Myopia is a major problem of public health in China, and even in the world, and slowing down the progress of myopia has become a hot issue of concern. However, the effects of the current therapeutic and interventional modalities to myopia, including optical lenses, chemical drugs, and laser surgery, the effect of treatment and intervention is not very satisfactory, and these modalities may incur some side effects. This situation suggests that the pathogenic and regulatory mechanisms of myopia remain elusive, and the myopia treatments lack the accurate and effective targets to the etiology. A complete visual experience depends on the entire visual pathway from the retina to the visual cortex, in which any structural and functional defect can lead to visual abnormalities. In recent years, with the advances in the infrared spectroscopy and the magnetic resonance imaging technology, more and more evidence has shown that the progression of myopia is related to the visual cortex. Improving the functional connectivity and blood prefusion between different regions of the visual cortex may impede myopia profession. In-depth understanding of the interaction between myopia and the visual cortex is helpful to search for accurate and effective myopia treatment targets and novel intervention strategies.
Normal brain aging and a serious of neurodegenerative diseases may lead to decline in memory, attention and executive ability and poorer quality of life. The mechanism of the decline is not clear now and is still a hot issue in the fields of neuroscience and medicine. A large number of researches showed that resting state functional brain networks based functional magnetic resonance imaging (fMRI) are sensitive and susceptive to the change of cognitive function. In this paper, the researches of brain functional connectivity based on resting fMRI in recent years were compared, and the results of subjects with different levels of cognitive decline including normal brain aging, mild cognitive impairment (MCI) and Alzheimer’s disease (AD) were reviewed. And the changes of brain functional networks under three different levels of cognitive decline are introduced in this paper, which will provide the basis for the detection of normal brain aging and clinical diseases.
Although a great number of studies have investigated the changes of resting-state functional connectivity (rsFC) in patients with mental disorders, such as depression and schizophrenia etc, little is known how stable the changes are, and whether temporal sad or happy mood can modulate the intrinsic rsFC. In our experiments, happy and sad video clips were used to induce temporally happy and sad mood states in 20 healthy young adults. We collected functional magnetic resonance imaging (fMRI) data while participants were watching happy or sad video clips, which were administrated in two consecutive days. Seed-based functional connectivity analyses were conducted using the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), and amygdala as seeds to investigate neural network related to executive function, attention, and emotion. We also investigated the association of the rsFC changes with emotional arousability level to understand individual differences. There is significantly stronger functional connectivity between the left DLPFC and posterior cingulate cortex (PCC) under sad mood than that under happy mood. The increased connectivity strength was positively correlated with subjects' emotional arousability. The increased positive correlation between the left DLPFC and PCC under sad relative to happy mood might reflect an increased processing of negative emotion-relevant stimuli. The easier one was induced by strong negative emotion (higher emotional arousability), the greater the left DLPFC-PCC connectivity was indicated, the greater the instability of the intrinsic rsFC was shown.
Developmental and epileptic encephalopathy (DEE) is a group of diseases that severely affects the neurological development of children, characterized by frequent seizures and significant neurodevelopmental impairments. These diseases not only impact the quality of life of affected children but also impose a heavy burden on families and society. In recent years, the development of brain network theory has provided a new perspective on understanding the pathological mechanisms of DEE, especially the role of structural and functional brain networks in the process of epilepsy. This review systematically summarized the research progress of structural and functional brain networks in DEE, highlighted their importance in seizure activity, disease progression, and prognosis evaluation.
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.
Brain aging can affect the strength of functional connectivity between brain regions. In recent years, studies have shown that functional connectivity is fluctuant over time, and can reflect more physiological and pathological information. Therefore, in the study resting state functional magnetic resonance imaging (fMRI) data of 32 elderly subjects and 36 younger subjects were selected, and the sliding window technique was used to estimate dynamic functional connectivity network. Then, the dependency of fluctuating energy difference on frequency band was studied using wavelet packet analysis, conducting the linear regression with age at the same time. Results showed that the fluctuating energy in older group was significantly higher than that in the young group in low frequency, and it was significantly lower than that in the young people in high frequency. These results suggested that the dynamic functional connectivity between networks in the elderly exist slow wave phenomenon, which may be related to the decreased reaction rate of the elderly. This article provides new ideas and methods for the research about brain aging, and promotes a theoretical basis for further understanding of the physiological significance of brain dynamic functional connectivity.
Repetitive transcranial magnetic stimulation (rTMS) can influence the stimulated brain regions and other distal brain regions connecting to them. The purpose of the study is to investigate the effects of low-frequency rTMS over primary motor cortex on brain by analyzing the brain functional connectivity and coordination between brain regions. 10 healthy subjects were recruited. 1 Hz rTMS was used to stimulate primary motor cortex for 20 min. 1 min resting state electroencephalography (EEG) was collected before and after the stimulation respectively. By performing phase synchronization analysis between the EEG electrodes, the brain functional network and its properties were calculated. Signed-rank test was used for statistical analysis. The result demonstrated that the global phase synchronization in alpha frequency band was decreased significantly after low-frequency rTMS (P<0.05). The phase synchronization was down-regulated between motor cortex and ipsilateral frontal/parietal cortex, and also between contralateral parietal cortex and bilateral frontal cortex. The mean degree and global efficiency of brain functional networks in alpha frequency band were significantly decreased (P<0.05), and the mean shortest path length were significantly increased (P<0.05), which suggested the information transmission of the brain networks and its efficiency was reduced after low-frequency rTMS. This study verified the inhibition function of the low-frequency rTMS to brain activities, and demonstrated that low-frequency rTMS stimulation could affect both stimulating brain regions and distal brain regions connected to them. The findings in this study could be of guidance to clinical application of low-frequency rTMS.
Anesthesia consciousness monitoring is an important issue in basic neuroscience and clinical applications, which has received extensive attention. In this study, in order to find the indicators for monitoring the state of clinical anesthesia, a total of 14 patients undergoing general anesthesia were collected for 5 minutes resting electroencephalogram data under three states of consciousness (awake, moderate and deep anesthesia). Sparse partial least squares (SPLS) and traditional synchronized likelihood (SL) are used to calculate brain functional connectivity, and the three conscious states before and after anesthesia were distinguished by the connection features. The results show that through the whole brain network analysis, SPLS and traditional SL method have the same trend of network parameters in different states of consciousness, and the results obtained by SPLS method are statistically significant (P<0.05). The connection features obtained by the SPLS method are classified by the support vector machine, and the classification accuracy is 87.93%, which is 7.69% higher than that of the connection feature classification obtained by SL method. The results of this study show that the functional connectivity based on the SPLS method has better performance in distinguishing three kinds of consciousness states, and may provides a new idea for clinical anesthesia monitoring.
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.