Hongming Li

Jan 25, 2025 | biorxiv.org | Hongming Li |Zaixu Cui |Matthew Cieslak |Taylor Salo

AbstractThe brain functional connectome development is fundamental to neurocognitive growth in youth. While brain age prediction has been widely used to assess connectome development at the individual level, traditional approaches providing a global index overlook the spatial variability and inter-individual heterogeneity of functional connectivity (FC) development across the cortex. In this study, we introduced a regional brain development index to assess spatially fine-grained FC development.

Dec 11, 2024 | biorxiv.org | Hao Zheng |Xiaoyang Chen |Hongming Li |Tingting Chen

AbstractDeep learning-based cortical surface reconstruction (CSR) methods heavily rely on pseudo ground truth (pGT) generated by conventional CSR pipelines as supervision, leading to dataset-specific challenges and lengthy training data preparation. We propose a new approach for reconstructing multiple cortical surfaces using weak supervision from brain MRI ribbon segmentations.

Nov 19, 2024 | nature.com | Hongkai Wang |Hongming Li |Chao Song

Based on deep mediatization theory and artificial intelligence (AI) technology, this study explores the effective improvement of museums’ social media communication by applying Convolutional Neural Network (CNN) technology. Firstly, the social media content from four different museums is collected, a dataset containing tens of thousands of images is constructed, and a CNN-based model is designed for automatic identification and classification of image content. The model is trained and tested through a series of experiments, evaluating its performance in enhancing museums’ social media communication. Experimental results indicate that the CNN model significantly enhances user participation, access rates, retention rates, and sharing rates of content. Specifically, user participation increased from 15 to 25%, reflecting a 66.7% rise. Content coverage increased from 20 to 35%, showing a 75% increase. User retention rate rose from 10 to 20%, indicating a 100% increase. Content sharing rate increased from 5 to 15%, reflecting a 200% rise. Additionally, the study discusses the model’s performance across various museum types, batch sizes, and learning rate settings, verifying its robustness and wide applicability.

Apr 29, 2024 | biorxiv.org | Yuncong Ma |Hongming Li |Zhen Zhou |Xiaoyang Chen

AbstractPersonalized functional networks (FNs) derived from functional magnetic resonance imaging (fMRI) data are useful for characterizing individual variations in the brain functional topography associated with the brain development, aging, and disorders.