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Chen Z, Tang Y, Liu X, Li W, Hu Y, Hu B, Xu T, Zhang R, Xia L, Zhang JX, Xiao Z, Chen J, Feng Z, Zhou Y, He Q, Qiu J, Lei X, Chen H, Qin S, Feng T. Edge-centric connectome-genetic markers of bridging factor to comorbidity between depression and anxiety. Nat Commun 2024; 15:10560. [PMID: 39632897 PMCID: PMC11618586 DOI: 10.1038/s41467-024-55008-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Accepted: 11/25/2024] [Indexed: 12/07/2024] Open
Abstract
Depression-anxiety comorbidity is commonly attributed to the occurrence of specific symptoms bridging the two disorders. However, the significant heterogeneity of most bridging symptoms presents challenges for psychopathological interpretation and clinical applicability. Here, we conceptually established a common bridging factor (cb factor) to characterize a general structure of these bridging symptoms, analogous to the general psychopathological p factor. We identified a cb factor from 12 bridging symptoms in depression-anxiety comorbidity network. Moreover, this cb factor could be predicted using edge-centric connectomes with robust generalizability, and was characterized by connectome patterns in attention and frontoparietal networks. In an independent twin cohort, we found that these patterns were moderately heritable, and identified their genetic connectome-transcriptional markers that were associated with the neurobiological enrichment of vasculature and cerebellar development, particularly during late-childhood-to-young-adulthood periods. Our findings revealed a general factor of bridging symptoms and its neurobiological architectures, which enriched neurogenetic understanding of depression-anxiety comorbidity.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science, School of Psychology, Third Military Medical University, Chongqing, China.
- School of Psychology, Southwest University, Chongqing, China.
- Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, China.
| | - Yancheng Tang
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science, School of Psychology, Third Military Medical University, Chongqing, China
| | - Wei Li
- Experimental Research Center for Medical and Psychological Science, School of Psychology, Third Military Medical University, Chongqing, China
| | - Yuanyuan Hu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Bowen Hu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ting Xu
- School of Psychology, Southwest University, Chongqing, China
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Rong Zhang
- School of Psychology, Southwest University, Chongqing, China
| | - Lei Xia
- Experimental Research Center for Medical and Psychological Science, School of Psychology, Third Military Medical University, Chongqing, China
| | - Jing-Xuan Zhang
- Experimental Research Center for Medical and Psychological Science, School of Psychology, Third Military Medical University, Chongqing, China
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ji Chen
- Center for Brain Health and Brain Technology, Global Institute of Future Technology, Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science, School of Psychology, Third Military Medical University, Chongqing, China
| | - Yuan Zhou
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
| | - Qinghua He
- School of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - Xu Lei
- School of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- School of Psychology, Southwest University, Chongqing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Tingyong Feng
- School of Psychology, Southwest University, Chongqing, China.
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2
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Khan AF, Iturria-Medina Y. Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms. Transl Psychiatry 2024; 14:386. [PMID: 39313512 PMCID: PMC11420368 DOI: 10.1038/s41398-024-03073-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 08/20/2024] [Accepted: 08/27/2024] [Indexed: 09/25/2024] Open
Abstract
From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.
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Affiliation(s)
- Ahmed Faraz Khan
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, Canada
| | - Yasser Iturria-Medina
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Canada.
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, Canada.
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3
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Shen T, McMillan CT. Evolutionary perspectives on mRNA signatures of neurodegeneration-related brain remodelling. Brain 2024; 147:2906-2908. [PMID: 39155065 PMCID: PMC11370795 DOI: 10.1093/brain/awae267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2024] [Accepted: 08/04/2024] [Indexed: 08/20/2024] Open
Abstract
This scientific commentary refers to ‘Frontotemporal lobar degeneration targets brain regions linked to expression of recently evolved genes’ by Pasquini et al. (https://doi.org/10.1093/brain/awae205).
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Affiliation(s)
- Ting Shen
- Department of Neurology, Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Corey T McMillan
- Department of Neurology, Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Tu Y, Liu Y, Fan S, Weng J, Li M, Zhang F, Fu Y, Hu J. Relationship between brain white matter damage and grey matter atrophy in hereditary spastic paraplegia types 4 and 5. Eur J Neurol 2024; 31:e16310. [PMID: 38651515 PMCID: PMC11235729 DOI: 10.1111/ene.16310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 03/11/2024] [Accepted: 04/04/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND AND PURPOSE White matter (WM) damage is the main target of hereditary spastic paraplegia (HSP), but mounting evidence indicates that genotype-specific grey matter (GM) damage is not uncommon. Our aim was to identify and compare brain GM and WM damage patterns in HSP subtypes and investigate how gene expression contributes to these patterns, and explore the relationship between GM and WM damage. METHODS In this prospective single-centre cohort study from 2019 to 2022, HSP patients and controls underwent magnetic resonance imaging evaluations. The alterations of GM and WM patterns were compared between groups by applying a source-based morphometry approach. Spearman rank correlation was used to explore the associations between gene expression and GM atrophy patterns in HSP subtypes. Mediation analysis was conducted to investigate the interplay between GM and WM damage. RESULTS Twenty-one spastic paraplegia type 4 (SPG4) patients (mean age 50.7 years ± 12.0 SD, 15 men), 21 spastic paraplegia type 5 (SPG5) patients (mean age 29.1 years ± 12.8 SD, 14 men) and 42 controls (sex- and age-matched) were evaluated. Compared to controls, SPG4 and SPG5 showed similar WM damage but different GM atrophy patterns. GM atrophy patterns in SPG4 and SPG5 were correlated with corresponding gene expression (ρ = 0.30, p = 0.008, ρ = 0.40, p < 0.001, respectively). Mediation analysis indicated that GM atrophy patterns were mediated by WM damage in HSP. CONCLUSIONS Grey matter atrophy patterns were distinct between SPG4 and SPG5 and were not only secondary to WM damage but also associated with disease-related gene expression. CLINICAL TRIAL REGISTRATION NO NCT04006418.
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Affiliation(s)
- Yuqing Tu
- Department of RadiologyFirst Affiliated Hospital of Fujian Medical UniversityFuzhouFujianChina
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated HospitalFujian Medical UniversityFuzhouChina
| | - Ying Liu
- Department of RadiologyFirst Affiliated Hospital of Fujian Medical UniversityFuzhouFujianChina
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated HospitalFujian Medical UniversityFuzhouChina
| | - Shuping Fan
- Department of RadiologyFirst Affiliated Hospital of Fujian Medical UniversityFuzhouFujianChina
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated HospitalFujian Medical UniversityFuzhouChina
| | - Jiaqi Weng
- Department of RadiologyFirst Affiliated Hospital of Fujian Medical UniversityFuzhouFujianChina
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated HospitalFujian Medical UniversityFuzhouChina
| | - Mengcheng Li
- Department of RadiologyFirst Affiliated Hospital of Fujian Medical UniversityFuzhouFujianChina
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated HospitalFujian Medical UniversityFuzhouChina
| | - Fan Zhang
- Department of RadiologyFirst Affiliated Hospital of Fujian Medical UniversityFuzhouFujianChina
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated HospitalFujian Medical UniversityFuzhouChina
| | - Ying Fu
- Department of Neurology and Institute of Neurology, First Affiliated Hospital, Institute of Neuroscience, and Fujian Key Laboratory of Molecular NeurologyFujian Medical UniversityFuzhouFujianChina
| | - Jianping Hu
- Department of RadiologyFirst Affiliated Hospital of Fujian Medical UniversityFuzhouFujianChina
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated HospitalFujian Medical UniversityFuzhouChina
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5
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Lawn T, Howard MA, Turkheimer F, Misic B, Deco G, Martins D, Dipasquale O. From neurotransmitters to networks: Transcending organisational hierarchies with molecular-informed functional imaging. Neurosci Biobehav Rev 2023; 150:105193. [PMID: 37086932 PMCID: PMC10390343 DOI: 10.1016/j.neubiorev.2023.105193] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 04/01/2023] [Accepted: 04/19/2023] [Indexed: 04/24/2023]
Abstract
The human brain exhibits complex interactions across micro, meso-, and macro-scale organisational principles. Recent synergistic multi-modal approaches have begun to link micro-scale information to systems level dynamics, transcending organisational hierarchies and offering novel perspectives into the brain's function and dysfunction. Specifically, the distribution of micro-scale properties (such as receptor density or gene expression) can be mapped onto macro-scale measures from functional MRI to provide novel neurobiological insights. Methodological approaches to enrich functional imaging analyses with molecular information are rapidly evolving, with several streams of research having developed relatively independently, each offering unique potential to explore the trans-hierarchical functioning of the brain. Here, we address the three principal streams of research - spatial correlation, molecular-enriched network, and in-silico whole brain modelling analyses - to provide a critical overview of the different sources of molecular information, how this information can be utilised within analyses of fMRI data, the merits and pitfalls of each methodology, and, through the use of key examples, highlight their promise to shed new light on key domains of neuroscientific inquiry.
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Affiliation(s)
- Timothy Lawn
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Matthew A Howard
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Bratislav Misic
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec, Canada
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, Barcelona 08005, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - Daniel Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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Baik JY, Kim M, Bao J, Long Q, Shen L. Identifying Alzheimer's genes via brain transcriptome mapping. BMC Med Genomics 2022; 15:116. [PMID: 35590321 PMCID: PMC9118564 DOI: 10.1186/s12920-022-01260-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 05/04/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is one of the most common neurodegenerative disorders characterized by progressive decline in cognitive function. Targeted genetic analyses, genome-wide association studies, and imaging genetic analyses have been performed to detect AD risk and protective genes and have successfully identified dozens of AD susceptibility loci. Recently, brain imaging transcriptomics analyses have also been conducted to investigate the relationship between neuroimaging traits and gene expression measures to identify interesting gene-traits associations. These imaging transcriptomic studies typically do not involve the disease outcome in the analysis, and thus the identified brain or transcriptomic markers may not be related or specific to the disease outcome. RESULTS We propose an innovative two-stage approach to identify genes whose expression profiles are related to diagnosis phenotype via brain transcriptome mapping. Specifically, we first map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model. Then, the gene-diagnosis association is assessed by spatially correlating the brain transcriptome map with the diagnostic effect map on the brain-wide imaging traits. To demonstrate the promise of our approach, we apply it to the integrative analysis of the brain transcriptome data from the Allen Human Brain Atlas (AHBA) and the amyloid imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our method identifies 12 genes whose brain-wide transcriptome patterns are highly correlated with six different diagnostic effect maps on the amyloid imaging traits. These 12 genes include four confirmatory findings (i.e., AD genes reported in DisGeNET) and eight novel genes that have not be associated with AD in DisGeNET. CONCLUSION We have proposed a novel disease-related brain transcriptomic mapping method to identify genes whose expression profiles spatially correlated with regional diagnostic effects on a studied brain trait. Our empirical study on the AHBA and ADNI data shows the promise of the approach, and the resulting AD gene discoveries provide valuable information for better understanding biological pathways from transcriptomic signatures to intermediate brain traits and to phenotypic disease outcomes.
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Affiliation(s)
- Jae Young Baik
- grid.25879.310000 0004 1936 8972School of Arts and Sciences, University of Pennsylvania, Philadelphia, USA
| | - Mansu Kim
- grid.411947.e0000 0004 0470 4224Department of Artificial intelligence, Catholic University of Korea, Bucheon, Republic of Korea
| | - Jingxuan Bao
- grid.25879.310000 0004 1936 8972Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - Qi Long
- grid.25879.310000 0004 1936 8972Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA.
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7
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Lukow PB, Martins D, Veronese M, Vernon AC, McGuire P, Turkheimer FE, Modinos G. Cellular and molecular signatures of in vivo imaging measures of GABAergic neurotransmission in the human brain. Commun Biol 2022; 5:372. [PMID: 35440709 PMCID: PMC9018713 DOI: 10.1038/s42003-022-03268-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 03/14/2022] [Indexed: 11/21/2022] Open
Abstract
Diverse GABAergic interneuron networks orchestrate information processing in the brain. Understanding the principles underlying the organisation of this system in the human brain, and whether these principles are reflected by available non-invasive in vivo neuroimaging methods, is crucial for the study of GABAergic neurotransmission. Here, we use human gene expression data and state-of-the-art imaging transcriptomics to uncover co-expression patterns between genes encoding GABAA receptor subunits and inhibitory interneuron subtype-specific markers, and their association with binding patterns of the gold-standard GABA PET radiotracers [11C]Ro15-4513 and [11C]flumazenil. We found that the inhibitory interneuron marker somatostatin covaries with GABAA receptor-subunit genes GABRA5 and GABRA2, and that their distribution followed [11C]Ro15-4513 binding. In contrast, the inhibitory interneuron marker parvalbumin covaried with GABAA receptor-subunit genes GABRA1, GABRB2 and GABRG2, and their distribution tracked [11C]flumazenil binding. Our findings indicate that existing PET radiotracers may provide complementary information about key components of the GABAergic system.
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Affiliation(s)
- Paulina Barbara Lukow
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, London, UK.
| | - Daniel Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, London, UK
- NIHR Maudsley Biomedical Research Centre, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Mattia Veronese
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, London, UK
- NIHR Maudsley Biomedical Research Centre, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
- Department of Information Engineering, University of Padua, Via Giovanni Gradenigo, 6, 35131, Padova, PD, Italy
| | - Anthony Christopher Vernon
- Department of Basic & Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, 5 Cutcombe Road, Brixton, London, SE5 9RT, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, New Hunt's House, Guy's Campus, London, UK
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, London, UK
- NIHR Maudsley Biomedical Research Centre, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Federico Edoardo Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, London, UK
| | - Gemma Modinos
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, London, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, New Hunt's House, Guy's Campus, London, UK
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8
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Martins D, Giacomel A, Williams SCR, Turkheimer F, Dipasquale O, Veronese M. Imaging transcriptomics: Convergent cellular, transcriptomic, and molecular neuroimaging signatures in the healthy adult human brain. Cell Rep 2021; 37:110173. [PMID: 34965413 DOI: 10.1016/j.celrep.2021.110173] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/30/2021] [Accepted: 12/03/2021] [Indexed: 12/12/2022] Open
Abstract
The integration of transcriptomic and neuroimaging data, "imaging transcriptomics," has recently emerged to generate hypotheses about potential biological pathways underlying regional variability in neuroimaging features. However, the validity of this approach is yet to be examined in depth. Here, we sought to bridge this gap by performing transcriptomic decoding of the regional distribution of well-known molecular markers spanning different elements of the biology of the healthy human brain. Imaging transcriptomics identifies biological and cell pathways that are consistent with the known biology of a wide range of molecular neuroimaging markers. The extent to which it can capture patterns of gene expression that align well with elements of the biology of the neuroinflammatory axis, at least in healthy controls without a proinflammatory challenge, is inconclusive. Imaging transcriptomics might constitute an interesting approach to improve our understanding of the biological pathways underlying regional variability in a wide range of neuroimaging phenotypes.
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Affiliation(s)
- Daniel Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK.
| | - Alessio Giacomel
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Steven C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Mattia Veronese
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK; Department of Information Engineering, University of Padua, Via Gradenigo, 6/b, 35131 Padova, Italy.
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9
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Kesler SR, Tang T, Henneghan AM, Wright M, Gaber MW, Palesh O. Cross-Sectional Characterization of Local Brain Network Connectivity Pre and Post Breast Cancer Treatment and Distinct Association With Subjective Cognitive and Psychological Function. Front Neurol 2021; 12:746493. [PMID: 34777216 PMCID: PMC8586413 DOI: 10.3389/fneur.2021.746493] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 10/05/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: We aimed to characterize local brain network connectivity in long-term breast cancer survivors compared to newly diagnosed patients. Methods: Functional magnetic resonance imaging (fMRI) and subjective cognitive and psychological function data were obtained from a group of 76 newly diagnosed, pre-treatment female patients with breast cancer (mean age 57 ± 7 years) and a separate group of 80, post-treatment, female breast cancer survivors (mean age 58 ± 8; mean time since treatment 44 ± 43 months). The network-based statistic (NBS) was used to compare connectivity of local brain edges between groups. Hubs were defined as nodes with connectivity indices one standard deviation or more above network mean and were further classified as provincial (higher intra-subnetwork connectivity) or connector (higher inter-subnetwork connectivity) using the participation coefficient. We determined the hub status of nodes encompassing significantly different edges and correlated the centralities of edges with behavioral measures. Results: The post-treatment group demonstrated significantly lower subjective cognitive function (W = 3,856, p = 0.004) but there were no group differences in psychological distress (W = 2,866, p = 0.627). NBS indicated significantly altered connectivity (p < 0.042, corrected) in the post-treatment group compared to the pre-treatment group largely in temporal, frontal-temporal and temporal-parietal areas. The majority of the regions projecting these connections (78%) met criteria for hub status and significantly less of these hubs were connectors in the post-treatment group (z = 1.85, p = 0.031). Subjective cognitive function and psychological distress were correlated with largely non-overlapping edges in the post-treatment group (p < 0.05). Conclusion: Widespread functional network alterations are evident in long-term survivors of breast cancer compared to newly diagnosed patients. We also demonstrated that there are both overlapping and unique brain network signatures for subjective cognitive function vs. psychological distress.
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Affiliation(s)
- Shelli R. Kesler
- School of Nursing, University of Texas at Austin, Austin, TX, United States
| | - Tien Tang
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States
| | | | - Michelle Wright
- School of Nursing, University of Texas at Austin, Austin, TX, United States
| | - M. Waleed Gaber
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States
| | - Oxana Palesh
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
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Arnatkeviciute A, Fulcher BD, Bellgrove MA, Fornito A. Imaging Transcriptomics of Brain Disorders. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 2:319-331. [PMID: 36324650 PMCID: PMC9616271 DOI: 10.1016/j.bpsgos.2021.10.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/06/2021] [Accepted: 10/11/2021] [Indexed: 01/05/2023] Open
Abstract
Noninvasive neuroimaging is a powerful tool for quantifying diverse aspects of brain structure and function in vivo, and it has been used extensively to map the neural changes associated with various brain disorders. However, most neuroimaging techniques offer only indirect measures of underlying pathological mechanisms. The recent development of anatomically comprehensive gene expression atlases has opened new opportunities for studying the transcriptional correlates of noninvasively measured neural phenotypes, offering a rich framework for evaluating pathophysiological hypotheses and putative mechanisms. Here, we provide an overview of some fundamental methods in imaging transcriptomics and outline their application to understanding brain disorders of neurodevelopment, adulthood, and neurodegeneration. Converging evidence indicates that spatial variations in gene expression are linked to normative changes in brain structure during age-related maturation and neurodegeneration that are in part associated with cell-specific gene expression markers of gene expression. Transcriptional correlates of disorder-related neuroimaging phenotypes are also linked to transcriptionally dysregulated genes identified in ex vivo analyses of patient brains. Modeling studies demonstrate that spatial patterns of gene expression are involved in regional vulnerability to neurodegeneration and the spread of disease across the brain. This growing body of work supports the utility of transcriptional atlases in testing hypotheses about the molecular mechanism driving disease-related changes in macroscopic neuroimaging phenotypes.
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Affiliation(s)
- Aurina Arnatkeviciute
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
- Address correspondence to Aurina Arnatkeviciute, Ph.D
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, Camperdown, New South Wales, Australia
| | - Mark A. Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
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