How delays matter in an oscillatory whole-brain spiking-neuron network model for MEG alpha-rhythms at rest.

In recent years the study of the intrinsic brain dynamics in a relaxed awake state in the absence of any specific task has gained increasing attention, as spontaneous neural activity has been found to be highly structured at a large scale. This so called resting-state activity has been found to be comprised by nonrandom spatiotemporal patterns and fluctuations, and several Resting-State Networks (RSN) have been found in BOLD-fMRI as well as in MEG signal power envelope correlations. The underlying anatomical connectivity structure between areas of the brain has been identified as being a key to the observed functional network connectivity, but the mechanisms behind this are still underdetermined. Theoretical large-scale brain models for fMRI data have corroborated the importance of the connectome in shaping network dynamics, while the importance of delays and noise differ between studies and depend on the models' specific dynamics. In the current study, we present a spiking neuron network model that is able to produce noisy, distributed alpha-oscillations, matching the power peak in the spectrum of group resting-state MEG recordings. We studied how well the model captured the inter-node correlation structure of the alpha-band power envelopes for different delays between brain areas, and found that the model performs best for propagation delays inside the physiological range (5-10 m/s). Delays also shift the transition from noisy to bursting oscillations to higher global coupling values in the model. Thus, in contrast to the asynchronous fMRI state, delays are important to consider in the presence of oscillation.

Bottom up modeling of the connectome: linking structure and function in the resting brain and their changes in aging.

With the increasing availability of advanced imaging technologies, we are entering a new era of neuroscience. Detailed descriptions of the complex brain network enable us to map out a structural connectome, characterize it with graph theoretical methods, and compare it to the functional networks with increasing detail. To link these two aspects and understand how dynamics and structure interact to form functional brain networks in task and in the resting state, we use theoretical models. The advantage of using theoretical models is that by recreating functional connectivity and time series explicitly from structure and pre-defined dynamics, we can extract critical mechanisms by linking structure and function in ways not directly accessible in the real brain. Recently, resting-state models with varying local dynamics have reproduced empirical functional connectivity patterns, and given support to the view that the brain works at a critical point at the edge of a bifurcation of the system. Here, we present an overview of a modeling approach of the resting brain network and give an application of a neural mass model in the study of complexity changes in aging.

Bimanual movement control is moderated by fixation strategies.

Our study examined the effects of performing a pointing movement with the left hand on the kinematics of a simultaneous grasping movement executed with the right hand. We were especially interested in the question of whether both movements can be controlled independently or whether interference effects occur. Since previous studies suggested that eye movements may play a crucial role in bimanual movement control, the effects of different fixation strategies were also studied. Human participants were either free to move their eyes (Experiment 1) or they had to fixate (Experiment 2) while doing the task. The results show that bimanual movement control differed fundamentally depending on the fixation condition: if free viewing was allowed, participants tended to perform the task sequentially, as reflected in grasping kinematics by a delayed grip opening and a poor adaptation of the grip to the object properties for the duration of the pointing movement. This behavior was accompanied by a serial fixation of the targets for the pointing and grasping movements. In contrast, when central fixation was required, both movements were performed fast and with no obvious interference effects. The results support the notion that bimanual movement control is moderated by fixation strategies. By default, participants seem to prefer a sequential behavior in which the eyes monitor what the hands are doing. However, when forced to fixate, they do surprisingly well in performing both movements in parallel.