1
|
Cao P, Li Y, Dong Y, Tang Y, Xu G, Si Q, Chen C, Yao Y, Li R, Sui Y. Different structural connectivity patterns in the subregions of the thalamus, hippocampus, and cingulate cortex between schizophrenia and psychotic bipolar disorder. J Affect Disord 2024; 363:269-281. [PMID: 39053628 DOI: 10.1016/j.jad.2024.07.077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 06/25/2024] [Accepted: 07/14/2024] [Indexed: 07/27/2024]
Abstract
OBJECTIVE Schizophrenia (SCZ) and psychotic bipolar disorder (PBD) are two major psychotic disorders with similar symptoms and tight associations on the psychopathological level, posing a clinical challenge for their differentiation. This study aimed to investigate and compare the structural connectivity patterns of the limbic system between SCZ and PBD, and to identify specific regional disruptions associated with psychiatric symptoms. METHODS Using sMRI data from 146 SCZ, 160 PBD, and 145 healthy control (HC) participants, we employed a data-driven approach to segment the hippocampus, thalamus, hypothalamus, amygdala, and cingulate cortex into subregions. We then investigated the structural connectivity patterns between these subregions at the global and nodal levels. Additionally, we assessed psychotic symptoms by utilizing the subscales of the Brief Psychiatric Rating Scale (BPRS) to examine correlations between symptom severity and network metrics between groups. RESULTS Patients with SCZ and PBD had decreased global efficiency (Eglob) (SCZ: adjusted P<0.001; PBD: adjusted P = 0.003), local efficiency (Eloc) (SCZ and PBD: adjusted P<0.001), and clustering coefficient (Cp) (SCZ and PBD: adjusted P<0.001), and increased path length (Lp) (SCZ: adjusted P<0.001; PBD: adjusted P = 0.004) compared to HC. Patients with SCZ showed more pronounced decreases in Eglob (adjusted P<0.001), Eloc (adjusted P<0.001), and Cp (adjusted P = 0.029), and increased Lp (adjusted P = 0.024) compared to patients with PBD. The most notable structural disruptions were observed in the hippocampus and thalamus, which correlated with different psychotic symptoms, respectively. CONCLUSION This study provides evidence of distinct structural connectivity disruptions in the limbic system of patients with SCZ and PBD. These findings might contribute to our understanding of the neuropathological basis for distinguishing SCZ and PBD.
Collapse
Affiliation(s)
- Peiyu Cao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China
| | - Yuting Li
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China; Huzhou Third People's Hospital, Huzhou 313000, Zhejiang, China
| | - Yingbo Dong
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China
| | - Yilin Tang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China
| | - Guoxin Xu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China
| | - Qi Si
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China; Huai'an No. 3 People's Hospital, Huai'an 223001, Jiangsu, China
| | - Congxin Chen
- Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing 210000, Jiangsu, China
| | - Ye Yao
- Nanjing Medical University, Nanjing 210000, Jiangsu, China
| | - Runda Li
- Vanderbilt University, Nashville 37240, TN, USA
| | - Yuxiu Sui
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing 210000, Jiangsu, China.
| |
Collapse
|
2
|
Yin Y, Qiu X, Nie L, Wang F, Luo X, Zhao C, Yu H, Luo D, Wang J, Liu H. Individual-based morphological brain network changes in children with Rolandic epilepsy. Clin Neurophysiol 2024; 165:90-96. [PMID: 38991378 DOI: 10.1016/j.clinph.2024.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 04/09/2024] [Accepted: 06/15/2024] [Indexed: 07/13/2024]
Abstract
OBJECTIVE To investigate the local cortical morphology and individual-based morphological brain networks (MBNs) changes in children with Rolandic epilepsy (RE). METHODS Based on the structural MRI data of 56 children with RE and 56 healthy controls (HC), we constructed four types of individual-based MBNs using morphological indices (cortical thickness [CT], fractal dimension [FD], gyrification index [GI], and sulcal depth [SD]). The global and nodal properties of the brain networks were analyzed using graph theory. The between-group difference in local morphology and network topology was estimated, and partial correlation analysis was further analyzed. RESULTS Compared with the HC, children with RE showed regional GI increases in the right posterior cingulate gyrus and SD increases in the right anterior cingulate gyrus and medial prefrontal cortex. Regarding the network level, RE exhibited increased characteristic path length in CT-based and FD-based networks, while decreased FD-based network node efficiency in the right inferior frontal gyrus. No significant correlation between altered morphological features and clinical variables was found in RE. CONCLUSIONS These findings indicated that children with RE have disrupted morphological brain network organization beyond local morphology changes. SIGNIFICANCE The present study could provide more theoretical basis for exploring the neuropathological mechanisms in RE.
Collapse
Affiliation(s)
- Yu Yin
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi 563003, China; Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Xiaofan Qiu
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Lisha Nie
- GE Healthcare, MR Research China, Beijing, China
| | - Fuqin Wang
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi 563003, China
| | - Xinyu Luo
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi 563003, China
| | - Chunfeng Zhao
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi 563003, China
| | - Haoyue Yu
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi 563003, China
| | - Dan Luo
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi 563003, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Heng Liu
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi 563003, China.
| |
Collapse
|
3
|
Vickery S, Patil KR, Dahnke R, Hopkins WD, Sherwood CC, Caspers S, Eickhoff SB, Hoffstaedter F. The uniqueness of human vulnerability to brain aging in great ape evolution. SCIENCE ADVANCES 2024; 10:eado2733. [PMID: 39196942 PMCID: PMC11352902 DOI: 10.1126/sciadv.ado2733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 07/24/2024] [Indexed: 08/30/2024]
Abstract
Aging is associated with progressive gray matter loss in the brain. This spatially specific, morphological change over the life span in humans is also found in chimpanzees, and the comparison between these great ape species provides a unique evolutionary perspective on human brain aging. Here, we present a data-driven, comparative framework to explore the relationship between gray matter atrophy with age and recent cerebral expansion in the phylogeny of chimpanzees and humans. In humans, we show a positive relationship between cerebral aging and cortical expansion, whereas no such relationship was found in chimpanzees. This human-specific association between strong aging effects and large relative cortical expansion is particularly present in higher-order cognitive regions of the ventral prefrontal cortex and supports the "last-in-first-out" hypothesis for brain maturation in recent evolutionary development of human faculties.
Collapse
Affiliation(s)
- Sam Vickery
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Center Jülich, Jülich, Germany
- Division of Physiotherapy, Department of Applied Health Sciences, Hochschule für Gesundheit (University of Applied Sciences), Bochum, Germany
| | - Kaustubh R. Patil
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Center Jülich, Jülich, Germany
| | - Robert Dahnke
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany
- Structural Brain Mapping Group, Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - William D. Hopkins
- Department of Comparative Medicine, Michale E. Keeling Center for Comparative Medicine and Research, The University of Texas MD Anderson Cancer Center, Bastrop, TX, USA
| | - Chet C. Sherwood
- Department of Anthropology and Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, DC, USA
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
| | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Center Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Center Jülich, Jülich, Germany
| |
Collapse
|
4
|
Cheng Y, Lin L, Hou W, Qiu H, Deng C, Yan Z, Qian L, Cui W, Li Y, Yang Z, Chen Q, Su S. Altered individual-level morphological similarity network in children with growth hormone deficiency. J Neurodev Disord 2024; 16:48. [PMID: 39187797 PMCID: PMC11346214 DOI: 10.1186/s11689-024-09566-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 08/12/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND Accumulating evidences indicate regional grey matter (GM) morphology alterations in pediatric growth hormone deficiency (GHD); however, large-scale morphological brain networks (MBNs) undergo these patients remains unclear. OBJECTIVE To investigate the topological organization of individual-level MBNs in pediatric GHD. METHODS Sixty-one GHD and 42 typically developing controls (TDs) were enrolled. Inter-regional morphological similarity of GM was taken to construct individual-level MBNs. Between-group differences of topological parameters and network-based statistics analysis were compared. Finally, association relationship between network properties and clinical variables was analyzed. RESULTS Compared to TDs, GHD indicated a disturbance in the normal small-world organization, reflected by increased Lp, γ, λ, σ and decreased Cp, Eglob (all PFDR < 0.017). Regarding nodal properties, GHD exhibited increased nodal profiles at cerebellum 4-5, central executive network-related left inferior frontal gyrus, limbic regions-related right posterior cingulate gyrus, left hippocampus, and bilateral pallidum, thalamus (all PFDR < 0.05). Meanwhile, GHD exhibited decreased nodal profiles at sensorimotor network -related bilateral paracentral lobule, default-mode network-related left superior frontal gyrus, visual network -related right lingual gyrus, auditory network-related right superior temporal gyrus and bilateral amygdala, right cerebellum 3, bilateral cerebellum 10, vermis 1-2, 3, 4-5, 6 (all PFDR < 0.05). Furthermore, serum markers and behavior scores in GHD group were correlated with altered nodal profiles (P ≤ 0.046, uncorrected). CONCLUSION GHD undergo an extensive reorganization in large-scale individual-level MBNs, probably due to abnormal cortico-striatal-thalamo-cerebellum loops, cortico-limbic-cerebellum, dorsal visual-sensorimotor-striatal, and auditory-cerebellum circuitry. This study highlights the crucial role of abnormal morphological connectivity underlying GHD, which might result in their relatively slower development in motor, cognitive, and linguistic functional within behavior problem performance.
Collapse
Affiliation(s)
- Yanglei Cheng
- Department of Endocrine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Liping Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Weifeng Hou
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huaqiong Qiu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chengfen Deng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zi Yan
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Long Qian
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China
| | - Wei Cui
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China
| | - Yanbing Li
- Department of Endocrine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhiyun Yang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qiuli Chen
- Department of Pediatric, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Shu Su
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
| |
Collapse
|
5
|
Fang K, Hou Y, Niu L, Han S, Zhang W. Individualized gray matter morphological abnormalities uncover two robust transdiagnostic biotypes. J Affect Disord 2024; 365:S0165-0327(24)01324-7. [PMID: 39173920 DOI: 10.1016/j.jad.2024.08.102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 07/22/2024] [Accepted: 08/19/2024] [Indexed: 08/24/2024]
Abstract
Psychiatric disorders exhibit a shared neuropathology, yet the diverse presentations among patients necessitate the identification of transdiagnostic subtypes to enhance diagnostic and treatment strategies. This study aims to unveil potential transdiagnostic subtypes based on personalized gray matter morphological abnormalities. A total of 496 patients with psychiatric disorders and 255 healthy controls (HCs) from three distinct datasets (one for discovery and two for validation) were enrolled. Individualized gray matter morphological abnormalities were determined using normative modeling to identify transdiagnostic subtypes. In the discovery dataset, two transdiagnostic subtypes with contrasting patterns of structural abnormalities compared to HCs were identified. Reproducibility and generalizability analyses demonstrated that these subtypes could be generalized to new patients and even to new disorders in the validation datasets. These subtypes were characterized by distinct disease epicenters. The gray matter abnormal pattern in subtype 1 was mainly linked to excitatory receptors, whereas subtype 2 showed a predominant association with inhibitory receptors. Furthermore, we observed that the gray matter abnormal pattern in subtype 2 was correlated with transcriptional profiles of inflammation-related genes, while subtype 1 did not show this association. Our findings reveal two robust transdiagnostic biotypes, offering novel insights into psychiatric nosology.
Collapse
Affiliation(s)
- Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, China; Henan Engineering Research Center for Tumor Precision Medicine and Comprehensive Evaluation, Henan Cancer Hospital, China; Henan Provincial Key Laboratory of Anticancer Drug Research, Henan Cancer Hospital, China
| | - Ying Hou
- Department of ultrasound, the affiliated cancer hospital of Zhengzhou University & Henan Cancer Hospital, China
| | - Lianjie Niu
- Department of Breast Disease, Henan Breast Cancer Center, the affiliated Cancer Hidospital of Zhengzhou University & Henan Cancer Hospital, China.
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Henan Province, China.
| | - Wenzhou Zhang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, China; Henan Engineering Research Center for Tumor Precision Medicine and Comprehensive Evaluation, Henan Cancer Hospital, China; Henan Provincial Key Laboratory of Anticancer Drug Research, Henan Cancer Hospital, China.
| |
Collapse
|
6
|
Shen J, Zhang Y, Zhu Z, Cheng Y, Cai B, Zhao Y, Zhao H. Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation. Neuroimage 2024; 297:120739. [PMID: 39009250 DOI: 10.1016/j.neuroimage.2024.120739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 07/07/2024] [Accepted: 07/12/2024] [Indexed: 07/17/2024] Open
Abstract
Heritability and genetic covariance/correlation quantify the marginal and shared genetic effects across traits. They offer insights on the genetic architecture of complex traits and diseases. To explore how genetic variations contribute to brain function variations, we estimated heritability and genetic correlation across cortical thickness, surface area, and volume of 33 anatomically predefined regions in left and right hemispheres, using summary statistics of genome-wide association analyses of 31,968 participants in the UK Biobank. To characterize the relationships between these regions of interest, we constructed a genetic network for these regions using recursive two-way cut-offs in similarity matrices defined by genetic correlations. The inferred genetic network matches the brain lobe mapping more closely than the network inferred from phenotypic similarities. We further studied the associations between the genetic network for brain regions and 30 complex traits through a novel composite-linkage disequilibrium score regression method. We identified seven significant pairs, which offer insights on the genetic basis for regions of interest mediated by cortical measures.
Collapse
Affiliation(s)
- Jiangnan Shen
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Yiliang Zhang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Zhaohan Zhu
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Youshu Cheng
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Biao Cai
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA; Department of Management Sciences, City University of Hong Kong, Hong Kong S.A.R, China
| | - Yize Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA; Department of Genetics, Yale University School of Medicine, New Haven, CT, USA; Program of Computational Biology and Biomedical Informatics, Yale University, New Haven, CT, USA.
| |
Collapse
|
7
|
Li H, Liu M, Zhang J, Liu S, Fang Z, Pan M, Sui X, Rang W, Xiao H, Jiang Y, Zheng Y, Ge X. The effect of preterm birth on thalamic development based on shape and structural covariance analysis. Neuroimage 2024; 297:120708. [PMID: 38950664 DOI: 10.1016/j.neuroimage.2024.120708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 05/31/2024] [Accepted: 06/24/2024] [Indexed: 07/03/2024] Open
Abstract
Acting as a central hub in regulating brain functions, the thalamus plays a pivotal role in controlling high-order brain functions. Considering the impact of preterm birth on infant brain development, traditional studies focused on the overall development of thalamus other than its subregions. In this study, we compared the volumetric growth and shape development of the thalamic hemispheres between the infants born preterm and full-term (Left volume: P = 0.027, Left normalized volume: P < 0.0001; Right volume: P = 0.070, Right normalized volume: P < 0.0001). The ventral nucleus region, dorsomedial nucleus region, and posterior nucleus region of the thalamus exhibit higher vulnerability to alterations induced by preterm birth. The structural covariance (SC) between the thickness of thalamus and insula in preterm infants (Left: corrected P = 0.0091, Right: corrected P = 0.0119) showed significant increase as compared to full-term controls. Current findings suggest that preterm birth affects the development of the thalamus and has differential effects on its subregions. The ventral nucleus region, dorsomedial nucleus region, and posterior nucleus region of the thalamus are more susceptible to the impacts of preterm birth.
Collapse
Affiliation(s)
- Hongzhuang Li
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Mengting Liu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
| | - Jianfeng Zhang
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Shujuan Liu
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Zhicong Fang
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Minmin Pan
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Xiaodan Sui
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Wei Rang
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Hang Xiao
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Yanyun Jiang
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Shandong, China.
| | - Xinting Ge
- School of Information Science and Engineering, Shandong Normal University, Shandong, China.
| |
Collapse
|
8
|
Ding H, Zhang Y, Xie Y, Du X, Ji Y, Lin L, Chang Z, Zhang B, Liang M, Yu C, Qin W. Individualized Texture Similarity Network in Schizophrenia. Biol Psychiatry 2024; 96:176-187. [PMID: 38218309 DOI: 10.1016/j.biopsych.2023.12.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/14/2023] [Accepted: 12/23/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND Structural covariance network disruption has been considered an important pathophysiological indicator for schizophrenia. Here, we introduced a novel individualized structural covariance network measure, referred to as a texture similarity network (TSN), and hypothesized that the TSN could reliably reveal unique intersubject heterogeneity and complex dysconnectivity patterns in schizophrenia. METHODS The TSN was constructed by measuring the covariance of 180 three-dimensional voxelwise gray-level co-occurrence matrix feature maps between brain areas in each participant. We first tested the validity and reproducibility of the TSN in characterizing the intersubject variability in 2 longitudinal test-retest healthy cohorts. The TSN was further applied to elucidate intersubject variability and dysconnectivity patterns in 10 schizophrenia case-control datasets (609 schizophrenia cases vs. 579 controls) as well as in a first-episode depression dataset (69 patients with depression vs. 69 control participants). RESULTS The test-retest analysis demonstrated higher TSN intersubject than intrasubject variability. Moreover, the TSN reliably revealed higher intersubject variability in both chronic and first-episode schizophrenia, but not in depression. The TSN also reproducibly detected coexistent increased and decreased TSN strength in widespread brain areas, increased global small-worldness, and the coexistence of both structural hyposynchronization in the central networks and hypersynchronization in peripheral networks in patients with schizophrenia but not in patients with depression. Finally, aberrant intersubject variability and covariance strength patterns revealed by the TSN showed a missing or weak correlation with other individualized structural covariance network measures, functional connectivity, and regional volume changes. CONCLUSIONS These findings support the reliability of a TSN in revealing unique structural heterogeneity and complex dysconnectivity in patients with schizophrenia.
Collapse
Affiliation(s)
- Hao Ding
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China; School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Yu Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaotong Du
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yi Ji
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Liyuan Lin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhongyu Chang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Bin Zhang
- Tianjin Anding Hospital, Tianjin Medical University, Tianjin, China; Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China; School of Medical Imaging, Tianjin Medical University, Tianjin, China.
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
| |
Collapse
|
9
|
Huang S, Li M, Huang C, Liu J. Acute limbic system connectivity predicts chronic cognitive function in mild traumatic brain injury: An individualized differential structural covariance network study. Pharmacol Res 2024; 206:107274. [PMID: 38906205 DOI: 10.1016/j.phrs.2024.107274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 06/16/2024] [Accepted: 06/18/2024] [Indexed: 06/23/2024]
Abstract
Mild traumatic brain injury (mTBI) is a known risk factor for neurodegenerative diseases, yet the precise pathophysiological mechanisms remain poorly understand, often obscured by group-level analysis in non-invasive neuroimaging studies. Individual-based method is critical to exploring heterogeneity in mTBI. We recruited 80 mTBI patients and 40 matched healthy controls, obtaining high-resolution structural MRI for constructing Individual Differential Structural Covariance Networks (IDSCN). Comparisons were conducted at both the individual and group levels. Connectome-based Predictive Modeling (CPM) was applied to predict cognitive performance based on whole-brain connectivity. During the acute stage of mTBI, patients exhibited significant heterogeneity in the count and direction of altered edges, obscured by group-level analysis. In the chronic stage, the number of altered edges decreased and became more consistent, aligning with clinical observations of acute cognitive impairment and gradual improvement. Subgroup analysis based on loss of consciousness/post-traumatic amnesia revealed distinct patterns of alterations. The temporal lobe, particularly regions related to the limbic system, significantly predicted cognitive function from acute to chronic stage. The use of IDSCN and CPM has provided valuable individual-level insights, reconciling discrepancies from previous studies. Additionally, the limbic system may be an appropriate target for future intervention efforts.
Collapse
Affiliation(s)
- Sihong Huang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China
| | - Mengjun Li
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China
| | - Chuxin Huang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Department of Radiology Quality Control Center, Hunan Province, Changsha, Hunan 410011, China; Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan 410011, China.
| |
Collapse
|
10
|
Da Silveira RV, Magalhães TNC, Balthazar MLF, Castellano G. Differences between Alzheimer's disease and mild cognitive impairment using brain networks from magnetic resonance texture analysis. Exp Brain Res 2024; 242:1947-1955. [PMID: 38910159 DOI: 10.1007/s00221-024-06871-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 06/07/2024] [Indexed: 06/25/2024]
Abstract
Several studies have aimed at identifying biomarkers in the initial phases of Alzheimer's disease (AD). Conversely, texture features, such as those from gray-level co-occurrence matrices (GLCMs), have highlighted important information from several types of medical images. More recently, texture-based brain networks have been shown to provide useful information in characterizing healthy individuals. However, no studies have yet explored the use of this type of network in the context of AD. This work aimed to employ texture brain networks to investigate the distinction between groups of patients with amnestic mild cognitive impairment (aMCI) and mild dementia due to AD, and a group of healthy subjects. Magnetic resonance (MR) images from the three groups acquired at two instances were used. Images were segmented and GLCM texture parameters were calculated for each region. Structural brain networks were generated using regions as nodes and the similarity among texture parameters as links, and graph theory was used to compute five network measures. An ANCOVA was performed for each network measure to assess statistical differences between groups. The thalamus showed significant differences between aMCI and AD patients for four network measures for the right hemisphere and one network measure for the left hemisphere. There were also significant differences between controls and AD patients for the left hippocampus, right superior parietal lobule, and right thalamus-one network measure each. These findings represent changes in the texture of these regions which can be associated with the cortical volume and thickness atrophies reported in the literature for AD. The texture networks showed potential to differentiate between aMCI and AD patients, as well as between controls and AD patients, offering a new tool to help understand these conditions and eventually aid early intervention and personalized treatment, thereby improving patient outcomes and advancing AD research.
Collapse
Affiliation(s)
- Rafael Vinícius Da Silveira
- Department of Cosmic Rays and Chronology, Gleb Wataghin Physics Institute, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil.
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil.
| | - Thamires Naela Cardoso Magalhães
- Department of Neurology and Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - Marcio Luiz Figueredo Balthazar
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
- Department of Neurology and Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - Gabriela Castellano
- Department of Cosmic Rays and Chronology, Gleb Wataghin Physics Institute, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| |
Collapse
|
11
|
Yang X, Wang Z, Li H, Qin W, Liu N, Liu Z, Wang S, Xu J, Wang J. Polygenic Score for Conscientiousness Is a Protective Factor for Reversion from Mild Cognitive Impairment to Normal Cognition. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2309889. [PMID: 38838096 PMCID: PMC11304237 DOI: 10.1002/advs.202309889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 05/21/2024] [Indexed: 06/07/2024]
Abstract
Spontaneous reversion from mild cognitive impairment (MCI) to normal cognition (NC) is little known. Based on the data of the Genetics of Personality Consortium and MCI participants from Alzheimer's Disease Neuroimaging Initiative, the authors investigate the effect of polygenic scores (PGS) for personality traits on the reversion of MCI to NC and its underlying neurobiology. PGS analysis reveals that PGS for conscientiousness (PGS-C) is a protective factor that supports the reversion from MCI to NC. Gene ontology enrichment analysis and tissue-specific enrichment analysis indicate that the protective effect of PGS-C may be attributed to affecting the glutamatergic synapses of subcortical structures, such as hippocampus, amygdala, nucleus accumbens, and caudate nucleus. The structural covariance network (SCN) analysis suggests that the left whole hippocampus and its subfields, and the left whole amygdala and its subnuclei show significantly stronger covariance with several high-cognition relevant brain regions in the MCI reverters compared to the stable MCI participants, which may help illustrate the underlying neural mechanism of the protective effect of PGS-C.
Collapse
Affiliation(s)
- Xuan Yang
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
- Department of RadiologyJining No.1 People's HospitalJiningShandong272000P. R. China
| | - Zirui Wang
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Haonan Li
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Wen Qin
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Nana Liu
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Zhixuan Liu
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Siqi Wang
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Jiayuan Xu
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Junping Wang
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | | |
Collapse
|
12
|
Pontillo G, Cepas MB, Broeders TAA, Koubiyr I, Schoonheim MM. Network Analysis in Multiple Sclerosis and Related Disorders. Neuroimaging Clin N Am 2024; 34:375-384. [PMID: 38942522 DOI: 10.1016/j.nic.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease of the central nervous system, commonly featuring disability and cognitive impairment. The pathologic hallmark of MS lies in demyelination and hence impaired structural and functional neuronal pathways. Recent studies have shown that MS shows extensive structural disconnection of key network hub areas like the thalamus, combined with a functional network reorganization that can mostly be related to poorer clinical functioning. As MS can, therefore, be considered a network disorder, this review outlines recent innovations in the field of network neuroscience in MS.
Collapse
Affiliation(s)
- Giuseppe Pontillo
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands; MS Center Amsterdam, Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands.
| | - Mar Barrantes Cepas
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Tommy A A Broeders
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Ismail Koubiyr
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| |
Collapse
|
13
|
Zhang P, Wan X, Jiang J, Liu Y, Wang D, Ai K, Liu G, Zhang X, Zhang J. A causal effect study of cortical morphology and related covariate networks in classical trigeminal neuralgia patients. Cereb Cortex 2024; 34:bhae337. [PMID: 39123310 DOI: 10.1093/cercor/bhae337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/17/2024] [Accepted: 07/27/2024] [Indexed: 08/12/2024] Open
Abstract
Structural covariance networks and causal effects within can provide critical information on gray matter reorganization and disease-related hierarchical changes. Based on the T1WI data of 43 classical trigeminal neuralgia patients and 45 controls, we constructed morphological similarity networks of cortical thickness, sulcal depth, fractal dimension, and gyrification index. Moreover, causal structural covariance network analyses were conducted in regions with morphological abnormalities or altered nodal properties, respectively. We found that patients showed reduced sulcal depth, gyrification index, and fractal dimension, especially in the salience network and the default mode network. Additionally, the integration of the fractal dimension and sulcal depth networks was significantly reduced, accompanied by decreased nodal efficiency of the bilateral temporal poles, and right pericalcarine cortex within the sulcal depth network. Negative causal effects existed from the left insula to the right caudal anterior cingulate cortex in the gyrification index map, also from bilateral temporal poles to right pericalcarine cortex within the sulcal depth network. Collectively, patients exhibited impaired integrity of the covariance networks in addition to the abnormal gray matter morphology in the salience network and default mode network. Furthermore, the patients may experience progressive impairment in the salience network and from the limbic system to the sensory system in network topology, respectively.
Collapse
Affiliation(s)
- Pengfei Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, Sichuan 610041, China
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Xinyue Wan
- Department of Radiology, Huashan Hospital, Fudan University, No. 12, Urumqi Middle Road, Jingan District, Shanghai 200040, China
| | - Jingqi Jiang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Yang Liu
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Danyang Wang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Kai Ai
- Department of Clinical and Technical Supports, Philips Healthcare, No. 64 West Section, South 2nd Ring Road, Yanta District, Xi'an 710000, China
| | - Guangyao Liu
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Xinding Zhang
- Department of Neurosurgery and Laboratory of Neurosurgery, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
| | - Jing Zhang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| |
Collapse
|
14
|
Baboli R, Cao M, Martin E, Halperin JM, Wu K, Li X. Distinct structural brain network properties in children with familial versus non-familial attention-deficit/hyperactivity disorder (ADHD). Cortex 2024; 179:1-13. [PMID: 39089096 DOI: 10.1016/j.cortex.2024.06.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/12/2024] [Accepted: 06/17/2024] [Indexed: 08/03/2024]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is among the most prevalent, inheritable, and heterogeneous childhood-onset neurodevelopmental disorders. Children with a hereditary background of ADHD have heightened risk of having ADHD and persistent impairment symptoms into adulthood. These facts suggest distinct familial-specific neuropathological substrates in ADHD that may exist in anatomical components subserving attention and cognitive control processing pathways during development. The objective of this study is to investigate the topological properties of the gray matter (GM) structural brain networks in children with familial ADHD (ADHD-F), non-familial ADHD (ADHD-NF), as well as matched controls. A total of 452 participants were involved, including 132, 165 and 155 in groups of ADHD-F, ADHD-NF and typically developed children, respectively. The GM structural brain network was constructed for each group using graph theoretical techniques with cortical and subcortical structures as nodes and correlations between volume of each pair of the nodes within each group as edges, while controlled for confounding factors using regression analysis. Relative to controls, children in both ADHD-F and ADHD-NF groups showed significantly higher nodal global and nodal local efficiencies in the left caudal middle frontal gyrus. Compared to controls and ADHD-NF, children with ADHD-F showed distinct structural network topological patterns associated with right precuneus (significantly higher nodal global efficiency and significantly higher nodal strength), left paracentral gyrus (significantly higher nodal strength and trend toward significantly higher nodal local efficiency) and left putamen (significantly higher nodal global efficiency and trend toward significantly higher nodal local efficiency). Our results for the first time in the field provide evidence of familial-specific structural brain network alterations in ADHD, that may contribute to distinct clinical/behavioral symptomology and developmental trajectories in children with ADHD-F.
Collapse
Affiliation(s)
- Rahman Baboli
- Department of Biomedical Engineering, New Jersey Institute of Technology, NJ, USA; Graduate School of Biomedical Sciences, Rutgers University, Newark, NJ, USA
| | - Meng Cao
- Department of Biomedical Engineering, New Jersey Institute of Technology, NJ, USA; Graduate School of Biomedical Sciences, Rutgers University, Newark, NJ, USA
| | - Elizabeth Martin
- Department of Biomedical Engineering, New Jersey Institute of Technology, NJ, USA
| | - Jeffrey M Halperin
- Department of Psychology, Queens College, City University of New York, NY, USA
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, NJ, USA; Department of Electrical and Computer Engineering, New Jersey Institute of Technology, NJ, USA.
| |
Collapse
|
15
|
Kozol RA, Canavan A, Tolentino B, Keene AC, Kowalko JE, Duboué ER. Evolution of a central dopamine circuit underlies adaptation of light-evoked sensorimotor response in the blind cavefish, Astyanax mexicanus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.25.605141. [PMID: 39091880 PMCID: PMC11291158 DOI: 10.1101/2024.07.25.605141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Adaptive behaviors emerge in novel environments through functional changes in neural circuits. While relationships between circuit function and behavior have been well studied, how evolution shapes those circuits and leads to behavioral adpation is poorly understood. The Mexican cavefish , Astyanax mexicanus , provides a unique genetically amendable model system, equipped with above ground eyed surface fish and multiple evolutionarily divergent populations of blind cavefish that have evolved in complete darkness. These differences in environment and vision provide an opprotunity to examine how a neural circuit is functionally influenced by the presence of light. Here, we examine differences in the detection, and behavioral response induced by non visual light reception. Both populations exhibit photokinetic behavior, with surface fish becoming hyperactive following sudden darkness and cavefish becoming hyperactive following sudden illumination. To define these photokinetic neural circuits, we integrated whole brain functional imaging with our Astyanax brain atlas for surface and cavefish responding to light changes. We identified the caudal posterior tuberculum as the central modulator for both light or dark stimulated photokinesis. To unconver how spatiotemporal neuronal activity differed between surface fish and cavefish, we used stable pan-neuronal GCaMP Astyanax transgenics to show that a subpopulation of darkness sensitve neurons in surface fish are now light senstive in cavefish. Further functional analysis revealed that this integrative switch is dependent on dopmane signaling, suggesting a key role for dopamine and a highly conserved dopamine circuit in modulating the evolution of a circuit driving an essential behavior. Together, these data shed light into how neural circuits evolved to adapte to novel settings, and reveal the power of Astyanax as a model to elucidate mechanistic ingiths underlying sensory adaptation. Abstract Figure
Collapse
|
16
|
Sheng Y, Wang Y, Wang X, Zhang Z, Zhu D, Zheng W. No sex difference in maturation of brain morphology during the perinatal period. Brain Struct Funct 2024:10.1007/s00429-024-02828-x. [PMID: 39020216 DOI: 10.1007/s00429-024-02828-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 07/04/2024] [Indexed: 07/19/2024]
Abstract
Accumulating evidence have documented sex differences in brain anatomy from early childhood to late adulthood. However, whether sex difference of brain structure emerges in the neonatal brain and how sex modulates the development of cortical morphology during the perinatal stage remains unclear. Here, we utilized T2-weighted MRI from the Developing Human Connectome Project (dHCP) database, consisting of 41 male and 40 female neonates born between 35 and 43 postmenstrual weeks (PMW). Neonates of each sex were arranged in a continuous ascending order of age to capture the progressive changes in cortical thickness and curvature throughout the developmental continuum. The maturational covariance network (MCN) was defined as the coupled developmental fluctuations of morphology measures between cortical regions. We constructed MCNs based on the two features, respectively, to illustrate their developmental interdependencies, and then compared the network topology between sexes. Our results showed that cortical structural development exhibited a localized pattern in both males and females, with no significant sex differences in the developmental trajectory of cortical morphology, overall organization, nodal importance, and modular structure of the MCN. Furthermore, by merging male and female neonates into a unified cohort, we identified evident dependencies influences in structural development between different brain modules using the Granger causality analysis (GCA), emanating from high-order regions toward primary cortices. Our findings demonstrate that the maturational pattern of cortical morphology may not differ between sexes during the perinatal period, and provide evidence for the developmental causality among cortical structures in perinatal brains.
Collapse
Affiliation(s)
- Yucen Sheng
- School of Foreign Languages, Lanzhou Jiaotong University, Lanzhou, People's Republic of China
| | - Ying Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Xiaomin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Zhe Zhang
- Institute of Brain Science, Hangzhou Normal University, Hangzhou, People's Republic of China
| | - Dalin Zhu
- Department of Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital Lanzhou, Lanzhou, People's Republic of China.
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China.
| |
Collapse
|
17
|
Wang Y, Zhu D, Zhao L, Wang X, Zhang Z, Hu B, Wu D, Zheng W. Profiling cortical morphometric similarity in perinatal brains: Insights from development, sex difference, and inter-individual variation. Neuroimage 2024; 295:120660. [PMID: 38815676 DOI: 10.1016/j.neuroimage.2024.120660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/17/2024] [Accepted: 05/28/2024] [Indexed: 06/01/2024] Open
Abstract
The topological organization of the macroscopic cortical networks important for the development of complex brain functions. However, how the cortical morphometric organization develops during the third trimester and whether it demonstrates sexual and individual differences at this particular stage remain unclear. Here, we constructed the morphometric similarity network (MSN) based on morphological and microstructural features derived from multimodal MRI of two independent cohorts (cross-sectional and longitudinal) scanned at 30-44 postmenstrual weeks (PMW). Sex difference and inter-individual variations of the MSN were also examined on these cohorts. The cross-sectional analysis revealed that both network integration and segregation changed in a nonlinear biphasic trajectory, which was supported by the results obtained from longitudinal analysis. The community structure showed remarkable consistency between bilateral hemispheres and maintained stability across PMWs. Connectivity within the primary cortex strengthened faster than that within high-order communities. Compared to females, male neonates showed a significant reduction in the participation coefficient within prefrontal and parietal cortices, while their overall network organization and community architecture remained comparable. Furthermore, by using the morphometric similarity as features, we achieved over 65 % accuracy in identifying an individual at term-equivalent age from images acquired after birth, and vice versa. These findings provide comprehensive insights into the development of morphometric similarity throughout the perinatal cortex, enhancing our understanding of the establishment of neuroanatomical organization during early life.
Collapse
Affiliation(s)
- Ying Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Dalin Zhu
- Department of Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Leilei Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiaomin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhe Zhang
- Institute of Brain Science, Hangzhou Normal University, Hangzhou, China; School of Physics, Hangzhou Normal University, Hangzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; School of Medical Technology, Beijing Institute of Technology, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| |
Collapse
|
18
|
Duan H, Shi R, Kang J, Banaschewski T, Bokde ALW, Büchel C, Desrivières S, Flor H, Grigis A, Garavan H, Gowland PA, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Papadopoulos Orfanos D, Poustka L, Hohmann S, Holz N, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Lin X, Feng J. Population clustering of structural brain aging and its association with brain development. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.09.24301030. [PMID: 38260410 PMCID: PMC10802651 DOI: 10.1101/2024.01.09.24301030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Structural brain aging has demonstrated strong inter-individual heterogeneity and mirroring patterns with brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, most of the existing research focused on the cross-sectional changes of brain aging. In this investigation, we present a data-driven approach that incorporate both cross-sectional changes and longitudinal trajectories of structural brain aging and identified two brain aging patterns among 37,013 healthy participants from UK Biobank. Participants with accelerated brain aging also demonstrated accelerated biological aging, cognitive decline and increased genetic susceptibilities to major neuropsychiatric disorders. Further, by integrating longitudinal neuroimaging studies from a multi-center adolescent cohort, we validated the "last in, first out" mirroring hypothesis and identified brain regions with manifested mirroring patterns between brain aging and brain development. Genomic analyses revealed risk loci and genes contributing to accelerated brain aging and delayed brain development, providing molecular basis for elucidating the biological mechanisms underlying brain aging and related disorders.
Collapse
Affiliation(s)
- Haojing Duan
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Runye Shi
- School of Data Science, Fudan University, Shanghai, China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | | | - Sylvane Desrivières
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, Vermont, USA
| | - Penny A Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Developmental Trajectories and Psychiatry", Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes; France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Developmental Trajectories and Psychiatry", Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- AP-HP. Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Developmental Trajectories and Psychiatry", Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes; France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein Kiel University, Kiel, Germany
| | | | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Nathalie Holz
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Nilakshi Vaidya
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität BerlinHumboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Gunter Schumann
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität BerlinHumboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Centre for Population Neuroscience and Stratified Medicine (PONS Centre), ISTBI, Fudan University, Shanghai, China
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Germany
| | - Xiaolei Lin
- School of Data Science, Fudan University, Shanghai, China
- Huashan Institute of Medicine, Huashan Hospital affiliated to Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
- School of Data Science, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| |
Collapse
|
19
|
Zhang S, Li P, Feng Q, Shen R, Zhou H, Zhao Z. Using individualized structural covariance networks to analyze the heterogeneity of cerebral small vessel disease with cognitive impairment. J Stroke Cerebrovasc Dis 2024; 33:107829. [PMID: 38901472 DOI: 10.1016/j.jstrokecerebrovasdis.2024.107829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 06/14/2024] [Accepted: 06/18/2024] [Indexed: 06/22/2024] Open
Abstract
BACKGROUND Cerebral small vessel disease (CSVD) includes vascular disorders characterized by heterogeneous pathomechanisms and different neuropathological clinical manifestations. Cognitive dysfunction in CSVD is associated with reductions in structural covariance networks (SCNs). A majority of research conducted on SCNs focused on group-level analysis. However, it is crucial to investigate the individualized variations in order to gain a better understanding of heterogeneous disorders such as CSVD. Therefore, this study aimed to utilize individualized differential structural covariance network (IDSCN) analysis to detect individualized structural covariance aberration. METHODS A total of 35 healthy controls and 33 CSVD patients with cognitive impairment participated in this investigation. Using the regional gray matter volume in their T1 images, the IDSCN was constructed for each participant. Finally, the differential structural covariance edges between the two groups were determined by comparing their IDSCN using paired-sample t-tests. On the basis of these differential edges, the two subtypes of cognitively impaired CSVD patients were identified. RESULTS The findings revealed that the differential structural covariance edges in CSVD patients with cognitive impairment showed a highly heterogeneous distribution, with the edges primarily cross-distributed between the occipital lobe (specifically inferior occipital gyrus and cuneus), temporal lobe (specifically superior temporal gyrus), and the cerebellum. To varying degrees, the inferior frontal gyrus and the superior parietal gyrus were also distributed. Subsequently, a correlation analysis was performed between the resulting differential edges and the cognitive scale scores. A significant negative association was observed between the cognitive scores and the differential edges distributed in the inferior frontal gyrus and inferior occipital gyrus, the superior temporal gyrus and inferior occipital gyrus, and within the temporal lobe. Particularly in the cognitive domain of attention, the two subtypes separated by differential edges exhibited differences in cognitive scale scores [Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA)]. The differential edges of the subtype 1, characterized by lower cognitive level, were mainly cross-distributed in the limbic lobe (specifically the cingulate gyrus and hippocampus), the parietal lobe (including the superior parietal gyrus and precuneus), and the cerebellum. In contrast, the differential edges of the subtype 2 with a relatively high level of cognition were distributed between the cuneus and the cerebellum. CONCLUSIONS The differential structural covariance was investigated between the healthy controls and the CSVD patients with cognitive impairment, showing that differential structural covariance existed between the two groups. The edge distributions in certain parts of the brain, such as cerebellum and occipital and temporal lobes, verified this. Significant associations were seen between cognitive scale scores and some of those differential edges .The two subtypes that differed in both differential edges and cognitive levels were also identified. The differential edges of subtype 1 with relatively lower cognitive levels were more distributed in the cingulate gyrus, hippocampus, superior parietal gyrus, and precuneus. This could potentially offer significant benefits in terms of accurate diagnosis and targeted treatment of heterogeneous disorders such as CSVD.
Collapse
Affiliation(s)
- Shiyu Zhang
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215000, China
| | - Ping Li
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215000, China
| | - Qian Feng
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215000, China
| | - Rong Shen
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215000, China
| | - Hua Zhou
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215000, China.
| | - Zhong Zhao
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215000, China.
| |
Collapse
|
20
|
Joo SW, Jo YT, Choi W, Kim SM, Yoo SY, Joe S, Lee J. Topological abnormalities of the morphometric similarity network of the cerebral cortex in schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:57. [PMID: 38886369 PMCID: PMC11183129 DOI: 10.1038/s41537-024-00477-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/30/2024] [Indexed: 06/20/2024]
Abstract
A morphometric similarity (MS) network can be constructed using multiple magnetic resonance imaging parameters of each cortical region. An MS network can be used to assess the similarity between cortical regions. Although MS networks can detect microstructural alterations and capture connections between histologically similar cortical areas, the influence of schizophrenia on the topological characteristics of MS networks remains unclear. We obtained T1- and diffusion-weighted images of 239 healthy controls and 190 individuals with schizophrenia to construct the MS network. Group comparisons of the mean MS of the cortical regions and subnetworks were performed. The strengths of the connections between the cortical regions and the global and nodal network indices were compared between the groups. Clinical associations with the network indices were tested using Spearman's rho. Compared with healthy controls, individuals with schizophrenia had significant group differences in the mean MS of several cortical regions and subnetworks. Individuals with schizophrenia had both superior and inferior strengths of connections between cortical regions compared with those of healthy controls. We observed regional abnormalities of the MS network in individuals with schizophrenia regarding lower centrality values of the pars opercularis, superior frontal, and superior temporal areas. Specific nodal network measures of the right pars opercularis and left superior temporal areas were associated with illness duration in individuals with schizophrenia. We identified regional abnormalities of the MS network in schizophrenia with the left superior temporal area possibly being a key region in topological organization and cortical connections.
Collapse
Affiliation(s)
- Sung Woo Joo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Tak Jo
- Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea
| | - Woohyeok Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sun Min Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - So Young Yoo
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Soohyun Joe
- Brain Laboratory, Department of Psychiatry, University of California San Diego, School of Medicine, San Diego, California, USA
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| |
Collapse
|
21
|
Song Z, Li H, Zhang Y, Zhu C, Jiang M, Song L, Wang Y, Ouyang M, Hu F, Zheng Q. s 2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer's disease solely from structural MRI. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01178-3. [PMID: 38869733 DOI: 10.1007/s10334-024-01178-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/19/2024] [Accepted: 06/04/2024] [Indexed: 06/14/2024]
Abstract
OBJECTIVE To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. METHODS A total of 3377 participants' sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s2MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy. RESULTS The s2MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (p < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation. CONCLUSION The s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization.
Collapse
Affiliation(s)
- Zhiwei Song
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Honglun Li
- Department of Radiology, Yantai Yuhuangding Hospital Affiliated with Qingdao University Medical College, Yantai, 264099, China
| | - Yiyu Zhang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Chuanzhen Zhu
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Minbo Jiang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Limei Song
- School of Medical Imaging, Weifang Medical University, Weifang, 261000, China
| | - Yi Wang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
- Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou, 423000, Hunan, China
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Fang Hu
- Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou, 423000, Hunan, China
| | - Qiang Zheng
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China.
| |
Collapse
|
22
|
Li L, Li Y, Li Z, Huang G, Liang Z, Zhang L, Wan F, Shen M, Han X, Zhang Z. Multimodal and hemispheric graph-theoretical brain network predictors of learning efficacy for frontal alpha asymmetry neurofeedback. Cogn Neurodyn 2024; 18:847-862. [PMID: 38826665 PMCID: PMC11143167 DOI: 10.1007/s11571-023-09939-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 12/29/2022] [Accepted: 01/31/2023] [Indexed: 02/23/2023] Open
Abstract
EEG neurofeedback using frontal alpha asymmetry (FAA) has been widely used for emotion regulation, but its effectiveness is controversial. Studies indicated that individual differences in neurofeedback training can be traced to neuroanatomical and neurofunctional features. However, they only focused on regional brain structure or function and overlooked possible neural correlates of the brain network. Besides, no neuroimaging predictors for FAA neurofeedback protocol have been reported so far. We designed a single-blind pseudo-controlled FAA neurofeedback experiment and collected multimodal neuroimaging data from healthy participants before training. We assessed the learning performance for evoked EEG modulations during training (L1) and at rest (L2), and investigated performance-related predictors based on a combined analysis of multimodal brain networks and graph-theoretical features. The main findings of this study are described below. First, both real and sham groups could increase their FAA during training, but only the real group showed a significant increase in FAA at rest. Second, the predictors during training blocks and at rests were different: L1 was correlated with the graph-theoretical metrics (clustering coefficient and local efficiency) of the right hemispheric gray matter and functional networks, while L2 was correlated with the graph-theoretical metrics (local and global efficiency) of the whole-brain and left the hemispheric functional network. Therefore, the individual differences in FAA neurofeedback learning could be explained by individual variations in structural/functional architecture, and the correlated graph-theoretical metrics of learning performance indices showed different laterality of hemispheric networks. These results provided insight into the neural correlates of inter-individual differences in neurofeedback learning. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-09939-x.
Collapse
Affiliation(s)
- Linling Li
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Yutong Li
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Zhaoxun Li
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Gan Huang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Zhen Liang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Li Zhang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Feng Wan
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | - Manjun Shen
- Department of Mental Health, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen 518060, China
| | - Xue Han
- Department of Mental Health, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen 518060, China
| | - Zhiguo Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518060, China
- Peng Cheng Laboratory, Shenzhen 518060, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen 518060, China
| |
Collapse
|
23
|
Solis-Urra P, Rodriguez-Ayllon M, Verdejo-Román J, Erickson KI, Verdejo-García A, Catena A, Ortega FB, Esteban-Cornejo I. Early life factors and structural brain network in children with overweight/obesity: The ActiveBrains project. Pediatr Res 2024; 95:1812-1817. [PMID: 38066249 DOI: 10.1038/s41390-023-02923-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 10/24/2023] [Accepted: 11/07/2023] [Indexed: 07/14/2024]
Abstract
BACKGROUND The aims of this study were to investigate the association of early life factors, including birth weight, birth length, and breastfeeding practices, with structural brain networks; and to test whether structural brain networks associated with early life factors were also associated with academic performance in children with overweight/obesity (OW/OB). METHOD 96 children with OW/OB aged 8-11 years (10.03 ± 1.16) from the ActiveBrains project were included. Early life factors were collected from birth records and reported by parents as weight, height, and months of breastfeeding. T1-weighted images were used to identify structural networks using a non-negative matrix factorization (NNMF) approach. Academic performance was evaluated by the Woodcock-Muñoz standardized test battery. RESULTS Birth weight and birth length were associated with seven networks involving the cerebellum, cingulate gyrus, occipital pole, and subcortical structures including hippocampus, caudate nucleus, putamen, pallidum, nucleus accumbens, and amygdala. No associations were found for breastfeeding practices. None of the networks linked to birth weight and birth length were linked to academic performance. CONCLUSIONS Birth weight and birth length, but not breastfeeding, were associated with brain structural networks in children with OW/OB. Thus, early life factors are related to brain networks, yet a link with academic performance was not observed. IMPACT Birth weight and birth length, but not breastfeeding, were associated with several structural brain networks involving the cerebellum, cingulate gyrus, occipital pole, and subcortical structures including hippocampus, caudate, putamen, pallidum, accumbens and amygdala in children with overweight/obesity, playing a role for a normal brain development. Despite no academic consequences, other behavioral consequences should be investigated. Interventions aimed at improving optimal intrauterine growth and development may be of importance to achieve a healthy brain later in life.
Collapse
Affiliation(s)
- Patricio Solis-Urra
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain.
- Servicio de Medicina Nuclear, Hospital Universitario Virgen de las Nieves, 18014, Granada, España.
- Faculty of Education and Social Sciences, Universidad Andres Bello, Viña del Mar, 2531015, Chile.
| | - Maria Rodriguez-Ayllon
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Juan Verdejo-Román
- Department of Personality, Assessment & Psychological Treatment, University of Granada, Granada, Spain
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain
| | - Kirk I Erickson
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
- AdventHealth Research Institute, Neuroscience, Orlando, FL, USA
| | - Antonio Verdejo-García
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Andrés Catena
- School of Psychology, University of Granada, Campus de Cartuja s/n, 18071, Granada, Spain
| | - Francisco B Ortega
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
| | - Irene Esteban-Cornejo
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain.
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain.
| |
Collapse
|
24
|
McWhinney SR, Hlinka J, Bakstein E, Dietze LMF, Corkum ELV, Abé C, Alda M, Alexander N, Benedetti F, Berk M, Bøen E, Bonnekoh LM, Boye B, Brosch K, Canales‐Rodríguez EJ, Cannon DM, Dannlowski U, Demro C, Diaz‐Zuluaga A, Elvsåshagen T, Eyler LT, Fortea L, Fullerton JM, Goltermann J, Gotlib IH, Grotegerd D, Haarman B, Hahn T, Howells FM, Jamalabadi H, Jansen A, Kircher T, Klahn AL, Kuplicki R, Lahud E, Landén M, Leehr EJ, Lopez‐Jaramillo C, Mackey S, Malt U, Martyn F, Mazza E, McDonald C, McPhilemy G, Meier S, Meinert S, Melloni E, Mitchell PB, Nabulsi L, Nenadić I, Nitsch R, Opel N, Ophoff RA, Ortuño M, Overs BJ, Pineda‐Zapata J, Pomarol‐Clotet E, Radua J, Repple J, Roberts G, Rodriguez‐Cano E, Sacchet MD, Salvador R, Savitz J, Scheffler F, Schofield PR, Schürmeyer N, Shen C, Sim K, Sponheim SR, Stein DJ, Stein F, Straube B, Suo C, Temmingh H, Teutenberg L, Thomas‐Odenthal F, Thomopoulos SI, Urosevic S, Usemann P, van Haren NEM, Vargas C, Vieta E, Vilajosana E, Vreeker A, Winter NR, Yatham LN, Thompson PM, Andreassen OA, Ching CRK, Hajek T. Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity. Hum Brain Mapp 2024; 45:e26682. [PMID: 38825977 PMCID: PMC11144951 DOI: 10.1002/hbm.26682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/21/2024] [Accepted: 03/28/2024] [Indexed: 06/04/2024] Open
Abstract
Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.
Collapse
Affiliation(s)
- Sean R. McWhinney
- Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada
| | - Jaroslav Hlinka
- Department of Complex SystemsInstitute of Computer Science, Czech Academy of SciencesPragueCzech Republic
- National Institute of Mental HealthKlecanyCzech Republic
| | - Eduard Bakstein
- National Institute of Mental HealthKlecanyCzech Republic
- Department of CyberneticsCzech Technical UniversityPragueCzech Republic
| | - Lorielle M. F. Dietze
- Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada
- Department of Medical NeuroscienceDalhousie UniversityHalifaxNova ScotiaCanada
| | | | - Christoph Abé
- Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden
| | - Martin Alda
- Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada
- National Institute of Mental HealthKlecanyCzech Republic
| | - Nina Alexander
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Francesco Benedetti
- Vita‐Salute San Raffaele UniversityMilanItaly
- Division of Neuroscience, Psychiatry and Psychobiology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Michael Berk
- Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon HealthDeakin UniversityGeelongVictoriaAustralia
| | - Erlend Bøen
- Unit for Psychosomatics/CL Outpatient Clinic for Adults, Division of Mental Health and AddictionOslo University HospitalOsloNorway
| | - Linda M. Bonnekoh
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Department of Child Adolescent Psychiatry and PsychotherapyUniversity of MünsterMünsterGermany
| | - Birgitte Boye
- Unit for Psychosomatics and C‐L Psychiatry for AdultsOslo University HospitalOsloNorway
- Department of Behavioural MedicineInstitute of Basic Medical Sciences, University of OsloOsloNorway
| | - Katharina Brosch
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
- Institute of Behavioral ScienceFeinstein Institutes for Medical ResearchManhassetNew YorkUSA
| | - Erick J. Canales‐Rodríguez
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain
- CIBERSAM, Instituto de Salud Carlos IIIBarcelonaSpain
| | - Dara M. Cannon
- Clinical Neuroimaging Laboratory, Galway Neuroscience CentreCollege of Medicine Nursing and Health Sciences, University of GalwayGalwayIreland
| | - Udo Dannlowski
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Caroline Demro
- Department of Psychiatry & Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Ana Diaz‐Zuluaga
- Research Group in Psychiatry GIPSI, Department of Psychiatry, Faculty of MedicineUniversidad de AntioquiaMedellinColombia
| | - Torbjørn Elvsåshagen
- Department of Behavioural MedicineInstitute of Basic Medical Sciences, University of OsloOsloNorway
- Institute of Clinical Medicine, Norwegian Centre for Mental Disorders Research (NORMENT)University of Oslo and Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of Neurology, Division of Clinical NeuroscienceOslo University HospitalOsloNorway
| | - Lisa T. Eyler
- Department of PsychiatryUniversity of California San DiegoLa JollaCaliforniaUSA
- Desert‐Pacific MIRECC, VA San Diego HealthcareSan DiegoCaliforniaUSA
| | - Lydia Fortea
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Instituto de Salud Carlos IIIUniversity of BarcelonaBarcelonaSpain
| | - Janice M. Fullerton
- Neuroscience Research AustraliaRandwickNew South WalesAustralia
- School of Biomedical Sciences, Faculty of Medicine & HealthUniversity of New South WalesSydneyNew South WalesAustralia
| | - Janik Goltermann
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Ian H. Gotlib
- Department of PsychologyStanford UniversityStanfordCaliforniaUSA
| | - Dominik Grotegerd
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Bartholomeus Haarman
- Department of PsychiatryUniversity Medical Center Groningen, University of GroningenGroningenThe Netherlands
| | - Tim Hahn
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Fleur M. Howells
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Hamidreza Jamalabadi
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Andreas Jansen
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
- Core‐Facility Brainimaging, Faculty of MedicineUniversity of MarburgGermany
| | - Tilo Kircher
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Anna Luisa Klahn
- Institute of Neuroscience and PhysiologySahlgrenska Academy at Gothenburg UniversityGothenburgSweden
| | | | - Elijah Lahud
- Department of Psychiatry & Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Mikael Landén
- Institute of Neuroscience and PhysiologySahlgrenska Academy at Gothenburg UniversityGothenburgSweden
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
| | - Elisabeth J. Leehr
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Carlos Lopez‐Jaramillo
- Research Group in Psychiatry GIPSI, Department of Psychiatry, Faculty of MedicineUniversidad de AntioquiaMedellinColombia
| | - Scott Mackey
- Department of PsychiatryUniversity of Vermont College of MedicineBurlingtonVermontUSA
| | - Ulrik Malt
- Unit for Psychosomatics/CL Outpatient Clinic for Adults, Division of Mental Health and AddictionOslo University HospitalOsloNorway
- Institute of Clinical Medicine, Department of NeurologyUniversity of OsloOsloNorway
| | - Fiona Martyn
- Clinical Neuroimaging Laboratory, Galway Neuroscience CentreCollege of Medicine Nursing and Health Sciences, University of GalwayGalwayIreland
| | - Elena Mazza
- Vita‐Salute San Raffaele UniversityMilanItaly
- Division of Neuroscience, Psychiatry and Psychobiology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, Galway Neuroscience CentreCollege of Medicine Nursing and Health Sciences, University of GalwayGalwayIreland
| | - Genevieve McPhilemy
- Clinical Neuroimaging Laboratory, Galway Neuroscience CentreCollege of Medicine Nursing and Health Sciences, University of GalwayGalwayIreland
| | - Sandra Meier
- Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada
| | - Susanne Meinert
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Institute for Translational NeuroscienceUniversity of MünsterMünsterGermany
| | - Elisa Melloni
- Vita‐Salute San Raffaele UniversityMilanItaly
- Division of Neuroscience, Psychiatry and Psychobiology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Philip B. Mitchell
- Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine & HealthUniversity of New South WalesSydneyNew South WalesAustralia
| | - Leila Nabulsi
- Clinical Neuroimaging Laboratory, Galway Neuroscience CentreCollege of Medicine Nursing and Health Sciences, University of GalwayGalwayIreland
| | - Igor Nenadić
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Robert Nitsch
- Institute for Translational NeuroscienceUniversity of MünsterMünsterGermany
| | - Nils Opel
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Department of Psychiatry and PsychotherapyJena University HospitalJenaGermany
- German Center for Mental Health (DZPG), Site Jena‐Magdeburg‐HalleGermany
| | - Roel A. Ophoff
- UCLA Center for Neurobehavioral GeneticsLos AngelesCaliforniaUSA
| | - Maria Ortuño
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | | | - Julian Pineda‐Zapata
- Research GroupInstituto de Alta Tecnología Médica, Ayudas diagnósticas SURAMedellinColombia
| | - Edith Pomarol‐Clotet
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain
- CIBERSAM, Instituto de Salud Carlos IIIBarcelonaSpain
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Instituto de Salud Carlos IIIUniversity of BarcelonaBarcelonaSpain
| | - Jonathan Repple
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Department of Psychiatry, Psychosomatic Medicine and PsychotherapyGoethe University Frankfurt, University HospitalFrankfurtGermany
| | - Gloria Roberts
- Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine & HealthUniversity of New South WalesSydneyNew South WalesAustralia
| | - Elena Rodriguez‐Cano
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain
- CIBERSAM, Instituto de Salud Carlos IIIBarcelonaSpain
| | - Matthew D. Sacchet
- Meditation Research Program, Department of PsychiatryMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain
- CIBERSAM, Instituto de Salud Carlos IIIBarcelonaSpain
| | - Jonathan Savitz
- Laureate Institute for Brain ResearchTulsaOklahomaUSA
- Oxley College of Health SciencesThe University of TulsaTulsaOklahomaUSA
| | - Freda Scheffler
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Peter R. Schofield
- Neuroscience Research AustraliaRandwickNew South WalesAustralia
- School of Biomedical Sciences, Faculty of Medicine & HealthUniversity of New South WalesSydneyNew South WalesAustralia
| | - Navid Schürmeyer
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Chen Shen
- Department of PsychologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Kang Sim
- West Region, Institute of Mental HealthSingaporeSingapore
- Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore
| | - Scott R. Sponheim
- Department of Psychiatry & Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA
- Minneapolis VA Health Care SystemMinneapolisMinnesotaUSA
| | - Dan J. Stein
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
- South African MRC Unit on Risk & Resilience in Mental DisordersUniversity of Cape TownCape TownSouth Africa
| | - Frederike Stein
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Benjamin Straube
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Henk Temmingh
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Lea Teutenberg
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | | | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Snezana Urosevic
- Department of Psychiatry & Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA
- Minneapolis VA Health Care SystemMinneapolisMinnesotaUSA
| | - Paula Usemann
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Neeltje E. M. van Haren
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
- Department of PsychiatryUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Cristian Vargas
- Research Group in Psychiatry GIPSI, Department of Psychiatry, Faculty of MedicineUniversidad de AntioquiaMedellinColombia
| | - Eduard Vieta
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Instituto de Salud Carlos III, Institute of NeuroscienceUniversity of Barcelona, Hospital ClínicBarcelonaSpain
| | - Enric Vilajosana
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - Annabel Vreeker
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
- Department of Psychology, Education and Child StudiesErasmus University RotterdamRotterdamThe Netherlands
| | - Nils R. Winter
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | | | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Ole A. Andreassen
- Institute of Clinical Medicine, Norwegian Centre for Mental Disorders Research (NORMENT)University of Oslo and Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Tomas Hajek
- Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada
- National Institute of Mental HealthKlecanyCzech Republic
| |
Collapse
|
25
|
Li Y, Nie H, Xiang P, Shen W, Yan M, Yan C, Su S, Qian L, Liang Y, Tang W, Yang Z, Li Y, Chen Y. Disrupted individual-level morphological brain network in spinal muscular atrophy types 2 and 3. CNS Neurosci Ther 2024; 30:e14804. [PMID: 38887183 PMCID: PMC11183166 DOI: 10.1111/cns.14804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 05/27/2024] [Accepted: 05/30/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND AND OBJECTIVE Spinal muscular atrophy (SMA) is one of the most common monogenic neuromuscular diseases, and the pathogenesis mechanisms, especially the brain network topological properties, remain unknown. This study aimed to use individual-level morphological brain network analysis to explore the brain neural network mechanisms in SMA. METHODS Individual-level gray matter (GM) networks were constructed by estimating the interregional similarity of GM volume distribution using both Kullback-Leibler divergence-based similarity (KLDs) and Jesen-Shannon divergence-based similarity (JSDs) measurements based on Automated Anatomical Labeling 116 and Hammersmith 83 atlases for 38 individuals with SMA types 2 and 3 and 38 age- and sex-matched healthy controls (HCs). The topological properties were analyzed by the graph theory approach and compared between groups by a nonparametric permutation test. Additionally, correlation analysis was used to assess the associations between altered topological metrics and clinical characteristics. RESULTS Compared with HCs, although global network topology remained preserved in individuals with SMA, brain regions with altered nodal properties mainly involved the right olfactory gyrus, right insula, bilateral parahippocampal gyrus, right amygdala, right thalamus, left superior temporal gyrus, left cerebellar lobule IV-V, bilateral cerebellar lobule VI, right cerebellar lobule VII, and vermis VII and IX. Further correlation analysis showed that the nodal degree of the right cerebellar lobule VII was positively correlated with the disease duration, and the right amygdala was negatively correlated with the Hammersmith Functional Motor Scale Expanded (HFMSE) scores. CONCLUSIONS Our findings demonstrated that topological reorganization may prioritize global properties over nodal properties, and disrupted topological properties in the cortical-limbic-cerebellum circuit in SMA may help to further understand the network pathogenesis underlying SMA.
Collapse
Affiliation(s)
- Yufen Li
- Department of RadiologyThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Huirong Nie
- Department of RadiologyThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Pei Xiang
- Department of RadiologyThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Wanqing Shen
- Department of Interventional OncologyThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Mengzhen Yan
- Department of Pediatric Intensive Care UnitThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Cui Yan
- Department of RadiologyThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Shu Su
- Department of RadiologyThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Long Qian
- Department of Biomedical Engineering, College of EngineeringPeking UniversityBeijingChina
| | - Yujian Liang
- Department of Pediatric Intensive Care UnitThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Wen Tang
- Department of Pediatric Intensive Care UnitThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Zhiyun Yang
- Department of RadiologyThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Yijuan Li
- Department of Pediatric Intensive Care UnitThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Yingqian Chen
- Department of RadiologyThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| |
Collapse
|
26
|
Dong C, Thalamuthu A, Jiang J, Mather KA, Sachdev PS, Wen W. Brain structural covariances in the ageing brain in the UK Biobank. Brain Struct Funct 2024; 229:1165-1177. [PMID: 38625555 PMCID: PMC11147885 DOI: 10.1007/s00429-024-02794-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 03/21/2024] [Indexed: 04/17/2024]
Abstract
The morphologic properties of brain regions co-vary or correlate with each other. Here we investigated the structural covariances of cortical thickness and subcortical volumes in the ageing brain, along with their associations with age and cognition, using cross-sectional data from the UK Biobank (N = 42,075, aged 45-83 years, 53% female). As the structural covariance should be estimated in a group of participants, all participants were divided into 84 non-overlapping, equal-sized age groups ranging from the youngest to the oldest. We examined 84 cortical thickness covariances and subcortical covariances. Our findings include: (1) there were significant differences in the variability of structural covariance in the ageing process, including an increased variance, and a decreased entropy. (2) significant enrichment in pairwise correlations between brain regions within the occipital lobe was observed in all age groups; (3) structural covariance in older age, especially after the age of around 64, was significantly different from that in the youngest group (median age 48 years); (4) sixty-two of the total 528 pairs of cortical thickness correlations and 10 of the total 21 pairs of subcortical volume correlations showed significant associations with age. These trends varied, with some correlations strengthening, some weakening, and some reversing in direction with advancing age. Additionally, as ageing was associated with cognitive decline, most of the correlations with cognition displayed an opposite trend compared to age associated patterns of correlations.
Collapse
Affiliation(s)
- Chao Dong
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia.
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
| | - Karen A Mather
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
- Neuropsychiatric Institute (NPI), Prince of Wales Hospital, Randwick, NSW, 2031, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
- Neuropsychiatric Institute (NPI), Prince of Wales Hospital, Randwick, NSW, 2031, Australia
| |
Collapse
|
27
|
Luo T, Zhang M, Li S, Situ M, Liu P, Wang M, Tao Y, Zhao S, Wang Z, Yang Y, Huang Y. Exome functional risk score and brain connectivity can predict social adaptability outcome of children with autism spectrum disorder in 4 years' follow up. Front Psychiatry 2024; 15:1384134. [PMID: 38818019 PMCID: PMC11137745 DOI: 10.3389/fpsyt.2024.1384134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 04/29/2024] [Indexed: 06/01/2024] Open
Abstract
Introduction Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder emerging in early childhood, with heterogeneous clinical outcomes across individuals. This study aims to recognize neuroimaging genetic factors associated with outcomes of ASD after a 4-year follow-up. Methods A total of 104 ASD children were included in this study; they underwent clinical assessments, MRI data acquisition, and the whole exome sequencing (WES). Exome functional risk score (EFRS) was calculated based on WES; and two modalities of brain connectivity were constructed based on MRI data, that is functional connectivity (FC) for functional MRI (fMRI), and individual differential structural covariance network (IDSCN) for structural MRI (sMRI), to explore the neuroimaging genetic biomarker of outcomes of ASD children. Results Regression analysis found EFRS predicts social adaptability at the 4-year follow-up (Y = -0.013X + 9.29, p = 0.003). We identified 19 pairs of FC associated with autism symptoms severity at follow-up, 10 pairs of FC and 4 pairs of IDSCN associated with social adaptability at follow-up, and 10 pairs of FC associated with ASD EFRS by support vector regression (SVR). Related brain regions with prognostic predictive effects are mainly distributed in superior frontal gyrus, occipital cortex, temporal cortex, parietal cortex, paracentral lobule, pallidum, and amygdala for FC, and temporal cortex, thalamus, and hippocampus for IDSCN. Mediation model showed that ASD EFRS affects the social communication of ASD children through the mediation of FC between left middle occipital gyrus and left pallidum (RMSEA=0.126, CMIN=80.66, DF=42, p< 0.001, CFI=0.867, AIC=152). Discussion Our findings underscore that both EFRS and brain connectivity can predict social adaptability, and that brain connectivity serving as mediator in the relationship of EFRS and behaviors of ASD, suggesting the intervention targets in the future clinical application.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Yi Huang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| |
Collapse
|
28
|
Haris EM, Bryant RA, Korgaonkar MS. Structural covariance, topological organization, and volumetric features of amygdala subnuclei in posttraumatic stress disorder. Neuroimage Clin 2024; 42:103619. [PMID: 38744025 PMCID: PMC11108976 DOI: 10.1016/j.nicl.2024.103619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/14/2024] [Accepted: 05/10/2024] [Indexed: 05/16/2024]
Abstract
The amygdala is divided into functional subnuclei which have been challenging to investigate due to functional magnetic resonance imaging (MRI) limitations in mapping small neural structures. Hence their role in the neurobiology of posttraumatic stress disorder (PTSD) remains poorly understood. Examination of covariance of structural MRI measures could be an alternate approach to circumvent this issue. T1-weighted anatomical scans from a 3 T scanner from non-trauma-exposed controls (NEC; n = 71, 75 % female) and PTSD participants (n = 67, 69 % female) were parcellated into 105 brain regions. Pearson's r partial correlations were computed for three and nine bilateral amygdala subnuclei and every other brain region, corrected for age, sex, and total brain volume. Pairwise correlation comparisons were performed to examine subnuclei covariance profiles between-groups. Graph theory was employed to investigate subnuclei network topology. Volumetric measures were compared to investigate structural changes. We found differences between amygdala subnuclei in covariance with the hippocampus for both groups, and additionally with temporal brain regions for the PTSD group. Network topology demonstrated the importance of the right basal nucleus in facilitating network communication only in PTSD. There were no between-group differences for any of the three structural metrics. These findings are in line with previous work that has failed to find structural differences for amygdala subnuclei between PTSD and controls. However, differences between amygdala subnuclei covariance profiles observed in our study highlight the need to investigate amygdala subnuclei functional connectivity in PTSD using higher field strength fMRI for better spatial resolution.
Collapse
Affiliation(s)
- Elizabeth M Haris
- Brain Dynamics Centre, Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW, Australia; School of Psychology, University of New South Wales, Sydney, Australia.
| | - Richard A Bryant
- Brain Dynamics Centre, Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW, Australia; School of Psychology, University of New South Wales, Sydney, Australia
| | - Mayuresh S Korgaonkar
- Brain Dynamics Centre, Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW, Australia; Discipline of Psychiatry, Sydney Medical School, Westmead, NSW, Australia; Department of Radiology, Western Sydney Local Health District, Westmead, NSW, Australia.
| |
Collapse
|
29
|
Huang Y, Wang N, Li W, Feng T, Zhang H, Fan X, Chen S, Wang Y, Shan Y, Wei P, Zhao G. Aberrant individual structure covariance network in patients with mesial temporal lobe epilepsy. Front Neurosci 2024; 18:1381385. [PMID: 38784092 PMCID: PMC11112066 DOI: 10.3389/fnins.2024.1381385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 04/08/2024] [Indexed: 05/25/2024] Open
Abstract
Objective Mesial temporal lobe epilepsy (mTLE) is a complex neurological disorder that has been recognized as a widespread global network disorder. The group-level structural covariance network (SCN) could reveal the structural connectivity disruption of the mTLE but could not reflect the heterogeneity at the individual level. Methods This study adopted a recently proposed individual structural covariance network (IDSCN) method to clarify the alternated structural covariance connection mode in mTLE and to associate IDSCN features with the clinical manifestations and regional brain atrophy. Results We found significant IDSCN abnormalities in the ipsilesional hippocampus, ipsilesional precentral gyrus, bilateral caudate, and putamen in mTLE patients than in healthy controls. Moreover, the IDSCNs of these areas were positively correlated with the gray matter atrophy rate. Finally, we identified several connectivities with weak associations with disease duration, frequency, and surgery outcome. Significance Our research highlights the role of hippo-thalamic-basal-cortical circuits in the pathophysiologic process of disrupted whole-brain morphological covariance networks in mTLE, and builds a bridge between brain-wide covariance network changes and regional brain atrophy.
Collapse
Affiliation(s)
- Yuda Huang
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Ningrui Wang
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
- Beijing Municipal Geriatric Medical Research Center, Beijing, China
| | - Wei Li
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Tao Feng
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Huaqiang Zhang
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Xiaotong Fan
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Sichang Chen
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Yihe Wang
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Yongzhi Shan
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Penghu Wei
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
- School of Public Health and Management, Ningxia Medical University, Yinchuan, China
- Clinical Research Center for Epilepsy Capital Medical University, Beijing, China
| | - Guoguang Zhao
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
- School of Public Health and Management, Ningxia Medical University, Yinchuan, China
- Clinical Research Center for Epilepsy Capital Medical University, Beijing, China
| |
Collapse
|
30
|
Cao HL, Meng YJ, Wei W, Li T, Li ML, Guo WJ. Altered individual gray matter structural covariance networks in early abstinence patients with alcohol dependence. Brain Imaging Behav 2024:10.1007/s11682-024-00888-5. [PMID: 38713331 DOI: 10.1007/s11682-024-00888-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2024] [Indexed: 05/08/2024]
Abstract
While alterations in cortical thickness have been widely observed in individuals with alcohol dependence, knowledge about cortical thickness-based structural covariance networks is limited. This study aimed to explore the topological disorganization of structural covariance networks based on cortical thickness at the single-subject level among patients with alcohol dependence. Structural imaging data were obtained from 61 patients with alcohol dependence during early abstinence and 59 healthy controls. The single-subject structural covariance networks were constructed based on cortical thickness data from 68 brain regions and were analyzed using graph theory. The relationships between network architecture and clinical characteristics were further investigated using partial correlation analysis. In the structural covariance networks, both patients with alcohol dependence and healthy controls displayed small-world topology. However, compared to controls, alcohol-dependent individuals exhibited significantly altered global network properties characterized by greater normalized shortest path length, greater shortest path length, and lower global efficiency. Patients exhibited lower degree centrality and nodal efficiency, primarily in the right precuneus. Additionally, scores on the Alcohol Use Disorder Identification Test were negatively correlated with the degree centrality and nodal efficiency of the left middle temporal gyrus. The results of this correlation analysis did not survive after multiple comparisons in the exploratory analysis. Our findings may reveal alterations in the topological organization of gray matter networks in alcoholism patients, which may contribute to understanding the mechanisms of alcohol addiction from a network perspective.
Collapse
Affiliation(s)
- Hai-Ling Cao
- Mental Health Center, West China Hospital, Sichuan University, No. 28 Dianxin South Street, Chengdu, Sichuan, 610041, China
| | - Ya-Jing Meng
- Mental Health Center, West China Hospital, Sichuan University, No. 28 Dianxin South Street, Chengdu, Sichuan, 610041, China
| | - Wei Wei
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310063, China
| | - Tao Li
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310063, China
| | - Ming-Li Li
- Mental Health Center, West China Hospital, Sichuan University, No. 28 Dianxin South Street, Chengdu, Sichuan, 610041, China.
| | - Wan-Jun Guo
- Mental Health Center, West China Hospital, Sichuan University, No. 28 Dianxin South Street, Chengdu, Sichuan, 610041, China.
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310063, China.
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China.
| |
Collapse
|
31
|
Dzialas V, Hoenig MC, Prange S, Bischof GN, Drzezga A, van Eimeren T. Structural underpinnings and long-term effects of resilience in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:94. [PMID: 38697984 PMCID: PMC11066097 DOI: 10.1038/s41531-024-00699-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 04/02/2024] [Indexed: 05/05/2024] Open
Abstract
Resilience in neuroscience generally refers to an individual's capacity to counteract the adverse effects of a neuropathological condition. While resilience mechanisms in Alzheimer's disease are well-investigated, knowledge regarding its quantification, neurobiological underpinnings, network adaptations, and long-term effects in Parkinson's disease is limited. Our study involved 151 Parkinson's patients from the Parkinson's Progression Marker Initiative Database with available Magnetic Resonance Imaging, Dopamine Transporter Single-Photon Emission Computed Tomography scans, and clinical information. We used an improved prediction model linking neuropathology to symptom severity to estimate individual resilience levels. Higher resilience levels were associated with a more active lifestyle, increased grey matter volume in motor-associated regions, a distinct structural connectivity network and maintenance of relative motor functioning for up to a decade. Overall, the results indicate that relative maintenance of motor function in Parkinson's patients may be associated with greater neuronal substrate, allowing higher tolerance against neurodegenerative processes through dynamic network restructuring.
Collapse
Affiliation(s)
- Verena Dzialas
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, 50937, Cologne, Germany
- University of Cologne, Faculty of Mathematics and Natural Sciences, 50923, Cologne, Germany
| | - Merle C Hoenig
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, 50937, Cologne, Germany
- Molecular Organization of the Brain, Institute for Neuroscience and Medicine II, Research Center Juelich, 52428, Juelich, Germany
| | - Stéphane Prange
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, 50937, Cologne, Germany
- Université de Lyon, Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR, 5229, Bron, France
| | - Gérard N Bischof
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, 50937, Cologne, Germany
- Molecular Organization of the Brain, Institute for Neuroscience and Medicine II, Research Center Juelich, 52428, Juelich, Germany
| | - Alexander Drzezga
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, 50937, Cologne, Germany
- Molecular Organization of the Brain, Institute for Neuroscience and Medicine II, Research Center Juelich, 52428, Juelich, Germany
- German Center for Neurodegenerative Diseases, 53127, Bonn, Germany
| | - Thilo van Eimeren
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, 50937, Cologne, Germany.
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, 50937, Cologne, Germany.
| |
Collapse
|
32
|
Shi L, Liu X, Zhang S, Zhou A. Association of gut microbiota with cerebral cortical thickness: A Mendelian randomization study. J Affect Disord 2024; 352:312-320. [PMID: 38382814 DOI: 10.1016/j.jad.2024.02.063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 02/11/2024] [Accepted: 02/16/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND The causal relationship between gut microbiota and cerebral cortex development remains unclear. We aimed to scrutinize the plausible causal impact of gut microbiota on cortical thickness via Mendelian randomization (MR) study. METHODS Genome-wide association study (GWAS) data for 196 gut microbiota phenotypes (N = 18,340) were obtained as exposures, and GWAS data for cortical thickness-related traits (N = 51,665) were selected as outcomes. Inverse variance weighted was used as the main estimate method. A series of sensitivity analyses was used to test the robustness of the estimates including Cochran's Q test, MR-Egger intercept analysis, Steiger filtering, scatter plot funnel plot and leave-one-out analysis. RESULTS Genetic prediction of high Bacillales (β = 0.005, P = 0.032) and Lactobacillales (β = 0.010, P = 0.012) abundance was associated with a potential increase in global cortical thickness. For specific functional brain subdivisions, genetically predicted order Lactobacillales would potentially increase the thickness of the fusiform (β = 0.014, P = 0.016) and supramarginal (β = 0.017, P = 0.003). Meanwhile, order Bacillales would increase the thickness of fusiform (β = 0.007, P = 0.039), insula (β = 0.011, P = 0.003), rostralanteriorcingulate (β = 0.014, P = 0.002) and supramarginal (β = 0.006, P = 0.043). No significant estimates of heterogeneity or pleiotropy were found. CONCLUSIONS Through MR studies, we discovered genetic prediction of the Lactobacillales and Bacillales orders potentially linked to cortical thickness, affirming gut microbiota may enhance brain structure. Genetically predicted supramarginal and fusiform may be potential targets.
Collapse
Affiliation(s)
- Lubo Shi
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing Digestive Disease Center Beijing, China
| | - Xiaoduo Liu
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Shutian Zhang
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing Digestive Disease Center Beijing, China.
| | - Anni Zhou
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing Digestive Disease Center Beijing, China.
| |
Collapse
|
33
|
Hoops D, Yee Y, Hammill C, Wong S, Manitt C, Bedell BJ, Cahill L, Lerch JP, Flores C, Sled JG. Disproportionate neuroanatomical effects of DCC haploinsufficiency in adolescence compared with adulthood: links to dopamine, connectivity, covariance, and gene expression brain maps in mice. J Psychiatry Neurosci 2024; 49:E157-E171. [PMID: 38692693 PMCID: PMC11068426 DOI: 10.1503/jpn.230106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 01/23/2024] [Accepted: 03/06/2024] [Indexed: 05/03/2024] Open
Abstract
BACKGROUND Critical adolescent neural refinement is controlled by the DCC (deleted in colorectal cancer) protein, a receptor for the netrin-1 guidance cue. We sought to describe the effects of reduced DCC on neuroanatomy in the adolescent and adult mouse brain. METHODS We examined neuronal connectivity, structural covariance, and molecular processes in a DCC-haploinsufficient mouse model, compared with wild-type mice, using new, custom analytical tools designed to leverage publicly available databases from the Allen Institute. RESULTS We included 11 DCC-haploinsufficient mice and 16 wild-type littermates. Neuroanatomical effects of DCC haploinsufficiency were more severe in adolescence than adulthood and were largely restricted to the mesocorticolimbic dopamine system. The latter finding was consistent whether we identified the regions of the mesocorticolimbic dopamine system a priori or used connectivity data from the Allen Brain Atlas to determine de novo where these dopamine axons terminated. Covariance analyses found that DCC haploinsufficiency disrupted the coordinated development of the brain regions that make up the mesocorticolimbic dopamine system. Gene expression maps pointed to molecular processes involving the expression of DCC, UNC5C (encoding DCC's co-receptor), and NTN1 (encoding its ligand, netrin-1) as underlying our structural findings. LIMITATIONS Our study involved a single sex (males) at only 2 ages. CONCLUSION The neuroanatomical phenotype of DCC haploinsufficiency described in mice parallels that observed in DCC-haploinsufficient humans. It is critical to understand the DCC-haploinsufficient mouse as a clinically relevant model system.
Collapse
Affiliation(s)
- Daniel Hoops
- From the Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ont. (Hoops, Yee, Hammill, Wong, Lerch, Sled); the Department of Medical Biophysics, University of Toronto, Ont. (Hoops, Yee, Lerch, Sled); the Department of Psychiatry, McGill University, Montréal, Que. (Hoops, Flores); the Douglas Mental Health University Institute, Montréal, Que. (Hoops, Manitt, Flores); the Department of Chemistry, Memorial University, St. John's, N.L. (Hoops, Cahill); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que. (Bedell, Flores); the Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, University of Oxford, U.K. (Lerch); the Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montréal, Que. (Flores)
| | - Yohan Yee
- From the Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ont. (Hoops, Yee, Hammill, Wong, Lerch, Sled); the Department of Medical Biophysics, University of Toronto, Ont. (Hoops, Yee, Lerch, Sled); the Department of Psychiatry, McGill University, Montréal, Que. (Hoops, Flores); the Douglas Mental Health University Institute, Montréal, Que. (Hoops, Manitt, Flores); the Department of Chemistry, Memorial University, St. John's, N.L. (Hoops, Cahill); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que. (Bedell, Flores); the Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, University of Oxford, U.K. (Lerch); the Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montréal, Que. (Flores)
| | - Christopher Hammill
- From the Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ont. (Hoops, Yee, Hammill, Wong, Lerch, Sled); the Department of Medical Biophysics, University of Toronto, Ont. (Hoops, Yee, Lerch, Sled); the Department of Psychiatry, McGill University, Montréal, Que. (Hoops, Flores); the Douglas Mental Health University Institute, Montréal, Que. (Hoops, Manitt, Flores); the Department of Chemistry, Memorial University, St. John's, N.L. (Hoops, Cahill); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que. (Bedell, Flores); the Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, University of Oxford, U.K. (Lerch); the Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montréal, Que. (Flores)
| | - Sammi Wong
- From the Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ont. (Hoops, Yee, Hammill, Wong, Lerch, Sled); the Department of Medical Biophysics, University of Toronto, Ont. (Hoops, Yee, Lerch, Sled); the Department of Psychiatry, McGill University, Montréal, Que. (Hoops, Flores); the Douglas Mental Health University Institute, Montréal, Que. (Hoops, Manitt, Flores); the Department of Chemistry, Memorial University, St. John's, N.L. (Hoops, Cahill); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que. (Bedell, Flores); the Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, University of Oxford, U.K. (Lerch); the Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montréal, Que. (Flores)
| | - Colleen Manitt
- From the Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ont. (Hoops, Yee, Hammill, Wong, Lerch, Sled); the Department of Medical Biophysics, University of Toronto, Ont. (Hoops, Yee, Lerch, Sled); the Department of Psychiatry, McGill University, Montréal, Que. (Hoops, Flores); the Douglas Mental Health University Institute, Montréal, Que. (Hoops, Manitt, Flores); the Department of Chemistry, Memorial University, St. John's, N.L. (Hoops, Cahill); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que. (Bedell, Flores); the Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, University of Oxford, U.K. (Lerch); the Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montréal, Que. (Flores)
| | - Barry J Bedell
- From the Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ont. (Hoops, Yee, Hammill, Wong, Lerch, Sled); the Department of Medical Biophysics, University of Toronto, Ont. (Hoops, Yee, Lerch, Sled); the Department of Psychiatry, McGill University, Montréal, Que. (Hoops, Flores); the Douglas Mental Health University Institute, Montréal, Que. (Hoops, Manitt, Flores); the Department of Chemistry, Memorial University, St. John's, N.L. (Hoops, Cahill); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que. (Bedell, Flores); the Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, University of Oxford, U.K. (Lerch); the Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montréal, Que. (Flores)
| | - Lindsay Cahill
- From the Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ont. (Hoops, Yee, Hammill, Wong, Lerch, Sled); the Department of Medical Biophysics, University of Toronto, Ont. (Hoops, Yee, Lerch, Sled); the Department of Psychiatry, McGill University, Montréal, Que. (Hoops, Flores); the Douglas Mental Health University Institute, Montréal, Que. (Hoops, Manitt, Flores); the Department of Chemistry, Memorial University, St. John's, N.L. (Hoops, Cahill); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que. (Bedell, Flores); the Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, University of Oxford, U.K. (Lerch); the Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montréal, Que. (Flores)
| | - Jason P Lerch
- From the Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ont. (Hoops, Yee, Hammill, Wong, Lerch, Sled); the Department of Medical Biophysics, University of Toronto, Ont. (Hoops, Yee, Lerch, Sled); the Department of Psychiatry, McGill University, Montréal, Que. (Hoops, Flores); the Douglas Mental Health University Institute, Montréal, Que. (Hoops, Manitt, Flores); the Department of Chemistry, Memorial University, St. John's, N.L. (Hoops, Cahill); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que. (Bedell, Flores); the Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, University of Oxford, U.K. (Lerch); the Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montréal, Que. (Flores)
| | - Cecilia Flores
- From the Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ont. (Hoops, Yee, Hammill, Wong, Lerch, Sled); the Department of Medical Biophysics, University of Toronto, Ont. (Hoops, Yee, Lerch, Sled); the Department of Psychiatry, McGill University, Montréal, Que. (Hoops, Flores); the Douglas Mental Health University Institute, Montréal, Que. (Hoops, Manitt, Flores); the Department of Chemistry, Memorial University, St. John's, N.L. (Hoops, Cahill); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que. (Bedell, Flores); the Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, University of Oxford, U.K. (Lerch); the Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montréal, Que. (Flores)
| | - John G Sled
- From the Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ont. (Hoops, Yee, Hammill, Wong, Lerch, Sled); the Department of Medical Biophysics, University of Toronto, Ont. (Hoops, Yee, Lerch, Sled); the Department of Psychiatry, McGill University, Montréal, Que. (Hoops, Flores); the Douglas Mental Health University Institute, Montréal, Que. (Hoops, Manitt, Flores); the Department of Chemistry, Memorial University, St. John's, N.L. (Hoops, Cahill); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que. (Bedell, Flores); the Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, University of Oxford, U.K. (Lerch); the Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montréal, Que. (Flores)
| |
Collapse
|
34
|
Han S, Fang K, Zheng R, Li S, Zhou B, Sheng W, Wen B, Liu L, Wei Y, Chen Y, Chen H, Cui Q, Cheng J, Zhang Y. Gray matter atrophy is constrained by normal structural brain network architecture in depression. Psychol Med 2024; 54:1318-1328. [PMID: 37947212 DOI: 10.1017/s0033291723003161] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
BACKGROUND There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression. METHODS In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis. RESULTS In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset. CONCLUSIONS These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.
Collapse
Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Shuying Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Baohong Wen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Liang Liu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Huafu Chen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| |
Collapse
|
35
|
Yan W, Tang S, Chen L, Lei T, Li H, Jiang Y, He M, Zhou L, Li Y, Zeng C, Li H. The thalamic covariance network is associated with cognitive deficits in patients with cerebral small vascular disease. Ann Clin Transl Neurol 2024; 11:1148-1159. [PMID: 38433494 DOI: 10.1002/acn3.52030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/15/2023] [Accepted: 01/11/2024] [Indexed: 03/05/2024] Open
Abstract
OBJECTIVE Abnormalities in the gray matter structure of cerebral small vessel disease (CSVD) have been observed throughout the brain. However, whether cortico-cortical connections exist between regions of gray matter atrophy in patients with CSVD has not been fully elucidated. This question was tested by comparing the gray matter covariance networks in CSVD patients with and without cognitive impairment (CI). METHODS We performed multivariate modeling of the gray matter volume measurements of 61 patients with CI (CSVD-CI), 85 patients without CI (CSVD-NC), and 108 healthy controls using source-based morphological analysis (SBM) to obtain gray matter structural covariance networks at the population level. Then, correlations between structural covariance networks and cognitive functions were analyzed in CSVD patients. Finally, a support vector machine (SVM) classifier was used with the gray matter covariance network as a classification feature to identify CI among the CSVD population. RESULTS The results of the analysis of all the subjects showed that compared with healthy controls, the expression of the thalamic covariance network, cerebellum covariance network, and calcarine cortex covariance network was reduced in patients with CSVD. Moreover, CSVD-CI patients showed a significant reduction in the expression of the thalamic covariance network, encompassing the thalamus and the parahippocampal gyrus, relative to CSVD-NC patients, which persisted after excluding CSVD patients with thalamic lacunes. In patients with CSVD, cognitive functions were positively correlated with measures of the thalamic covariance network. More than 80% of CSVD patients with CI were correctly identified by the SVM classifier. INTERPRETATION Our findings provide new evidence to explain the distribution state of gray matter reduction in CSVD patients, and the thalamic covariance network is the core region for early gray matter reduction during the development of CSVD disease, which is related to cognitive deficits. Reduced expression of thalamic covariance networks may provide a neuroimaging biomarker for the early identification of cognitive impairment in CSVD patients.
Collapse
Affiliation(s)
- Wei Yan
- Department of Radiology, Affilated Hospital of North Sichuan Medical College, NanChong, 637000, Sichuan, China
| | - Siwei Tang
- Department of Radiology, Affilated Hospital of North Sichuan Medical College, NanChong, 637000, Sichuan, China
| | - Li Chen
- Department of Radiology, Affilated Hospital of North Sichuan Medical College, NanChong, 637000, Sichuan, China
| | - Ting Lei
- Department of Radiology, Affilated Hospital of North Sichuan Medical College, NanChong, 637000, Sichuan, China
| | - Haiqing Li
- Department of Radiology, Affilated Hospital of North Sichuan Medical College, NanChong, 637000, Sichuan, China
| | - Yuxing Jiang
- Department of Radiology, Affilated Hospital of North Sichuan Medical College, NanChong, 637000, Sichuan, China
| | - Miao He
- Department of Radiology, Affilated Hospital of North Sichuan Medical College, NanChong, 637000, Sichuan, China
| | - Lijing Zhou
- Department of Radiology, Affilated Hospital of North Sichuan Medical College, NanChong, 637000, Sichuan, China
| | - Yajun Li
- Department of Radiology, Affilated Hospital of North Sichuan Medical College, NanChong, 637000, Sichuan, China
| | - Chen Zeng
- Department of Radiology, Affilated Hospital of North Sichuan Medical College, NanChong, 637000, Sichuan, China
| | - Hongjian Li
- Department of Radiology, Affilated Hospital of North Sichuan Medical College, NanChong, 637000, Sichuan, China
| |
Collapse
|
36
|
Long JY, Qin K, Pan N, Fan WL, Li Y. Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set. Br J Psychiatry 2024; 224:170-178. [PMID: 38602159 PMCID: PMC11039554 DOI: 10.1192/bjp.2024.41] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/20/2024] [Accepted: 02/11/2024] [Indexed: 04/12/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD. AIMS Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes. METHOD A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings. RESULTS Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms. CONCLUSIONS Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.
Collapse
Affiliation(s)
- Jing-Yi Long
- Wuhan Mental Health Center, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China; and Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China
| | - Kun Qin
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Nanfang Pan
- Huaxi Magnetic Resonance Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Wen-Liang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; and Department of Radiology, Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yi Li
- Wuhan Mental Health Center, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China; and Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China
| |
Collapse
|
37
|
Kim RY, Joo Y, Ha E, Hong H, Suh C, Shim Y, Lee H, Kim Y, Cho JH, Yoon S, Lyoo IK. Alterations in Brain Morphometric Networks and Their Relationship with Memory Dysfunction in Patients with Type 2 Diabetes Mellitus. Exp Neurobiol 2024; 33:107-117. [PMID: 38724480 PMCID: PMC11089400 DOI: 10.5607/en24005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 03/31/2024] [Accepted: 04/09/2024] [Indexed: 05/15/2024] Open
Abstract
Cognitive dysfunction, a significant complication of type 2 diabetes mellitus (T2DM), can potentially manifest even from the early stages of the disease. Despite evidence of global brain atrophy and related cognitive dysfunction in early-stage T2DM patients, specific regions vulnerable to these changes have not yet been identified. The study enrolled patients with T2DM of less than five years' duration and without chronic complications (T2DM group, n=100) and demographically similar healthy controls (control group, n=50). High-resolution T1-weighted magnetic resonance imaging data were subjected to independent component analysis to identify structurally significant components indicative of morphometric networks. Within these networks, the groups' gray matter volumes were compared, and distinctions in memory performance were assessed. In the T2DM group, the relationship between changes in gray matter volume within these networks and declines in memory performance was examined. Among the identified morphometric networks, the T2DM group exhibited reduced gray matter volumes in both the precuneus (Bonferroni-corrected p=0.003) and insular-opercular (Bonferroni-corrected p=0.024) networks relative to the control group. Patients with T2DM demonstrated significantly lower memory performance than the control group (p=0.001). In the T2DM group, reductions in gray matter volume in both the precuneus (r=0.316, p=0.001) and insular-opercular (r=0.199, p=0.047) networks were correlated with diminished memory performance. Our findings indicate that structural alterations in the precuneus and insular-opercular networks, along with memory dysfunction, can manifest within the first 5 years following a diagnosis of T2DM.
Collapse
Affiliation(s)
- Rye Young Kim
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
| | - Yoonji Joo
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
| | - Eunji Ha
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
| | - Haejin Hong
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
| | - Chaewon Suh
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
| | - Youngeun Shim
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Hyeonji Lee
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Yejin Kim
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Jae-Hyoung Cho
- Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
| | - Sujung Yoon
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, Korea
| | - In Kyoon Lyoo
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, Korea
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea
| |
Collapse
|
38
|
Lee S, Song Y, Hong H, Joo Y, Ha E, Shim Y, Hong SN, Kim J, Lyoo IK, Yoon S, Kim DW. Changes in Structural Covariance among Olfactory-related Brain Regions in Anosmia Patients. Exp Neurobiol 2024; 33:99-106. [PMID: 38724479 PMCID: PMC11089402 DOI: 10.5607/en24007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/01/2024] [Accepted: 04/09/2024] [Indexed: 05/15/2024] Open
Abstract
Anosmia, characterized by the loss of smell, is associated not only with dysfunction in the peripheral olfactory system but also with changes in several brain regions involved in olfactory processing. Specifically, the orbitofrontal cortex is recognized for its pivotal role in integrating olfactory information, engaging in bidirectional communication with the primary olfactory regions, including the olfactory cortex, amygdala, and entorhinal cortex. However, little is known about alterations in structural connections among these brain regions in patients with anosmia. In this study, high-resolution T1-weighted images were obtained from participants. Utilizing the volumes of key brain regions implicated in olfactory function, we employed a structural covariance approach to investigate brain reorganization patterns in patients with anosmia (n=22) compared to healthy individuals (n=30). Our structural covariance analysis demonstrated diminished connectivity between the amygdala and entorhinal cortex, components of the primary olfactory network, in patients with anosmia compared to healthy individuals (z=-2.22, FDR-corrected p=0.039). Conversely, connectivity between the orbitofrontal cortex-a major region in the extended olfactory network-and amygdala was found to be enhanced in the anosmia group compared to healthy individuals (z=2.32, FDR-corrected p=0.039). However, the structural connections between the orbitofrontal cortex and entorhinal cortex did not differ significantly between the groups (z=0.04, FDR-corrected p=0.968). These findings suggest a potential structural reorganization, particularly of higher-order cortical regions, possibly as a compensatory effort to interpret the limited olfactory information available in individuals with olfactory loss.
Collapse
Affiliation(s)
- Suji Lee
- College of Pharmacy, Dongduk Women’s University, Seoul 02748, Korea
| | - Yumi Song
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Haejin Hong
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
| | - Yoonji Joo
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
| | - Eunji Ha
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
| | - Youngeun Shim
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Seung-No Hong
- Department of Otorhinolaryngology-Head & Neck Surgery, Boramae Medical Center, Seoul National University College of Medicine, Seoul 07061, Korea
| | - Jungyoon Kim
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, Korea
| | - In Kyoon Lyoo
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, Korea
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Sujung Yoon
- Ewha Brain Institute, Ewha Womans University, Seoul 03760, Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, Korea
| | - Dae Woo Kim
- Department of Otorhinolaryngology-Head & Neck Surgery, Boramae Medical Center, Seoul National University College of Medicine, Seoul 07061, Korea
| |
Collapse
|
39
|
Chang Z, Liu L, Lin L, Wang G, Zhang C, Tian H, Liu W, Wang L, Zhang B, Ren J, Zhang Y, Xie Y, Du X, Wei X, Wei L, Luo Y, Dong H, Li X, Zhao Z, Liang M, Zhang C, Wang X, Yu C, Qin W, Liu H. Selective disrupted gray matter volume covariance of amygdala subregions in schizophrenia. Front Psychiatry 2024; 15:1349989. [PMID: 38742128 PMCID: PMC11090100 DOI: 10.3389/fpsyt.2024.1349989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 04/11/2024] [Indexed: 05/16/2024] Open
Abstract
Objective Although extensive structural and functional abnormalities have been reported in schizophrenia, the gray matter volume (GMV) covariance of the amygdala remain unknown. The amygdala contains several subregions with different connection patterns and functions, but it is unclear whether the GMV covariance of these subregions are selectively affected in schizophrenia. Methods To address this issue, we compared the GMV covariance of each amygdala subregion between 807 schizophrenia patients and 845 healthy controls from 11 centers. The amygdala was segmented into nine subregions using FreeSurfer (v7.1.1), including the lateral (La), basal (Ba), accessory-basal (AB), anterior-amygdaloid-area (AAA), central (Ce), medial (Me), cortical (Co), corticoamygdaloid-transition (CAT), and paralaminar (PL) nucleus. We developed an operational combat harmonization model for 11 centers, subsequently employing a voxel-wise general linear model to investigate the differences in GMV covariance between schizophrenia patients and healthy controls across these subregions and the entire brain, while adjusting for age, sex and TIV. Results Our findings revealed that five amygdala subregions of schizophrenia patients, including bilateral AAA, CAT, and right Ba, demonstrated significantly increased GMV covariance with the hippocampus, striatum, orbitofrontal cortex, and so on (permutation test, P< 0.05, corrected). These findings could be replicated in most centers. Rigorous correlation analysis failed to identify relationships between the altered GMV covariance with positive and negative symptom scale, duration of illness, and antipsychotic medication measure. Conclusion Our research is the first to discover selectively impaired GMV covariance patterns of amygdala subregion in a large multicenter sample size of patients with schizophrenia.
Collapse
Affiliation(s)
- Zhongyu Chang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Liping Liu
- Department of Psychiatry, The First Psychiatric Hospital of Harbin, Harbin, Heilongjiang, China
| | - Liyuan Lin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Gang Wang
- Wuhan Mental Health Center, The Ninth Clinical School, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chen Zhang
- Department of Biochemistry and Psychopharmacology, Shanghai Mental Health Center, Shanghai, China
| | - Hongjun Tian
- Department of Psychiatry, Tianjin Fourth Center Hospital, The Fourth Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Wei Liu
- Department of Psychiatry, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lina Wang
- Department of Psychiatry, Tianjin Fourth Center Hospital, The Fourth Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Bin Zhang
- Department of Psychiatry, Tianjin Fourth Center Hospital, The Fourth Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Juanjuan Ren
- Department of Biochemistry and Psychopharmacology, Shanghai Mental Health Center, Shanghai, China
| | - Yu Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaotong Du
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaotong Wei
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Luli Wei
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yun Luo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Haoyang Dong
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xin Li
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhen Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Congpei Zhang
- Department of Psychiatry, The First Psychiatric Hospital of Harbin, Harbin, Heilongjiang, China
| | - Xijin Wang
- Department of Psychiatry, The First Psychiatric Hospital of Harbin, Harbin, Heilongjiang, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
- State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Huaigui Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| |
Collapse
|
40
|
Liu B, Mao Z, Yan X, Yang H, Xu J, Feng Z, Zhang Y, Yu X. Structural network topologies are associated with deep brain stimulation outcomes in Meige syndrome. Neurotherapeutics 2024; 21:e00367. [PMID: 38679556 DOI: 10.1016/j.neurot.2024.e00367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 05/01/2024] Open
Abstract
Deep brain stimulation (DBS) is an effective therapy for Meige syndrome (MS). However, the DBS efficacy varies across MS patients and the factors contributing to the variable responses remain enigmatic. We aim to explain the difference in DBS efficacy from a network perspective. We collected preoperative T1-weighted MRI images of 76 MS patients who received DBS in our center. According to the symptomatic improvement rates, all MS patients were divided into two groups: the high improvement group (HIG) and the low improvement group (LIG). We constructed group-level structural covariance networks in each group and compared the graph-based topological properties and interregional connections between groups. Subsequent functional annotation and correlation analyses were also conducted. The results indicated that HIG showed a higher clustering coefficient, longer characteristic path length, lower small-world index, and lower global efficiency compared with LIG. Different nodal betweennesses and degrees between groups were mainly identified in the precuneus, sensorimotor cortex, and subcortical nuclei, among which the gray matter volume of the left precentral gyrus and left thalamus were positively correlated with the symptomatic improvement rates. Moreover, HIG had enhanced interregional connections within the somatomotor network and between the somatomotor network and default-mode network relative to LIG. We concluded that the high and low DBS responders have notable differences in large-scale network architectures. Our study sheds light on the structural network underpinnings of varying DBS responses in MS patients.
Collapse
Affiliation(s)
- Bin Liu
- Medical School of Chinese PLA, Beijing, 100853, China; Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China
| | - Zhiqi Mao
- Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China
| | - Xinyuan Yan
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Hang Yang
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Junpeng Xu
- Medical School of Chinese PLA, Beijing, 100853, China; Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China
| | - Zhebin Feng
- Medical School of Chinese PLA, Beijing, 100853, China; Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China
| | - Yanyang Zhang
- Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China.
| | - Xinguang Yu
- Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China.
| |
Collapse
|
41
|
Chen TY, Zhu JD, Tsai SJ, Yang AC. Exploring morphological similarity and randomness in Alzheimer's disease using adjacent grey matter voxel-based structural analysis. Alzheimers Res Ther 2024; 16:88. [PMID: 38654366 PMCID: PMC11036786 DOI: 10.1186/s13195-024-01448-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 04/01/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Alzheimer's disease is characterized by large-scale structural changes in a specific pattern. Recent studies developed morphological similarity networks constructed by brain regions similar in structural features to represent brain structural organization. However, few studies have used local morphological properties to explore inter-regional structural similarity in Alzheimer's disease. METHODS Here, we sourced T1-weighted MRI images of 342 cognitively normal participants and 276 individuals with Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative database. The relationships of grey matter intensity between adjacent voxels were defined and converted to the structural pattern indices. We conducted the information-based similarity method to evaluate the structural similarity of structural pattern organization between brain regions. Besides, we examined the structural randomness on brain regions. Finally, the relationship between the structural randomness and cognitive performance of individuals with Alzheimer's disease was assessed by stepwise regression. RESULTS Compared to cognitively normal participants, individuals with Alzheimer's disease showed significant structural pattern changes in the bilateral posterior cingulate gyrus, hippocampus, and olfactory cortex. Additionally, individuals with Alzheimer's disease showed that the bilateral insula had decreased inter-regional structural similarity with frontal regions, while the bilateral hippocampus had increased inter-regional structural similarity with temporal and subcortical regions. For the structural randomness, we found significant decreases in the temporal and subcortical areas and significant increases in the occipital and frontal regions. The regression analysis showed that the structural randomness of five brain regions was correlated with the Mini-Mental State Examination scores of individuals with Alzheimer's disease. CONCLUSIONS Our study suggested that individuals with Alzheimer's disease alter micro-structural patterns and morphological similarity with the insula and hippocampus. Structural randomness of individuals with Alzheimer's disease changed in temporal, frontal, and occipital brain regions. Morphological similarity and randomness provide valuable insight into brain structural organization in Alzheimer's disease.
Collapse
Affiliation(s)
- Ting-Yu Chen
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jun-Ding Zhu
- Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Albert C Yang
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| |
Collapse
|
42
|
Xu F, Ma J, Wang W, Li H. A longitudinal study of the brain structure network changes in HIV patients with ANI: combined VBM with SCN. Front Neurol 2024; 15:1388616. [PMID: 38694776 PMCID: PMC11061470 DOI: 10.3389/fneur.2024.1388616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/04/2024] [Indexed: 05/04/2024] Open
Abstract
Background Despite the widespread adoption of combination antiretroviral therapy (cART) in managing HIV, the virus's impact on the brain structure of patients remains significant. This study aims to longitudinally explore the persistent effects of HIV on brain structure, focusing on changes in gray matter volume (GMV) and structural covariance network (SCN) among patients at the Asymptomatic Neurocognitive Impairment (ANI) stage. Methods This research involved 45 HIV patients diagnosed with ANI and 45 demographically matched healthy controls (HCs). The participants were observed over a 1.5-year period. Differences in GMV between groups were analyzed using voxel-based morphometry (VBM), while the graph theory model facilitated the establishment of topological metrics for assessing network indices. These differences were evaluated using two-sample t-tests and paired-sample t-tests, applying the network-based statistics method. Additionally, the study examined correlations between GMV and cognitive performance, as well as clinical variables. Results Compared with HCs, HIV patients demonstrated reduced GMV in the right middle temporal gyrus and left middle frontal gyrus (FWE, p < 0.05), along with decreased betweenness centrality (BC) in the left anterior cingulate and paracingulate cortex. Conversely, an increase in the clustering coefficient (Cp) was observed (FDR, p < 0.05). During the follow-up period, a decline in GMV in the right fusiform gyrus (FWE, p < 0.05) and a reduction in node efficiency (Ne) in the triangular part of the inferior frontal gyrus were noted compared with baseline measurements (FDR, p < 0.05). The SCN of HIV patients exhibited small-world properties across most sparsity levels (Sigma >1), and area under the curve (AUC) analysis revealed no significant statistical differences between groups. Conclusion The findings suggest that despite the administration of combination antiretroviral therapy (cART), HIV continues to exert slow and sustained damage on brain structures. However, when compared to HCs, the small-world properties of the patients' SCNs did not significantly differ, and the clustering coefficient, indicative of the overall information-processing capacity of the brain network, was slightly elevated in HIV patients. This elevation may relate to compensatory effects of brain area functions, the impact of cART, functional reorganization, or inflammatory responses.
Collapse
Affiliation(s)
| | | | | | - Hongjun Li
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
43
|
Xu CX, Kong L, Jiang H, Jiang Y, Sun YH, Bian LG, Feng Y, Sun QF. Analysis of brain structural covariance network in Cushing disease. Heliyon 2024; 10:e28957. [PMID: 38601682 PMCID: PMC11004566 DOI: 10.1016/j.heliyon.2024.e28957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 03/27/2024] [Accepted: 03/27/2024] [Indexed: 04/12/2024] Open
Abstract
Background Cushing disease (CD) is a rare clinical neuroendocrine disease. CD is characterized by abnormal hypercortisolism induced by a pituitary adenoma with the secretion of adrenocorticotropic hormone. Individuals with CD usually exhibit atrophy of gray matter volume. However, little is known about the alterations in topographical organization of individuals with CD. This study aimed to investigate the structural covariance networks of individuals with CD based on the gray matter volume using graph theory analysis. Methods High-resolution T1-weighted images of 61 individuals with CD and 53 healthy controls were obtained. Gray matter volume was estimated and the structural covariance network was analyzed using graph theory. Network properties such as hubs of all participants were calculated based on degree centrality. Results No significant differences were observed between individuals with CD and healthy controls in terms of age, gender, and education level. The small-world features were conserved in individuals with CD but were higher than those in healthy controls. The individuals with CD showed higher global efficiency and modularity, suggesting higher integration and segregation as compared to healthy controls. The hub nodes of the individuals with CD were Short insular gyri (G_insular_short_L), Anterior part of the cingulate gyrus and sulcus (G_and_S_cingul-Ant_R), and Superior frontal gyrus (G_front_sup_R). Conclusions Significant differences in the structural covariance network of patients with CD were found based on graph theory. These findings might help understanding the pathogenesis of individuals with CD and provide insight into the pathogenesis of this CD.
Collapse
Affiliation(s)
- Can-Xin Xu
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Linghan Kong
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
- Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hong Jiang
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yue Jiang
- Department of Neurosurgery, The First Affiliated Hospital of Xinxiang Medical University, Weihui, Henan, 453100, China
| | - Yu-Hao Sun
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Liu-Guan Bian
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yuan Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
- Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, China
| | - Qing-Fang Sun
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| |
Collapse
|
44
|
Zhou B, Zhao Y, Wu X. Differences of individual gray matter networks between MCI patients who converted to AD within 3 Years and nonconverters. Heliyon 2024; 10:e28874. [PMID: 38623255 PMCID: PMC11016615 DOI: 10.1016/j.heliyon.2024.e28874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 03/24/2024] [Accepted: 03/26/2024] [Indexed: 04/17/2024] Open
Abstract
Objective Here we aimed to explore the differences in individual gray matter (GM) networks at baseline in mild cognitive impairment patients who converted to Alzheimer's disease (AD) within 3 years (MCI-C) and nonconverters (MCI-NC). Materials and methods Data from 461 MCI patients (180 MCI-C and 281 MCI-NC) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject, a GM network was constructed using 3D-T1 imaging and the Kullback-Leibler divergence method. Gradient and topological analyses of individual GM networks were performed, and partial correlations were calculated to evaluate relationships among network properties, cognitive function, and apolipoprotein E (APOE) €4 alleles. Subsequently, a support vector machine (SVM) model was constructed to discriminate the MCI-C and MCI-NC patients at baseline. Results The gradient analysis revealed that the principal gradient score distribution was more compressed in the MCI-C group than in the MCI-NC group, with scores for the left lingual gyrus, right fusiform gyrus and left middle temporal gyrus being increased in the MCI-C group (p < 0.05, FDR corrected). The topological analysis showed significant differences in nodal efficiency in four nodes between the two groups. Furthermore, the regional gradient scores or nodal efficiency were found to be significantly related to the neuropsychological test scores, and the left middle temporal gyrus gradient scores were positively associated with the number of APOE €4 alleles (r = 0.192, p = 0.002). Ultimately, the SVM model achieved a balanced accuracy of 79.4% in classifying MCI-C and MCI-NC patients (p < 0.001). Conclusion The whole-brain GM network hierarchy in the MCI-C group was more compressed than that in the MCI-NC group, suggesting more serious cognitive impairments in the MCI-C group. The left middle temporal gyrus gradient scores were related to both cognitive function and APOE €4 alleles, thus serving as potential biomarkers distinguishing MCI-C from MCI-NC at baseline.
Collapse
Affiliation(s)
- Baiwan Zhou
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yueqi Zhao
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaojia Wu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| |
Collapse
|
45
|
Saha R, Saha DK, Fu Z, Duda M, Silva RF, Calhoun VD. Analysis of Longitudinal Change Patterns in Developing Brain Using Functional and Structural Magnetic Resonance Imaging via Multimodal Fusion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.07.588473. [PMID: 38645216 PMCID: PMC11030394 DOI: 10.1101/2024.04.07.588473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Functional and structural magnetic resonance imaging (fMRI and sMRI) are complementary approaches that can be used to study longitudinal brain changes in adolescents. Each individual modality offers distinct insights into the brain. Each individual modality may overlook crucial aspects of brain analysis. By combining them, we can uncover hidden brain connections and gain a more comprehensive understanding. In previous work, we identified multivariate patterns of change in whole-brain function during adolescence. In this work, we focus on linking functional change patterns (FCPs) to brain structure. We introduce two approaches and applied them to data from the Adolescent Brain and Cognitive Development (ABCD) dataset. First, we evaluate voxelwise sMRI-FCP coupling to identify structural patterns linked to our previously identified FCPs. Our approach revealed multiple interesting patterns in functional network connectivity (FNC) and gray matter volume (GMV) data that were linked to subject level variation. FCP components 2 and 4 exhibit extensive associations between their loadings and voxel-wise GMV data. Secondly, we leveraged a symmetric multimodal fusion technique called multiset canonical correlation analysis (mCCA) + joint independent component analysis (jICA). Using this approach, we identify structured FCPs such as one showing increased connectivity between visual and sensorimotor domains and decreased connectivity between sensorimotor and cognitive control domains, linked to structural change patterns (SCPs) including alterations in the bilateral sensorimotor cortex. Interestingly, females exhibit stronger coupling between brain functional and structural changes than males, highlighting sex-related differences. The combined results from both asymmetric and symmetric multimodal fusion methods underscore the intricate sex-specific nuances in neural dynamics. By utilizing two complementary multimodal approaches, our study enhances our understanding of the dynamic nature of brain connectivity and structure during the adolescent period, shedding light on the nuanced processes underlying adolescent brain development.
Collapse
Affiliation(s)
- Rekha Saha
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Debbrata K. Saha
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Marlena Duda
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Rogers F. Silva
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, USA
| |
Collapse
|
46
|
Xu F, Wang Y, Wang W, Liang W, Tang Y, Liu S. Preterm Birth Alters the Regional Development and Structural Covariance of Cerebellum at Term-Equivalent Age. CEREBELLUM (LONDON, ENGLAND) 2024:10.1007/s12311-024-01691-0. [PMID: 38581612 DOI: 10.1007/s12311-024-01691-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/01/2024] [Indexed: 04/08/2024]
Abstract
Preterm birth is associated with increased risk for a spectrum of neurodevelopmental disabilities. The cerebellum is implicated in a wide range of cognitive functions extending beyond sensorimotor control and plays an increasingly recognized role in brain development. Morphometric studies based on volume analyses have revealed impaired cerebellar development in preterm infants. However, the structural covariance between the cerebellum and cerebral cortex has not been studied during the neonatal period, and the extent to which structural covariance is affected by preterm birth remains unknown. In this study, using the structural MR images of 52 preterm infants scanned at term-equivalent age and 312 full-term controls from the Developing Human Connectome Project, we compared volumetric growth, local cerebellum shape development and cerebello-cerebral structural covariance between the two groups. We found that although there was no significant difference in the overall volume measurements between preterm and full-term infants, the shape measurements were different. Compared with the control infants, preterm infants had significantly larger thickness in the vermis and lower thickness in the lateral portions of the bilateral cerebral hemispheres. The structural covariance between the cerebellum and frontal and parietal lobes was significantly greater in preterm infants than in full-term controls. The findings in this study suggested that cerebellar development and cerebello-cerebral structural covariance may be affected by premature birth.
Collapse
Affiliation(s)
- Feifei Xu
- Department of Anatomy and Neurobiology, Institute for Sectional Anatomy and Digital Human, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Yu Wang
- Department of Anatomy and Neurobiology, Institute for Sectional Anatomy and Digital Human, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Wenjun Wang
- Department of Anatomy and Neurobiology, Institute for Sectional Anatomy and Digital Human, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Wenjia Liang
- Department of Anatomy and Neurobiology, Institute for Sectional Anatomy and Digital Human, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Yuchun Tang
- Department of Anatomy and Neurobiology, Institute for Sectional Anatomy and Digital Human, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Shuwei Liu
- Department of Anatomy and Neurobiology, Institute for Sectional Anatomy and Digital Human, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China.
| |
Collapse
|
47
|
Lee P, Chou K, Lee W, Peng L, Chen L, Lin C, Liang C, Chung C. Altered cerebellar and caudate gray-matter volumes and structural covariance networks preceding dual cognitive and mobility impairments in older people. Alzheimers Dement 2024; 20:2420-2433. [PMID: 38298159 PMCID: PMC11032519 DOI: 10.1002/alz.13714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/17/2023] [Accepted: 12/16/2023] [Indexed: 02/02/2024]
Abstract
INTRODUCTION The neuroanatomical changes driving both cognitive and mobility impairments, an emerging preclinical dementia syndrome, are not fully understood. We examined gray-matter volumes (GMVs) and structural covariance networks (SCNs) abnormalities in community-based older people preceding the conversion to physio-cognitive decline syndrome (PCDS). METHODS Voxel-wise brain GMV and established SCNs were compared between PCDS and non-PCDS converters. RESULTS The study included 343 individuals (60.2 ± 6.9 years, 49.6% men) with intact cognitive and mobility functions. Over an average 5.6-year follow-up, 116 transitioned to PCDS. Identified regions with abnormal GMVs in PCDS converters were over cerebellum and caudate, which served as seeds for SCNs establishment. Significant differences in cerebellum-based (to right frontal pole and left middle frontal gyrus) and caudate-based SCNs (to right caudate putamen, right planum temporale, left precentral gyrus, right postcentral gyrus, and left parietal operculum) between converters and nonconverters were observed. DISCUSSION This study reveals early neuroanatomic changes, emphasizing the cerebellum's role, in dual cognitive and mobility impairments. HIGHLIGHTS Neuroanatomic precursors of dual cognitive and mobility impairments are identified. Cerebellar GMV reductions and increased right caudate GMV precede the onset of PCDS. Altered cerebellum- and caudate-based SCNs drive PCDS transformation. This research establishes a foundation for understanding PCDS as a specific dementia syndrome.
Collapse
Affiliation(s)
- Pei‐Lin Lee
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Institute of NeuroscienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
| | - Kun‐Hsien Chou
- Institute of NeuroscienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Brain Research CenterNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
| | - Wei‐Ju Lee
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Department of Family MedicineTaipei Veterans General Hospital Yuanshan BranchYi‐LanTaiwan
| | - Li‐Ning Peng
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Center for Geriatric and GerontologyTaipei Veterans General HospitalTaipeiTaiwan
| | - Liang‐Kung Chen
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Center for Geriatric and GerontologyTaipei Veterans General HospitalTaipeiTaiwan
- Taipei Municipal Gan‐Dau Hospital (managed by Taipei Veterans General Hospital)TaipeiTaiwan
| | - Ching‐Po Lin
- Institute of NeuroscienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Brain Research CenterNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Department of Education and ResearchTaipei City HospitalTaipeiTaiwan
| | - Chih‐Kuang Liang
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Center for Geriatrics and GerontologyKaohsiung Veterans General HospitalKaohsiungTaiwan
- Division of NeurologyDepartment of Internal MedicineKaohsiung Veterans General HospitalKaohsiungTaiwan
| | - Chih‐Ping Chung
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Department of NeurologyNeurological InstituteTaipei Veterans General HospitalTaipeiTaiwan
| |
Collapse
|
48
|
Chen Y, Liang L, Wei Y, Liu Y, Li X, Zhang Z, Li L, Deng D. Disrupted morphological brain network organization in subjective cognitive decline and mild cognitive impairment. Brain Imaging Behav 2024; 18:387-395. [PMID: 38147273 DOI: 10.1007/s11682-023-00839-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/01/2023] [Indexed: 12/27/2023]
Abstract
We aim to investigate the alterations in gray matter for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) from the perspective of the human connectome. High-resolution T1-weighted images were acquired from 54 patients with SCD, 95 patients with MCI, and 65 healthy controls (HC). Morphological brain networks (MBN) were constructed using similarities in the distribution of gray matter volumes between regions. The strength of morphological connections and topographic metrics derived from the graph-theoretical analysis were compared. Furthermore, we assessed the relationship between the observed morphological abnormalities and disease severity. According to the results, we found a significantly decreased morphological connection between the somatomotor network and ventral attention network in SCD compared to HC and MCI compared to SCD. The graph-theoretic analysis illustrated disruptions in the whole network organization, where the normalized shortest path increased and the global efficiency (Eg) decreased in MCI compared to SCD. In addition, Montreal Cognitive Assessment scores of SCD patients had a significantly negative correlation with Eg. The primary limitations of the present study include the cross-sectional design, no enrolled AD patients, no assessment of amyloidosis, and the need for more comprehensive neuropsychological tests. Our findings indicate the abnormalities of morphological networks at early stages in the AD continuum, which could be interpreted as compensatory changes to retain a normal level of cognitive function. The present study could provide new insight into the mechanism of AD.
Collapse
Affiliation(s)
- Yuxin Chen
- Medical College of Guangxi University, Guangxi University, Nanning, Guangxi, China
- Department of Radiology, the People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Lingyan Liang
- Department of Radiology, the People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Yichen Wei
- Department of Radiology, the People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Ying Liu
- Department of Radiology, the People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Xiaocheng Li
- Department of Radiology, the People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Zhiguo Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China
- Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Linling Li
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China.
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China.
| | - Demao Deng
- Medical College of Guangxi University, Guangxi University, Nanning, Guangxi, China.
- Department of Radiology, the People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China.
| |
Collapse
|
49
|
Hsieh H, Xu Q, Zhang Q, Yang F, Xu Y, Liu G, Liu R, Yu Q, Zhang Z, Lu G, Gu X, Zhang Z. Mapping progressive damage epicenters in epilepsy with generalized tonic-clonic seizures by causal structural covariance network density (CaSCNd). Brain Res 2024; 1828:148766. [PMID: 38242522 DOI: 10.1016/j.brainres.2024.148766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 11/28/2023] [Accepted: 01/12/2024] [Indexed: 01/21/2024]
Abstract
AIMS Mapping progressive patterns of structural damage in epilepsies with idiopathic and secondarily generalized tonic-clonic seizures with causal structural covariance networks and multiple analysis strategies. METHODS Patients with idiopathic generalized tonic-clonic seizures (IGTCS) (n = 114) and secondarily generalized tonic-clonic seizures (SGTCS) (n = 125) were recruited. Morphometric parameter of gray matter volume was analyzed on structural MRI. Structural covariance network based on granger causality analysis (CaSCN) was performed on the cross-sectional morphometric data sorted by disease durations of patients. Seed-based CaSCN analysis was firstly carried out to map the progressive and influential patterns of damage to thalamus-related structures. A novel technique for voxel-based CaSCN density (CaSCNd) analysis was further proposed, enabling for identifying the epicenter of structural brain damage during the disease process. RESULTS The thalamus-associated CaSCNs demonstrated different patterns of progressive damage in two types of generalized tonic-clonic seizures. In IGTCS, the structural damage was predominantly driven from the thalamus, and expanded to the cortex, while in SGTCS, the damage was predominantly driven from the cortex, and expanded to the thalamus through the basal ganglia. CaSCNd analysis revealed that the IGTCS had an out-effect epicenter in the thalamus, whereas the SGTCS had equipotent in- and out-effects in the thalamus, cortex, and basal ganglia. CONCLUSION CaSCN revealed distinct damage patterns in the two types of GTCS, featuring with measurement of structural brain damage from the accumulating effect over a relatively long time period. Our work provided evidence for understanding network impairment mechanism underlying different GTCSs.
Collapse
Affiliation(s)
- Hsinyu Hsieh
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
| | - Qiang Xu
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
| | - Qirui Zhang
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
| | - Fang Yang
- Department of Neurology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
| | - Yin Xu
- Institute of Neurology Anhui, University of Chinese Medicine, Hefei 230061, China
| | - Gaoping Liu
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
| | - Ruoting Liu
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
| | - Qianqian Yu
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
| | - Zixuan Zhang
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
| | - Guangming Lu
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China
| | - Xing Gu
- Department of Ultrasound, YanCheng 1(st) People Hospital, China
| | - Zhiqiang Zhang
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China; State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing 210093, China.
| |
Collapse
|
50
|
Mo X, Jiang P, Sun J, Lu L, Li L, Huang X, Xu J, Li J, Zhang J, Gong Q. Mapping structural covariance networks of emotional withdrawal symptoms in males with methamphetamine use disorder during abstinence. Addict Biol 2024; 29:e13394. [PMID: 38627958 PMCID: PMC11021798 DOI: 10.1111/adb.13394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 01/15/2024] [Accepted: 03/18/2024] [Indexed: 04/19/2024]
Abstract
Individuals with methamphetamine use disorder (MUD) often experience anxiety and depressive symptoms during abstinence, which can worsen the likelihood of relapse. Thus, it is essential to understand the neuro-mechanism behind methamphetamine use and its associated emotional withdrawal symptoms in order to develop effective clinical strategies. This study aimed to evaluate associations between emotional withdrawal symptoms and structural covariance networks (SCNs) based on cortical thickness (CTh) across the brain. The CTh measures were obtained from Tl-weighted MRI data from a sample of 48 males with MUD during abstinence and 48 male healthy controls. The severity of anxiety and depressive symptoms was assessed by the Hamilton Anxiety Scale (HAMA) and depression (HAMD) scales. Two important nodes belonging to the brain reward system, the right rostral anterior cingulate cortex (rACC) and medial prefrontal cortex (medPFC), were selected as seeds to conduct SCNs and modulation analysis by emotional symptoms. MUDs showed higher structural covariance between the right rACC and regions in the dorsal attention, right frontoparietal, auditory, visual and limbic networks. They also displayed higher structural covariance between the right medPFC and regions in the limbic network. Moreover, the modulation analysis showed that higher scores on HAMA were associated with increased covariance between the right rACC and the left parahippocampal and isthmus cingulate cortex in the default mode network. These outcomes shed light on the complex neurobiological mechanisms underlying methamphetamine use and its associated emotional withdrawal symptoms and may provide new insights into the development of effective treatments for MUD.
Collapse
Affiliation(s)
- Xian Mo
- West China Biomedical Big Data Center, West China HospitalSichuan UniversityChengduChina
- College of Electrical EngineeringSichuan UniversityChengduChina
| | - Ping Jiang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China HospitalSichuan UniversityChengduChina
- West China Medical Publishers, West China HospitalSichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Jiayu Sun
- Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
| | - Lu Lu
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China HospitalSichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Lei Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China HospitalSichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Xiaoqi Huang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China HospitalSichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Jiajun Xu
- Mental Health CenterWest China Hospital of Sichuan UniversityChengduChina
| | - Jing Li
- Mental Health CenterWest China Hospital of Sichuan UniversityChengduChina
| | - Junran Zhang
- College of Electrical EngineeringSichuan UniversityChengduChina
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China HospitalSichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| |
Collapse
|