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  • Springer Science and Business Media LLC  (5)
  • Wang, Fei  (5)
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  • Springer Science and Business Media LLC  (5)
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  • 1
    Online Resource
    Online Resource
    A longitudinal resource for population neuroscience of school-age children and adolescents in China
    Springer Science and Business Media LLC ; 2023
    In:  Scientific Data Vol. 10, No. 1 ( 2023-08-21)
    Details
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2023-08-21)
    Abstract: During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013–2022), the first ten-year stage of the lifespan CCNP (2013–2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0–17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the “Chinese Data-sharing Warehouse for In-vivo Imaging Brain” in the Chinese Color Nest Project (CCNP) – Lifespan Brain-Mind Development Data Community ( https://ccnp.scidb.cn ) at the Science Data Bank.
    Type of Medium: Online Resource
    ISSN: 2052-4463
    URL: Article
    DOI: 10.1038/s41597-023-02377-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2775191-0
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Age- and gender-related dispersion of brain networks across the lifespan
    Springer Science and Business Media LLC ; 2023
    In:  GeroScience Vol. 46, No. 1 ( 2023-08-05), p. 1303-1318
    Details
    In: GeroScience, Springer Science and Business Media LLC, Vol. 46, No. 1 ( 2023-08-05), p. 1303-1318
    Type of Medium: Online Resource
    ISSN: 2509-2723
    URL: Article
    DOI: 10.1007/s11357-023-00900-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2886418-9
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Reduced nucleus accumbens functional connectivity in reward network and default mode network in patients with recurrent major depressive disorder
    Springer Science and Business Media LLC ; 2022
    In:  Translational Psychiatry Vol. 12, No. 1 ( 2022-06-06)
    Details
    In: Translational Psychiatry, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-06-06)
    Abstract: The nucleus accumbens (NAc) is considered a hub of reward processing and a growing body of evidence has suggested its crucial role in the pathophysiology of major depressive disorder (MDD). However, inconsistent results have been reported by studies on reward network-focused resting-state functional MRI (rs-fMRI). In this study, we examined functional alterations of the NAc-based reward circuits in patients with MDD via meta- and mega-analysis. First, we performed a coordinated-based meta-analysis with a new SDM-PSI method for all up-to-date rs-fMRI studies that focused on the reward circuits of patients with MDD. Then, we tested the meta-analysis results in the REST-meta-MDD database which provided anonymous rs-fMRI data from 186 recurrent MDDs and 465 healthy controls. Decreased functional connectivity (FC) within the reward system in patients with recurrent MDD was the most robust finding in this study. We also found disrupted NAc FCs in the DMN in patients with recurrent MDD compared with healthy controls. Specifically, the combination of disrupted NAc FCs within the reward network could discriminate patients with recurrent MDD from healthy controls with an optimal accuracy of 74.7%. This study confirmed the critical role of decreased FC in the reward network in the neuropathology of MDD. Disrupted inter-network connectivity between the reward network and DMN may also have contributed to the neural mechanisms of MDD. These abnormalities have potential to serve as brain-based biomarkers for individual diagnosis to differentiate patients with recurrent MDD from healthy controls.
    Type of Medium: Online Resource
    ISSN: 2158-3188
    URL: Article
    DOI: 10.1038/s41398-022-01995-x
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2609311-X
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  • 4
    Online Resource
    Online Resource
    Disrupted intrinsic functional brain topology in patients with major depressive disorder
    Springer Science and Business Media LLC ; 2021
    In:  Molecular Psychiatry Vol. 26, No. 12 ( 2021-12), p. 7363-7371
    Details
    In: Molecular Psychiatry, Springer Science and Business Media LLC, Vol. 26, No. 12 ( 2021-12), p. 7363-7371
    Abstract: Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.
    Type of Medium: Online Resource
    ISSN: 1359-4184 , 1476-5578
    URL: Article
    DOI: 10.1038/s41380-021-01247-2
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 1502531-7
    detail.hit.zdb_id: 1330655-8
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  • 5
    Online Resource
    Online Resource
    Connectomics-based resting-state functional network alterations predict suicidality in major depressive disorder
    Springer Science and Business Media LLC ; 2023
    In:  Translational Psychiatry Vol. 13, No. 1 ( 2023-11-27)
    Details
    In: Translational Psychiatry, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2023-11-27)
    Abstract: Suicidal behavior is a major concern for patients who suffer from major depressive disorder (MDD). However, dynamic alterations and dysfunction of resting-state networks (RSNs) in MDD patients with suicidality have remained unclear. Thus, we investigated whether subjects with different severity of suicidal ideation and suicidal behavior may have different disturbances in brain RSNs and whether these changes could be used as the diagnostic biomarkers to discriminate MDD with or without suicidal ideation and suicidal behavior. Then a multicenter, cross-sectional study of 528 MDD patients with or without suicidality and 998 healthy controls was performed. We defined the probability of dying by the suicide of the suicidality components as a ‘suicidality gradient’. We constructed ten RSNs, including default mode (DMN), subcortical (SUB), ventral attention (VAN), and visual network (VIS). The network connections of RSNs were analyzed among MDD patients with different suicidality gradients and healthy controls using ANCOVA, chi-squared tests, and network-based statistical analysis. And support vector machine (SVM) model was designed to distinguish patients with mild-to-severe suicidal ideation, and suicidal behavior. We found the following abnormalities with increasing suicidality gradient in MDD patients: within-network connectivity values initially increased and then decreased, and one-versus-other network values decreased first and then increased. Besides, within- and between-network connectivity values of the various suicidality gradients are mainly negatively correlated with HAMD anxiety and positively correlated with weight. We found that VIS and DMN-VIS values were affected by age ( p   〈  0.05), cingulo-opercular network, and SUB-VAN values were statistically influenced by sex ( p   〈  0.05). Furthermore, the SVM model could distinguish MDD patients with different suicidality gradients (AUC range, 0.73–0.99). In conclusion, we have identified that disrupted brain connections were present in MDD patients with different suicidality gradient. These findings provided useful information about the pathophysiological mechanisms of MDD patients with suicidality.
    Type of Medium: Online Resource
    ISSN: 2158-3188
    URL: Article
    DOI: 10.1038/s41398-023-02655-4
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2609311-X
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