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Shuping Tan

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5 papers

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YNIMG Journal 2024 Journal Article

Segregation of the regional radiomics similarity network exhibited an increase from late childhood to early adolescence: A developmental investigation

Lei Chu
Debin Zeng
Yirong He
Xiaoxi Dong
Qiongling Li
Xuhong Liao
Tengda Zhao
Xiaodan Chen

Brain development is characterized by an increase in structural and functional segregation, which supports the specialization of cognitive processes within the context of network neuroscience. In this study, we investigated age-related changes in morphological segregation using individual Regional Radiomics Similarity Networks (R2SNs) constructed with a longitudinal dataset of 494 T1-weighted MR scans from 309 typically developing children aged 6.2 to 13 years at baseline. Segertation indices were defined as the relative difference in connectivity strengths within and between modules and cacluated at the global, system and local levels. Linear mixed-effect models revealed longitudinal increases in both global and system segregation indices, particularly within the limbic and dorsal attention network, and decreases within the ventral attention network. Superior performance in working memory and inhibitory control was associated with higher system-level segregation indices in default, frontoparietal, ventral attention, somatomotor and subcortical systems, and lower local segregation indices in visual network regions, regardless of age. Furthermore, gene enrichment analysis revealed correlations between age-related changes in local segregation indices and regional expression levels of genes related to developmental processes. These findings provide novel insights into typical brain developmental changes using R2SN-derived segregation indices, offering a valuable tool for understanding human brain structural and cognitive maturation.

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YNIMG Journal 2021 Journal Article

Development of the default-mode network during childhood and adolescence: A longitudinal resting-state fMRI study

Fengmei Fan
Xuhong Liao
Tianyuan Lei
Tengda Zhao
Mingrui Xia
Weiwei Men
Yanpei Wang
Mingming Hu

The default-mode network (DMN) is a set of functionally connected regions that play crucial roles in internal cognitive processing. Previous resting-state fMRI studies have demonstrated that the intrinsic functional organization of the DMN undergoes remarkable reconfigurations during childhood and adolescence. However, these studies have mainly focused on cross-sectional designs with small sample sizes, limiting the consistency and interpretations of the findings. Here, we used a large sample of longitudinal resting-state fMRI data comprising 305 typically developing children (6-12 years of age at baseline, 491 scans in total) and graph theoretical approaches to delineate the developmental trajectories of the functional architecture of the DMN. For each child, the DMN was constructed according to a prior parcellation with 32 brain nodes. We showed that the overall connectivity increased in strength from childhood to adolescence and became spatially similar to that in the young adult group (N = 61, 18-28 years of age). These increases were primarily located in the midline structures. Global and local network efficiency in the DMN also increased with age, indicating an enhanced capability in parallel information communication within the brain system. Based on the divergent developmental rates of nodal centrality, we identified three subclusters within the DMN, with the fastest rates in the cluster mainly comprising the anterior medial prefrontal cortex and posterior cingulate cortex. Together, our findings highlight the developmental patterns of the functional architecture in the DMN from childhood to adolescence, which has implications for the understanding of network mechanisms underlying the cognitive development of individuals.

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YNICL Journal 2020 Journal Article

Abnormal amygdala subregional-sensorimotor connectivity correlates with positive symptom in schizophrenia

Meng Zhang
Fude Yang
Fengmei Fan
Zhiren Wang
Xiang Hong
Yunlong Tan
Shuping Tan
L. Elliot Hong

Altered resting-state functional connectivity (rsFC) of the amygdala has been demonstrated to be implicated in schizophrenia neuronal pathophysiology. However, whether rsFC of amygdala subregions is differentially affected in schizophrenia remains unclear. This study compared the functional networks of each amygdala subdivision between healthy controls (HC) and patients with first-episode schizophrenia (FES). In total, 47 HC and 78 patients with FES underwent resting-state functional magnetic resonance imaging. The amygdala was divided into the following three subregions using the Juelich histological atlas: basolateral amygdala (BLA), centromedial amygdala (CMA), and superficial amygdala (SFA). The rsFC of the three amygdala subdivisions was computed and compared between the two groups. Significantly increased rsFC of the right CMA with the right postcentral gyrus and decreased rsFC of the right BLA with the left precentral gyrus were observed in the FES group compared with the HC group. Notably, the right BLA-left precentral gyrus connectivity was negatively correlated with positive symptoms and conceptual disorganization in patients with FES. In conclusion, this study found that patients with FES had abnormal functional connectivity in the amygdala subregions, and the altered rsFC was associated with positive symptoms. The present findings demonstrate the disruptive rsFC patterns of amygdala subregional-sensorimotor networks in FES and may provide new insights into the neuronal pathophysiology of FES.

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YNIMG Journal 2020 Journal Article

Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA

Joaquim Radua
Eduard Vieta
Russell Shinohara
Peter Kochunov
Yann Quidé
Melissa J. Green
Cynthia S. Weickert
Thomas Weickert

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

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YNIMG Journal 2019 Journal Article

Unbiased age-specific structural brain atlases for Chinese pediatric population

Tengda Zhao
Xuhong Liao
Vladimir S. Fonov
Qiushi Wang
Weiwei Men
Yanpei Wang
Shaozheng Qin
Shuping Tan

In magnetic resonance (MR) imaging studies of child brain development, structural brain atlases usually serve as important references for the pediatric population, in which individual images are spatially normalized into a common or standard stereotactic space. However, the popular existing pediatric brain atlases (e. g. , National Institutes of Health pediatric atlases, NIH-PD) are mostly based on MR images obtained from Caucasian populations and thus are not ideal for the characterization of the brains of Chinese children due to neuroanatomical differences related to genetic and environmental factors. Here, we use an unbiased template construction algorithm to create a set of age-specific Chinese pediatric (CHN-PD) atlases based on high-quality T1-and T2-weighted MR images from 328 cognitively normal Chinese children aged 6–12 years. The CHN-PD brain atlases include asymmetric and symmetric templates, sex-specific templates and tissue probability templates, and contain multiple age-specific templates at one-year intervals. A direct comparison of the CHN-PD and NIH-PD atlases reveals dramatic anatomical differences mainly in the bilateral frontal and parietal regions. After applying the CHN-PD and NIH-PD atlases to two independent Chinese pediatric datasets (N = 114 and N = 71), we find that the CHN-PD atlases result in significantly higher accuracy than the NIH-PD atlases in both predicting “brain age” and guiding brain tissue segmentation. These results suggest that the CHN-PD brain atlases are necessary for studies of the typical and atypical development of the Chinese pediatric population. These CHN-PD atlases have been released on the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) website (https: //www. nitrc. org/projects/chn-pd).

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