ObjectiveTo reveal impairments in the perceptual networks in tuberous sclerosis complex (TSC) with epilepsy by functional connectivity MRI (fcMRI). MethodsThe fcMRI-based independent component analysis (ICA) was used to measure the resting state functional connectivity in nine TSC patients with epilepsy recruited from June 2010 to June 2012 and perceptual networks including the sensorimotor network (SMN), visual network (VN), and auditory network (AN) were investigated. The correlation between Z values in regions of interest (ROIs) and age of seizure onset or duration of epilepsy were analyzed. ResultsCompared with the controls, the TSC patients with epilepsy presented decreased functional connectivity in primary visual cortex within the VN networks and there were no increased connectivity. Increased connectivity in left middle temporal gyrus and inferior temporal gyrus was found and decreased connectivity was detected in right inferior frontal gyrus within AN networks. Decreased connectivity was detected at the right inferior frontal gyrus and the increase in connectivity was found in right thalamus within SMN netwoks. No significant correlations were found between Z values in ROIs including the primary visual cortex within the VN, right thalamus and inferior frontal gyrus within SMN, left temporal lobe and right inferior frontal gyrus within AN and the duration of the disease or the age of onset. ConclusionFhere is altered (both increased and decreased) functional connectivity in the perceptual networks of TSC patients with epilepsy. The decreased functional connectivity may reflect the dysfunction of correlative perceptual networks in TSC patients, and the increased functional connectivity may indicate the compensatory mechanism or reorganization of cortical networks. Our fcMRI study may contribute to the understanding of neuropathophysiological mechanisms underlying perceptual impairments in TSC patients with epilepsy.
The research shows that personality assessment can be achieved by regression model based on electroencephalogram (EEG). Most of existing researches use event-related potential or power spectral density for personality assessment, which can only represent the brain information of a single region. But some research shows that human cognition is more dependent on the interaction of brain regions. In addition, due to the distribution difference of EEG features among subjects, the trained regression model can not get accurate results of cross subject personality assessment. In order to solve the problem, this research proposes a personality assessment method based on EEG functional connectivity and domain adaption. This research collected EEG data from 45 normal people under different emotional pictures (positive, negative and neutral). Firstly, the coherence of 59 channels in 5 frequency bands was taken as the original feature set. Then the feature-based domain adaptation was used to map the feature to a new feature space. It can reduce the distribution difference between training and test set in the new feature space, so as to reduce the distribution difference between subjects. Finally, the support vector regression model was trained and tested based on the transformed feature set by leave-one-out cross-validation. What’s more, this paper compared the methods used in previous researches. The results showed that the method proposed in this paper improved the performance of regression model and obtained better personality assessment results. This research provides a new method for personality assessment.
In recent years, due to the emergence of ultrafast ultrasound imaging technology, the sensitivity of detecting slow and micro blood flow with ultrasound has been dramatically improved, and functional ultrasound imaging (fUSI) has been developed. fUSI is a novel technology for neurological imaging that utilizes neurovascular coupling to detect the functional activity of the central nervous system (CNS) with high spatiotemporal resolution and high sensitivity, which is dynamic, non-invasive or minimally invasive. fUSI fills the gap between functional magnetic resonance imaging (fMRI) and optical imaging with its high accessibility and portability. Moreover, it is compatible with electrophysiological recording and optogenetics. In this paper, we review the developments of fUSI and its applications in neuroimaging. To date, fUSI has been used in various animals ranging from mice to non-human primates, as well as in clinical surgeries and bedside functional brain imaging of neonates. In conclusion, fUSI has great potential in neuroscience research and is expected to become an important tool for neuroscientists, pathologists and pharmacologists.