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Fengmei Fan

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4 papers

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YNICL Journal 2023 Journal Article

Frequency-dependent and time-variant alterations of neural activity in post-stroke depression: A resting-state fMRI study

Xiumei Wu
Luoyu Wang
Haibo Jiang
Yanhui Fu
Tiantian Wang
Zhenqiang Ma
Xiaoyan Wu
Yiying Wang

BACKGROUND: Post-stroke depression (PSD) is one of the most frequent psychiatric disorders after stroke. However, the underlying brain mechanism of PSD remains unclarified. Using the amplitude of low-frequency fluctuation (ALFF) approach, we aimed to investigate the abnormalities of neural activity in PSD patients, and further explored the frequency and time properties of ALFF changes in PSD. METHODS: Resting-state fMRI data and clinical data were collected from 39 PSD patients (PSD), 82 S patients without depression (Stroke), and 74 age- and sex-matched healthy controls (HC). ALFF across three frequency bands (ALFF-Classic: 0.01-0.08 Hz; ALFF-Slow4: 0.027-0.073 Hz; ALFF-Slow5: 0.01-0.027 Hz) and dynamic ALFF (dALFF) were computed and compared among three groups. Ridge regression analyses and spearman's correlation analyses were further applied to explore the relationship between PSD-specific alterations and depression severity in PSD. RESULTS: We found that PSD-specific alterations of ALFF were frequency-dependent and time-variant. Specially, compared to both Stroke and HC groups, PSD exhibited increased ALFF in the contralesional dorsolateral prefrontal cortex (DLPFC) and insula in all three frequency bands. Increased ALFF in ipsilesional DLPFC were observed in both slow-4 and classic frequency bands which were positively correlated with depression scales in PSD, while increased ALFF in the bilateral hippocampus and contralesional rolandic operculum were only found in slow-5 frequency band. These PSD-specific alterations in different frequency bands could predict depression severity. Moreover, decreased dALFF in contralesional superior temporal gyrus were observed in PSD group. LIMITATIONS: Longitudinal studies are required to explore the alterations of ALFF in PSD as the disease progress. CONCLUSIONS: The frequency-dependent and time-variant properties of ALFF could reflect the PSD-specific alterations in complementary ways, which may assist to elucidate underlying neural mechanisms and be helpful for early diagnosis and interventions for the disease.

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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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