1
|
Yu L, Yi M, Guo J, Li J, Zeng H, Cui L, Xu X, Liu G, Fan Y, Zeng J, Xing S, Chen Y, Wang M, Tan S, Jin LY, Kumar D, Vipin A, Ann SS, Binte Zailan FZ, Sandhu GK, Kandiah N, Dang C. Lower serum uric acid and impairment of right cerebral hemisphere structural brain networks are related to depressive symptoms in cerebral small vessel disease: A cross-sectional study. Heliyon 2024; 10:e27947. [PMID: 38509880 PMCID: PMC10950715 DOI: 10.1016/j.heliyon.2024.e27947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 03/03/2024] [Accepted: 03/08/2024] [Indexed: 03/22/2024] Open
Abstract
Cerebral small vessel disease (SVD) may be associated with an increased risk of depressive symptoms. Serum uric acid (SUA), an antioxidant, may be involved in the occurrence and development of depressive symptoms, but the mechanism remains unknown. Moreover, the relationship between structural brain networks and SUA has not been explored. This study examined the relationship between SUA and depressive symptoms in patients with SVD using graph theory analysis. We recruited 208 SVD inpatients and collected fasting blood samples upon admission. Depressive symptoms were assessed using the 24-item Hamilton Depression Rating Scale (HAMD-24). Magnetic resonance imaging was used to evaluate SVD, and diffusion tensor images were used to analyze structural brain networks using graph theory. Patients with depressive symptoms (n = 34, 25.76%) compared to those without (334.53 vs 381.28 μmol/L, p = 0.017) had lower SUA levels. Graph theoretical analyses showed a positive association of SUA with betweenness centrality, nodal efficiency, and clustering coefficients and a negative correlation with the shortest path length in SVD with depressive symptoms group. HAMD scores were significantly associated with nodal network metrics in the right cerebral hemisphere. Our findings suggested that lower SUA levels are significantly associated with disrupted structural brain networks in the right cerebral hemisphere of patients with SVD who have depressive symptoms.
Collapse
Affiliation(s)
- Lei Yu
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
| | - Ming Yi
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
| | - Jiayu Guo
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
| | - Jinbiao Li
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
| | - Huixing Zeng
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
| | - Liqian Cui
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
| | - Xiangming Xu
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
| | - Gang Liu
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
| | - Yuhua Fan
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
| | - Jinsheng Zeng
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
| | - Shihui Xing
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
| | - Yicong Chen
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
| | - Meng Wang
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
| | - Shuangquan Tan
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
| | - Leow Yi Jin
- Dementia Research Center Singapore (DRCS), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Dilip Kumar
- Dementia Research Center Singapore (DRCS), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Ashwati Vipin
- Dementia Research Center Singapore (DRCS), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Soo See Ann
- Dementia Research Center Singapore (DRCS), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Fatin Zahra Binte Zailan
- Dementia Research Center Singapore (DRCS), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Gurveen Kaur Sandhu
- Dementia Research Center Singapore (DRCS), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Nagaendran Kandiah
- Dementia Research Center Singapore (DRCS), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Chao Dang
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, China
- Dementia Research Center Singapore (DRCS), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| |
Collapse
|
2
|
Ge R, Liu X, Long D, Frangou S, Vila-Rodriguez F. Sex effects on cortical morphological networks in healthy young adults. Neuroimage 2021; 233:117945. [PMID: 33711482 DOI: 10.1016/j.neuroimage.2021.117945] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/17/2021] [Accepted: 03/03/2021] [Indexed: 12/30/2022] Open
Abstract
Understanding sex-related differences across the human cerebral cortex is an important step in elucidating the basis of psychological, behavioural and clinical differences between the sexes. Prior structural neuroimaging studies primarily focused on regional sex differences using univariate analyses. Here we focus on sex differences in cortical morphological networks (CMNs) derived using multivariate modelling of regional cortical measures of volume and surface from high-quality structural MRI scans from healthy participants in the Human Connectome Project (HCP) (n = 1,063) and the Southwest University Longitudinal Imaging Multimodal (SLIM) study (n = 549). The functional relevance of the CMNs was inferred using the NeuroSynth decoding function. Sex differences were widespread but not uniform. In general, females had higher volume, thickness and cortical folding in networks that involve prefrontal (both ventral and dorsal regions including the anterior cingulate) and parietal regions while males had higher volume, thickness and cortical folding in networks that primarily include temporal and posterior cortical regions. CMN loading coefficients were used as input features to linear discriminant analyses that were performed separately in the HCP and SLIM; sex was predicted with a high degree of accuracy (81%-85%) across datasets. The availability of behavioral data in the HCP enabled us to show that male-biased surface-based CMNs were associated with externalizing behaviors. These results extend previous literature on regional sex-differences by identifying CMNs that can reliably predict sex, are relevant to the expression of psychopathology and provide the foundation for the future investigation of their functional significance in clinical populations.
Collapse
Affiliation(s)
- Ruiyang Ge
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, BC, Canada
| | - Xiang Liu
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, BC, Canada
| | - David Long
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, BC, Canada
| | - Sophia Frangou
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, BC, Canada; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, BC, Canada.
| |
Collapse
|
3
|
Wang L, Lin FV, Cole M, Zhang Z. Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized Cognition. Neuroimage 2021; 225:117493. [PMID: 33127479 PMCID: PMC7826449 DOI: 10.1016/j.neuroimage.2020.117493] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 10/17/2020] [Accepted: 10/21/2020] [Indexed: 12/23/2022] Open
Abstract
Structural brain networks constructed from diffusion MRI are important biomarkers for understanding human brain structure and its relation to cognitive functioning. There is increasing interest in learning differences in structural brain networks between groups of subjects in neuroimaging studies, leading to a variable selection problem in network classification. Traditional methods often use independent edgewise tests or unstructured generalized linear model (GLM) with regularization on vectorized networks to select edges distinguishing the groups, which ignore the network structure and make the results hard to interpret. In this paper, we develop a symmetric bilinear logistic regression (SBLR) with elastic-net penalty to identify a set of small clique subgraphs in network classification. Clique subgraphs, consisting of all the interconnections among a subset of brain regions, have appealing neurological interpretations as they may correspond to some anatomical circuits in the brain related to the outcome. We apply this method to study differences in the structural connectome between adolescents with high and low crystallized cognitive ability, using the crystallized cognition composite score, picture vocabulary and oral reading recognition tests from NIH Toolbox. A few clique subgraphs containing several small sets of brain regions are identified between different levels of functioning, indicating their importance in crystallized cognition.
Collapse
Affiliation(s)
- Lu Wang
- Department of Statistics, Central South University, China.
| | - Feng Vankee Lin
- Elaine C. Hubbard Center for Nursing Research On Aging, School of Nursing, University of Rochester Medical Center, USA; Department of Psychiatry, School of Medicine and Dentistry, University of Rochester Medical Center, USA; Department of Brain and Cognitive Sciences, University of Rochester, USA; Department of Neuroscience, University of Rochester Medical Center, USA; Department of Neurology, School of Medicine and Dentistry, University of Rochester Medical Center, USA
| | - Martin Cole
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, USA
| | - Zhengwu Zhang
- Department of Neuroscience, University of Rochester Medical Center, USA; Department of Biostatistics and Computational Biology, University of Rochester Medical Center, USA.
| |
Collapse
|
4
|
Chung MK, Xie L, Huang SG, Wang Y, Yan J, Shen L. Rapid Acceleration of the Permutation Test via Transpositions. Connect Neuroimaging (2019) 2019; 11848:42-53. [PMID: 34514470 DOI: 10.1007/978-3-030-32391-2_5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The permutation test is an often used test procedure for determining statistical significance in brain network studies. Unfortunately, generating every possible permutation for large-scale brain imaging datasets such as HCP and ADNI with hundreds of subjects is not practical. Many previous attempts at speeding up the permutation test rely on various approximation strategies such as estimating the tail distribution with known parametric distributions. In this study, we propose the novel transposition test that exploits the underlying algebraic structure of the permutation group. The method is applied to a large number of diffusion tensor images in localizing the regions of the brain network differences.
Collapse
|