Characterizing the Visual Cortex Function in Cognitive Task-Induced Mental Load: An Functional Magnetic Resonance Imaging Study.

Introduction: The mental load caused by simultaneous multitasking can affect visual information processing and reduce its ability. This study investigated the effect of mental load caused by cognitive tasks simultaneously with visual task on the number of active voxels in the visual cortex. Methods: This study recruited 22 individuals with a mean age of 24.72 ± 5.47 years. 3-Tesla functional magnetic resonance imaging (fMRI) was used to examine the functions of the visual cortex and amygdala region during three different task conditions: visual task alone, visual task with an auditory n-back task, and visual task with an arithmetic task. The visual stimuli consisted of Gabor patches with a contrast of 55% at spatial frequencies of 0.25, 4, and 9 cycles per degree (cpd). These were presented in three trials of eight blocks with a stimulation time of 12 sec and a rest time of 14 sec. Results: Activated brain voxels in the primary, secondary, and associated visual cortex areas were reduced in response to the mental load imposed by the n-back and arithmetic tasks. This reduction was greater for a spatial frequency of 0.25 cpd in the n-back task condition and spatial frequency of 9 cpd in the arithmetic task condition. In addition, the amygdala was stimulated in 2-back task and arithmetic task conditions. Conclusions: This study revealed a decline in the number of activated voxels of the visual cortex due to the mental load caused by simultaneous cognitive tasks, confirming the findings of previous psychophysical studies.

A PID controller for synchronization between master-slave neurons in fractional-order of neocortical network model.

Modeling of the biological neurons is a way to understand the architecture of neural networks of the brain. A complex brain network includes the synchronization between some groups of neurons. The dynamic behavior of interactions between groups of slave-master neurons in the neocortical network is unpredictable and challenging. The purpose of synchronizing a neural interaction is to reduce the synchronization error between the chaotic slave-master neurons. This paper uses a proportional-integral-derivative (PID) controller to synchronize master-slave neurons in the fractional-order of the neocortical network model based on dendritic spike frequency adaptation (DSFA) uncertainties and unknown disturbance effects. The purpose of this article is in two parts: First, we implemented the effect of previous states of the neuron conditions by fractional-order of the differential equations in the neocortical network model. Second, by synchronizing the FO neocortical master-slave model by PID controller, we investigated the connection strength of the complex network in chaotic point of view. The optimized PID coefficients and fractional-order were calculated using root mean square error (RMSE) criteria to control the membrane voltage synchronization. The chaotic behavior of the system was evaluated by numerical techniques such as attractor analysis and time series diagrams. The optimal RMSE value for master-slave neurons occurred at fractional-orders 0.89. It is shown that the synchronization of master-slave neurons improves over time, and eventually they are fully synchronized while the controller error is reduced.

Chaos in memory function of sleep: A nonlinear dynamical analysis in thalamocortical study.

Studying the dynamical behaviors of neuronal models may help in better understanding of real nervous system. In addition, it can help researchers to understand some specific phenomena in neuronal system. The thalamocortical network is made of neurons in the thalamus and cortex. In it, the memory function is consolidated in sleep by creating up and down state oscillations (1 Hz) and fast (13-17 Hz) - slow (8-12 Hz) spindles. Recently, a nonlinear biological model for up-down oscillations and fast-slow spindles of the thalamocortical network has been proposed. In this research, the power spectral for the fast-slow spindle of the model is extracted. Dynamical properties of the model, such as the bifurcation diagrams, and attractors are investigated. The results show that the variation of the synaptic power between the excitatory neurons of the cortex and the reticular neurons in the thalamus changes the spindles' activity. According to previous experimental findings, it is an essential rule for consolidating the memory function during sleep. It is also pointed out that when the fast-slow spindles of the brain increase, the dynamics of the thalamocortical system tend to chaos.

Characterizing resting-state networks in Parkinson's disease: A multi-aspect functional connectivity study.

PURPOSE: Resting-state functional magnetic resonance imaging (Rs-fMRI) can be used to investigate the alteration of resting-state brain networks (RSNs) in patients with Parkinson's disease (PD) when compared with healthy controls (HCs). The aim of this study was to identify the differences between individual RSNs and reveal the most important discriminatory characteristic of RSNs between the HCs and PDs. METHODS: This study used Rs-fMRI data of 23 patients with PD and 18 HCs. Group independent component analysis (ICA) was performed, and 23 components were extracted by spatially overlapping the components with a template RSN. The extracted components were used in the following three methods to compare RSNs of PD patients and HCs: (1) a subject-specific score based on group RSNs and a dual-regression approach (namely RSN scores); (2) voxel-wise comparison of the RSNs in the PD patient and HC groups using a nonparametric permutation test; and (3) a hierarchical clustering analysis of RSNs in the PD patient and HC groups. RESULTS: The results of RSN scores showed a significant decrease in connectivity in seven ICs in patients with PD compared with HCs, and this decrease was particularly striking on the lateral and medial posterior occipital cortices. The results of hierarchical clustering of the RSNs revealed that the cluster of the default mode network breaks down into the three other clusters in PD patients. CONCLUSION: We found various characteristics of the alteration of the RSNs in PD patients compared with HCs. Our results suggest that different characteristics of RSNs provide insights into the biological mechanism of PD.