Zang YF papers

Autism Res. 2024 Dec 31. doi: 10.1002/aur.3303. Online ahead of print.

ABSTRACT

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and its underlying neuroanatomical mechanisms still remain unclear. The scaled subprofile model of principal component analysis (SSM-PCA) is a data-driven multivariate technique for capturing stable disease-related spatial covariance pattern. Here, SSM-PCA is innovatively applied to obtain robust ASD-related gray matter volume pattern associated with clinical symptoms. We utilized T1-weighted structural MRI images (sMRI) of 576 subjects (288 ASDs and 288 typically developing (TD) controls) aged 7-29 years from the Autism Brain Imaging Data Exchange II (ABIDE II) dataset. These images were analyzed with SSM-PCA to identify the ASD-related spatial covariance pattern. Subsequently, we investigated the relationship between the pattern and clinical symptoms and verified its robustness. Then, the applicability of the pattern under different age stages were further explored. The results revealed that the ASD-related pattern primarily involves the thalamus, putamen, parahippocampus, orbitofrontal cortex, and cerebellum. The expression of this pattern correlated with Social Response Scale and Social Communication Questionnaire scores. Moreover, the ASD-related pattern was robust for the ABIDE I dataset. Regarding the applicability of the pattern for different age stages, the effect sizes of its expression in ASD were medium in the children and adults, while small in adolescents. This study identified a robust ASD-related pattern based on gray matter volume that is associated with social deficits. Our findings provide new insights into the neuroanatomical mechanisms of ASD and may facilitate its future intervention.

PMID:39737534 | DOI:10.1002/aur.3303

CNS Neurosci Ther. 2024 Dec;30(12):e70120. doi: 10.1111/cns.70120.

ABSTRACT

INTRODUCTION: The centromedian nucleus (CM) of the thalamus is essential for arousal, attention, sensory processing, and motor control. Neuromodulation targeting CM dysfunction has shown efficacy in various neurological disorders. However, its individualized precise transcranial magnetic stimulation (TMS) remains unreported. Using resting-state functional MRI, we mapped CM-based functional connectivity (CM-FC) to develop a personalized TMS scheme for neurological conditions.

METHODS: We first analyzed the CM-FC patterns of healthy subjects via 10 scanning sessions in three MRI scanners spanning two subject groups: one from the Human Connectome Project (n = 20, four sessions) dataset and the other from Hangzhou Normal University (n = 20, three sessions of 3 T MRI and three sessions of 1.5 T MRI). Pearson's correlation was used for CM-FC evaluation. Then, we proposed an overlapping index ranging from 1 to 10, and group-level clusters with the highest overlapping index located 4 cm beneath the scalp were identified. In the individual CM-FC map, watershed image segmentation was used to obtain an individual cluster. The peak voxel with the highest FC value within the individual cluster was defined as a potential individualized target for future TMS.

RESULTS: The spatial FC patterns were remarkably similar between the left and right CMs. CMs have widespread positive connectivity with cortical areas, including the sensorimotor cortex, supplementary motor area, middle frontal cortex, medial temporal cortex, and middle cingulate. Among the group-level FC patterns of the left and right CMs, only the left CM had a group cluster in the left primary sensorimotor cortex (PSMC, cluster size = 51) with an overlapping index of 10, that is, 10 sessions showed significant CM-FC.

CONCLUSIONS: The left PSMC exhibited reproducible FC with the left CM. The individual peak FC location in the left PSMC could be used as a TMS target for indirect modulation of CM activity and aid in the treatment of CM-related neurological disorders.

PMID:39648650 | DOI:10.1111/cns.70120

Brain Imaging Behav. 2024 Nov 13. doi: 10.1007/s11682-024-00923-5. Online ahead of print.

ABSTRACT

In recent years, brain signal complexity has gained attention as an indicator of brain well-being and a predictor of disease and dysfunction. Brain entropy quantifies this complexity. Assessment of functional network centrality and connectivity reveals that information communication induces neural signal oscillations in certain brain regions. However, their relationship is uncertain. This work studied brain signal complexity, network centrality, and connectivity in both healthy and depressed individuals. The current work comprised a sample of 124 first-episode drug-naïve patients with major depressive disorder (MDD) and 105 healthy controls (HC). Six functional networks were created for each person using resting-state functional magnetic resonance imaging. For each network, entropy, centrality, and connectivity were computed. Using structural equation modeling, this study examined the associations between brain network entropy, centrality, and connectivity. The findings demonstrated substantial correlations of entropy with both centrality and connectivity in HC and these correlation patterns were disrupted in MDD. Compared to HC, MDD exhibited higher entropy in four networks and demonstrated changes in centralities across all networks. The structural equation modeling showed that network centralities, connectivity, and depression severity had impacts on brain entropy. Nevertheless, no impacts were observed in the opposite directions. This study indicated that the complexity of brain signals was influenced not only by the interactions among different areas of the brain but also by the severity level of depression. These findings enhanced our comprehension of the associations of brain entropy with its influential factors.

PMID:39532824 | DOI:10.1007/s11682-024-00923-5

Sci Bull (Beijing). 2024 Nov 1:S2095-9273(24)00794-1. doi: 10.1016/j.scib.2024.11.001. Online ahead of print.

NO ABSTRACT

PMID:39532560 | DOI:10.1016/j.scib.2024.11.001

Clin Neurophysiol. 2024 Oct 9;168:25-33. doi: 10.1016/j.clinph.2024.09.026. Online ahead of print.

ABSTRACT

OBJECTIVE: In order to examine whether individualized peak functional connectivity could potentially serve as a target for repetitive transcranial magnetic stimulation (rTMS) therapy, we investigated the location of peak functional connectivity (FC) between the cortical motor area and the key brain region, the globus pallidus internus (GPi), in Tourette syndrome, and explored the relationship between the severity of the disease and these aberrant functional connections.

METHODS: The study involved a cohort of 103 children diagnosed with Tourette syndrome and 66 age-matched typically developing children. The GPi was served as the seed, and the study compared individualized peak FC strength in the supplementary motor area (SMA) and premotor area between the two groups. Spatial distribution of peak FC in the motor area and GPi-based voxel-wise FC were also analyzed.

RESULTS: Children with Tourette syndrome exhibited lower peak FC in the left SMA when using left GPi as the seed. This reduction in peak FC demonstrated a significant and negative correlation with the Yale Global Tic Severity Scale scores.

CONCLUSIONS: SMA-GPi FC is one of the key pathological circuit in Tourette syndrome.

SIGNIFICANCE: The individual peak FC location in the left SMA potentially serve as stimulation targets for rTMS treatment of TS.

PMID:39426023 | DOI:10.1016/j.clinph.2024.09.026