Alzheimer’s disease (AD) is characterized by progressive deterioration of brain function among elderly people. Studies revealed aberrant correlations in spontaneous blood oxygen level-dependent (BOLD) signals in resting-state functional magnetic resonance imaging (rs-fMRI) over a wide range of temporal scales. However, the study of the temporal dynamics of BOLD signals in subjects with AD and mild cognitive impairment (MCI) remains largely unexplored. Multiscale entropy (MSE) analysis is a method for estimating the complexity of finite time series over multiple time scales. In this research, we applied MSE analysis to investigate the abnormal complexity of BOLD signals using the rs-fMRI data from the Alzheimer’s disease neuroimaging initiative (ADNI) database. There were 30 normal controls (NCs), 33 early MCI (EMCI), 32 late MCI (LMCI), and 29 AD patients. Following preprocessing of the BOLD signals, whole-brain MSE maps across six time scales were generated using the Complexity Toolbox. One-way analysis of variance (ANOVA) analysis on the MSE maps of four groups revealed significant differences in the thalamus, insula, lingual gyrus and inferior occipital gyrus, superior frontal gyrus and olfactory cortex, supramarginal gyrus, superior temporal gyrus, and middle temporal gyrus on multiple time scales. Compared with the NC group, MCI and AD patients had significant reductions in the complexity of BOLD signals and AD patients demonstrated lower complexity than that of the MCI subjects. Additionally, the complexity of BOLD signals from the regions of interest (ROIs) was found to be significantly associated with cognitive decline in patient groups on multiple time scales. Consequently, the complexity or MSE of BOLD signals may provide an imaging biomarker of cognitive impairments in MCI and AD.

Research has shown that abnormal brain networks in patients with schizophrenia appear at different frequencies, but the relationship between these different frequencies is unclear. Therefore, it is necessary to use a multilayer network model to evaluate the integration of information from different frequency bands. To explore the mechanism of integration and separation in the multilayer network of schizophrenia, we constructed multilayer frequency brain network models in 50 patients with schizophrenia and 69 healthy subjects, and the entropy of the multiplex degree (EMD) and multilayer clustering coefficient (MCC) were calculated. The results showed that the ability to integrate and separate information in the multilayer network of patients was significantly higher than that of normal people. This difference was mainly reflected in the default mode network, sensorimotor network, subcortical network, and visual network. Among them, the subcortical network was different in both MCC and EMD outcomes. Furthermore, differences were found in the posterior cingulate gyrus, hippocampus, amygdala, putamen, pallidum, and thalamus. The thalamus and posterior cingulate gyrus were associated with the patient’s symptom scores. Our results showed that the cross-frequency interaction ability of patients with schizophrenia was significantly enhanced, among which the subcortical network was the most active. This interaction may serve as a compensation mechanism for intralayer dysfunction.

Epilepsy is a common neurological disorder in which abnormal brain waves propagate rapidly in the brain in the form of a graph network during seizures, and seizures are extremely sudden. So, designing accurate and reliable prediction methods can provide early warning for patients, which is crucial for improving their lives. In recent years, a large number of studies have been conducted using deep learning models on epileptic open electroencephalogram (EEG) datasets with good results, but due to individual differences there are still some subjects whose seizure features cannot be accurately captured and are more difficult to differentiate, with poor prediction results. Important time-varying information may be overlooked if only graph space features during seizures are considered. To address these issues, we propose a synchronization-based graph spatio-temporal attention network (SGSTAN). This model effectively leverages the intricate information embedded within EEG recordings through spatio-temporal correlations. Experimental results on public datasets demonstrate the efficacy of our approach. On the CHB-MIT dataset, our method achieves accuracy, specificity, and sensitivity scores of 98.2%, 98.07%, and 97.85%, respectively. In the case of challenging subjects that are difficult to classify, we achieved an outstanding average classification accuracy of 97.59%, surpassing the results of previous studies.

In recent years, there has been growing interest in studying the complexity of resting-state functional magnetic resonance imaging (rs-fMRI) brain signals. As one of the most commonly used complexity methods, entropy measures have been used to quantitatively characterize abnormal brain activity in aged individuals and patients with psychopathic and neurological disorders, and most studies have analyzed brain signals from a single channel. The widely used entropy methods include approximate entropy (AE), sample entropy (SE), permutation entropy (PE), and fuzzy entropy (FE). However, the test-retest reliability of different entropy methods remains to be explored. In this study, we investigated the distribution and test-retest reliability of four entropy measures and a new entropy algorithm we proposed, permutation fuzzy entropy (PFE), in three independent data sets at three levels, i.e., based on voxels, brain regions, and functional networks. Our results showed that analyzing fMRI signals with entropy showed strong tissue sensitivity. The highest reliability was achieved with PFE, and PE and FE were superior to AE and SE at all three levels. The percentage of nodes with good to excellent reliability in PFE, PE, FE, SE and AE was 94.31%, 52.65%, 18.56%, 11.36% and 0.76%, respectively. PFE and PE showed fair to good reliability in the visual network, auditory network, default-mode network, etc. In conclusion, characterizing brain entropy may provide an informative tool to assess the complexity of brain functions. Our results suggested that PFE and PE had better reliability and reflected more topological information related to normal and disordered functioning of the human brain.

It has been shown that the functional dependency of the brain exists in both direct and indirect regional relationships. Therefore, it is necessary to map higher-order coupling in brain structure and function to understand brain dynamic. However, how to quantify connections between not directly regions remains unknown to schizophrenia. The word2vec is a common algorithm through create embeddings of words to solve these problems. We apply the node2vec embedding representation to characterize features on each node, their pairwise relationship can give rise to correspondence relationships between brain regions. Then we adopt pearson correlation to quantify the higher-order coupling between structure and function in normal controls and schizophrenia. In addition, we construct direct and indirect connections to quantify the coupling between their respective functional connections. The results showed that higher-order coupling is significantly higher in schizophrenia. Importantly, the anomalous cause of coupling mainly focus on indirect structural connections. The indirect structural connections play an essential role in functional connectivity–structural connectivity (SC–FC) coupling. The similarity between embedded representations capture more subtle network underlying information, our research provides new perspectives for understanding SC–FC coupling. A strong indication that the structural backbone of the brain has an intimate influence on the resting-state functional.