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Ottino-González J, Cupertino RB, Cao Z, Hahn S, Pancholi D, Albaugh MD, Brumback T, Baker FC, Brown SA, Clark DB, de Zambotti M, Goldston DB, Luna B, Nagel BJ, Nooner KB, Pohl KM, Tapert SF, Thompson WK, Jernigan TL, Conrod P, Mackey S, Garavan H. Brain structural covariance network features are robust markers of early heavy alcohol use. Addiction 2024; 119:113-124. [PMID: 37724052 PMCID: PMC10872365 DOI: 10.1111/add.16330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/27/2023] [Indexed: 09/20/2023]
Abstract
BACKGROUND AND AIMS Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies. DESIGN AND SETTING Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years). CASES Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected. MEASUREMENTS Graph theory metrics of segregation and integration were used to summarize SCN. FINDINGS Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar. CONCLUSION Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.
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Affiliation(s)
- Jonatan Ottino-González
- Division of Endocrinology, The Saban Research Institute, Children’s Hospital Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry, University of Vermont Larner College of Medicine, Burlington, VT, USA
| | - Renata B. Cupertino
- Department of Genetics, University of California San Diego, San Diego, CA, USA
| | - Zhipeng Cao
- Department of Psychiatry, University of Vermont Larner College of Medicine, Burlington, VT, USA
| | - Sage Hahn
- Department of Psychiatry, University of Vermont Larner College of Medicine, Burlington, VT, USA
| | - Devarshi Pancholi
- Department of Psychiatry, University of Vermont Larner College of Medicine, Burlington, VT, USA
| | - Matthew D. Albaugh
- Department of Psychiatry, University of Vermont Larner College of Medicine, Burlington, VT, USA
| | - Ty Brumback
- Department of Psychological Science, Northern Kentucky University, Highland Heights, KY, USA
| | - Fiona C. Baker
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
| | - Sandra A. Brown
- Departments of Psychology and Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Duncan B. Clark
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - David B. Goldston
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bonnie J. Nagel
- Departments of Psychiatry and Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
| | - Kate B. Nooner
- Department of Psychology, University of North Carolina Wilmington, Wilmington, NC, USA
| | - Kilian M. Pohl
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Susan F. Tapert
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Wesley K. Thompson
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Terry L. Jernigan
- Center for Human Development, University of California, San Diego, CA, USA
| | - Patricia Conrod
- Department of Psychiatry, Université de Montreal, CHU Ste Justine Hospital, Montreal, Québec, Canada
| | - Scott Mackey
- Department of Psychiatry, University of Vermont Larner College of Medicine, Burlington, VT, USA
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont Larner College of Medicine, Burlington, VT, USA
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Ottino-González J, Garavan H. Brain structural covariance network differences in adults with alcohol dependence and heavy-drinking adolescents. Addiction 2022; 117:1312-1325. [PMID: 34907616 DOI: 10.1111/add.15772] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 11/05/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND AIMS Graph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy-drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol. DESIGN Cross-sectional sample of adults and a cohort of adolescents. Correlation matrices for cortical thicknesses across 68 regions were summarized with graph theoretic metrics. SETTING AND PARTICIPANTS A total of 745 adults with AD and 979 non-dependent controls from 24 sites curated by the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA)-Addiction consortium, and 297 hazardous drinking adolescents and 594 controls at ages 19 and 14 from the IMAGEN study, all from Europe. MEASUREMENTS Metrics of network segregation (modularity, clustering coefficient and local efficiency) and integration (average shortest path length and global efficiency). FINDINGS The younger AD adults had lower network segregation and higher integration relative to non-dependent controls. Compared with controls, the hazardous drinkers at age 19 showed lower modularity [area-under-the-curve (AUC) difference = -0.0142, 95% confidence interval (CI) = -0.1333, 0.0092; P-value = 0.017], clustering coefficient (AUC difference = -0.0164, 95% CI = -0.1456, 0.0043; P-value = 0.008) and local efficiency (AUC difference = -0.0141, 95% CI = -0.0097, 0.0034; P-value = 0.010), as well as lower average shortest path length (AUC difference = -0.0405, 95% CI = -0.0392, 0.0096; P-value = 0.021) and higher global efficiency (AUC difference = 0.0044, 95% CI = -0.0011, 0.0043; P-value = 0.023). The same pattern was present at age 14 with lower clustering coefficient (AUC difference = -0.0131, 95% CI = -0.1304, 0.0033; P-value = 0.024), lower average shortest path length (AUC difference = -0.0362, 95% CI = -0.0334, 0.0118; P-value = 0.019) and higher global efficiency (AUC difference = 0.0035, 95% CI = -0.0011, 0.0038; P-value = 0.048). CONCLUSIONS Cross-sectional analyses indicate that a specific structural covariance network profile is an early marker of alcohol dependence in adults. Similar effects in a cohort of heavy-drinking adolescents, observed at age 19 and prior to substantial alcohol exposure at age 14, suggest that this pattern may be a pre-existing risk factor for problematic drinking.
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Affiliation(s)
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, USA
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Li D, Liu C, Huang Z, Li H, Xu Q, Zhou B, Hu C, Zhang Y, Wang Y, Nie J, Qiao Z, Yin D, Xu X. Common and Distinct Disruptions of Cortical Surface Morphology Between Autism Spectrum Disorder Children With and Without SHANK3 Deficiency. Front Neurosci 2021; 15:751364. [PMID: 34776852 PMCID: PMC8581670 DOI: 10.3389/fnins.2021.751364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 09/29/2021] [Indexed: 11/13/2022] Open
Abstract
SH3 and Multiple Ankyrin Repeat Domains 3 (SHANK3)-caused autism spectrum disorder (ASD) may present a unique opportunity to clarify the heterogeneous neuropathological mechanisms of ASD. However, the specificity and commonality of disrupted large-scale brain organization in SHANK3-deficient children remain largely unknown. The present study combined genetic tests, neurobehavioral evaluations, and magnetic resonance imaging, aiming to explore the disruptions of both local and networked cortical structural organization in ASD children with and without SHANK3 deficiency. Multiple surface morphological parameters such as cortical thickness (CT) and sulcus depth were estimated, and the graph theory was adopted to characterize the topological properties of structural covariance networks (SCNs). Finally, a correlation analysis between the alterations in brain morphological features and the neurobehavioral evaluations was performed. Compared with typically developed children, increased CT and reduced nodal degree were found in both ASD children with and without SHANK3 defects mainly in the lateral temporal cortex, prefrontal cortex (PFC), temporo-parietal junction (TPJ), superior temporal gyrus (STG), and limbic/paralimbic regions. Besides commonality, our findings showed some distinct abnormalities in ASD children with SHANK3 defects compared to those without. Locally, more changes in the STG and orbitofrontal cortex were exhibited in ASD children with SHANK3 defects, while more changes in the TPJ and inferior parietal lobe (IPL) in those without SHANK3 defects were observed. For the SCNs, a trend toward regular network topology was observed in ASD children with SHANK3 defects, but not in those without. In addition, ASD children with SHANK3 defects showed more alterations of nodal degrees in the anterior and posterior cingulate cortices and right insular, while there were more disruptions in the sensorimotor areas and the left insular and dorsomedial PFC in ASD without SHANK3 defects. Our findings indicate dissociable disruptions of local and networked brain morphological features in ASD children with and without SHANK3 deficiency. Moreover, this monogenic study may provide a valuable path for parsing the heterogeneity of brain disturbances in ASD.
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Affiliation(s)
- Dongyun Li
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Chunxue Liu
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Ziyi Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, Affiliated Mental Health Center, East China Normal University, Shanghai, China.,School of Psychology, South China Normal University, Guangzhou, China
| | - Huiping Li
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Qiong Xu
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Bingrui Zhou
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Chunchun Hu
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Ying Zhang
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Yi Wang
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Jingxin Nie
- School of Psychology, South China Normal University, Guangzhou, China
| | - Zhongwei Qiao
- Department of Radiology, Children's Hospital of Fudan University, Shanghai, China
| | - Dazhi Yin
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, Affiliated Mental Health Center, East China Normal University, Shanghai, China
| | - Xiu Xu
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
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Yang Y, Cheng Y, Wang X, Upreti B, Cui R, Liu S, Shan B, Yu H, Luo C, Xu J. Gout Is Not Just Arthritis: Abnormal Cortical Thickness and Structural Covariance Networks in Gout. Front Neurol 2021; 12:662497. [PMID: 34603178 PMCID: PMC8481804 DOI: 10.3389/fneur.2021.662497] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 08/12/2021] [Indexed: 12/27/2022] Open
Abstract
Background: Hyperuricemia is the cause of gout. The antioxidant and neuroprotective effects of uric acid seem to benefit some patients with central nervous system injury. However, changes in the brain structure have not been discovered in patients with gout. Object: Clarify the changes in cortical thickness in patients with gout and the alteration of the structural covariance networks (SCNs) based on cortical thickness. Methods: We collected structural MRIs of 23 male gout patients and 23 age-matched healthy controls. After calculating and comparing the difference in cortical thickness between the two groups, we constructed and analyzed the cortical thickness covariance networks of the two groups, and we investigated for any changes in SCNs of gout patients. Results: Gout patients have thicker cortices in the left postcentral, left supramarginal, right medial temporal, and right medial orbitofrontal regions; and thinner cortices were found in the left insula, left superior frontal, right pericalcarine, and right precentral regions. In SCN analysis, between-group differences in global network measures showed that gout patients have a higher global efficiency. In regional network measures, more nodes in gout patients have increased centrality. In network hub analysis, we found that the transfer of the core hub area, rather than the change in number, may be the characteristic of the gout's cortical thickness covariance network. Conclusion: This is the first study on changes in brain cortical thickness and SCN based on graph theory in patients with gout. The present study found that, compared with healthy controls, gout patients show regional cortical thinning or thickening, and variation in the properties of the cortical thickness covariance network also changed. These alterations may be the combined effect of disease damage and physiological compensation. More research is needed to fully understand the complex underlying mechanisms of gout brain variation.
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Affiliation(s)
- Yifan Yang
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiangyu Wang
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Bibhuti Upreti
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Ruomei Cui
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shuang Liu
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Baoci Shan
- Nuclear Analysis Technology Key Laboratory, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Hongjun Yu
- Magnetic Resonance Imaging Center, The First Hospital of Kunming, Kunming, China
| | - Chunrong Luo
- Magnetic Resonance Imaging Center, The First Hospital of Kunming, Kunming, China
| | - Jian Xu
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
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5
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Keo A, Dzyubachyk O, van der Grond J, Hafkemeijer A, van de Berg WDJ, van Hilten JJ, Reinders MJT, Mahfouz A. Cingulate networks associated with gray matter loss in Parkinson's disease show high expression of cholinergic genes in the healthy brain. Eur J Neurosci 2021; 53:3727-3739. [PMID: 33792979 PMCID: PMC8251922 DOI: 10.1111/ejn.15216] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/16/2021] [Accepted: 03/21/2021] [Indexed: 12/25/2022]
Abstract
Structural covariance networks are able to identify functionally organized brain regions by gray matter volume covariance across a population. We examined the transcriptomic signature of such anatomical networks in the healthy brain using postmortem microarray data from the Allen Human Brain Atlas. A previous study revealed that a posterior cingulate network and anterior cingulate network showed decreased gray matter in brains of Parkinson's disease patients. Therefore, we examined these two anatomical networks to understand the underlying molecular processes that may be involved in Parkinson's disease. Whole brain transcriptomics from the healthy brain revealed upregulation of genes associated with serotonin, GPCR, GABA, glutamate, and RAS-signaling pathways. Our results also suggest involvement of the cholinergic circuit, in which genes NPPA, SOSTDC1, and TYRP1 may play a functional role. Finally, both networks were enriched for genes associated with neuropsychiatric disorders that overlap with Parkinson's disease symptoms. The identified genes and pathways contribute to healthy functions of the posterior and anterior cingulate networks and disruptions to these functions may in turn contribute to the pathological and clinical events observed in Parkinson's disease.
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Affiliation(s)
- Arlin Keo
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands.,Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Oleh Dzyubachyk
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Anne Hafkemeijer
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Department of Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Wilma D J van de Berg
- Department of Anatomy and Neurosciences, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
| | - Jacobus J van Hilten
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Marcel J T Reinders
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands.,Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Ahmed Mahfouz
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands.,Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.,Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
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6
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Jackson SR, Sigurdsson HP, Dyke K, Condon M, Jackson GM. The role of the cingulate cortex in the generation of motor tics and the experience of the premonitory urge-to-tic in Tourette syndrome. J Neuropsychol 2021; 15:340-362. [PMID: 33774919 DOI: 10.1111/jnp.12242] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 01/07/2021] [Indexed: 12/26/2022]
Abstract
Tourette syndrome (TS) is a neurological disorder of childhood onset that is characterized by the occurrence of motor and vocal tics. TS is associated with cortical-striatal-thalamic-cortical circuit [CSTC] dysfunction and hyper-excitability of cortical limbic and motor regions that are thought to lead to the occurrence of tics. Individuals with TS often report that their tics are preceded by 'premonitory sensory/urge phenomena' (PU) that are described as uncomfortable bodily sensations that precede the execution of a tic and are experienced as a strong urge for motor discharge. While the precise role played by PU in the occurrence of tics is largely unknown, they are nonetheless of considerable theoretical and clinical importance as they form a core component of many behavioural therapies used in the treatment of tic disorders. Recent evidence indicates that the cingulate cortex may play an important role in the generation of PU in TS, and in 'urges-for-action' more generally. In the current study, we utilized voxel-based morphometry (VBM) techniques, together with 'seed-to-voxel' structural covariance network (SCN) mapping, to investigate the putative role played by the cingulate cortex in the generation of motor tics and the experience of PU in a relatively large group of young people with TS. Whole-brain VBM analysis revealed that TS was associated with clusters of significantly reduced grey matter volumes bilaterally within: the orbito-frontal cortex; the cerebellum; and the anterior and mid-cingulate cortex. Similarly, analysis of SCNs associated with bilateral mid- and anterior cingulate 'seed' regions demonstrated that TS is associated with increased structural covariance primarily with the bilateral motor cerebellum; the inferior frontal cortex; and the posterior cingulate cortex.
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Affiliation(s)
- Stephen R Jackson
- School of Psychology, University of Nottingham, UK.,Institute of Mental Health, School of Medicine, University of Nottingham, UK
| | | | | | - Maria Condon
- School of Psychology, University of Nottingham, UK
| | - Georgina M Jackson
- Institute of Mental Health, School of Medicine, University of Nottingham, UK
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7
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Wang E, Jia Y, Ya Y, Xu J, Mao C, Luo W, Fan G, Jiang Z. Abnormal Topological Organization of Sulcal Depth-Based Structural Covariance Networks in Parkinson's Disease. Front Aging Neurosci 2021; 12:575672. [PMID: 33519416 PMCID: PMC7843381 DOI: 10.3389/fnagi.2020.575672] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 12/14/2020] [Indexed: 11/13/2022] Open
Abstract
Recent research on Parkinson's disease (PD) has demonstrated the topological abnormalities of structural covariance networks (SCNs) using various morphometric features from structural magnetic resonance images (sMRI). However, the sulcal depth (SD)-based SCNs have not been investigated. In this study, we used SD to investigate the topological alterations of SCNs in 60 PD patients and 56 age- and gender-matched healthy controls (HC). SCNs were constructed by thresholding SD correlation matrices of 68 regions and analyzed using graph theoretical approaches. Compared with HC, PD patients showed increased normalized clustering coefficient and normalized path length, as well as a reorganization of degree-based and betweenness-based hubs (i.e., less frontal hubs). Moreover, the degree distribution analysis showed more high-degree nodes in PD patients. In addition, we also found the increased assortativity and reduced robustness under a random attack in PD patients compared to HC. Taken together, these findings indicated an abnormal topological organization of SD-based SCNs in PD patients, which may contribute in understanding the pathophysiology of PD at the network level.
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Affiliation(s)
- Erlei Wang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yujing Jia
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yang Ya
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Jin Xu
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Chengjie Mao
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Weifeng Luo
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Guohua Fan
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhen Jiang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
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Yang F, Qu M, Zhang Y, Zhao L, Xing W, Zhou G, Tang J, Wu J, Zhang Y, Liao W. Aberrant Brain Network Integration and Segregation in Diabetic Peripheral Neuropathy Revealed by Structural Connectomics. Front Neurosci 2020; 14:585588. [PMID: 33343281 PMCID: PMC7746555 DOI: 10.3389/fnins.2020.585588] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 11/16/2020] [Indexed: 11/13/2022] Open
Abstract
Diabetic peripheral neuropathy (DPN) is one of the most common forms of peripheral neuropathy, and its incidence has been increasing. Mounting evidence has shown that patients with DPN have been associated with widespread alterations in the structure, function and connectivity of the brain, suggesting possible alterations in large-scale brain networks. Using structural covariance networks as well as advanced graph-theory-based computational approaches, we investigated the topological abnormalities of large-scale brain networks for a relatively large sample of patients with DPN (N = 67) compared to matched healthy controls (HCs; N = 88). Compared with HCs, the structural covariance networks of patients with DPN showed an increased characteristic path length, clustering coefficient, sigma, transitivity, and modularity, suggestive of inefficient global integration and increased local segregation. These findings may improve our understanding of the pathophysiological mechanisms underlying alterations in the central nervous system of patients with DPN from the perspective of large-scale structural brain networks.
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Affiliation(s)
- Fangxue Yang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Minli Qu
- Department of Endocrinology, Xiangya Hospital, Central South University, Changsha, China
| | - Youming Zhang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Linmei Zhao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Wu Xing
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Gaofeng Zhou
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Jingyi Tang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Jing Wu
- Department of Endocrinology, Xiangya Hospital, Central South University, Changsha, China
| | - Yuanchao Zhang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Weihua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,Molecular Imaging Research Center of Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders (XiangYa), Changsha, China
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9
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Tur C, Kanber B, Eshaghi A, Altmann DR, Khaleeli Z, Prados F, Ourselin S, Thompson AJ, Gandini Wheeler-Kingshott CA, Toosy AT, Ciccarelli O. Clinical relevance of cortical network dynamics in early primary progressive MS. Mult Scler 2020; 26:442-456. [PMID: 30799709 DOI: 10.1177/1352458519831400] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
BACKGROUND Structural cortical networks (SCNs) reflect the covariance between the cortical thickness of different brain regions, which may share common functions and a common developmental evolution. SCNs appear abnormal in neurodegenerative conditions such as Alzheimer's and Parkinson's diseases, but have never been assessed in primary progressive multiple sclerosis (PPMS). OBJECTIVE The aim of this study was to test whether SCNs are abnormal in early PPMS and change over 5 years, and correlate with disability worsening. METHODS A total of 29 PPMS patients and 13 healthy controls underwent clinical and brain magnetic resonance imaging (MRI) assessments for 5 years. Baseline and 5-year follow-up cortical thickness values were obtained and used to build correlation matrices, considered as weighted graphs to obtain network metrics. Bootstrap-based statistics assessed SCN differences between patients and controls and between patients with fast and slow progression. RESULTS At baseline, patients showed features of lower connectivity (p = 0.02) and efficiency (p < 0.001) than controls. Over 5 years, patients, especially those with fastest clinical progression, showed significant changes suggesting an increase in network connectivity (p < 0.001) and efficiency (p < 0.02), not observed in controls. CONCLUSION SCNs are abnormal in early PPMS. Longitudinal SCN changes demonstrated a switch from low- to high-efficiency networks especially among fast progressors, indicating their clinical relevance.
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Affiliation(s)
- Carmen Tur
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK
| | - Baris Kanber
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
| | - Arman Eshaghi
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK/Department of Computer Science, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
| | - Dan R Altmann
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK/Department of Medical Statistics, London School of Hygiene & Tropical Medicine, University of London, London, UK
| | - Zhaleh Khaleeli
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK
| | - Ferran Prados
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK/Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
| | - Sebastian Ourselin
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK/School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences & Medicine, King's College London and St Thomas' Hospital, London, UK
| | - Alan J Thompson
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK/National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Claudia Am Gandini Wheeler-Kingshott
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK/Brain MRI 3T Research Center, C. Mondino National Neurological Institute, Pavia, Italy/Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Ahmed T Toosy
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK
| | - Olga Ciccarelli
- Queen Square MS Centre, UCL Institute of Neurology, University College London (UCL), London, UK/National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
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10
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Wang X, Yu Y, Zhao W, Li Q, Li X, Li S, Yin C, Han Y. Altered Whole-Brain Structural Covariance of the Hippocampal Subfields in Subcortical Vascular Mild Cognitive Impairment and Amnestic Mild Cognitive Impairment Patients. Front Neurol 2018; 9:342. [PMID: 29872419 PMCID: PMC5972219 DOI: 10.3389/fneur.2018.00342] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 04/30/2018] [Indexed: 11/17/2022] Open
Abstract
The hippocampus plays important roles in memory processing. However, the hippocampus is not a homogeneous structure, which consists of several subfields. The hippocampal subfields are differently affected by many neurodegenerative diseases, especially mild cognitive impairment (MCI). Amnestic mild cognitive impairment (aMCI) and subcortical vascular mild cognitive impairment (svMCI) are the two subtypes of MCI. aMCI is characterized by episodic memory loss, and svMCI is characterized by extensive white matter hyperintensities and multiple lacunar infarctions on magnetic resonance imaging. The primary cognitive impairment in svMCI is executive function, attention, and semantic memory. Some variations or disconnections within specific large-scale brain networks have been observed in aMCI and svMCI patients. The aim of this study was to investigate abnormalities in structural covariance networks (SCNs) between hippocampal subfields and the whole cerebral cortex in aMCI and svMCI patients, and whether these abnormalities are different between the two groups. Automated segmentation of hippocampal subfields was performed with FreeSurfer 5.3, and we selected five hippocampal subfields as the seeds of SCN analysis: CA1, CA2/3, CA4/dentate gyrus (DG), subiculum, and presubiculum. SCNs were constructed based on these hippocampal subfield seeds for each group. Significant correlations between hippocampal subfields, fusiform gyrus (FFG), and entorhinal cortex (ERC) in gray matter volume were found in each group. We also compared the differences in the strength of structural covariance between any two groups. In the aMCI group, compared to the normal controls (NC) group, we observed an increased association between the left CA1/CA4/DG/subiculum and the left temporal pole. Additionally, the hippocampal subfields (bilateral CA1, left CA2/3) significantly covaried with the orbitofrontal cortex in the svMCI group compared to the NC group. In the aMCI group compared to the svMCI group, we observed decreased association between hippocampal subfields and the right FFG, while we also observed an increased association between the bilateral subiculum/presubiculum and bilateral ERC. These findings provide new evidence that there is altered whole-brain structural covariance of the hippocampal subfields in svMCI and aMCI patients and provide insights to the pathological mechanisms of different MCI subtypes.
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Affiliation(s)
- Xuetong Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Yang Yu
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Department of Neurology, XuanWu Hospital, Capital Medical University, Beijing, China
| | - Weina Zhao
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Department of Neurology, XuanWu Hospital, Capital Medical University, Beijing, China.,Department of Neurology, Hongqi Hospital, Mudanjiang Medical University, Mudanjiang, China
| | - Qiongling Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Xinwei Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Shuyu Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Changhao Yin
- Department of Neurology, Hongqi Hospital, Mudanjiang Medical University, Mudanjiang, China
| | - Ying Han
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Department of Neurology, XuanWu Hospital, Capital Medical University, Beijing, China
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11
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Pereira JB, Mijalkov M, Kakaei E, Mecocci P, Vellas B, Tsolaki M, Kloszewska I, Soininen H, Spenger C, Lovestone S, Simmons A, Wahlund LO, Volpe G, Westman E. Disrupted Network Topology in Patients with Stable and Progressive Mild Cognitive Impairment and Alzheimer's Disease. Cereb Cortex 2016; 26:3476-3493. [PMID: 27178195 PMCID: PMC4961019 DOI: 10.1093/cercor/bhw128] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Recent findings suggest that Alzheimer's disease (AD) is a disconnection syndrome characterized by abnormalities in large-scale networks. However, the alterations that occur in network topology during the prodromal stages of AD, particularly in patients with stable mild cognitive impairment (MCI) and those that show a slow or faster progression to dementia, are still poorly understood. In this study, we used graph theory to assess the organization of structural MRI networks in stable MCI (sMCI) subjects, late MCI converters (lMCIc), early MCI converters (eMCIc), and AD patients from 2 large multicenter cohorts: ADNI and AddNeuroMed. Our findings showed an abnormal global network organization in all patient groups, as reflected by an increased path length, reduced transitivity, and increased modularity compared with controls. In addition, lMCIc, eMCIc, and AD patients showed a decreased path length and mean clustering compared with the sMCI group. At the local level, there were nodal clustering decreases mostly in AD patients, while the nodal closeness centrality detected abnormalities across all patient groups, showing overlapping changes in the hippocampi and amygdala and nonoverlapping changes in parietal, entorhinal, and orbitofrontal regions. These findings suggest that the prodromal and clinical stages of AD are associated with an abnormal network topology.
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Affiliation(s)
- Joana B Pereira
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Patricia Mecocci
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Bruno Vellas
- INSERM U 558, University of Toulouse, Toulouse, France
| | - Magda Tsolaki
- Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Hilka Soininen
- University of Eastern Finland, Joensuu, Finland.,University Hospital of Kuopio, Kuopio, Finland
| | - Christian Spenger
- Department of Clinical Science, Intervention and Technology at Karolinska Institutet, Division of Medical Imaging and Technology, Stockholm, Sweden.,Department of Radiology, Karolinska University Hospital in Huddinge, Solna, Sweden
| | | | - Andrew Simmons
- NIHR Biomedical Research Centre for Mental Health, London, UK
| | - Lars-Olof Wahlund
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Giovanni Volpe
- Department of Physics, Soft Matter Lab.,UNAM-National Nanotechnology Research Center, Bilkent University, Ankara, Turkey
| | - Eric Westman
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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12
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Hafkemeijer A, Altmann‐Schneider I, Craen AJM, Slagboom PE, Grond J, Rombouts SARB. Associations between age and gray matter volume in anatomical brain networks in middle-aged to older adults. Aging Cell 2014; 13:1068-74. [PMID: 25257192 PMCID: PMC4326918 DOI: 10.1111/acel.12271] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/21/2014] [Indexed: 12/25/2022] Open
Abstract
Aging is associated with cognitive decline, diminished brain function, regional brain atrophy, and disrupted structural and functional brain connectivity. Understanding brain networks in aging is essential, as brain function depends on large-scale distributed networks. Little is known of structural covariance networks to study inter-regional gray matter anatomical associations in aging. Here, we investigate anatomical brain networks based on structural covariance of gray matter volume among 370 middle-aged to older adults of 45-85 years. For each of 370 subjects, we acquired a T1-weighted anatomical MRI scan. After segmentation of structural MRI scans, nine anatomical networks were defined based on structural covariance of gray matter volume among subjects. We analyzed associations between age and gray matter volume in anatomical networks using linear regression analyses. Age was negatively associated with gray matter volume in four anatomical networks (P < 0.001, corrected): a subcortical network, sensorimotor network, posterior cingulate network, and an anterior cingulate network. Age was not significantly associated with gray matter volume in five networks: temporal network, auditory network, and three cerebellar networks. These results were independent of gender and white matter hyperintensities. Gray matter volume decreases with age in networks containing subcortical structures, sensorimotor structures, posterior, and anterior cingulate cortices. Gray matter volume in temporal, auditory, and cerebellar networks remains relatively unaffected with advancing age.
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Affiliation(s)
- Anne Hafkemeijer
- Institute of Psychology Leiden University Leiden The Netherlands
- Department of Radiology Leiden University Medical Center Leiden The Netherlands
- Leiden Institute for Brain and Cognition Leiden University Leiden The Netherlands
| | - Irmhild Altmann‐Schneider
- Department of Radiology Leiden University Medical Center Leiden The Netherlands
- Department of Molecular Epidemiology Netherlands Consortium for Healthy Ageing Leiden University Medical Center Leiden The Netherlands
| | - Anton J. M. Craen
- Department of Molecular Epidemiology Netherlands Consortium for Healthy Ageing Leiden University Medical Center Leiden The Netherlands
- Department of Gerontology and Geriatrics Leiden University Medical Center Leiden The Netherlands
| | - P. Eline Slagboom
- Department of Molecular Epidemiology Netherlands Consortium for Healthy Ageing Leiden University Medical Center Leiden The Netherlands
- Department of Molecular Epidemiology Leiden University Medical Center Leiden The Netherlands
| | - Jeroen Grond
- Department of Radiology Leiden University Medical Center Leiden The Netherlands
| | - Serge A. R. B. Rombouts
- Institute of Psychology Leiden University Leiden The Netherlands
- Department of Radiology Leiden University Medical Center Leiden The Netherlands
- Leiden Institute for Brain and Cognition Leiden University Leiden The Netherlands
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13
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Tewarie P, Steenwijk MD, Tijms BM, Daams M, Balk LJ, Stam CJ, Uitdehaag BMJ, Polman CH, Geurts JJG, Barkhof F, Pouwels PJW, Vrenken H, Hillebrand A. Disruption of structural and functional networks in long-standing multiple sclerosis. Hum Brain Mapp 2014; 35:5946-61. [PMID: 25053254 DOI: 10.1002/hbm.22596] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Revised: 07/10/2014] [Accepted: 07/14/2014] [Indexed: 11/09/2022] Open
Abstract
Both gray matter atrophy and disruption of functional networks are important predictors for physical disability and cognitive impairment in multiple sclerosis (MS), yet their relationship is poorly understood. Graph theory provides a modality invariant framework to analyze patterns of gray matter morphology and functional coactivation. We investigated, how gray matter and functional networks were affected within the same MS sample and examined their interrelationship. Magnetic resonance imaging and magnetoencephalography (MEG) were performed in 102 MS patients and 42 healthy controls. Gray matter networks were computed at the group-level based on cortical thickness correlations between 78 regions across subjects. MEG functional networks were computed at the subject level based on the phase-lag index between time-series of regions in source-space. In MS patients, we found a more regular network organization for structural covariance networks and for functional networks in the theta band, whereas we found a more random network organization for functional networks in the alpha2 band. Correlation analysis revealed a positive association between covariation in thickness and functional connectivity in especially the theta band in MS patients, and these results could not be explained by simple regional gray matter thickness measurements. This study is a first multimodal graph analysis in a sample of MS patients, and our results suggest that a disruption of gray matter network topology is important to understand alterations in functional connectivity in MS as regional gray matter fails to take into account the inherent connectivity structure of the brain.
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Affiliation(s)
- Prejaas Tewarie
- Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
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