![]() With just one exception, discussed below, this schema also substantially agrees with the signs obtained from the temporal correlation analysis (see Section 3.1 and Supplementary Materials). We started from the basic schema depicted in Figure 2, assuming that the connections from one SMAp and the contralateral M1h are inhibitory (this choice reproduces the pattern reported in ), whereas the others are excitatory. While several studies suggest inhibition other suggest that these connections are excitatory, especially when directed toward the performing hand. Parameter Estimation Method Assumptions on Parameters and Network Topologyģc-A more difficult problem concerns connections between one area in one hemisphere and a non-homologous area in the other hemisphere. The computation time required to perform one single simulation (including spectra calculation) on a notebook (i7 last generation CPU) was in the range of about 80 s. Finally, computation of power spectrum density and coherence was applied to these model signals. In particular, the output signals of the model are the post-synaptic membrane potentials of the pyramidal population in each ROI (i.e., quantity ν p in Equation (4) of the Supplementary Materials), which is representative of local mean field potential. Afterwards, data were passed through a low-pass antialiasing filter, re-sampled at 100 Hz (sampling period 0.01 s) and stored for subsequent processing. ![]() The first second of the simulation was then excluded to eliminate the initial transient response. Each simulation lasted 11 s starting from a null initial value of the state variables. ![]() We tested the method’s accuracy by performing some simulations with a much smaller step (hence longer computation times), without observing significant changes in the results. Numerical integration of the differential equations was performed with the Euler integration method, with a simulation step as low as 10 −4 s. Our idea is that linear models might overestimate the task–dependent component and underestimate the role of structural links, which remain stable across tasks. In recent studies, using a neural mass model to generate reliable signals in an interconnected network, we demonstrated that the estimated functional connectivity can vary dramatically, even in the presence of the same model network, depending on the presence of non–linear phenomena (such as saturation in neural activity). On the other hand, structural causal connectivity (defined as the existence of anatomical connections physically linking brain regions) cannot exhibit such large variations in a task–dependent fashion, and in a brief time scale. Depending on the particular task, in fact, one area can transmit more or less information to another area, conditioned by the level of activities and non-linear phenomena involved. This point is certainly acceptable if one refers to the amount of correlation or mutual information between two signals. ![]() The proposed procedure represents an innovative method to assess a brain circuit, which does not rely on a task-dependent connectivity network and allows brain rhythms and desynchronization to be assessed on a quantitative basis.įirst, as specified above, most studies accept the idea that connectivity may dramatically change between one task and another. The presented model can simulate the three conditions using a single set of connectivity parameters, assuming that only inputs to the ROIs change from one condition to the other. Parameters of the model have been assigned to simulate both power spectral densities and coherences of a patient with left-hemisphere stroke during resting condition, movement of the affected, and movement of the unaffected hand. The dynamics of each region is simulated using a neural mass model, which reproduces the oscillatory activity through the interaction among four neural populations. The model considers six cortical regions of interest (ROIs) involved in hand movement. This work aims to propose a new method for motor connectivity assessment based on the hypothesis of a task-independent connectivity network, assuming nonlinear behavior. Traditional methods provide connectivity estimations which may vary depending on the task. Knowledge of motor cortex connectivity is of great value in cognitive neuroscience, in order to provide a better understanding of motor organization and its alterations in pathological conditions. ![]()
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