Alzheimer’s disease (AD) is a common neurodegenerative disease characterized by progressive dementia. Accumulation of β–amyloid peptide 1–42 and phosphorylation of tau protein in the brain are the two main pathological features of AD. However, comprehensive studies have shown that neuroinflammation also plays a crucial role in the pathogenesis of AD. Neuroinflammation is associated with neuronal death and abnormal protein aggregation and promotes the pathological process of β-amyloid peptide 1–42 and tau protein. The inflammatory components associated with AD include glial cells, complement system, cytokines and chemokines. In recent years, some researchers have focused on exosomes, a type of membrane nano vesicles. Exosomes can transport proteins, lipids, microRNAs and other signaling molecules to participate in a variety of signaling pathways for signal transmission or immune response, affecting the activity of target cells and participating in important pathophysiological processes. Therefore, exosomes play an essential role in intercellular communication and may mediate neuroinflammation to promote the development of AD. This paper reviews the occurrence and development of neuroinflammation and exosomes in AD, providing a deeper understanding of the pathogenesis of AD. Furthermore, the role of exosomes in the pathogenesis and treatment of AD is further described, demonstrating their potential as therapeutic targets for neuroinflammation and AD in the future.

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Recognizing emotion from Electroencephalography (EEG) is a promising and valuable research issue in the field of affective brain-computer interfaces (aBCI). To improve the accuracy of emotion recognition, an emotional feature extraction method is proposed based on the temporal information in the EEG signal. This study adopts microstate analysis as a spatio-temporal analysis for EEG signals. Microstates are defined as a series of momentary quasi-stable scalp electric potential topographies. Brain electrical activity could be modeled as being composed of a time sequence of microstates. Microstate sequences provide an ideal macroscopic window for observing the temporal dynamics of spontaneous brain activity. To further analyze the fine structure of the microstate sequence, we propose a feature extraction method based on k-mer. K-mer is a k-length substring of a given sequence. It has been widely used in computational genomics and sequence analysis. We extract features that are based on the D2∗ statistic of k-mer. In addition, we also extract four parameters (duration, occurrence, time coverage, GEV) of each microstate class as features at the coarse level. We conducted experiments on the DEAP dataset to evaluate the performance of the proposed features. The experimental results demonstrate that the fusion of features in fine and coarse levels can effectively improve classification accuracy.

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Objectives Analysis of FDG‐PET imaging commonly shows that hypometabolism extends into extra‐epileptogenic zones (extra‐EZ). This study investigates the distribution patterns of hypometabolism in frontal lobe epilepsy (FLE) originating in different frontal regions. Methods Sixty‐four patients with FLE were grouped by EZ localization according to Brodmann areas (BAs): Group 1 (the frontal motor and premotor area), BAs 4, 6, and 8; Group 2 (the inferior frontal gyrus and opercular area), BAs 44, 45, and 47; Group 3 (the dorsal prefrontal area), BAs 9, 10, 11, and 46; and Group 4 (the medial frontal and anterior cingulate gyrus), BAs 32 and 24. Regions of extra‐EZ hypometabolism were statistically analyzed between FLE groups and healthy controls. Correlation analysis was performed to identify relationships between the intensity of hypometabolism and clinical characteristics. Results Significant hypometabolism in the ipsilateral (Groups 1 and 4) or bilateral (Groups 2 and 3) anterior insulae was found. Groups 1 and 4 presented with limited distribution of extra‐EZ hypometabolism, whereas Groups 2 and 3 showed widely distributed extra‐EZ hypometabolism in the rectus gyrus, cingulate gyrus, and other regions. Additionally, the intensity of hypometabolism was correlated with epilepsy duration in Groups 2 and 3. Conclusions All FLE groups showed hypometabolism in the anterior insula. In addition, distinct patterns of extra‐EZ hypometabolism were identified for each FLE group. This quantitative FDG‐PET analysis expanded our understanding of the topography of epileptic networks and can guide EZ localization in the future.