TdCCA with Dual-Modal Signal Fusion: Degenerated Occipital and Frontal Connectivity of Adult Moyamoya Disease for Early Identification.

Cognitive impairment in patients with moyamoya disease (MMD) manifests earlier than clinical symptoms. Early identification of brain connectivity changes is essential for uncovering the pathogenesis of cognitive impairment in MMD. We proposed a temporally driven canonical correlation analysis (TdCCA) method to achieve dual-modal synchronous information fusion from electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) for exploring the differences in brain connectivity between MMD and normal control groups.

Developmental dynamics of brain network modularity and temporal co-occurrence diversity in childhood.

Objective: Brain development during childhood involves significant structural, functional, and connectivity changes, reflecting the interplay between modularity, information interaction, and functional segregation. This study aims to understand the dynamic properties of brain connectivity and their impact on cognitive development, focusing on temporal co-occurrence diversity patterns.

Methods: We recruited 481 children aged 6 to 12 years from the Healthy Brain Network database.

Classification of gastric cancerous tissues by a residual network based on optical coherence tomography images.

Optical coherence tomography (OCT) is a noninvasive, radiation-free, and high-resolution imaging technology. The intraoperative classification of normal and cancerous tissue is critical for surgeons to guide surgical operations. Accurate classification of gastric cancerous OCT images is beneficial to improve the effect of surgical treatment based on the deep learning method.