Zeqiang Linli

Numerous studies indicate that the brain demonstrates functional lateralization with distinct patterns between males and females, but they primarily have focused on gray matter, with only a handful considering white matter (WM) and sex differences. Utilizing rs-fMRI from the Southwest University Longitudinal Imaging Multimodal (SLIM) dataset, we quantified lateralization of WM as the difference in each region’s synchronization with the left versus right hemisphere, and examined its temporal fluctuations and sex differences. Then, the Louvain community detection algorithm clustered lateralization patterns, and sex differences within each cluster were analyzed. We established the functional connectivity network, assessed its properties, and utilized correlation analysis to examine the relationship between lateralization and network. Our analyses revealed that WM demonstrated a left-lateralized trend in the left hemisphere and a right-lateralized trend in the right hemisphere. Moreover, functional lateralization was more pronounced in males than females. We also categorized the regions into three spatial clusters, revealing sex differences in the lateralization patterns within each cluster. Additionally, lateralization patterns are intricately associated with the functional connectivity network. These findings provide insights into functional lateralization and sex differences, and may inform future investigations of the neural mechanisms associated with functional specialization, cognition, behavior, and neurological disorders.

Background: Structural white matter (WM) alterations are recognized in Autism Spectrum Disorder (ASD), yet the functional connectivity (FC) of WM networks and its clinical significance remain largely underexplored. Methods: This study aimed to investigate aberrant FC patterns within intra‐WM (WM–WM) and WM–grey matter (WM–GM) networks in a large ASD cohort. Resting‐state fMRI data from 272 ASD individuals and 368 typical controls (TC) from the ABIDE‐II dataset were analysed. We constructed WM–WM and WM–GM FC networks using Pearson correlations between atlas‐defined regions, applied ComBat harmonization and employed Network‐Based Statistic (NBS) to identify group differences. Associations with clinical symptoms were assessed using the Social Responsiveness Scale (SRS) scores, and a CatBoost algorithm was used for diagnostic classification based on connectivity features. Results: NBS analyses revealed significantly increased connectivity in ASD for 116 WM–WM pairs and 58 WM–GM pairs ( p < 0.05, FWER‐corrected). Critically, the strength of these aberrant WM–WM functional connections exhibited a significant negative correlation with SRS total scores ( r = −0.22, p < 0.001), whereas WM–GM connectivity showed no such significant association. The hybrid CatBoost classifier, integrating both WM–WM and WM–GM features, achieved moderate diagnostic discrimination (AUC = 0.669 ± 0.040). Conclusion: These results offer novel insights into the aberrant functional architecture of WM‐related networks in ASD, particularly linking intra‐WM dysconnectivity to symptom severity, thereby enhancing our understanding of the neural substrates underlying social‐communicative deficits.

Background: Structural white matter (WM) alterations are recognized in Autism Spectrum Disorder (ASD), yet the functional connectivity (FC) of WM networks and its clinical significance remain largely under-explored. Methods: This study aimed to investigate aberrant FC patterns within intra-WM (WM-WM) and WM-gray matter (WM-GM) networks in a large ASD cohort. Resting-state fMRI data from 272 ASD individuals and 368 typical controls (TC) from the ABIDE-II dataset were analyzed. We constructed WM-WM and WM-GM FC networks using Pearson correlations between atlas-defined regions, applied ComBat harmonization, and employed Network-Based Statistics (NBS) to identify group differences. Associations with clinical symptoms were assessed using Social Responsiveness Scale (SRS) scores, and a CatBoost algorithm was used for diagnostic classification based on connectivity features. Results: NBS analyses revealed significantly increased connectivity in ASD for 116 WM-WM pairs and 58 WM-GM pairs (P<0.05, FWER-corrected). Critically, the strength of these aberrant WM-WM functional connections exhibited a significant negative correlation with SRS total scores (r = -0.22, P < 0.001), whereas WM-GM connectivity showed no such significant association. The hybrid CatBoost classifier, integrating both WM-WM and WM-GM features, achieved moderate diagnostic discrimination (AUC = 0.669 ± 0.040). Conclusion: These results offer novel insights into the aberrant functional architecture of WM-related networks in ASD, particularly linking intra-WM dysconnectivity to symptom severity, thereby enhancing our understanding of the neural substrates underlying social-communicative deficits.

Resting-state functional connectivity (FC) studies have predominantly centered on gray matter (GM), largely overlooking the functional contributions of white matter (WM). However, emerging evidence indicates WM blood-oxygen-level-dependent (BOLD) signals actively shape large-scale brain networks. Current methods to integrate WM face limitations, including challenges in assessing global network properties from bipartite GM-WM connections or the nascent stage of unified connectome models. Here, we introduce the Gray-White Matter Heterogeneous Fusion Network (GWM-HFN), a framework that defines GM-GM functional links via the covariance of their interaction profiles with WM bundles defined by a standardized atlas. Validated across six independent datasets, GWM-HFN demonstrates fair short-term (ICC ~ 0.36) and slight-to-fair long-term (ICC ~ 0.20) test-retest reliability, comparable to conventional GM-based FC. GWM-HFN exhibits distinct topological features, including small-worldness and enhanced modular segregation compared to GM-GM networks, capturing over 40% unique variance and a unique sensitivity to the connectivity patterns that differentiate individuals. Lifespan analyses reveal global linear declines and complex non-linear age effects in GWM-HFN connectivity, with peak connectivity in early adulthood ( ~ 34 years). Clinically, individuals with autism spectrum disorder (ASD) show GWM-HFN-specific hyperconnectivity, which correlates with symptom severity and offers greater sensitivity than GM-GM FC. Furthermore, GWM-HFN connectivity patterns predict individual differences in cognitive performance, notably in language tasks. The GWM-HFN framework provides a robust and more comprehensive approach to understanding WM-mediated neural communication, integrating functional signals across both GM and WM, and offers promising avenues for developing neuroimaging biomarkers for aging and neuropsychiatric disorders.

Background and Purpose: An understanding of the modular structure of brain functional networks and their changes with age is beneficial in uncovering the neural mechanisms that underlie cognitive decline during the ageing process. Compared to simpler networks, such as social networks, the execution of brain functions always depends on extensive interactions among multiple brain regions, which complicates the detection of accurate, stable and physiologically meaningful community structures. However, although previous work has focused on the modular organization of the brain, there has been insufficient research on its specific dynamic changes and how these evolve with age. In this case, this paper investigates the modular structure of human brain functional networks and their dynamic changes across different age groups, revealing the impact of ageing on brain network functionality.Methods: Firstly, we constructed brain networks using a dynamic sliding‐window subnetwork voting method. Further, this paper, based on public datasets and the Girvan–Newman (GN) community detection algorithm, effectively divided the community structure of brain networks and calculated modularity. It focused on analysing the brain network characteristics of 439 participants under rs‐fMRI data.Results: The results of the variance analysis indicate significant differences in modularity across different age groups. The brain networks of younger participants exhibited pronounced modular characteristics and higher efficiency in information processing. In contrast, older participants displayed a significant reduction in modularity, reflecting a trend towards functional integration.Conclusion: These changes are closely related to the decline in cognitive abilities and the degeneration of neural connections.

Background and Purpose: Extensive research has been carried out to investigate changes in various gray matter (GM) regions during the aging process using resting‐state functional MRI. However, the impact of aging on the functional connectivity (FC) between white matter (WM) and GM, particularly white matter–gray matter functional connectivity (WM–GM FC), remains largely unknown. This study proposes a novel method for constructing functional networks that integrate both WM and GM.Methods: By utilizing data from a lifespan cohort of 439 healthy adults, we devised a covariance‐based approach to establish a gray matter–white matter–gray matter (GM–WM–GM) mediated network. The FC between GM and WM was quantified using the Johns Hopkins University International Consortium of Brain Mapping‐Diffusion Tensor Imaging‐81 WM atlas in combination with the Automated Anatomical Labeling atlas. First, the WM–GM FC was calculated via Pearson correlation coefficients between WM and GM regions, followed by the standardization of the resulting matrix. The GM–WM–GM FC was then constructed using the covariance matrix. Furthermore, topological properties were calculated for GM–WM–GM networks. Finally, the age effect of GM–WM–GM and its topology were explored.Results: Our findings reveal a significant age‐related decline in intranetwork connectivity and global network efficiency, while internetwork connectivity followed an inverted U‐shaped pattern, suggesting functional dedifferentiation in the aging brain. Despite relatively stable local efficiency, the observed reduction in global efficiency indicates a weakening of long‐range neural connections. Additionally, a decrease in network modularity further supports this trend.Conclusion: These results offer novel insights into the age‐associated reorganization of brain networks, enhancing our understanding of the neural mechanisms underlying normal aging.

Resting-state functional connectivity (FC) studies have predominantly centered on gray matter (GM), largely overlooking the functional contributions of white matter (WM). However, emerging evidence indicates WM blood-oxygen-level-dependent (BOLD) signals actively shape large-scale brain networks. Current methods to integrate WM face limitations, including challenges in assessing global network properties from bipartite GM-WM connections or the nascent stage of unified connectome models. Here, we introduce the Gray-White Matter Heterogeneous Fusion Network (GWM-HFN), a novel framework that defines GM-GM functional links via the covariance of their interaction profiles with WM bundles defined by a standardized atlas. Validated across six independent datasets, GWM-HFN demonstrates fair short-term (ICC ∼0.36) and slight-to-fair long-term (ICC ∼0.20) test-retest reliability, comparable to conventional GM-based FC. GWM-HFN exhibits distinct topological features, including small-worldness and enhanced modular segregation compared to GM-GM networks, capturing over 40% unique variance and an enhanced ability to resolve the unique connectivity patterns that differentiate individuals. Lifespan analyses reveal global linear declines and complex non-linear age effects in GWM-HFN connectivity, with peak connectivity in early adulthood (∼34 years). Clinically, individuals with autism spectrum disorder (ASD) show GWM-HFN-specific hyperconnectivity, which correlates with symptom severity and offers greater sensitivity than GM-GM FC. Furthermore, GWM-HFN connectivity patterns predict individual differences in cognitive performance, notably in language tasks. The GWM-HFN framework provides a robust and more comprehensive approach to understanding WM-mediated neural communication, integrating functional signals across both GM and WM, and offers promising avenues for developing neuroimaging biomarkers for aging and neuropsychiatric disorders.