MR-based spatiotemporal anisotropic atrophy evaluation of hippocampus in Alzheimer's disease progression by multiscale skeletal representation

Shedding light on subiculum's role in human brain disorders

Subiculum is a pivotal output component of the hippocampal formation, a structure often overlooked in neuroscientific research. Here, this review aims to explore the role of the subiculum in various brain disorders, shedding light on its significance within the functional-neuroanatomical perspective on neurological diseases. The subiculum's involvement in multiple brain disorders was thoroughly examined. In Alzheimer's disease, subiculum alterations precede cognitive decline, while in epilepsy, the subiculum plays a critical role in seizure initiation. Stress involves the subiculum's impact on the hypothalamic-pituitary-adrenocortical axis. Moreover, the subiculum exhibits structural and functional changes in anxiety, schizophrenia, and Parkinson's disease, contributing to cognitive deficits. Bipolar disorder is linked to subiculum structural abnormalities, while autism spectrum disorder reveals an alteration of inward deformation in the subiculum. Lastly, frontotemporal dementia shows volumetric differences in the subiculum, emphasizing its contribution to the disorder's complexity. Taken together, this review consolidates existing knowledge on the subiculum's role in brain disorders, and may facilitate future research, diagnostic strategies, and therapeutic interventions for various neurological conditions.

EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification

Because of the intricate topological structure and connection of the human brain, extracting deep spatial features from electroencephalograph (EEG) signals is a challenging and time-consuming task. The extraction of topological spatial information plays a crucial role in EEG classification, and the architecture of the spatial convolution greatly affects the performance and complexity of convolutional neural network (CNN) based EEG classification models. In this study, a progressive convolution CNN architecture named EEGProgress is proposed, aiming to efficiently extract the topological spatial information of EEG signals from multi-scale levels (electrode, brain region, hemisphere, global) with superior speed. To achieve this, the raw EEG data is permuted using the empirical topological permutation rule, integrating the EEG data with numerous topological properties. Subsequently, the spatial features are extracted by a progressive feature extractor including prior, electrode, region, and hemisphere convolution blocks, progressively extracting the deep spatial features with reduced parameters and speed. Finally, the comparison and ablation experiments under both cross-subject and within-subject scenarios are conducted on a public dataset to verify the performance of the proposed EEGProgress and the effectiveness of the topological permutation. The results demonstrate the superior feature extraction ability of the proposed EEGProgress, with an average increase of 4.02% compared to other CNN-based EEG classification models under both cross-subject and within-subject scenarios. Furthermore, with the obtained average testing time, FLOPs, and parameters, the proposed EEGProgress outperforms other comparison models in terms of model complexity.