Functional networks

While anatomical connectivity changes slowly through synaptic learning, the functional connectivity of neurons changes rapidly with ongoing activity of neurons and their functional interactions. Functional networks of neurons and neural populations reflect how their interactions change with behaviors, stimulus types, and internal states. Therefore, the information propagation across a network can be analyzed through the varying topological properties of the functional networks. Our study investigates the functional networks of the visual cortex at both the single-cell and population levels. Ou

Normal brain function results from directed causal interactions between brain regions. However, the large-scale spatial and temporal structure of these interactions remains unclear. Lagged correlations between the activity of different brain regions may reflect their directed causal interactions. Thus, we combined magnetoencephalography (MEG) and machine learning approaches to characterize and disentangle the structure of lagged correlations in the human brain. We computed lagged correlations between all pairs of cortical regions and frequencies of neural activity. We employed curvature-regul

Resting-state functional connectivity MRI (rs-fMRI) provides a task-free methodology that allows for the non-invasive measurement of global brain network architecture. The use of rs-fMRI has promising potential for animal models, especially rodents, whose functional network architecture is highly translatable to humans. To minimise both head motion and stress, brain imaging of rodents typically involves using anaesthetic drugs to keep the animals still. However, these anaesthetic protocols negatively affect brain function, limiting the scope of studies. There is increasing interest in the use

Normal brain function results from directed causal interactions between brain regions. However, the large-scale spatial and temporal structure of these interactions remains unclear. Lagged correlations between the activity of different brain regions may reflect their directed causal interactions. Thus, we combined magnetoencephalography (MEG) and machine learning approaches to characterize and disentangle the structure of lagged correlations in the human brain.We computed lagged correlations between all pairs of cortical regions and frequencies of neural activity. We employed variational autoe