Motor tasks, in our daily life, could be performed through execution and imagination. The brain response underlying
these movements has been investigated by many studies. Neuroimaging studies have reported that both execution and imagination could activate several brain regions including supplementary motor area (SMA), premotor area (PMA),
primary sensorimotor area (M1/S1), posterior parietal lobe (PPL), striatum, thalamus and cerebellum. These findings
were based on the regional activation, and brain regions have been indicated to functionally interact with each other
when performing tasks. Therefore further investigation in these brain regions with functional connectivity measurements may provide new insights into the neural mechanism of execution and imagination. As a fundamental measurement of functional connectivity, connection strength of graph theory has been used to identify the key nodes of connection and their strength-priorities. Thus, we performed a comparative investigation between execution and imagination tasks with functional magnetic resonance imaging (fMRI), and further explored the key nodes of connection and their strength-priorities based on the results of functional activations. Our results revealed that bilateral SMA, contralateral PMA, thalamus and M1/S1 were involved in both tasks as key nodes of connection. These nodes may play important roles in motor control and motor coordination during execution and imagination. Notably, the strength-priorities of contralateral PMA and thalamus were different between the two tasks. Higher strength-priority was detected in PMA for imagination, implicating that motor planning may be more involved in the imagination task.
Mesial-temporal lobe epilepsy (mTLE), a neurological disorder characterized by abnormal synchronous discharges in a
large cell population, affects the hemodynamic activities of functional networks remote from the epileptogenic zone and causes widespread deficits in brain functions. Although a number of resting-state fMRI studies have found altered spatial patterns in the canonical resting-state networks (RSNs) in patients with mTLE, including the default mode network (DMN), dorsal lateral attention network (DAN), auditory network (AUN), somatosensory network (SMN) and visual network (VIN), none of these studies has addressed the question whether the frequencies of hemodynamic oscillations in these RSNs were altered. In the present study, we have proposed a network-based temporal clustering analysis (TCA) method to characterize the resting hemodynamic activity of a large-scale functional network. First, the RSNs were identified in healthy controls as well in the left mTLE patients using independent component analysis (ICA). Then, a time course representing the hemodynamic activity of each RSN was extracted by counting the number of the voxels that were activated simultaneously at each time point within the network. Finally, the power spectral density (PSD) of the time course was estimated. Our results have demonstrated significant differences in the frequency profiles of the SMN, VIN and left DAN between the patients and controls: the peaks of these spectra shifted toward a lower frequency in the patients, while more power was distributed over higher frequency bands in the healthy controls. However, no significant difference has been found in the AUN, DMN and right DAN. These features might serve as biomarkers to differentiate the patients from controls.
The disgust system arises phylogenetically in response to dangers to the internal milieu from pathogens and their
toxic products. Functional imaging studies have demonstrated that a much wider range of neural structures was involved
in triggering disgust reactions. However, less is known regarding how and what neural pathways these neural structures
interact. To address this issue, we adopted an effective connectivity based analysis, namely the multivariate Granger
causality approach, to explore the causal interactions within these brain networks. Results presented that disgust can
induce a wide range of brain activities, such as the insula, the anterior cingulate cortex, the parahippocampus lobe, the
dorsal lateral prefrontal cortex, the superior occipital gyrus, and the supplementary motor cortex. These brain areas
constitute as a whole, with much denser connectivity following disgust stimuli, in comparison with that of the neutral
condition. Moreover, the anterior insula, showing multiple casual interactions with limbic and subcortical areas, was
implicated as a central hub in organizing multiple information processing in the disgust system.
This paper discussed the evaluation problem of image scrambling degree (ISD). Inspired by the evaluation method of image texture characteristics, three new metrics for assessing objectively the ISD were proposed. The first method utilized the performance of energy concentration of Walsh transformation (WT), which took into account the properties that a good ISD measurement method should be contented. The second method used angular second moment (ASM) of image gray level co-occurrence matrix (GLCM). The third method combined the entropy of GLCM with image texture characteristic. Experimental results show that the proposed metrics are effective to assess the ISD, which correlates well with subjective assessment. Considering the computational complexity, the first evaluation method based on WT is remarkably superior to the method based on ASM and GLCM in terms of the time cost.