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Wall J, Xie H, Wang X. Temporal Interactions between Maintenance of Cerebral Cortex Thickness and Physical Activity from an Individual Person Micro-Longitudinal Perspective and Implications for Precision Medicine. J Pers Med 2024; 14:127. [PMID: 38392561 PMCID: PMC10890462 DOI: 10.3390/jpm14020127] [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: 11/28/2023] [Revised: 12/30/2023] [Accepted: 01/09/2024] [Indexed: 02/24/2024] Open
Abstract
Maintenance of brain structure is essential for neurocognitive health. Precision medicine has interests in understanding how maintenance of an individual person's brain, including cerebral cortical structure, interacts with lifestyle factors like physical activity. Cortical structure, including cortical thickness, has recognized relationships with physical activity, but concepts of these relationships come from group, not individual, focused findings. Whether or how group-focused concepts apply to an individual person is fundamental to precision medicine interests but remains unclear. This issue was studied in a healthy man using concurrent micro-longitudinal tracking of magnetic resonance imaging-defined cortical thickness and accelerometer-defined steps/day over six months. These data permitted detailed examination of temporal relationships between thickness maintenance and physical activity at an individual level. Regression analyses revealed graded significant and trend-level temporal interactions between preceding activity vs. subsequent thickness maintenance and between preceding thickness maintenance vs. subsequent activity. Interactions were bidirectional, delayed/prolonged over days/weeks, positive, bilateral, directionally asymmetric, and limited in strength. These novel individual-focused findings in some ways are predicted, but in other ways remain unaddressed or undetected, by group-focused work. We suggest that individual-focused concepts of temporal interactions between maintenance of cortical structure and activity can provide needed new insight for personalized tailoring of physical activity, cortical, and neurocognitive health.
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Affiliation(s)
- John Wall
- Department of Neurosciences, University of Toledo College of Medicine & Life Sciences, Toledo, OH 43614, USA
| | - Hong Xie
- Department of Neurosciences, University of Toledo College of Medicine & Life Sciences, Toledo, OH 43614, USA
| | - Xin Wang
- Department of Neurosciences, University of Toledo College of Medicine & Life Sciences, Toledo, OH 43614, USA
- Department of Psychiatry, University of Toledo College of Medicine & Life Sciences, Toledo, OH 43614, USA
- Department of Radiology, University of Toledo College of Medicine & Life Sciences, Toledo, OH 43614, USA
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Omlor W, Rabe F, Fuchs S, Cecere G, Homan S, Surbeck W, Kallen N, Georgiadis F, Spiller T, Seifritz E, Weickert T, Bruggemann J, Weickert C, Potkin S, Hashimoto R, Sim K, Rootes-Murdy K, Quide Y, Houenou J, Banaj N, Vecchio D, Piras F, Piras F, Spalletta G, Salvador R, Karuk A, Pomarol-Clotet E, Rodrigue A, Pearlson G, Glahn D, Tomecek D, Spaniel F, Skoch A, Kirschner M, Kaiser S, Kochunov P, Fan FM, Andreassen OA, Westlye LT, Berthet P, Calhoun VD, Howells F, Uhlmann A, Scheffler F, Stein D, Iasevoli F, Cairns MJ, Carr VJ, Catts SV, Di Biase MA, Jablensky A, Green MJ, Henskens FA, Klauser P, Loughland C, Michie PT, Mowry B, Pantelis C, Rasser PE, Schall U, Scott R, Zalesky A, de Bartolomeis A, Barone A, Ciccarelli M, Brunetti A, Cocozza S, Pontillo G, Tranfa M, Di Giorgio A, Thomopoulos SI, Jahanshad N, Thompson PM, van Erp T, Turner J, Homan P. Estimating multimodal brain variability in schizophrenia spectrum disorders: A worldwide ENIGMA study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.22.559032. [PMID: 37961617 PMCID: PMC10634976 DOI: 10.1101/2023.09.22.559032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Objective Schizophrenia is a multifaceted disorder associated with structural brain heterogeneity. Despite its relevance for identifying illness subtypes and informative biomarkers, structural brain heterogeneity in schizophrenia remains incompletely understood. Therefore, the objective of this study was to provide a comprehensive insight into the structural brain heterogeneity associated with schizophrenia. Methods This meta- and mega-analysis investigated the variability of multimodal structural brain measures of white and gray matter in individuals with schizophrenia versus healthy controls. Using the ENIGMA dataset of MRI-based brain measures from 22 international sites with up to 6139 individuals for a given brain measure, we examined variability in cortical thickness, surface area, folding index, subcortical volume and fractional anisotropy. Results We found that individuals with schizophrenia are distinguished by higher heterogeneity in the frontotemporal network with regard to multimodal structural measures. Moreover, individuals with schizophrenia showed higher homogeneity of the folding index, especially in the left parahippocampal region. Conclusions Higher multimodal heterogeneity in frontotemporal regions potentially implies different subtypes of schizophrenia that converge on impaired frontotemporal interaction as a core feature of the disorder. Conversely, more homogeneous folding patterns in the left parahippocampal region might signify a consistent characteristic of schizophrenia shared across subtypes. These findings underscore the importance of structural brain variability in advancing our neurobiological understanding of schizophrenia, and aid in identifying illness subtypes as well as informative biomarkers.
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Schetter M, Romascano D, Gaujard M, Rummel C, Denervaud S. Learning by Heart or with Heart: Brain Asymmetry Reflects Pedagogical Practices. Brain Sci 2023; 13:1270. [PMID: 37759871 PMCID: PMC10526483 DOI: 10.3390/brainsci13091270] [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: 07/26/2023] [Revised: 08/14/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
Brain hemispheres develop rather symmetrically, except in the case of pathology or intense training. As school experience is a form of training, the current study tested the influence of pedagogy on morphological development through the cortical thickness (CTh) asymmetry index (AI). First, we compared the CTh AI of 111 students aged 4 to 18 with 77 adults aged > 20. Second, we investigated the CTh AI of the students as a function of schooling background (Montessori or traditional). At the whole-brain level, CTh AI was not different between the adult and student groups, even when controlling for age. However, pedagogical experience was found to impact CTh AI in the temporal lobe, within the parahippocampal (PHC) region. The PHC region has a functional lateralization, with the right PHC region having a stronger involvement in spatiotemporal context encoding, while the left PHC region is involved in semantic encoding. We observed CTh asymmetry toward the left PHC region for participants enrolled in Montessori schools and toward the right for participants enrolled in traditional schools. As these participants were matched on age, intelligence, home-life and socioeconomic conditions, we interpret this effect found in memory-related brain regions to reflect differences in learning strategies. Pedagogy modulates how new concepts are encoded, with possible long-term effects on knowledge transfer.
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Affiliation(s)
- Martin Schetter
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, 1005 Lausanne, Switzerland
| | - David Romascano
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, 1005 Lausanne, Switzerland
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital—Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Mathilde Gaujard
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, 1005 Lausanne, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital—Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Solange Denervaud
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, 1005 Lausanne, Switzerland
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Long J, Li J, Xie B, Jiao Z, Shen G, Liao W, Song X, Le H, Xia J, Wu S. Morphometric similarity network alterations in COVID-19 survivors correlate with behavioral features and transcriptional signatures. Neuroimage Clin 2023; 39:103498. [PMID: 37643521 PMCID: PMC10474075 DOI: 10.1016/j.nicl.2023.103498] [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: 04/17/2023] [Revised: 07/24/2023] [Accepted: 08/15/2023] [Indexed: 08/31/2023]
Abstract
OBJECTIVES To explore the differences in the cortical morphometric similarity network (MSN) between COVID-19 survivors and healthy controls, and the correlation between these differences and behavioralfeatures and transcriptional signatures. MATERIALS & METHODS 39 COVID-19 survivors and 39 age-, sex- and education years-matched healthy controls (HCs) were included. All participants underwent MRI and behavioral assessments (PCL-17, GAD-7, PHQ-9). MSN analysis was used to compute COVID-19 survivors vs. HCs differences across brain regions. Correlation analysis was used to determine the associations between regional MSN differences and behavioral assessments, and determine the spatial similarities between regional MSN differences and risk genes transcriptional activity. RESULTS COVID-19 survivors exhibited decreased regional MSN in insula, precuneus, transverse temporal, entorhinal, para-hippocampal, rostral middle frontal and supramarginal cortices, and increased regional MSN in pars triangularis, lateral orbitofrontal, superior frontal, superior parietal, postcentral, and inferior temporal cortices. Regional MSN value of lateral orbitofrontal cortex was positively associated with GAD-7 and PHQ-9 scores, and rostral middle frontal was negatively related to PHQ-9 scores. The analysis of spatial similarities showed that seven risk genes (MFGE8, MOB2, NUP62, PMPCA, SDSL, TMEM178B, and ZBTB11) were related to regional MSN values. CONCLUSION The MSN differences were associated with behavioral and transcriptional signatures, early psychological counseling or intervention may be required to COVID-19 survivors. Our study provided a new insight into understanding the altered coordination of structure in COVID-19 and may offer a new endophenotype to further investigate the brain substrate.
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Affiliation(s)
- Jia Long
- Department of Radiology, South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China
| | - Jiao Li
- School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, 610054, PR China
| | - Bing Xie
- Department of Radiology, South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China
| | - Zhuomin Jiao
- Department of Neurology, South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China
| | - Guoqiang Shen
- Department of Radiology, South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China
| | - Wei Liao
- School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, 610054, PR China
| | - Xiaomin Song
- Department of Radiology, South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China
| | - Hongbo Le
- Department of Radiology, South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China.
| | - Jun Xia
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen 518035, PR China.
| | - Song Wu
- South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China.
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Xie Y, Sun J, Man W, Zhang Z, Zhang N. Personalized estimates of brain cortical structural variability in individuals with Autism spectrum disorder: the predictor of brain age and neurobiology relevance. Mol Autism 2023; 14:27. [PMID: 37507798 PMCID: PMC10375633 DOI: 10.1186/s13229-023-00558-1] [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: 03/24/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a heritable condition related to brain development that affects a person's perception and socialization with others. Here, we examined variability in the brain morphology in ASD children and adolescent individuals at the level of brain cortical structural profiles and the level of each brain regional measure. METHODS We selected brain structural MRI data in 600 ASDs and 729 normal controls (NCs) from Autism Brain Imaging Data Exchange (ABIDE). The personalized estimate of similarity between gray matter volume (GMV) profiles of an individual to that of others in the same group was assessed by using the person-based similarity index (PBSI). Regional contributions to PBSI score were utilized for brain age gap estimation (BrainAGE) prediction model establishment, including support vector regression (SVR), relevance vector regression (RVR), and Gaussian process regression (GPR). The association between BrainAGE prediction in ASD and clinical performance was investigated. We further explored the related inter-regional profiles of gene expression from the Allen Human Brain Atlas with variability differences in the brain morphology between groups. RESULTS The PBSI score of GMV was negatively related to age regardless of the sample group, and the PBSI score was significantly lower in ASDs than in NCs. The regional contributions to the PBSI score of 126 brain regions in ASDs showed significant differences compared to NCs. RVR model achieved the best performance for predicting brain age. Higher inter-individual brain morphology variability was related to increased brain age, specific to communication symptoms. A total of 430 genes belonging to various pathways were identified as associated with brain cortical morphometric variation. The pathways, including short-term memory, regulation of system process, and regulation of nervous system process, were dominated mainly by gene sets for manno midbrain neurotypes. LIMITATIONS There is a sample mismatch between the gene expression data and brain imaging data from ABIDE. A larger sample size can contribute to the model training of BrainAGE and the validation of the results. CONCLUSIONS ASD has personalized heterogeneity brain morphology. The brain age gap estimation and transcription-neuroimaging associations derived from this trait are replenished in an additional direction to boost the understanding of the ASD brain.
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Affiliation(s)
- Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Jie Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Weiqi Man
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
- Department of Radiology, Tianjin First Central Hospital, Tianjin, 300192, China
| | - Zhang Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China.
| | - Ningnannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China.
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Sun J, Zhao W, Xie Y, Zhou F, Wu L, Li Y, Li H, Li Y, Zeng C, Han X, Liu Y, Zhang N. Personalized estimates of morphometric similarity in multiple sclerosis and neuromyelitis optica spectrum disorders. Neuroimage Clin 2023; 39:103454. [PMID: 37343344 PMCID: PMC10509529 DOI: 10.1016/j.nicl.2023.103454] [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: 03/10/2023] [Revised: 05/21/2023] [Accepted: 06/16/2023] [Indexed: 06/23/2023]
Abstract
Brain morphometric alterations involve multiple brain regions on progression of the disease in multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) and exhibit age-related degenerative changes during the pathological aging. Recent advance in brain morphometry as measured using MRI have leveraged Person-Based Similarity Index (PBSI) approach to assess the extent of within-diagnosis similarity or heterogeneity of brain neuroanatomical profiles between individuals of healthy populations and validate in neuropsychiatric disorders. Brain morphometric changes throughout the lifespan would be invaluable for understanding regional variability of age-related structural degeneration and the substrate of inflammatory demyelinating disease. Here, we aimed to quantify the neuroanatomical profiles with PBSI measures of cortical thickness (CT) and subcortical volumes (SV) in 263 MS, 207 NMOSD, and 338 healthy controls (HC) from six separate central datasets (aged 11-80). We explored the between-group comparisons of PBSI measures, as well as the advancing age and sex effects on PBSI measures. Compared to NMOSD, MS showed a lower extent of within-diagnosis similarity. Significant differences in regional contributions to PBSI score were observed in 29 brain regions between MS and NMOSD (P < 0.05/164, Bonferroni corrected), of which bilateral cerebellum in MS and bilateral parahippocampal gyrus in NMOSD represented the highest divergence between the two patient groups, with a high similarity effect within each group. The PBSI scores were generally lower with advancing age, but their associations showed different patterns depending on the age range. For MS, CT profiles were significantly negatively correlated with age until the early 30 s (ρ = -0.265, P = 0.030), while for NMOSD, SV profiles were significantly negatively correlated with age with 51 year-old and older (ρ = -0.365, P = 0.008). The current study suggests that PBSI approach could be used to quantify the variation in brain morphometric changes in CNS inflammatory demyelinating disease, and exhibited a greater neuroanatomical heterogeneity pattern in MS compared with NMOSD. Our results reveal that, as an MR marker, PBSI may be sensitive to distribute the disease-associated grey matter diversity and complexity. Disease-driven production of regionally selective and age stage-dependency changes in the neuroanatomical profile of MS and NMOSD should be considered to facilitate the prediction of clinical outcomes and assessment of treatment responses.
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Affiliation(s)
- Jie Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Wenjin Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Fuqing Zhou
- Department of Radiology, The First Afliated Hospital, Nanchang University, Nanchang 330006, Jiangxi Province, China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang 330006, Jiangxi Province, China
| | - Lin Wu
- Department of Radiology, The First Afliated Hospital, Nanchang University, Nanchang 330006, Jiangxi Province, China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang 330006, Jiangxi Province, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Haiqing Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yongmei Li
- Department of Radiology, The First Afliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Chun Zeng
- Department of Radiology, The First Afliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xuemei Han
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun 130031, Jilin Province, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119, The West Southern 4th Ring Road, Fengtai District, Beijing 100070, China
| | - Ningnannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
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Wu X, Palaniyappan L, Yu G, Zhang K, Seidlitz J, Liu Z, Kong X, Schumann G, Feng J, Sahakian BJ, Robbins TW, Bullmore E, Zhang J. Morphometric dis-similarity between cortical and subcortical areas underlies cognitive function and psychiatric symptomatology: a preadolescence study from ABCD. Mol Psychiatry 2023; 28:1146-1158. [PMID: 36473996 DOI: 10.1038/s41380-022-01896-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022]
Abstract
Preadolescence is a critical period characterized by dramatic morphological changes and accelerated cortico-subcortical development. Moreover, the coordinated development of cortical and subcortical regions underlies the emerging cognitive functions during this period. Deviations in this maturational coordination may underlie various psychiatric disorders that begin during preadolescence, but to date these deviations remain largely uncharted. We constructed a comprehensive whole-brain morphometric similarity network (MSN) from 17 neuroimaging modalities in a large preadolescence sample (N = 8908) from Adolescent Brain Cognitive Development (ABCD) study and investigated its association with 10 cognitive subscales and 27 psychiatric subscales or diagnoses. Based on the MSNs, each brain was clustered into five modules with distinct cytoarchitecture and evolutionary relevance. While morphometric correlation was positive within modules, it was negative between modules, especially between isocortical and paralimbic/subcortical modules; this developmental dissimilarity was genetically linked to synapse and neurogenesis. The cortico-subcortical dissimilarity becomes more pronounced longitudinally in healthy children, reflecting developmental differentiation of segregated cytoarchitectonic areas. Higher cortico-subcortical dissimilarity (between the isocortical and paralimbic/subcortical modules) were related to better cognitive performance. In comparison, children with poor modular differentiation between cortex and subcortex displayed higher burden of externalizing and internalizing symptoms. These results highlighted cortical-subcortical morphometric dissimilarity as a dynamic maturational marker of cognitive and psychiatric status during the preadolescent stage and provided insights into brain development.
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Affiliation(s)
- Xinran Wu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, QC, Canada
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Gechang Yu
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, 999077, Hong Kong SAR, China
| | - Kai Zhang
- School of Computer Science and Technology, East China Normal University, 200062, Shanghai, China
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhaowen Liu
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Xiangzhen Kong
- Department of Psychology and Behavioral Sciences, Zhejiang University, Zhejiang, China
| | - Gunter Schumann
- The Centre for Population Neuroscience and Stratified Medicine (PONS), ISTBI, Fudan University, Shanghai, China
- PONS Centre and SGDP Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- PONS Centre, Charite Mental Health, Dept. of Psychiatry and Psychotherapie, CCM, Charite Universitaetsmedizin Berlin, Berlin, Germany
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Shanghai Center for Mathematical Sciences, Shanghai, 200433, China
- Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Barbara J Sahakian
- Department of Psychiatry, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Trevor W Robbins
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
- Cambridge shire and Peterborough NHS Trust, Elizabeth House, Fulbourn Hospital, Cambridge, UK
| | - Edward Bullmore
- Department of Psychiatry and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
- Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China.
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
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Meng Y, Yang S, Xiao J, Lu Y, Li J, Chen H, Liao W. Cortical gradient of a human functional similarity network captured by the geometry of cytoarchitectonic organization. Commun Biol 2022; 5:1152. [PMID: 36310240 PMCID: PMC9618576 DOI: 10.1038/s42003-022-04148-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 10/20/2022] [Indexed: 11/09/2022] Open
Abstract
Mapping the functional topology from a multifaceted perspective and relating it to underlying cross-scale structural principles is crucial for understanding the structural-functional relationships of the cerebral cortex. Previous works have described a sensory-association gradient axis in terms of coupling relationships between structure and function, but largely based on single specific feature, and the mesoscopic underpinnings are rarely determined. Here we show a gradient pattern encoded in a functional similarity network based on data from Human Connectome Project and further link it to cytoarchitectonic organizing principles. The spatial distribution of the primary gradient follows an inferior-anterior to superior-posterior axis. The primary gradient demonstrates converging relationships with layer-specific microscopic gene expression and mesoscopic cortical layer thickness, and is captured by the geometric representation of a myelo- and cyto-architecture based laminar differentiation theorem, involving a dual origin theory. Together, these findings provide a gradient, which describes the functional topology, and more importantly, linking the macroscale functional landscape with mesoscale laminar differentiation principles. Analysis of functional MRI data from the Human Connectome Project reveals the presence of a cortical gradient, which reflects functional topology and links the macroscale functional landscape with mesoscale laminar differentiation principles.
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Thng G, Shen X, Stolicyn A, Harris MA, Adams MJ, Barbu MC, Kwong ASF, Frangou S, Lawrie SM, McIntosh AM, Romaniuk L, Whalley HC. Comparing personalized brain-based and genetic risk scores for major depressive disorder in large population samples of adults and adolescents. Eur Psychiatry 2022; 65:e44. [PMID: 35899848 PMCID: PMC9393914 DOI: 10.1192/j.eurpsy.2022.2301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 06/20/2022] [Accepted: 07/01/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Major depressive disorder (MDD) is a polygenic disorder associated with brain alterations but until recently, there have been no brain-based metrics to quantify individual-level variation in brain morphology. Here, we evaluated and compared the performance of a new brain-based 'Regional Vulnerability Index' (RVI) with polygenic risk scores (PRS), in the context of MDD. We assessed associations with syndromal MDD in an adult sample (N = 702, age = 59 ± 10) and with subclinical depressive symptoms in a longitudinal adolescent sample (baseline N = 3,825, age = 10 ± 1; 2-year follow-up N = 2,081, age = 12 ± 1). METHODS MDD-RVIs quantify the correlation of the individual's corresponding brain metric with the expected pattern for MDD derived in an independent sample. Using the same methodology across samples, subject-specific MDD-PRS and six MDD-RVIs based on different brain modalities (subcortical volume, cortical thickness, cortical surface area, mean diffusivity, fractional anisotropy, and multimodal) were computed. RESULTS In adults, MDD-RVIs (based on white matter and multimodal measures) were more strongly associated with MDD (β = 0.099-0.281, PFDR = 0.001-0.043) than MDD-PRS (β = 0.056-0.152, PFDR = 0.140-0.140). In adolescents, depressive symptoms were associated with MDD-PRS at baseline and follow-up (β = 0.084-0.086, p = 1.38 × 10-4-4.77 × 10-4) but not with any MDD-RVIs (β < 0.05, p > 0.05). CONCLUSIONS Our results potentially indicate the ability of brain-based risk scores to capture a broader range of risk exposures than genetic risk scores in adults and are also useful in helping us to understand the temporal origins of depression-related brain features. Longitudinal data, specific to the developmental period and on white matter measures, will be useful in informing risk for subsequent psychiatric illness.
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Affiliation(s)
- Gladi Thng
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh, United Kingdom
| | - Xueyi Shen
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh, United Kingdom
| | - Aleks Stolicyn
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh, United Kingdom
| | - Mathew A. Harris
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh, United Kingdom
| | - Mark J. Adams
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh, United Kingdom
| | - Miruna C. Barbu
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh, United Kingdom
| | - Alex S. F. Kwong
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh, United Kingdom
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Sophia Frangou
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Stephen M. Lawrie
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh, United Kingdom
| | - Andrew M. McIntosh
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh, United Kingdom
| | - Liana Romaniuk
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh, United Kingdom
| | - Heather C. Whalley
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh, United Kingdom
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10
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Neuroanatomical heterogeneity and homogeneity in individuals at clinical high risk for psychosis. Transl Psychiatry 2022; 12:297. [PMID: 35882855 PMCID: PMC9325730 DOI: 10.1038/s41398-022-02057-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/28/2022] [Accepted: 07/01/2022] [Indexed: 12/12/2022] Open
Abstract
Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the 'normativeness' of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation.
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11
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Vuksanović V. Brain morphometric similarity and flexibility. Cereb Cortex Commun 2022; 3:tgac024. [PMID: 35854840 PMCID: PMC9283106 DOI: 10.1093/texcom/tgac024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 05/31/2022] [Accepted: 06/05/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
The cerebral cortex is represented through multiple multilayer morphometric similarity networks to study their modular structures. The approach introduces a novel way for studying brain networks' metrics across individuals, and can quantify network properties usually not revealed using conventional network analyses.
Methods
A total of 8 combinations or types of morphometric similarity networks were constructed – 4 combinations of the inter-regional cortical features on 2 brain atlases. The networks' modular structures were investigated by identifying those modular interactions that stay consistent across the combinations of inter-regional morphometric features and individuals.
Results
The results provide evidence of the community structures as the property of (i) cortical lobar divisions, and also as (ii) the product of different combinations of morphometric features used for the construction of the multilayer representations of the cortex. For the first time, this study has mapped out flexible and inflexible morphometric similarity hubs, and evidence has been provided about variations of the modular network topology across the multilayers with age and IQ.
Conclusions
The results contribute to understanding of intra-regional characteristics in cortical interactions, which potentially can be used to map heterogeneous neurodegeneration patterns in diseased brains.
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Affiliation(s)
- Vesna Vuksanović
- Health Data Science, Swansea University Medical School, Swansea University, Data Science Building , Swansea SA2 8PP, Wales, United Kingdom
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12
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Bolton TAW, Van De Ville D, Régis J, Witjas T, Girard N, Levivier M, Tuleasca C. Graph Theoretical Analysis of Structural Covariance Reveals the Relevance of Visuospatial and Attentional Areas in Essential Tremor Recovery After Stereotactic Radiosurgical Thalamotomy. Front Aging Neurosci 2022; 14:873605. [PMID: 35677202 PMCID: PMC9168220 DOI: 10.3389/fnagi.2022.873605] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Essential tremor (ET) is the most common movement disorder. Its pathophysiology is only partially understood. Here, we leveraged graph theoretical analysis on structural covariance patterns quantified from morphometric estimates for cortical thickness, surface area, and mean curvature in patients with ET before and one year after (to account for delayed clinical effect) ventro-intermediate nucleus (Vim) stereotactic radiosurgical thalamotomy. We further contrasted the observed patterns with those from matched healthy controls (HCs). Significant group differences at the level of individual morphometric properties were specific to mean curvature and the post-/pre-thalamotomy contrast, evidencing brain plasticity at the level of the targeted left thalamus, and of low-level visual, high-level visuospatial and attentional areas implicated in the dorsal visual stream. The introduction of cross-correlational analysis across pairs of morphometric properties strengthened the presence of dorsal visual stream readjustments following thalamotomy, as cortical thickness in the right lingual gyrus, bilateral rostral middle frontal gyrus, and left pre-central gyrus was interrelated with mean curvature in the rest of the brain. Overall, our results position mean curvature as the most relevant morphometric feature to understand brain plasticity in drug-resistant ET patients following Vim thalamotomy. They also highlight the importance of examining not only individual features, but also their interactions, to gain insight into the routes of recovery following intervention.
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Affiliation(s)
- Thomas A. W. Bolton
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Connectomics Laboratory, Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Jean Régis
- Stereotactic and Functional Neurosurgery Service and Gamma Knife Unit, Assistance Publique-Hôpitaux de Marseille, Centre Hospitalier Universitaire de la Timone, Marseille, France
| | - Tatiana Witjas
- Neurology Department, Assistance Publique-Hôpitaux de Marseille, Centre Hospitalier Universitaire de la Timone, Marseille, France
| | - Nadine Girard
- Department of Diagnostic and Interventional Neuroradiology, Centre de Résonance Magnétique Biologique et Médicale, Assistance Publique-Hôpitaux de Marseille, Centre Hospitalier Universitaire de la Timone, Marseille, France
| | - Marc Levivier
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Constantin Tuleasca
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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13
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Cheon EJ, Bearden CE, Sun D, Ching CRK, Andreassen OA, Schmaal L, Veltman DJ, Thomopoulos SI, Kochunov P, Jahanshad N, Thompson PM, Turner JA, van Erp TG. Cross disorder comparisons of brain structure in schizophrenia, bipolar disorder, major depressive disorder, and 22q11.2 deletion syndrome: A review of ENIGMA findings. Psychiatry Clin Neurosci 2022; 76:140-161. [PMID: 35119167 PMCID: PMC9098675 DOI: 10.1111/pcn.13337] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 11/29/2021] [Accepted: 01/21/2022] [Indexed: 12/25/2022]
Abstract
This review compares the main brain abnormalities in schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and 22q11.2 Deletion Syndrome (22q11DS) determined by ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium investigations. We obtained ranked effect sizes for subcortical volumes, regional cortical thickness, cortical surface area, and diffusion tensor imaging abnormalities, comparing each of these disorders relative to healthy controls. In addition, the studies report on significant associations between brain imaging metrics and disorder-related factors such as symptom severity and treatments. Visual comparison of effect size profiles shows that effect sizes are generally in the same direction and scale in severity with the disorders (in the order SZ > BD > MDD). The effect sizes for 22q11DS, a rare genetic syndrome that increases the risk for psychiatric disorders, appear to be much larger than for either of the complex psychiatric disorders. This is consistent with the idea of generally larger effects on the brain of rare compared to common genetic variants. Cortical thickness and surface area effect sizes for 22q11DS with psychosis compared to 22q11DS without psychosis are more similar to those of SZ and BD than those of MDD; a pattern not observed for subcortical brain structures and fractional anisotropy effect sizes. The observed similarities in effect size profiles for cortical measures across the psychiatric disorders mimic those observed for shared genetic variance between these disorders reported based on family and genetic studies and are consistent with shared genetic risk for SZ and BD and structural brain phenotypes.
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Affiliation(s)
- Eun-Jin Cheon
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, 5251 California Ave, Irvine, CA, 92617, USA
- Department of Psychiatry, Yeungnam University College of Medicine, Yeungnam University Medical Center, Daegu, Republic of Korea
| | - Carrie E. Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles
| | - Daqiang Sun
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles
- Department of Mental Health, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ole A. Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
- Orygen, Parkville, Australia
| | - Dick J. Veltman
- Department of Psychiatry, Amsterdam UMC, location VUMC, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jessica A. Turner
- Psychology Department and Neuroscience Institute, Georgia State University, Atlant, GA, USA
| | - Theo G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, 5251 California Ave, Irvine, CA, 92617, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, 309 Qureshey Research Lab, Irvine, CA, 92697, USA
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14
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Pelkmans W, Ossenkoppele R, Dicks E, Strandberg O, Barkhof F, Tijms BM, Pereira JB, Hansson O. Tau-related grey matter network breakdown across the Alzheimer's disease continuum. Alzheimers Res Ther 2021; 13:138. [PMID: 34389066 PMCID: PMC8364121 DOI: 10.1186/s13195-021-00876-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 07/09/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND Changes in grey matter covariance networks have been reported in preclinical and clinical stages of Alzheimer's disease (AD) and have been associated with amyloid-β (Aβ) deposition and cognitive decline. However, the role of tau pathology on grey matter networks remains unclear. Based on previously reported associations between tau pathology, synaptic density and brain structural measures, tau-related connectivity changes across different stages of AD might be expected. We aimed to assess the relationship between tau aggregation and grey matter network alterations across the AD continuum. METHODS We included 533 individuals (178 Aβ-negative cognitively unimpaired (CU) subjects, 105 Aβ-positive CU subjects, 122 Aβ-positive patients with mild cognitive impairment, and 128 patients with AD dementia) from the BioFINDER-2 study. Single-subject grey matter networks were extracted from T1-weighted images and graph theory properties including degree, clustering coefficient, path length, and small world topology were calculated. Associations between tau positron emission tomography (PET) values and global and regional network measures were examined using linear regression models adjusted for age, sex, and total intracranial volume. Finally, we tested whether the association of tau pathology with cognitive performance was mediated by grey matter network disruptions. RESULTS Across the whole sample, we found that higher tau load in the temporal meta-ROI was associated with significant changes in degree, clustering, path length, and small world values (all p < 0.001), indicative of a less optimal network organisation. Already in CU Aβ-positive individuals associations between tau burden and lower clustering and path length were observed, whereas in advanced disease stages elevated tau pathology was progressively associated with more brain network abnormalities. Moreover, the association between higher tau load and lower cognitive performance was only partly mediated (9.3 to 9.5%) through small world topology. CONCLUSIONS Our data suggest a close relationship between grey matter network disruptions and tau pathology in individuals with abnormal amyloid. This might reflect a reduced communication between neighbouring brain areas and an altered ability to integrate information from distributed brain regions with tau pathology, indicative of a more random network topology across different AD stages.
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Affiliation(s)
- Wiesje Pelkmans
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Ellen Dicks
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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15
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West A, Hamlin N, Frangou S, Wilson TW, Doucet GE. Person-Based Similarity Index for Cognition and Its Neural Correlates in Late Adulthood: Implications for Cognitive Reserve. Cereb Cortex 2021; 32:397-407. [PMID: 34255824 DOI: 10.1093/cercor/bhab215] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/10/2021] [Accepted: 06/10/2021] [Indexed: 11/13/2022] Open
Abstract
Healthy aging is typically associated with some level of cognitive decline, but there is substantial variation in such decline among older adults. The mechanisms behind such heterogeneity remain unclear but some have suggested a role for cognitive reserve. In this work, we propose the "person-based similarity index" for cognition (PBSI-Cog) as a proxy for cognitive reserve in older adults, and use the metric to quantify similarity between the cognitive profiles of healthy older and younger participants. In the current study, we computed this metric in 237 healthy older adults (55-88 years) using a reference group of 156 younger adults (18-39 years) taken from the Cambridge Center for Ageing and Neuroscience dataset. Our key findings revealed that PBSI-Cog scores in older adults were: 1) negatively associated with age (rho = -0.25, P = 10-4) and positively associated with higher education (t = 2.4, P = 0.02), 2) largely explained by fluid intelligence and executive function, and 3) predicted more by functional connectivity between lower- and higher-order resting-state networks than brain structural morphometry or education. Particularly, we found that higher segregation between the sensorimotor and executive networks predicted higher PBSI-Cog scores. Our results support the notion that brain network functional organization may underly variability in cognitive reserve in late adulthood.
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Affiliation(s)
- Anna West
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USA
| | - Noah Hamlin
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USA
| | - Sophia Frangou
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USA
| | - Gaelle E Doucet
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USA
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16
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Antoniades M, Haas SS, Modabbernia A, Bykowsky O, Frangou S, Borgwardt S, Schmidt A. Personalized Estimates of Brain Structural Variability in Individuals With Early Psychosis. Schizophr Bull 2021; 47:1029-1038. [PMID: 33547470 PMCID: PMC8266574 DOI: 10.1093/schbul/sbab005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Early psychosis in first-episode psychosis (FEP) and clinical high-risk (CHR) individuals has been associated with alterations in mean regional measures of brain morphology. Examination of variability in brain morphology could assist in quantifying the degree of brain structural heterogeneity in clinical relative to healthy control (HC) samples. METHODS Structural magnetic resonance imaging data were obtained from CHR (n = 71), FEP (n = 72), and HC individuals (n = 55). Regional brain variability in cortical thickness (CT), surface area (SA), and subcortical volume (SV) was assessed with the coefficient of variation (CV). Furthermore, the person-based similarity index (PBSI) was employed to quantify the similarity of CT, SA, and SV profile of each individual to others within the same diagnostic group. Normative modeling of the PBSI-CT, PBSI-SA, and PBSI-SV was used to identify CHR and FEP individuals whose scores deviated markedly from those of the healthy individuals. RESULTS There was no effect of diagnosis on the CV for any regional measure (P > .38). CHR and FEP individuals differed significantly from the HC group in terms of PBSI-CT (P < .0001), PBSI-SA (P < .0001), and PBSI-SV (P = .01). In the clinical groups, normative modeling identified 32 (22%) individuals with deviant PBSI-CT, 12 (8.4%) with deviant PBSI-SA, and 21 (15%) with deviant PBSI-SV; differences of small effect size indicated that individuals with deviant PBSI scores had lower IQ and higher psychopathology. CONCLUSIONS Examination of brain structural variability in early psychosis indicated heterogeneity at the level of individual profiles and encourages further large-scale examination to identify individuals that deviate markedly from normative reference data.
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Affiliation(s)
- Mathilde Antoniades
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Oleg Bykowsky
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
- Centre for Brain Health, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Stefan Borgwardt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - André Schmidt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- To whom correspondence should be addressed; Wilhelm Klein-Strasse 27, 4002 Basel, Switzerland; tel: +41 0(61) 325 59 29, fax: +41 (0)61 325 55 82, e-mail:
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17
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Zanin M, Aitya NA, Basilio J, Baumbach J, Benis A, Behera CK, Bucholc M, Castiglione F, Chouvarda I, Comte B, Dao TT, Ding X, Pujos-Guillot E, Filipovic N, Finn DP, Glass DH, Harel N, Iesmantas T, Ivanoska I, Joshi A, Boudjeltia KZ, Kaoui B, Kaur D, Maguire LP, McClean PL, McCombe N, de Miranda JL, Moisescu MA, Pappalardo F, Polster A, Prasad G, Rozman D, Sacala I, Sanchez-Bornot JM, Schmid JA, Sharp T, Solé-Casals J, Spiwok V, Spyrou GM, Stalidzans E, Stres B, Sustersic T, Symeonidis I, Tieri P, Todd S, Van Steen K, Veneva M, Wang DH, Wang H, Wang H, Watterson S, Wong-Lin K, Yang S, Zou X, Schmidt HH. An Early Stage Researcher's Primer on Systems Medicine Terminology. NETWORK AND SYSTEMS MEDICINE 2021; 4:2-50. [PMID: 33659919 PMCID: PMC7919422 DOI: 10.1089/nsm.2020.0003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/27/2020] [Indexed: 12/19/2022] Open
Abstract
Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.
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Affiliation(s)
- Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Nadim A.A. Aitya
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - José Basilio
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Jan Baumbach
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Arriel Benis
- Faculty of Technology Management, Holon Institute of Technology (HIT), Holon, Israel
| | - Chandan K. Behera
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Magda Bucholc
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Ioanna Chouvarda
- Lab of Computing, Medical Informatics, and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Blandine Comte
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Tien-Tuan Dao
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Xuemei Ding
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Estelle Pujos-Guillot
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Nenad Filipovic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - David P. Finn
- Pharmacology and Therapeutics, School of Medicine, Galway Neuroscience Centre, National University of Ireland, Galway, Republic of Ireland
| | - David H. Glass
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Nissim Harel
- Faculty of Sciences, Holon Institute of Technology (HIT), Holon, Israel
| | - Tomas Iesmantas
- Department of Mathematics and Natural Sciences, Kaunas University of Technology, Kaunas, Lithuania
| | - Ilinka Ivanoska
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, Macedonia
| | - Alok Joshi
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Karim Zouaoui Boudjeltia
- Laboratory of Experimental Medicine (ULB 222), Medicine Faculty, Université libre de Bruxelles, CHU de Charleroi, Charleroi, Belgium
| | - Badr Kaoui
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Daman Kaur
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Liam P. Maguire
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Paula L. McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Niamh McCombe
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - João Luís de Miranda
- Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre, Portalegre, Portugal
- Centro de Recursos Naturais e Ambiente (CERENA), Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | | | | | - Annikka Polster
- Centre for Molecular Medicine Norway (NCMM), Forskningparken, Oslo, Norway
| | - Girijesh Prasad
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Ioan Sacala
- Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Jose M. Sanchez-Bornot
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Johannes A. Schmid
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Trevor Sharp
- Department of Pharmacology, University of Oxford, Oxford, United Kingdom
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic–Central University of Catalonia, Vic, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- College of Artificial Intelligence, Nankai University, Tianjin, China
| | - Vojtěch Spiwok
- Department of Biochemistry and Microbiology, University of Chemistry and Technology, Prague, Czech Republic
| | - George M. Spyrou
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Egils Stalidzans
- Computational Systems Biology Group, Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Blaž Stres
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Tijana Sustersic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - Ioannis Symeonidis
- Center for Research and Technology Hellas, Hellenic Institute of Transport, Thessaloniki, Greece
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Stephen Todd
- Altnagelvin Area Hospital, Western Health and Social Care Trust, Altnagelvin, United Kingdom
| | - Kristel Van Steen
- BIO3-Systems Genetics, GIGA-R, University of Liege, Liege, Belgium
- BIO3-Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Da-Hui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, and School of Systems Science, Beijing Normal University, Beijing, China
| | - Haiying Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Hui Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry, United Kingdom
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Su Yang
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Xin Zou
- Shanghai Centre for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Harald H.H.W. Schmidt
- Faculty of Health, Medicine & Life Science, Maastricht University, Maastricht, The Netherlands
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18
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Bergamelli E, Del Fabro L, Delvecchio G, D’Agostino A, Brambilla P. The Impact of Lithium on Brain Function in Bipolar Disorder: An Updated Review of Functional Magnetic Resonance Imaging Studies. CNS Drugs 2021; 35:1275-1287. [PMID: 34773217 PMCID: PMC9537229 DOI: 10.1007/s40263-021-00869-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/18/2021] [Indexed: 11/13/2022]
Abstract
Lithium remains a gold standard treatment for bipolar disorder (BD), and functional magnetic resonance imaging (fMRI) studies have contributed to clarifying its impact on neural circuitries in affected individuals. However, the specific neurobiological mechanisms through which lithium exerts its effects on brain function are not fully understood. In this review, we aimed to summarize the results of recent fMRI studies evaluating the impact of lithium on brain functional activity and connectivity in patients diagnosed with BD. We performed a literature search of available sources found in the PubMed database reported in English since 2016, when the last available review on this topic was published. Five fMRI studies in resting-state condition and six studies performed during the execution of emotional tasks met the inclusion criteria. Overall, the available evidence supports normalizing effects of lithium on brain activity and connectivity. Most of these studies reported a normalization in prefrontal regions and interconnected areas involved in emotion regulation and processing, regardless of the task employed. Importantly, lithium treatment showed distinct patterns of activity/connectivity changes compared with other treatments. Finally, lithium modulation of neural circuitries was found to be associated with clinical improvement in BD. These results are consistent with the hypothesis that selective abnormalities in neural circuitries supporting emotion processing and regulation improve during lithium treatment in BD. However, the heterogeneity of the examined studies regarding study design, sample selection, and analysis methods might limit the generalizability of the findings and lead to difficulties in comparing the results. Therefore, in future studies, larger cohorts and homogeneous experimental tasks are needed to further corroborate these findings.
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Affiliation(s)
- Emilio Bergamelli
- grid.415093.a0000 0004 1793 3800Department of Mental Health and Addiction, ASST Santi Paolo e Carlo, San Paolo Hospital, Milan, Italy ,grid.4708.b0000 0004 1757 2822Department of Health Sciences, University of Milan, Milan, Italy
| | - Lorenzo Del Fabro
- grid.4708.b0000 0004 1757 2822Department of Pathophysiology and Transplantation, University of Milan, via F. Sforza 35, 20122 Milan, Italy ,grid.414818.00000 0004 1757 8749Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Armando D’Agostino
- grid.415093.a0000 0004 1793 3800Department of Mental Health and Addiction, ASST Santi Paolo e Carlo, San Paolo Hospital, Milan, Italy ,grid.4708.b0000 0004 1757 2822Department of Health Sciences, University of Milan, Milan, Italy
| | - Paolo Brambilla
- grid.4708.b0000 0004 1757 2822Department of Pathophysiology and Transplantation, University of Milan, via F. Sforza 35, 20122 Milan, Italy ,grid.414818.00000 0004 1757 8749Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
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19
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Cox SR, Harris MA, Ritchie SJ, Buchanan CR, Valdés Hernández MC, Corley J, Taylor AM, Madole JW, Harris SE, Whalley HC, McIntosh AM, Russ TC, Bastin ME, Wardlaw JM, Deary IJ, Tucker-Drob EM. Three major dimensions of human brain cortical ageing in relation to cognitive decline across the eighth decade of life. Mol Psychiatry 2021; 26:2651-2662. [PMID: 33398085 PMCID: PMC8254824 DOI: 10.1038/s41380-020-00975-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 11/17/2020] [Accepted: 11/30/2020] [Indexed: 12/28/2022]
Abstract
Different brain regions can be grouped together, based on cross-sectional correlations among their cortical characteristics; this patterning has been used to make inferences about ageing processes. However, cross-sectional brain data conflate information on ageing with patterns that are present throughout life. We characterised brain cortical ageing across the eighth decade of life in a longitudinal ageing cohort, at ages ~73, ~76, and ~79 years, with a total of 1376 MRI scans. Volumetric changes among cortical regions of interest (ROIs) were more strongly correlated (average r = 0.805, SD = 0.252) than were cross-sectional volumes of the same ROIs (average r = 0.350, SD = 0.178). We identified a broad, cortex-wide, dimension of atrophy that explained 66% of the variance in longitudinal changes across the cortex. Our modelling also discovered more specific fronto-temporal and occipito-parietal dimensions that were orthogonal to the general factor and together explained an additional 20% of the variance. The general factor was associated with declines in general cognitive ability (r = 0.431, p < 0.001) and in the domains of visuospatial ability (r = 0.415, p = 0.002), processing speed (r = 0.383, p < 0.001) and memory (r = 0.372, p < 0.001). Individual differences in brain cortical atrophy with ageing are manifest across three broad dimensions of the cerebral cortex, the most general of which is linked with cognitive declines across domains. Longitudinal approaches are invaluable for distinguishing lifelong patterns of brain-behaviour associations from patterns that are specific to aging.
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Affiliation(s)
- S. R. Cox
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Department of Psychology, The University of Edinburgh, Edinburgh, UK ,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - M. A. Harris
- grid.4305.20000 0004 1936 7988Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - S. J. Ritchie
- grid.13097.3c0000 0001 2322 6764Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK
| | - C. R. Buchanan
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Department of Psychology, The University of Edinburgh, Edinburgh, UK ,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - M. C. Valdés Hernández
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - J. Corley
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - A. M. Taylor
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - J. W. Madole
- grid.55460.320000000121548364Department of Psychology, University of Texas, Austin, TX USA
| | - S. E. Harris
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - H. C. Whalley
- grid.4305.20000 0004 1936 7988Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - A. M. McIntosh
- grid.4305.20000 0004 1936 7988Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - T. C. Russ
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Division of Psychiatry, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Alzheimer Scotland Dementia Research Centre, The University of Edinburgh, Edinburgh, UK
| | - M. E. Bastin
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - J. M. Wardlaw
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988UK Dementia Research Institute at The University of Edinburgh, Edinburgh, UK
| | - I. J. Deary
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - E. M. Tucker-Drob
- grid.55460.320000000121548364Department of Psychology, University of Texas, Austin, TX USA
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20
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Doucet GE, Lin D, Du Y, Fu Z, Glahn DC, Calhoun VD, Turner J, Frangou S. Personalized estimates of morphometric similarity in bipolar disorder and schizophrenia. NPJ SCHIZOPHRENIA 2020; 6:39. [PMID: 33277498 PMCID: PMC7718905 DOI: 10.1038/s41537-020-00128-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 10/06/2020] [Indexed: 12/29/2022]
Abstract
Bipolar disorder and schizophrenia are associated with brain morphometry alterations. This study investigates inter-individual variability in brain structural profiles, both within diagnostic groups and between patients and healthy individuals. Brain morphometric measures from three independent samples of patients with schizophrenia (n = 168), bipolar disorder (n = 122), and healthy individuals (n = 180) were modeled as single vectors to generated individualized profiles of subcortical volumes and regional cortical thickness. These profiles were then used to compute a person-based similarity index (PBSI) for subcortical volumes and for regional cortical thickness, to quantify the within-group similarity of the morphometric profile of each individual to that of the other participants in the same diagnostic group. There was no effect of diagnosis on the PBSI for subcortical volumes. In contrast, compared to healthy individuals, the PBSI for cortical thickness was lower in patients with schizophrenia (effect size = 0.4, p ≤ 0.0002), but not in patients with bipolar disorder. The results were robust and reproducible across samples. We conclude that disease mechanisms for these disorders produce modest inter-individual variations in brain morphometry that should be considered in future studies attempting to cluster patients in subgroups.
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Affiliation(s)
- Gaelle E Doucet
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Boys Town National Research Hospital, Omaha, NE, USA
| | - Dongdong Lin
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.,School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - David C Glahn
- Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's Hospital, Harvard University, Boston, MA, USA
| | - Vincent D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Jessica Turner
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.,Department of Psychology, Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.
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21
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Person-based similarity in brain structure and functional connectivity in bipolar disorder. J Affect Disord 2020; 276:38-44. [PMID: 32697714 PMCID: PMC7568424 DOI: 10.1016/j.jad.2020.06.041] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 05/26/2020] [Accepted: 06/16/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Bipolar disorder shows significant variability in clinical presentation. Here we adopt a personalized approach to quantify the brain structural and functional similarity of each individual patient to other patients and to healthy individuals. METHODS Brain morphometric and resting-state functional connectivity measures from two independent samples of patients with bipolar disorder and healthy individuals (total number of participants=215) were modeled as single vectors to generated individualized morphometric and connectivity profiles. These profiles were then used to compute a person-based similarity indices which quantified the similarity in neuroimaging profiles amongst patients and between patients and health individuals. RESULTS The morphometric and connectivity profiles of patients showed within-diagnosis similarity which was comparable to that observed in healthy individuals. They also showed minimal deviance from those of healthy individuals; the correlation between the profiles of patients and healthy individuals was high (range: 0.71-0.94, p<10-5). The degree of similarity between imaging profiles was associated with IQ (for cortical thickness) and age (functional integration) rather than clinical variables. Patients who were prescribed lithium, compared to those who were not, showed greater similarity to healthy individuals in terms of network integration (t = 2.2, p = 0.03). LIMITATIONS We focused on patients with Bipolar disorder, type I only. CONCLUSIONS High inter-individual similarity in neuroimaging profiles was observed amongst patients with bipolar disorder and between patients and healthy individuals. We infer that brain alterations associated with bipolar disorder may be nested within the normal biological diversity consistent with the high prevalence of mood symptoms in the general population.
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22
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Janssen J, Díaz-Caneja CM, Alloza C, Schippers A, de Hoyos L, Santonja J, Gordaliza PM, Buimer EEL, van Haren NEM, Cahn W, Arango C, Kahn RS, Hulshoff Pol HE, Schnack HG. Dissimilarity in Sulcal Width Patterns in the Cortex can be Used to Identify Patients With Schizophrenia With Extreme Deficits in Cognitive Performance. Schizophr Bull 2020; 47:552-561. [PMID: 32964935 PMCID: PMC7965061 DOI: 10.1093/schbul/sbaa131] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Schizophrenia is a biologically complex disorder with multiple regional deficits in cortical brain morphology. In addition, interindividual heterogeneity of cortical morphological metrics is larger in patients with schizophrenia when compared to healthy controls. Exploiting interindividual differences in the severity of cortical morphological deficits in patients instead of focusing on group averages may aid in detecting biologically informed homogeneous subgroups. The person-based similarity index (PBSI) of brain morphology indexes an individual's morphometric similarity across numerous cortical regions amongst a sample of healthy subjects. We extended the PBSI such that it indexes the morphometric similarity of an independent individual (eg, a patient) with respect to healthy control subjects. By employing a normative modeling approach on longitudinal data, we determined an individual's degree of morphometric dissimilarity to the norm. We calculated the PBSI for sulcal width (PBSI-SW) in patients with schizophrenia and healthy control subjects (164 patients and 164 healthy controls; 656 magnetic resonance imaging scans) and associated it with cognitive performance and cortical sulcation index. A subgroup of patients with markedly deviant PBSI-SW showed extreme deficits in cognitive performance and cortical sulcation. Progressive reduction of PBSI-SW in the schizophrenia group relative to healthy controls was driven by these deviating individuals. By explicitly leveraging interindividual differences in the severity of PBSI-SW deficits, neuroimaging-driven subgrouping of patients is feasible. As such, our results pave the way for future applications of morphometric similarity indices for subtyping of clinical populations.
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Affiliation(s)
- Joost Janssen
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, C/ Ibiza, 43. 28009 Madrid, Spain,Ciber del Área de Salud Mental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain,Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands,To whom correspondence should be addressed; tel: 0034914265005, fax: 0034914265004, e-mail:
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, C/ Ibiza, 43. 28009 Madrid, Spain,Ciber del Área de Salud Mental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain,School of Medicine, Universidad Complutense, Madrid, Spain
| | - Clara Alloza
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, C/ Ibiza, 43. 28009 Madrid, Spain,Ciber del Área de Salud Mental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Anouck Schippers
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, C/ Ibiza, 43. 28009 Madrid, Spain,Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lucía de Hoyos
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, C/ Ibiza, 43. 28009 Madrid, Spain
| | - Javier Santonja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, C/ Ibiza, 43. 28009 Madrid, Spain
| | - Pedro M Gordaliza
- Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid, Madrid, Spain
| | - Elizabeth E L Buimer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Neeltje E M van Haren
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands,Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Sophia Children’s Hospital, Rotterdam, The Netherlands
| | - Wiepke Cahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, C/ Ibiza, 43. 28009 Madrid, Spain,Ciber del Área de Salud Mental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain,School of Medicine, Universidad Complutense, Madrid, Spain
| | - René S Kahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hugo G Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
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23
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Wall J, Xie H, Wang X. Interaction of Sleep and Cortical Structural Maintenance From an Individual Person Microlongitudinal Perspective and Implications for Precision Medicine Research. Front Neurosci 2020; 14:769. [PMID: 32848551 PMCID: PMC7411006 DOI: 10.3389/fnins.2020.00769] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 06/30/2020] [Indexed: 12/18/2022] Open
Abstract
Sleep and maintenance of brain structure are essential for the continuity of a person's cognitive/mental health. Interestingly, whether normal structural maintenance of the brain and sleep continuously interact in some way over day-week-month times has never been assessed at an individual-person level. This study used unconventional microlongitudinal sampling, structural magnetic resonance imaging, and n-of-1 analyses to assess normal interactions between fluctuations in the structural maintenance of cerebral cortical thickness and sleep duration for day, week, and multi-week intervals over a 6-month period in a healthy adult man. Correlation and time series analyses provided indications of "if-then," i.e., "if" this preceded "then" this followed, sleep-to-thickness maintenance and thickness maintenance-to-sleep bidirectional inverse interactions. Inverse interaction patterns were characterized by concepts of graded influences across nights, bilaterally positive relationships, continuity across successive weeks, and longer delayed/prolonged effects in the thickness maintenance-to-sleep than sleep-to-thickness maintenance direction. These interactions are proposed to involve normal circadian/allostatic/homeostatic mechanisms that continuously influence, and are influenced by, cortical substrate remodeling/turnover and sleep/wake cycle. Understanding interactions of individual person "-omics" is becoming a central interest in precision medicine research. The present n-of-1 findings contribute to this interest and have implications for precision medicine research use of a person's cortical structural and sleep "-omics" to optimize the continuous maintenance of that individual's cortical structure, sleep, and cognitive/mental health.
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Affiliation(s)
- John Wall
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
| | - Hong Xie
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
| | - Xin Wang
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
- Department of Psychiatry, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
- Department of Radiology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
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24
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Vermunt L, Dicks E, Wang G, Dincer A, Flores S, Keefe SJ, Berman SB, Cash DM, Chhatwal JP, Cruchaga C, Fox NC, Ghetti B, Graff-Radford NR, Hassenstab J, Karch CM, Laske C, Levin J, Masters CL, McDade E, Mori H, Morris JC, Noble JM, Perrin RJ, Schofield PR, Xiong C, Scheltens P, Visser PJ, Bateman RJ, Benzinger TLS, Tijms BM, Gordon BA. Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease. Brain Commun 2020; 2:fcaa102. [PMID: 32954344 PMCID: PMC7475695 DOI: 10.1093/braincomms/fcaa102] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 05/25/2020] [Accepted: 06/18/2020] [Indexed: 12/12/2022] Open
Abstract
Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer's disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer's disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset -9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer's disease, which is alike sporadic Alzheimer's disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer's disease.
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Affiliation(s)
- Lisa Vermunt
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Amsterdam, UMC, VU University, Netherlands
| | - Ellen Dicks
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Amsterdam, UMC, VU University, Netherlands
| | - Guoqiao Wang
- Division of Biostatistics, Washington University in St. Louis, MO, USA
| | - Aylin Dincer
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
| | - Shaney Flores
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
| | - Sarah J Keefe
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
| | - Sarah B Berman
- Department of Neurology, Alzheimer’s Disease Research Center, Pittsburgh, PA
- Pittsburgh Institute for Neurodegenerative Diseases, University of Pittsburgh, Pittsburgh, PA
| | - David M Cash
- UCL Queen Square Institute of Neurology, London, UK
| | - Jasmeer P Chhatwal
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis, MO, USA
- Hope Center for Neurological Disorders, . Washington University in St. Louis, MO, USA
- NeuroGenomics and Informatics, Washington University in St. Louis, St. Louis, MO, USA
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, UK
- Dementia Research Institute at UCL, UCL Institute of Neurology, London, UK
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University, IN, USA
| | | | - Jason Hassenstab
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, MO, USA
- Department of Psychological & Brain Sciences, Washington University in St. Louis, MO, USA
| | - Celeste M Karch
- Department of Psychiatry, Washington University in St. Louis, MO, USA
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Germany
| | | | - Colin L Masters
- Florey Institute, Melbourne, Australia
- The University of Melbourne, Melbourne, Australia
| | - Eric McDade
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, MO, USA
| | - Hiroshi Mori
- Department of Clinical Neuroscience, Osaka City University Medical School, Japan
| | - John C Morris
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, MO, USA
| | - James M Noble
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain, GH Sergievsky Center, Columbia University Medical Center, NY, USA
| | - Richard J Perrin
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis MO, USA
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, Australia
- School of Medical Sciences, UNSW Sydney, Sydney, Australia
| | - Chengjie Xiong
- Division of Biostatistics, Washington University in St. Louis, MO, USA
| | - Philip Scheltens
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Amsterdam, UMC, VU University, Netherlands
| | - Pieter Jelle Visser
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Amsterdam, UMC, VU University, Netherlands
- Department of Psychiatry and Neuropsychology, Maastricht University, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Netherlands
| | - Randall J Bateman
- Department of Psychiatry, Washington University in St. Louis, MO, USA
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, MO, USA
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA
| | - Betty M Tijms
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Amsterdam, UMC, VU University, Netherlands
| | - Brian A Gordon
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA
- Department of Psychological & Brain Sciences, Washington University in St. Louis, MO, USA
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25
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Yu Y, Yan LF, Sun Q, Hu B, Zhang J, Yang Y, Dai YJ, Cui WX, Xiu SJ, Hu YC, Heng CN, Liu QQ, Hou JF, Pan YY, Zhai LH, Han TH, Cui GB, Wang W. Neurovascular decoupling in type 2 diabetes mellitus without mild cognitive impairment: Potential biomarker for early cognitive impairment. Neuroimage 2019; 200:644-658. [PMID: 31252056 DOI: 10.1016/j.neuroimage.2019.06.058] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 06/22/2019] [Accepted: 06/24/2019] [Indexed: 12/15/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) is a significant risk factor for mild cognitive impairment (MCI) and the acceleration of MCI to dementia. The high glucose level induce disturbance of neurovascular (NV) coupling is suggested to be one potential mechanism, however, the neuroimaging evidence is still lacking. To assess the NV decoupling pattern in early diabetic status, 33 T2DM without MCI patients and 33 healthy control subjects were prospectively enrolled. Then, they underwent resting state functional MRI and arterial spin labeling imaging to explore the hub-based networks and to estimate the coupling of voxel-wise cerebral blood flow (CBF)-degree centrality (DC), CBF-mean amplitude of low-frequency fluctuation (mALFF) and CBF- mean regional homogeneity (mReHo). We further evaluated the relationship between NV coupling pattern and cognitive performance (false discovery rate corrected). T2DM without MCI patients displayed significant decrease in the absolute CBF-mALFF, CBF-mReHo coupling of CBFnetwork and in the CBF-DC coupling of DCnetwork. Besides, networks which involved CBF and DC hubs mainly located in the default mode network (DMN). Furthermore, less severe disease and better cognitive performance in T2DM patients were significantly correlated with higher coupling of CBF-DC, CBF-mALFF or CBF-mReHo, especially for the cognitive dimensions of general function and executive function. Thus, coupling of CBF-DC, CBF-mALFF and CBF-mReHo may serve as promising indicators to reflect NV coupling state and to explain the T2DM related early cognitive impairment.
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Affiliation(s)
- Ying Yu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Lin-Feng Yan
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Qian Sun
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Bo Hu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Jin Zhang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Yang Yang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Yu-Jie Dai
- Department of Clinical Nutrition, Xijing Hospital, Fourth Military Medical University (Air Force Medical University), 15 West Changle Road, Xi'an, 710032, Shaanxi, China.
| | - Wu-Xun Cui
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Si-Jie Xiu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Yu-Chuan Hu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Chun-Ni Heng
- Department of Endocrinology, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Qing-Quan Liu
- Department of Endocrinology, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Jun-Feng Hou
- Department of Endocrinology, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Yu-Yun Pan
- Student Brigade, Fourth Military Medical University (Air Force Medical University), 169 Changle Road, Xi'an, 710032, Shaanxi, China.
| | - Liang-Hao Zhai
- Student Brigade, Fourth Military Medical University (Air Force Medical University), 169 Changle Road, Xi'an, 710032, Shaanxi, China.
| | - Teng-Hui Han
- Student Brigade, Fourth Military Medical University (Air Force Medical University), 169 Changle Road, Xi'an, 710032, Shaanxi, China.
| | - Guang-Bin Cui
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Wen Wang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
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