and lesioned nodes are shown in black), mean firing rate for all nodes over the last 2 s of simulation and time-series signals extracted for the three seed ROIs rCAC (node 193, shown in black), rISTC (node 205, shown in green) and lPCUN (node 830, shown in magenta).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4338755&req=5

Figure 7: Analysis of lesioned (focal) brain activity vs. noise. 2D plots for neural activity following a focal lesion in areas rCUN, rLOCC and rPCUN for the same four simulation runs shown in Figure 6. Subplots (A–D) respectively refer to noise amplitudes 0.01, 0.05, 0.07, and 0.1. Each subplot shows three graphics: 2D distribution of nodes with activity indicated by colors from the color bar (warmer colors refer to higher activation and lesioned nodes are shown in black), mean firing rate for all nodes over the last 2 s of simulation and time-series signals extracted for the three seed ROIs rCAC (node 193, shown in black), rISTC (node 205, shown in green) and lPCUN (node 830, shown in magenta).
Mentions: Having looked at the healthy resting-state network above, we now show how lesions can be simulated in BrainX3. We consider two lesion types, (i) focal lesions, which occur in the case of stroke patients, and (ii) diffuse lesions, which typically occur in patients with multiple sclerosis. Figures 6, 7 shows results for the former lesion type with the same four levels of noise as above. Figures 8, 9 shows results with diffuse lesions. The focal lesion is constructed on the right hemisphere by severing all white matter fibers connections from all nodes in regions rCUN, rLOCC, and rPCUN. These are a total of 52 disconnected ROIs, amounting to 6.64% of the total connections. The diffuse lesions are constructed by randomly disconnecting individual ROIs distributed throughout the network. To compare with the focal case, we chose 50 scattered ROIs, which amount to 4.91% of the total connections.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Synthetic Perceptive Emotive and Cognitive Systems Lab, Center of Autonomous Systems and Neurorobotics, Universitat Pompeu Fabra Barcelona, Spain.

ABSTRACT
BrainX(3) 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 BrainX(3) 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. BrainX(3) 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.

No MeSH data available.