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Totzek JF, Chakravarty MM, Joober R, Malla A, Shah JL, Raucher-Chéné D, Young AL, Hernaus D, Lepage M, Lavigne KM. Longitudinal inference of multiscale markers in psychosis: from hippocampal centrality to functional outcome. Mol Psychiatry 2024:10.1038/s41380-024-02549-x. [PMID: 38605172 DOI: 10.1038/s41380-024-02549-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 03/28/2024] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
Multiscale neuroscience conceptualizes mental illness as arising from aberrant interactions across and within multiple biopsychosocial scales. We leverage this framework to propose a multiscale disease progression model of psychosis, in which hippocampal-cortical dysconnectivity precedes impairments in episodic memory and social cognition, which lead to more severe negative symptoms and lower functional outcome. As psychosis represents a heterogeneous collection of biological and behavioral alterations that evolve over time, we further predict this disease progression for a subtype of the patient sample, with other patients showing normal-range performance on all variables. We sampled data from two cross-sectional datasets of first- and multi-episode psychosis, resulting in a sample of 163 patients and 119 non-clinical controls. To address our proposed disease progression model and evaluate potential heterogeneity, we applied a machine-learning algorithm, SuStaIn, to the patient data. SuStaIn uniquely integrates clustering and disease progression modeling and identified three patient subtypes. Subtype 0 showed normal-range performance on all variables. In comparison, Subtype 1 showed lower episodic memory, social cognition, functional outcome, and higher negative symptoms, while Subtype 2 showed lower hippocampal-cortical connectivity and episodic memory. Subtype 1 deteriorated from episodic memory to social cognition, negative symptoms, functional outcome to bilateral hippocampal-cortical dysconnectivity, while Subtype 2 deteriorated from bilateral hippocampal-cortical dysconnectivity to episodic memory and social cognition, functional outcome to negative symptoms. This first application of SuStaIn in a multiscale psychiatric model provides distinct disease trajectories of hippocampal-cortical connectivity, which might underlie the heterogeneous behavioral manifestations of psychosis.
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Affiliation(s)
- Jana F Totzek
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - M Mallar Chakravarty
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Ridha Joober
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Ashok Malla
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Jai L Shah
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Delphine Raucher-Chéné
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Alexandra L Young
- Department of Computer Science, University College London, London, United Kingdom
| | - Dennis Hernaus
- Department of Psychiatry & Neuropsychology, School for Mental Health and NeuroScience MHeNS, Maastricht University, Maastricht, The Netherlands
| | - Martin Lepage
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Katie M Lavigne
- Department of Psychiatry, McGill University, Montreal, QC, Canada.
- Douglas Research Centre, Montreal, QC, Canada.
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2
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Chang YJ, Chen YI, Yeh HC, Santacruz SR. Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics. Sci Rep 2024; 14:5145. [PMID: 38429297 PMCID: PMC10907713 DOI: 10.1038/s41598-024-54593-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] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 02/14/2024] [Indexed: 03/03/2024] Open
Abstract
Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-scale to reveal the connections between the dynamical patterns and the behaviors, investigations of neural population dynamics are mainly limited to single-scale analysis. Our goal is to develop a cross-scale dynamical model for the collective activity of neuronal populations. Here we introduce a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales. Our model not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. We also show that the NBGNet robustly predicts held-out data across a long time scale (2 weeks) without retraining. We further validate the effective connectivity defined from our model by demonstrating that neural connectivity during motor behaviour agrees with the established neuroanatomical hierarchy of motor control in the literature. The NBGNet approach opens the door to revealing a comprehensive understanding of brain computation, where network mechanisms of multi-scale activity are critical.
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Affiliation(s)
- Yin-Jui Chang
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Yuan-I Chen
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Hsin-Chih Yeh
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
- Texas Materials Institute, The University of Texas at Austin, Austin, TX, USA
| | - Samantha R Santacruz
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
- Institute for Neuroscience, The University of Texas at Austin, Austin, TX, USA.
- Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.
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3
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Li J, Long Z, Sheng W, Du L, Qiu J, Chen H, Liao W. Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes. Biol Psychiatry 2024; 95:414-425. [PMID: 37573006 DOI: 10.1016/j.biopsych.2023.08.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 07/31/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied. METHODS Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes. RESULTS We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies. CONCLUSIONS Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.
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Affiliation(s)
- Jiao Li
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Zhiliang Long
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China
| | - Wei Sheng
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Lian Du
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, P.R. China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China
| | - Huafu Chen
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Wei Liao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China.
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4
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Gu YW, Fan JW, Zhao SW, Liu XF, Yin H, Cui LB. Large-scale mechanism hypothesis and research prospects of cognitive impairment in schizophrenia based on magnetic resonance imaging. Heliyon 2024; 10:e25915. [PMID: 38404811 PMCID: PMC10884805 DOI: 10.1016/j.heliyon.2024.e25915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 01/09/2024] [Accepted: 02/05/2024] [Indexed: 02/27/2024] Open
Abstract
Cognitive impairments in schizophrenia are pivotal clinical issues that need to be solved urgently. However, the mechanism remains unknown. It has been suggested that cognitive impairments in schizophrenia are associated with connectome damage, and are especially relevant to the disrupted hub nodes in the frontal and parietal lobes. Activating the dorsolateral prefrontal cortex (DLPFC) via repetitive transcranial magnetic stimulation (rTMS) could result in improved cognition. Based on several previous magnetic resonance imaging (MRI) studies on schizophrenia, we found that the first-episode patients showed connectome damage, as well as abnormal activation and connectivity of the DLPFC and inferior parietal lobule (IPL). Accordingly, we proposed that DLPFC-IPL pathway destruction might mediate connectome damage of cognitive impairments in schizophrenia. In the meantime, with the help of multimodal MRI and noninvasive neuromodulation tool, we may not only validate the hypothesis, but also find IPL as the potential intervention target for cognitive impairments in schizophrenia.
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Affiliation(s)
- Yue-Wen Gu
- Shaanxi Provincial Key Laboratory of Clinic Genetics, Fourth Military Medical University, Xi’an, China
- Schizophrenia Imaging Lab, Xijing Hospital, Fourth Military Medical University, Xi’an, China
- Department of Radiology, The General Hospital of Western Theater Command, Chengdu, China
| | - Jing-Wen Fan
- Shaanxi Provincial Key Laboratory of Clinic Genetics, Fourth Military Medical University, Xi’an, China
- Schizophrenia Imaging Lab, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Shu-Wan Zhao
- Shaanxi Provincial Key Laboratory of Clinic Genetics, Fourth Military Medical University, Xi’an, China
- Schizophrenia Imaging Lab, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Xiao-Fan Liu
- Shaanxi Provincial Key Laboratory of Clinic Genetics, Fourth Military Medical University, Xi’an, China
- Schizophrenia Imaging Lab, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Hong Yin
- Schizophrenia Imaging Lab, Xijing Hospital, Fourth Military Medical University, Xi’an, China
- Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), Xi'an, China
| | - Long-Biao Cui
- Shaanxi Provincial Key Laboratory of Clinic Genetics, Fourth Military Medical University, Xi’an, China
- Schizophrenia Imaging Lab, Xijing Hospital, Fourth Military Medical University, Xi’an, China
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
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5
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Yang Y, Ye C, Ma T. A deep connectome learning network using graph convolution for connectome-disease association study. Neural Netw 2023; 164:91-104. [PMID: 37148611 DOI: 10.1016/j.neunet.2023.04.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/01/2023] [Accepted: 04/16/2023] [Indexed: 05/08/2023]
Abstract
Multivariate analysis approaches provide insights into the identification of phenotype associations in brain connectome data. In recent years, deep learning methods including convolutional neural network (CNN) and graph neural network (GNN), have shifted the development of connectome-wide association studies (CWAS) and made breakthroughs for connectome representation learning by leveraging deep embedded features. However, most existing studies remain limited by potentially ignoring the exploration of region-specific features, which play a key role in distinguishing brain disorders with high intra-class variations, such as autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD). Here, we propose a multivariate distance-based connectome network (MDCN) that addresses the local specificity problem by efficient parcellation-wise learning, as well as associating population and parcellation dependencies to map individual differences. The approach incorporating an explainable method, parcellation-wise gradient and class activation map (p-GradCAM), is feasible for identifying individual patterns of interest and pinpointing connectome associations with diseases. We demonstrate the utility of our method on two largely aggregated multicenter public datasets by distinguishing ASD and ADHD from healthy controls and assessing their associations with underlying diseases. Extensive experiments have demonstrated the superiority of MDCN in classification and interpretation, where MDCN outperformed competitive state-of-the-art methods and achieved a high proportion of overlap with previous findings. As a CWAS-guided deep learning method, our proposed MDCN framework may narrow the bridge between deep learning and CWAS approaches, and provide new insights for connectome-wide association studies.
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Affiliation(s)
- Yanwu Yang
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China.
| | - Chenfei Ye
- Peng Cheng Laboratory, Shenzhen, China; International Research Institute for Artificial Intelligence, Harbin Institute of Technology at Shenzhen, Shenzhen, China.
| | - Ting Ma
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China; International Research Institute for Artificial Intelligence, Harbin Institute of Technology at Shenzhen, Shenzhen, China; Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
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6
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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: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [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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7
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McGowan AL, Sayed F, Boyd ZM, Jovanova M, Kang Y, Speer ME, Cosme D, Mucha PJ, Ochsner KN, Bassett DS, Falk EB, Lydon-Staley DM. Dense Sampling Approaches for Psychiatry Research: Combining Scanners and Smartphones. Biol Psychiatry 2023; 93:681-689. [PMID: 36797176 PMCID: PMC10038886 DOI: 10.1016/j.biopsych.2022.12.012] [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: 05/13/2022] [Revised: 11/22/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022]
Abstract
Together, data from brain scanners and smartphones have sufficient coverage of biology, psychology, and environment to articulate between-person differences in the interplay within and across biological, psychological, and environmental systems thought to underlie psychopathology. An important next step is to develop frameworks that combine these two modalities in ways that leverage their coverage across layers of human experience to have maximum impact on our understanding and treatment of psychopathology. We review literature published in the last 3 years highlighting how scanners and smartphones have been combined to date, outline and discuss the strengths and weaknesses of existing approaches, and sketch a network science framework heretofore underrepresented in work combining scanners and smartphones that can push forward our understanding of health and disease.
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Affiliation(s)
- Amanda L McGowan
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychology, Concordia University, Montréal, Québec, Canada
| | - Farah Sayed
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zachary M Boyd
- Department of Mathematics, Brigham Young University, Provo, Utah
| | - Mia Jovanova
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yoona Kang
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Megan E Speer
- Department of Psychology, Columbia University, New York, New York
| | - Danielle Cosme
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, New Hampshire
| | - Kevin N Ochsner
- Department of Psychology, Columbia University, New York, New York
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Santa Fe Institute, Santa Fe, New Mexico
| | - Emily B Falk
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania; Marketing Department, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania; Operations, Information and Decisions, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania
| | - David M Lydon-Staley
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania.
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8
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Wei Y, de Lange SC, Savage JE, Tissink E, Qi T, Repple J, Gruber M, Kircher T, Dannlowski U, Posthuma D, van den Heuvel MP. Associated Genetics and Connectomic Circuitry in Schizophrenia and Bipolar Disorder. Biol Psychiatry 2022:S0006-3223(22)01719-X. [PMID: 36803976 DOI: 10.1016/j.biopsych.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 10/15/2022] [Accepted: 11/02/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric conditions that can involve symptoms of psychosis and cognitive dysfunction. The 2 conditions share symptomatology and genetic etiology and are regularly hypothesized to share underlying neuropathology. Here, we examined how genetic liability to SCZ and BD shapes normative variations in brain connectivity. METHODS We examined the effect of the combined genetic liability for SCZ and BD on brain connectivity from two perspectives. First, we examined the association between polygenic scores for SCZ and BD for 19,778 healthy subjects from the UK Biobank and individual variation in brain structural connectivity reconstructed by means of diffusion weighted imaging data. Second, we conducted genome-wide association studies using genotypic and imaging data from the UK Biobank, taking SCZ-/BD-involved brain circuits as phenotypes of interest. RESULTS Our findings showed brain circuits of superior parietal and posterior cingulate regions to be associated with polygenic liability for SCZ and BD, circuitry that overlaps with brain networks involved in disease conditions (r = 0.239, p < .001). Genome-wide association study analysis showed 9 significant genomic loci associated with SCZ-involved circuits and 14 loci associated with BD-involved circuits. Genes related to SCZ-/BD-involved circuits were significantly enriched in gene sets previously reported in genome-wide association studies for SCZ and BD. CONCLUSIONS Our findings suggest that polygenic liability of SCZ and BD is associated with normative individual variation in brain circuitry.
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Affiliation(s)
- Yongbin Wei
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
| | - Siemon C de Lange
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Elleke Tissink
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Ting Qi
- Department of Neurology, School of Medicine, University of California San Francisco, San Francisco, California
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, the Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, the Netherlands.
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9
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Cruces RR, Royer J, Herholz P, Larivière S, Vos de Wael R, Paquola C, Benkarim O, Park BY, Degré-Pelletier J, Nelson MC, DeKraker J, Leppert IR, Tardif C, Poline JB, Concha L, Bernhardt BC. Micapipe: A pipeline for multimodal neuroimaging and connectome analysis. Neuroimage 2022; 263:119612. [PMID: 36070839 PMCID: PMC10697132 DOI: 10.1016/j.neuroimage.2022.119612] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 08/20/2022] [Accepted: 09/03/2022] [Indexed: 11/25/2022] Open
Abstract
Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and to consolidate findings across different spatial scales. Here, we present micapipe, an open processing pipeline for multimodal MRI datasets. Based on BIDS-conform input data, micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. The above matrices can be automatically generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations, allowing researchers to replicate findings easily across different spatial scales. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, function, cand connectivity.
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Affiliation(s)
- Raúl R Cruces
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
| | - Peer Herholz
- NeuroDataScience - ORIGAMI lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada; Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Bo-Yong Park
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada; Department of Data Science, Inha University, Incheon, Republic of Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Janie Degré-Pelletier
- Labo IDEA, Département de Psychologie, Université du Québec à Montréal, Montréal, Québec, Canada
| | - Mark C Nelson
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Jordan DeKraker
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Ilana R Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Christine Tardif
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Jean-Baptiste Poline
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Luis Concha
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Mexico
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
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10
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Aussel A, Ranta R, Aron O, Colnat-Coulbois S, Maillard L, Buhry L. Cell to network computational model of the epileptic human hippocampus suggests specific roles of network and channel dysfunctions in the ictal and interictal oscillations. J Comput Neurosci 2022; 50:519-535. [PMID: 35971033 DOI: 10.1007/s10827-022-00829-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 07/03/2022] [Accepted: 07/12/2022] [Indexed: 10/15/2022]
Abstract
The mechanisms underlying the generation of hippocampal epileptic seizures and interictal events and their interactions with the sleep-wake cycle are not yet fully understood. Indeed, medial temporal lobe epilepsy is associated with hippocampal abnormalities both at the neuronal (channelopathies, impaired potassium and chloride dynamics) and network level (neuronal and axonal loss, mossy fiber sprouting), with more frequent seizures during wakefulness compared with slow-wave sleep. In this article, starting from our previous computational modeling work of the hippocampal formation based on realistic topology and synaptic connectivity, we study the role of micro- and mesoscale pathological conditions of the epileptic hippocampus in the generation and maintenance of seizure-like theta and interictal oscillations. We show, through the simulations of hippocampal activity during slow-wave sleep and wakefulness that: (i) both mossy fiber sprouting and sclerosis account for seizure-like theta activity, (ii) but they have antagonist effects (seizure-like activity occurrence increases with sprouting but decreases with sclerosis), (iii) though impaired potassium and chloride dynamics have little influence on the generation of seizure-like activity, they do play a role on the generation of interictal patterns, and (iv) seizure-like activity and fast ripples are more likely to occur during wakefulness and interictal spikes during sleep.
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Affiliation(s)
- Amélie Aussel
- Laboratoire Lorrain de Recherche en Informatique et ses applications (LORIA UMR 7503), University of Lorraine, 54506, Nancy, France. .,Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France.
| | - Radu Ranta
- Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France
| | - Olivier Aron
- Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France.,Department of Neurology, CHU de Nancy, Nancy, France
| | - Sophie Colnat-Coulbois
- Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France.,Department of Neurology, CHU de Nancy, Nancy, France
| | - Louise Maillard
- Centre de Recherche en Automatique de Nancy, University of Lorraine, CRAN-CNRS UMR 7039, Nancy, France.,Department of Neurology, CHU de Nancy, Nancy, France
| | - Laure Buhry
- Laboratoire Lorrain de Recherche en Informatique et ses applications (LORIA UMR 7503), University of Lorraine, 54506, Nancy, France
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11
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Ramezanian-Panahi M, Abrevaya G, Gagnon-Audet JC, Voleti V, Rish I, Dumas G. Generative Models of Brain Dynamics. Front Artif Intell 2022; 5:807406. [PMID: 35910192 PMCID: PMC9335006 DOI: 10.3389/frai.2022.807406] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 06/10/2022] [Indexed: 01/28/2023] Open
Abstract
This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.
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Affiliation(s)
| | - Germán Abrevaya
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Instituto de Física de Buenos Aires (IFIBA), CONICET, Universidad de Buenos Aires, Buenos Aires, Argentina
| | | | - Vikram Voleti
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
| | - Irina Rish
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
| | - Guillaume Dumas
- Mila-Quebec AI Institute, Montréal, QC, Canada
- Université de Montréal, Montréal, QC, Canada
- Department of Psychiatry, CHU Sainte-Justine Research Center, Mila-Quebec AI Institute, Université de Montréal, Montréal, QC, Canada
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12
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Scheijbeler EP, van Nifterick AM, Stam CJ, Hillebrand A, Gouw AA, de Haan W. Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer's disease? Netw Neurosci 2022; 6:382-400. [PMID: 35733433 PMCID: PMC9208018 DOI: 10.1162/netn_a_00224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/15/2021] [Indexed: 11/24/2022] Open
Abstract
Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer's disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Local activity was characterized by permutation entropy (PE). Network-level interactions were studied using the inverted joint permutation entropy (JPEinv), corrected for volume conduction. The JPEinv showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta band entropy. Between-group differences were widespread across brain regions. Receiver operating characteristic (ROC) analysis of classification of MCI versus SCD subjects revealed that a logistic regression model trained on JPEinv features (78.4% [62.5-93.3%]) slightly outperformed PE (76.9% [60.3-93.4%]) and relative theta power-based models (76.9% [60.4-93.3%]). Classification performance of theta JPEinv was at least as good as the relative theta power benchmark. The JPEinv is therefore a potential biomarker for early-stage AD that should be explored in larger studies.
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Affiliation(s)
- Elliz P. Scheijbeler
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Anne M. van Nifterick
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Alida A. Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Willem de Haan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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13
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Wang Y, Chai L, Chu C, Li D, Gao C, Wu X, Yang Z, Zhang Y, Xu J, Nyengaard JR, Eickhoff SB, Liu B, Madsen KH, Jiang T, Fan L. Uncovering the genetic profiles underlying the intrinsic organization of the human cerebellum. Mol Psychiatry 2022; 27:2619-2634. [PMID: 35264730 DOI: 10.1038/s41380-022-01489-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 02/01/2022] [Accepted: 02/14/2022] [Indexed: 11/09/2022]
Abstract
The functional diversity of the human cerebellum is largely believed to be derived more from its extensive connections rather than being limited to its mostly invariant architecture. However, whether and how the determination of cerebellar connections in its intrinsic organization interact with microscale gene expression is still unknown. Here we decode the genetic profiles of the cerebellar functional organization by investigating the genetic substrates simultaneously linking cerebellar functional heterogeneity and its drivers, i.e., the connections. We not only identified 443 network-specific genes but also discovered that their co-expression pattern correlated strongly with intra-cerebellar functional connectivity (FC). Ninety of these genes were also linked to the FC of cortico-cerebellar cognitive-limbic networks. To further discover the biological functions of these genes, we performed a "virtual gene knock-out" by observing the change in the coupling between gene co-expression and FC and divided the genes into two subsets, i.e., a positive gene contribution indicator (GCI+) involved in cerebellar neurodevelopment and a negative gene set (GCI-) related to neurotransmission. A more interesting finding is that GCI- is significantly linked with the cerebellar connectivity-behavior association and many recognized brain diseases that are closely linked with the cerebellar functional abnormalities. Our results could collectively help to rethink the genetic substrates underlying the cerebellar functional organization and offer possible micro-macro interacted mechanistic interpretations of the cerebellum-involved high order functions and dysfunctions in neuropsychiatric disorders.
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Affiliation(s)
- Yaping Wang
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Lin Chai
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Congying Chu
- University of Chinese Academy of Sciences, 100190, Beijing, China. .,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
| | - Deying Li
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Chaohong Gao
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Xia Wu
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Zhengyi Yang
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Yu Zhang
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, 311100, China
| | - Junhai Xu
- School of Computer Science and Technology, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300350, China
| | - Jens Randel Nyengaard
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,Core Centre for Molecular Morphology, Section for Stereology and Microscopy, Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark.,Department of Pathology, Aarhus University Hospital, 8200, Aarhus, Denmark
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, 100875, Beijing, China
| | - Kristoffer Hougaard Madsen
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,Department of Informatics and Mathematical Modelling, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, 2650, Hvidovre, Denmark
| | - Tianzi Jiang
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Lingzhong Fan
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China. .,University of Chinese Academy of Sciences, 100190, Beijing, China. .,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
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14
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Sprugnoli G, Rigolo L, Faria M, Juvekar P, Tie Y, Rossi S, Sverzellati N, Golby AJ, Santarnecchi E. Tumor BOLD connectivity profile correlates with glioma patients' survival. Neurooncol Adv 2022; 4:vdac153. [PMID: 36532508 PMCID: PMC9753902 DOI: 10.1093/noajnl/vdac153] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Presence of residual neurovascular activity within glioma lesions have been recently demonstrated via functional MRI (fMRI) along with active electrical synapses between glioma cells and healthy neurons that influence survival. In this study, we aimed to investigate whether gliomas demonstrate synchronized neurovascular activity with the rest of the brain, by measuring Blood Oxygen Level Dependent (BOLD) signal synchronization, that is, functional connectivity (FC), while also testing whether the strength of such connectivity might predict patients' overall survival (OS). METHODS Resting-state fMRI scans of patients who underwent pre-surgical brain mapping were analyzed (total sample, n = 54; newly diagnosed patients, n = 18; recurrent glioma group, n = 36). A seed-to-voxel analysis was conducted to estimate the FC signal profile of the tumor mass. A regression model was then built to investigate the potential correlation between tumor FC and individual OS. Finally, an unsupervised, cross-validated clustering analysis was performed including tumor FC and clinical OS predictors (e.g., Karnofsky Performance Status - KPS - score, tumor volume, and genetic profile) to verify the performance of tumor FC in predicting OS with respect to validated radiological, demographic, genetic and clinical prognostic factors. RESULTS In both newly diagnosed and recurrent glioma patients a significant pattern of BOLD synchronization between the solid tumor and distant brain regions was found. Crucially, glioma-brain FC positively correlated with variance in individual survival in both newly diagnosed glioma group (r = 0.90-0.96; P < .001; R 2 = 81-92%) and in the recurrent glioma group (r = 0.72; P < .001; R 2 = 52%), outperforming standard clinical, radiological and genetic predictors. CONCLUSIONS Results suggest glioma's synchronization with distant brain regions should be further explored as a possible diagnostic and prognostic biomarker.
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Affiliation(s)
- Giulia Sprugnoli
- Precision Neuroscience & Neuromodulation Program and Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Radiology Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
- Image Guided Neurosurgery Laboratory, Department of Neurosurgery and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Laura Rigolo
- Image Guided Neurosurgery Laboratory, Department of Neurosurgery and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Meghan Faria
- Image Guided Neurosurgery Laboratory, Department of Neurosurgery and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Parikshit Juvekar
- Image Guided Neurosurgery Laboratory, Department of Neurosurgery and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yanmei Tie
- Image Guided Neurosurgery Laboratory, Department of Neurosurgery and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Simone Rossi
- Department of Medicine, Surgery and Neuroscience, Unit of Neurology and Clinical Neurophysiology, Siena Brain Investigation and Neuromodulation Lab (Si-BIN Lab), University of Siena, Italy
| | - Nicola Sverzellati
- Radiology Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Alexandra J Golby
- Alexandra J. Golby, MD, Image Guided Neurosurgery Laboratory, Department of Neurosurgery and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Neurosciences Center, 60 Fenwood Road, 1st Floor, Hale Building for Transformative Medicine, Boston, MA, 02115, USA ()
| | - Emiliano Santarnecchi
- Corresponding Authors: Emiliano Santarnecchi, PhD, PhD, Precision Neuroscience & Neuromodulation Program and Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA ()
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15
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Guan F, Ni T, Zhu W, Williams LK, Cui LB, Li M, Tubbs J, Sham PC, Gui H. Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction. Mol Psychiatry 2022; 27:113-126. [PMID: 34193973 PMCID: PMC11018294 DOI: 10.1038/s41380-021-01201-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 06/15/2021] [Accepted: 06/17/2021] [Indexed: 02/06/2023]
Abstract
Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies - genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics - have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.
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Affiliation(s)
- Fanglin Guan
- Department of Forensic Psychiatry, School of Medicine & Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Tong Ni
- Department of Forensic Psychiatry, School of Medicine & Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Weili Zhu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Long-Biao Cui
- Department of Clinical Psychology, School of Medical Psychology, Air Force Medical University, Xi'an, Shaanxi, China
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Justin Tubbs
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Pak-Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China.
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China.
| | - Hongsheng Gui
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA.
- Behavioral Health Services, Henry Ford Health System, Detroit, MI, USA.
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16
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Li J, Wu GR, Li B, Fan F, Zhao X, Meng Y, Zhong P, Yang S, Biswal BB, Chen H, Liao W. Transcriptomic and macroscopic architectures of intersubject functional variability in human brain white-matter. Commun Biol 2021; 4:1417. [PMID: 34931033 PMCID: PMC8688465 DOI: 10.1038/s42003-021-02952-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 11/30/2021] [Indexed: 12/18/2022] Open
Abstract
Intersubject variability is a fundamental characteristic of brain organizations, and not just "noise". Although intrinsic functional connectivity (FC) is unique to each individual and varies across brain gray-matter, the underlying mechanisms of intersubject functional variability in white-matter (WM) remain unknown. This study identified WMFC variabilities and determined the genetic basis and macroscale imaging in 45 healthy subjects. The functional localization pattern of intersubject variability across WM is heterogeneous, with most variability observed in the heteromodal cortex. The variabilities of heteromodal regions in expression profiles of genes are related to neuronal cells, involved in synapse-related and glutamic pathways, and associated with psychiatric disorders. In contrast, genes overexpressed in unimodal regions are mostly expressed in glial cells and were related to neurological diseases. Macroscopic variability recapitulates the functional and structural specializations and behavioral phenotypes. Together, our results provide clues to intersubject variabilities of the WMFC with convergent transcriptomic and cellular signatures, which relate to macroscale brain specialization.
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Affiliation(s)
- Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, 400715, P.R. China
| | - Bing Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Feiyang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Xiaopeng Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Peng Zhong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07103, USA
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
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17
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Wei Y, de Lange SC, Pijnenburg R, Scholtens LH, Ardesch DJ, Watanabe K, Posthuma D, van den Heuvel MP. Statistical testing in transcriptomic-neuroimaging studies: A how-to and evaluation of methods assessing spatial and gene specificity. Hum Brain Mapp 2021; 43:885-901. [PMID: 34862695 PMCID: PMC8764473 DOI: 10.1002/hbm.25711] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 10/22/2021] [Accepted: 10/23/2021] [Indexed: 11/14/2022] Open
Abstract
Multiscale integration of gene transcriptomic and neuroimaging data is becoming a widely used approach for exploring the molecular underpinnings of large‐scale brain organization in health and disease. Proper statistical evaluation of determined associations between imaging‐based phenotypic and transcriptomic data is key in these explorations, in particular to establish whether observed associations exceed “chance level” of random, nonspecific effects. Recent approaches have shown the importance of statistical models that can correct for spatial autocorrelation effects in the data to avoid inflation of reported statistics. Here, we discuss the need for examination of a second category of statistical models in transcriptomic‐neuroimaging analyses, namely those that can provide “gene specificity.” By means of a couple of simple examples of commonly performed transcriptomic‐neuroimaging analyses, we illustrate some of the potentials and challenges of transcriptomic‐imaging analyses, showing that providing gene specificity on observed transcriptomic‐neuroimaging effects is of high importance to avoid reports of nonspecific effects. Through means of simulations we show that the rate of reported nonspecific effects (i.e., effects that cannot be specifically linked to a specific gene or gene‐set) can run as high as 60%, with only less than 5% of transcriptomic‐neuroimaging associations observed through ordinary linear regression analyses showing both spatial and gene specificity. We provide a discussion, a tutorial, and an easy‐to‐use toolbox for the different options of null models in transcriptomic‐neuroimaging analyses.
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Affiliation(s)
- Yongbin Wei
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Siemon C de Lange
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Rory Pijnenburg
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lianne H Scholtens
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Dirk Jan Ardesch
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Kyoko Watanabe
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
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18
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Kawatake-Kuno A, Murai T, Uchida S. A Multiscale View of the Mechanisms Underlying Ketamine's Antidepressant Effects: An Update on Neuronal Calcium Signaling. Front Behav Neurosci 2021; 15:749180. [PMID: 34658809 PMCID: PMC8514675 DOI: 10.3389/fnbeh.2021.749180] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 09/14/2021] [Indexed: 12/21/2022] Open
Abstract
Major depressive disorder (MDD) is a debilitating disease characterized by depressed mood, loss of interest or pleasure, suicidal ideation, and reduced motivation or hopelessness. Despite considerable research, mechanisms underlying MDD remain poorly understood, and current advances in treatment are far from satisfactory. The antidepressant effect of ketamine is among the most important discoveries in psychiatric research over the last half-century. Neurobiological insights into the ketamine’s effects have shed light on the mechanisms underlying antidepressant efficacy. However, mechanisms underlying the rapid and sustained antidepressant effects of ketamine remain controversial. Elucidating such mechanisms is key to identifying new therapeutic targets and developing therapeutic strategies. Accumulating evidence demonstrates the contribution of the glutamatergic pathway, the major excitatory neurotransmitter system in the central nervous system, in MDD pathophysiology and antidepressant effects. The hypothesis of a connection among the calcium signaling cascade stimulated by the glutamatergic system, neural plasticity, and epigenetic regulation of gene transcription is further supported by its associations with ketamine’s antidepressant effects. This review briefly summarizes the potential mechanisms of ketamine’s effects with a specific focus on glutamatergic signaling from a multiscale perspective, including behavioral, cellular, molecular, and epigenetic aspects, to provide a valuable overview of ketamine’s antidepressant effects.
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Affiliation(s)
- Ayako Kawatake-Kuno
- SK Project, Medical Innovation Center, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Toshiya Murai
- SK Project, Medical Innovation Center, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shusaku Uchida
- SK Project, Medical Innovation Center, Kyoto University Graduate School of Medicine, Kyoto, Japan
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19
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McColgan P, Helbling S, Vaculčiaková L, Pine K, Wagstyl K, Attar FM, Edwards L, Papoutsi M, Wei Y, Van den Heuvel MP, Tabrizi SJ, Rees G, Weiskopf N. Relating quantitative 7T MRI across cortical depths to cytoarchitectonics, gene expression and connectomics. Hum Brain Mapp 2021; 42:4996-5009. [PMID: 34272784 PMCID: PMC8449108 DOI: 10.1002/hbm.25595] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 06/26/2021] [Accepted: 07/06/2021] [Indexed: 12/24/2022] Open
Abstract
Ultra-high field MRI across the depth of the cortex has the potential to provide anatomically precise biomarkers and mechanistic insights into neurodegenerative disease like Huntington's disease that show layer-selective vulnerability. Here we compare multi-parametric mapping (MPM) measures across cortical depths for a 7T 500 μm whole brain acquisition to (a) layer-specific cell measures from the von Economo histology atlas, (b) layer-specific gene expression, using the Allen Human Brain atlas and (c) white matter connections using high-fidelity diffusion tractography, at a 1.3 mm isotropic voxel resolution, from a 300mT/m Connectom MRI system. We show that R2*, but not R1, across cortical depths is highly correlated with layer-specific cell number and layer-specific gene expression. R1- and R2*-weighted connectivity strength of cortico-striatal and intra-hemispheric cortical white matter connections was highly correlated with grey matter R1 and R2* across cortical depths. Limitations of the layer-specific relationships demonstrated are at least in part related to the high cross-correlations of von Economo atlas cell counts and layer-specific gene expression across cortical layers. These findings demonstrate the potential and limitations of combining 7T MPMs, gene expression and white matter connections to provide an anatomically precise framework for tracking neurodegenerative disease.
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Affiliation(s)
- Peter McColgan
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Huntington's Disease Research Centre, Institute of NeurologyUniversity College LondonLondon
| | - Saskia Helbling
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Lenka Vaculčiaková
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Kerrin Pine
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Konrad Wagstyl
- The Wellcome Centre for Human Neuroimaging, Institute of NeurologyUniversity College LondonLondonUK
| | | | - Luke Edwards
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Marina Papoutsi
- Huntington's Disease Research Centre, Institute of NeurologyUniversity College LondonLondon
| | - Yongbin Wei
- Vrije Universiteit AmsterdamComplex Traits Genetics LabAmsterdamNetherlands
| | | | - Sarah J Tabrizi
- Huntington's Disease Research Centre, Institute of NeurologyUniversity College LondonLondon
| | - Geraint Rees
- The Wellcome Centre for Human Neuroimaging, Institute of NeurologyUniversity College LondonLondonUK
| | - Nikolaus Weiskopf
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Felix Bloch Institute for Solid State PhysicsFaculty of Physics and Earth Sciences, Leipzig UniversityLeipzigGermany
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20
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Zhao SW, Xu X, Wang XY, Yan TC, Cao Y, Yan QH, Chen K, Jin YC, Zhang YH, Yin H, Cui LB. Shaping the Trans-Scale Properties of Schizophrenia via Cerebral Alterations on Magnetic Resonance Imaging and Single-Nucleotide Polymorphisms of Coding and Non-Coding Regions. Front Hum Neurosci 2021; 15:720239. [PMID: 34566604 PMCID: PMC8458928 DOI: 10.3389/fnhum.2021.720239] [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: 06/04/2021] [Accepted: 08/05/2021] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia is a complex mental illness with genetic heterogeneity, which is often accompanied by alterations in brain structure and function. The neurobiological mechanism of schizophrenia associated with heredity remains unknown. Recently, the development of trans-scale and multi-omics methods that integrate gene and imaging information sheds new light on the nature of schizophrenia. In this article, we summarized the results of brain structural and functional changes related to the specific single-nucleotide polymorphisms (SNPs) in the past decade, and the SNPs were divided into non-coding regions and coding regions, respectively. It is hoped that the relationship between SNPs and cerebral alterations can be displayed more clearly and intuitively, so as to provide fresh approaches for the discovery of potential biomarkers and the development of clinical accurate individualized treatment decision-making.
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Affiliation(s)
- Shu-Wan Zhao
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China.,Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Xian Xu
- Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xian-Yang Wang
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Tian-Cai Yan
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Yang Cao
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Qing-Hong Yan
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Kun Chen
- Department of Anatomy and K. K. Leung Brain Research Centre, Fourth Military Medical University, Xi'an, China
| | - Yin-Chuan Jin
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Ya-Hong Zhang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Long-Biao Cui
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China.,Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China
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21
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Paredes O, López JB, Covantes-Osuna C, Ocegueda-Hernández V, Romo-Vázquez R, Morales JA. A Transcriptome Community-and-Module Approach of the Human Mesoconnectome. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1031. [PMID: 34441171 PMCID: PMC8393183 DOI: 10.3390/e23081031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/03/2021] [Accepted: 08/06/2021] [Indexed: 12/15/2022]
Abstract
Graph analysis allows exploring transcriptome compartments such as communities and modules for brain mesostructures. In this work, we proposed a bottom-up model of a gene regulatory network to brain-wise connectome workflow. We estimated the gene communities across all brain regions from the Allen Brain Atlas transcriptome database. We selected the communities method to yield the highest number of functional mesostructures in the network hierarchy organization, which allowed us to identify specific brain cell functions (e.g., neuroplasticity, axonogenesis and dendritogenesis communities). With these communities, we built brain-wise region modules that represent the connectome. Our findings match with previously described anatomical and functional brain circuits, such the default mode network and the default visual network, supporting the notion that the brain dynamics that carry out low- and higher-order functions originate from the modular composition of a GRN complex network.
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Affiliation(s)
| | | | | | | | - Rebeca Romo-Vázquez
- Computer Sciences Department, Exact Sciences and Engineering University Centre, Universidad de Guadalajara, Guadalajara 44430, Mexico; (O.P.); (J.B.L.); (C.C.-O.); (V.O.-H.)
| | - J. Alejandro Morales
- Computer Sciences Department, Exact Sciences and Engineering University Centre, Universidad de Guadalajara, Guadalajara 44430, Mexico; (O.P.); (J.B.L.); (C.C.-O.); (V.O.-H.)
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22
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Sprugnoli G, Rossi S, Rotenberg A, Pascual-Leone A, El-Fakhri G, Golby AJ, Santarnecchi E. Personalised, image-guided, noninvasive brain stimulation in gliomas: Rationale, challenges and opportunities. EBioMedicine 2021; 70:103514. [PMID: 34391090 PMCID: PMC8365310 DOI: 10.1016/j.ebiom.2021.103514] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 07/12/2021] [Accepted: 07/19/2021] [Indexed: 11/22/2022] Open
Abstract
Malignant brain tumours are among the most aggressive human cancers, and despite intensive efforts made over the last decades, patients’ survival has scarcely improved. Recently, high-grade gliomas (HGG) have been found to be electrically integrated with healthy brain tissue, a communication that facilitates tumour mitosis and invasion. This link to neuronal activity has provided new insights into HGG pathophysiology and opened prospects for therapeutic interventions based on electrical modulation of neural and synaptic activity in the proximity of tumour cells, which could potentially slow tumour growth. Noninvasive brain stimulation (NiBS), a group of techniques used in research and clinical settings to safely modulate brain activity and plasticity via electromagnetic or electrical stimulation, represents an appealing class of interventions to characterise and target the electrical properties of tumour-neuron interactions. Beyond neuronal activity, NiBS may also modulate function of a range of substrates and dynamics that locally interacts with HGG (e.g., vascular architecture, perfusion and blood-brain barrier permeability). Here we discuss emerging applications of NiBS in patients with brain tumours, covering potential mechanisms of action at both cellular, regional, network and whole-brain levels, also offering a conceptual roadmap for future research to prolong survival or promote wellbeing via personalised NiBS interventions.
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Affiliation(s)
- Giulia Sprugnoli
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Radiology Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy; Image Guided Neurosurgery laboratory, Department of Neurosurgery and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Brain investigation and Neuromodulation Laboratory (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Unit, University of Siena, Siena, Italy
| | - Simone Rossi
- Brain investigation and Neuromodulation Laboratory (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Unit, University of Siena, Siena, Italy
| | - Alexander Rotenberg
- Department of Neurology and Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alvaro Pascual-Leone
- Hinda and Arthur Marcus Institute for Aging Research and Center for Memory Health, Hebrew Senior Life, Boston, MA, USA; Guttmann Brain Health Institute, Institut Guttmann, Universitat Autonoma, Barcelona, Spain
| | - Georges El-Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandra J Golby
- Image Guided Neurosurgery laboratory, Department of Neurosurgery and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
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23
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Paredes O, Morales JA, Mendizabal AP, Romo-Vázquez R. Metacode: One code to rule them all. Biosystems 2021; 208:104486. [PMID: 34274462 DOI: 10.1016/j.biosystems.2021.104486] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/07/2021] [Accepted: 07/09/2021] [Indexed: 12/13/2022]
Abstract
The code of codes or metacode is a microcosm where biological layers, as well as their codes, interact together allowing the continuity of information flow in organisms by increasing biological entities' complexity. Through this novel organic code, biological systems scale towards niches with higher informatic freedom building structures that increase the entropy in the universe. Code biology has developed a novel informational framework where biological entities strive themselves through the information flow carried out through organic codes consisting of two molecular or functional landscapes intertwined through arbitrary linkages via an adaptor whose nature is autonomous from molecular determinism. Here we will integrate genomic and epigenomic codes according to the evidence released in ENCODE (phase 3), psychENCODE and GTEx project, outlining the principles of the metacode, to address the continuous nature of biological systems and their inter-layered information flow. This novel complex metacode maps from very constrained sets of elements (i.e., regulation sites modulating gene expression) to new ones with greater freedom of decoding (i.e., a continuous cell phenotypic space). This leads to a new domain in code biology where biological systems are informatic attractors that navigate an energy metaspace through a complexity-noise balance, stalling in emergent niches where organic codes take meaning.
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Affiliation(s)
- Omar Paredes
- Computer Sciences Department, CUCEI, Universidad de Guadalajara, Mexico
| | | | - Adriana P Mendizabal
- Molecular Biology Laboratory, Farmacobiology Department, CUCEI, Universidad de Guadalajara, Mexico
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24
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Larabi DI, Marsman JBC, Aleman A, Tijms BM, Opmeer EM, Pijnenborg GHM, van der Meer L, van Tol MJ, Ćurčić-Blake B. Insight does not come at random: Individual gray matter networks relate to clinical and cognitive insight in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2021; 109:110251. [PMID: 33493651 DOI: 10.1016/j.pnpbp.2021.110251] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 12/11/2020] [Accepted: 01/13/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND Impaired clinical and cognitive insight are prevalent in schizophrenia and relate to poorer outcome. Good insight has been suggested to depend on social cognitive and metacognitive abilities requiring global integration of brain signals. Impaired insight has been related to numerous focal gray matter (GM) abnormalities distributed across the brain suggesting dysconnectivity at the global level. In this study, we test whether global integration deficiencies reflected in gray matter network connectivity underlie individual variations in insight. METHODS We used graph theory to examine whether individual GM-network metrics relate to insight in patients with a psychotic disorder (n = 114). Clinical insight was measured with the Schedule for the Assessment of Insight-Expanded and item G12 of the Positive and Negative Syndrome Scale, and cognitive insight with the Beck Cognitive Insight Scale. Individual GM-similarity networks were created from GM-segmentations of T1-weighted MRI-scans. Graph metrics were calculated using the Brain Connectivity Toolbox. RESULTS Networks of schizophrenia patients with poorer clinical insight showed less segregation (i.e. clustering coefficient) into specialized subnetworks at the global level. Schizophrenia patients with poorer cognitive insight showed both less segregation and higher connectedness (i.e. lower path length) of their brain networks, making their network topology more "random". CONCLUSIONS Our findings suggest less segregated processing of information in patients with poorer cognitive and clinical insight, in addition to higher connectedness in patients with poorer cognitive insight. The ability to take a critical perspective on one's symptoms (clinical insight) or views (cognitive insight) might depend especially on segregated specialized processing within distinct subnetworks.
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Affiliation(s)
- Daouia I Larabi
- Department of Biomedical Sciences of Cells and Systems, Cognitive Neuroscience Center, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 2, 9713 AW Groningen, the Netherlands; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52428 Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Gurlittstraße 55, 40223 Düsseldorf, Germany.
| | - Jan-Bernard C Marsman
- Department of Biomedical Sciences of Cells and Systems, Cognitive Neuroscience Center, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 2, 9713 AW Groningen, the Netherlands
| | - André Aleman
- Department of Biomedical Sciences of Cells and Systems, Cognitive Neuroscience Center, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 2, 9713 AW Groningen, the Netherlands; Department of Clinical and Developmental Neuropsychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, the Netherlands
| | - Betty M Tijms
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, Amsterdam University Medical Center, PO Box 7057, 1007 MB Amsterdam, the Netherlands
| | - Esther M Opmeer
- Department of Biomedical Sciences of Cells and Systems, Cognitive Neuroscience Center, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 2, 9713 AW Groningen, the Netherlands; Department of Health and Welfare, University of Applied Sciences Windesheim, Campus 2, 8017 CA Zwolle, the Netherlands
| | - Gerdina H M Pijnenborg
- Department of Clinical and Developmental Neuropsychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, the Netherlands; Department of Psychotic Disorders, GGZ Drenthe, Dennenweg 9, 9404 LA Assen, the Netherlands
| | - Lisette van der Meer
- Department of Clinical and Developmental Neuropsychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, the Netherlands; Department of Psychiatric Rehabilitation, Lentis Psychiatric Institute, Lagerhout E35, 9741 KE Zuidlaren, the Netherlands; Rob Giel Research Center, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands
| | - Marie-José van Tol
- Department of Biomedical Sciences of Cells and Systems, Cognitive Neuroscience Center, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 2, 9713 AW Groningen, the Netherlands
| | - Branislava Ćurčić-Blake
- Department of Biomedical Sciences of Cells and Systems, Cognitive Neuroscience Center, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 2, 9713 AW Groningen, the Netherlands
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25
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Myelo- and cytoarchitectonic microstructural and functional human cortical atlases reconstructed in common MRI space. Neuroimage 2021; 239:118274. [PMID: 34146709 DOI: 10.1016/j.neuroimage.2021.118274] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/14/2021] [Accepted: 06/15/2021] [Indexed: 11/23/2022] Open
Abstract
The parcellation of the brain's cortical surface into anatomically and/or functionally distinct areas is a topic of ongoing investigation and interest. We provide digital versions of six classical human brain atlases in common MRI space. The cortical atlases represent a range of modalities, including cyto- and myeloarchitecture (Campbell, Smith, Brodmann and Von Economo), myelogenesis (Flechsig), and mappings of symptomatic information in relation to the spatial location of brain lesions (Kleist). Digital reconstructions of these important cortical atlases widen the range of modalities for which cortex-wide imaging atlases are currently available and offer the opportunity to compare and combine microstructural and lesion-based functional atlases with in-vivo imaging-based atlases.
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26
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McCutcheon RA, Pillinger T, Rogdaki M, Bustillo J, Howes OD. Glutamate connectivity associations converge upon the salience network in schizophrenia and healthy controls. Transl Psychiatry 2021; 11:322. [PMID: 34045446 PMCID: PMC8159959 DOI: 10.1038/s41398-021-01455-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 05/04/2021] [Accepted: 05/14/2021] [Indexed: 11/27/2022] Open
Abstract
Alterations in cortical inter-areal functional connectivity, and aberrant glutamatergic signalling are implicated in the pathophysiology of schizophrenia but the relationship between the two is unclear. We used multimodal imaging to identify areas of convergence between the two systems. Two separate cohorts were examined, comprising 195 participants in total. All participants received resting state functional MRI to characterise functional brain networks and proton magnetic resonance spectroscopy (1H-MRS) to measure glutamate concentrations in the frontal cortex. Study A investigated the relationship between frontal cortex glutamate concentrations and network connectivity in individuals with schizophrenia and healthy controls. Study B also used 1H-MRS, and scanned individuals with schizophrenia and healthy controls before and after a challenge with the glutamatergic modulator riluzole, to investigate the relationship between changes in glutamate concentrations and changes in network connectivity. In both studies the network based statistic was used to probe associations between glutamate and connectivity, and glutamate associated networks were then characterised in terms of their overlap with canonical functional networks. Study A involved 76 individuals with schizophrenia and 82 controls, and identified a functional network negatively associated with glutamate concentrations that was concentrated within the salience network (p < 0.05) and did not differ significantly between patients and controls (p > 0.85). Study B involved 19 individuals with schizophrenia and 17 controls and found that increases in glutamate concentrations induced by riluzole were linked to increases in connectivity localised to the salience network (p < 0.05), and the relationship did not differ between patients and controls (p > 0.4). Frontal cortex glutamate concentrations are associated with inter-areal functional connectivity of a network that localises to the salience network. Changes in network connectivity in response to glutamate modulation show an opposite effect compared to the relationship observed at baseline, which may complicate pharmacological attempts to simultaneously correct glutamatergic and connectivity aberrations.
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Affiliation(s)
- Robert A McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London, SE5 8AF, UK. .,Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, London, W12 0NN, UK. .,Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK. .,South London and Maudsley NHS Foundation Trust, London, UK.
| | - Toby Pillinger
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London, SE5 8AF, UK.,Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, London, W12 0NN, UK.,Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK.,South London and Maudsley NHS Foundation Trust, London, UK
| | - Maria Rogdaki
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London, SE5 8AF, UK.,Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, London, W12 0NN, UK.,Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK.,South London and Maudsley NHS Foundation Trust, London, UK
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA.,Department of Neurosciences, University of New Mexico, Albuquerque, NM, USA
| | - Oliver D Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London, SE5 8AF, UK.,Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, London, W12 0NN, UK.,Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK.,South London and Maudsley NHS Foundation Trust, London, UK
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27
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Huang M, Dong W, Sun Y, He B. Two dimensional automatic active shape model of degenerative disc repaired by low-intensity laser. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:4358-4371. [PMID: 34198441 DOI: 10.3934/mbe.2021219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
OBJECTIVE Intervertebral disc degeneration is the main factor causing low back pain, and the related long-term treatment can improve the situation of degeneration. This study aimed to investigate the effect of low-intensity laser irradiation on the repair of degenerative intervertebral disc by two dimensional automatic active shape model (2D-AASM). METHODS Nine Bama miniature pigs were randomly divided into three groups: control group (Con), model group (Mod) and laser treatment group (Las). After one month, the discs were treated with low-energy laser for another month. MRI was performed for one month, and the statistical shape model and 2D-AASM of intervertebral disc were established based on the minimum description length method. RESULTS The model established by the proposed method is more accurate and the segmentation result is more accurate. From the segmented T2-weighted image, the signal intensity of the Mod group decreased significantly, and the signal intensity in the Las group was moderate and high compared with the Mod group. The HE staining display the structure of Con group was damaged, and the construction of Las group was restored compared with Mod group. CONCLUSIONS The 2D-AASM method effectively improves the accuracy of intervertebral disc segmentation. The low-intensity laser has a protective effect on the repair of the degenerative intervertebral disc.
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Affiliation(s)
- Ming Huang
- Department of Anesthesiology, General Hospital of Northern Theater, Shenyang 110016, China
| | - Wenfei Dong
- Department of Anesthesiology, General Hospital of Northern Theater, Shenyang 110016, China
| | - Yingjie Sun
- Department of Anesthesiology, General Hospital of Northern Theater, Shenyang 110016, China
| | - Baowen He
- Department of Anesthesiology, General Hospital of Northern Theater, Shenyang 110016, China
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28
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Wylie KP, Kronberg E, Legget KT, Sutton B, Tregellas JR. Stable Meta-Networks, Noise, and Artifacts in the Human Connectome: Low- to High-Dimensional Independent Components Analysis as a Hierarchy of Intrinsic Connectivity Networks. Front Neurosci 2021; 15:625737. [PMID: 34025337 PMCID: PMC8134552 DOI: 10.3389/fnins.2021.625737] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 03/23/2021] [Indexed: 11/29/2022] Open
Abstract
Connectivity within the human connectome occurs between multiple neuronal systems-at small to very large spatial scales. Independent component analysis (ICA) is potentially a powerful tool to facilitate multi-scale analyses. However, ICA has yet to be fully evaluated at very low (10 or fewer) and ultra-high dimensionalities (200 or greater). The current investigation used data from the Human Connectome Project (HCP) to determine the following: (1) if larger networks, or meta-networks, are present at low dimensionality, (2) if nuisance sources increase with dimensionality, and (3) if ICA is prone to overfitting. Using bootstrap ICA, results suggested that, at very low dimensionality, ICA spatial maps consisted of Visual/Attention and Default/Control meta-networks. At fewer than 10 components, well-known networks such as the Somatomotor Network were absent from results. At high dimensionality, nuisance sources were present even in denoised high-quality data but were identifiable by correlation with tissue probability maps. Artifactual overfitting occurred to a minor degree at high dimensionalities. Basic summary statistics on spatial maps (maximum cluster size, maximum component weight, and average weight outside of maximum cluster) quickly and easily separated artifacts from gray matter sources. Lastly, by using weighted averages of bootstrap stability, even ultra-high dimensional ICA resulted in highly reproducible spatial maps. These results demonstrate how ICA can be applied in multi-scale analyses, reliably and accurately reproducing the hierarchy of meta-networks, large-scale networks, and subnetworks, thereby characterizing cortical connectivity across multiple spatial scales.
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Affiliation(s)
- Korey P. Wylie
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, United States
| | - Eugene Kronberg
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, United States
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Kristina T. Legget
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, United States
- Research Service, Rocky Mountain Regional VA Medical Center, Aurora, CO, United States
| | - Brianne Sutton
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, United States
| | - Jason R. Tregellas
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, United States
- Research Service, Rocky Mountain Regional VA Medical Center, Aurora, CO, United States
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29
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Li J, Seidlitz J, Suckling J, Fan F, Ji GJ, Meng Y, Yang S, Wang K, Qiu J, Chen H, Liao W. Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures. Nat Commun 2021; 12:1647. [PMID: 33712584 PMCID: PMC7955076 DOI: 10.1038/s41467-021-21943-5] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 02/12/2021] [Indexed: 01/08/2023] Open
Abstract
Major depressive disorder (MDD) has been shown to be associated with structural abnormalities in a variety of spatially diverse brain regions. However, the correlation between brain structural changes in MDD and gene expression is unclear. Here, we examine the link between brain-wide gene expression and morphometric changes in individuals with MDD, using neuroimaging data from two independent cohorts and a publicly available transcriptomic dataset. Morphometric similarity network (MSN) analysis shows replicable cortical structural differences in individuals with MDD compared to control subjects. Using human brain gene expression data, we observe that the expression of MDD-associated genes spatially correlates with MSN differences. Analysis of cell type-specific signature genes suggests that microglia and neuronal specific transcriptional changes account for most of the observed correlation with MDD-specific MSN differences. Collectively, our findings link molecular and structural changes relevant for MDD. The correlation between brain structural changes in major depressive disorder (MDD) and gene expression is unclear. Here, the authors explore the correlation between cell type-specific gene expression changes and cortical structural difference in individuals with major depressive disorder.
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Affiliation(s)
- Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Jakob Seidlitz
- Children's Hospital of Philadelphia, Department of Child and Adolescent Psychiatry and Behavioral Science, Philadelphia, PA, USA.,University of Pennsylvania, Department of Psychiatry, Philadelphia, PA, USA
| | - John Suckling
- University of Cambridge, Department of Psychiatry, Cambridge, UK
| | - Feiyang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Gong-Jun Ji
- Department of Medical Psychology, Chaohu Clinical Medical College, Anhui Medical University, Hefei, P.R. China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Kai Wang
- Department of Medical Psychology, Chaohu Clinical Medical College, Anhui Medical University, Hefei, P.R. China
| | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, P.R. China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China. .,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China.
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China. .,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China.
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30
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de Boer NS, de Bruin LC, Geurts JJG, Glas G. The Network Theory of Psychiatric Disorders: A Critical Assessment of the Inclusion of Environmental Factors. Front Psychol 2021; 12:623970. [PMID: 33613399 PMCID: PMC7890010 DOI: 10.3389/fpsyg.2021.623970] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 01/18/2021] [Indexed: 11/13/2022] Open
Abstract
Borsboom and colleagues have recently proposed a "network theory" of psychiatric disorders that conceptualizes psychiatric disorders as relatively stable networks of causally interacting symptoms. They have also claimed that the network theory should include non-symptom variables such as environmental factors. How are environmental factors incorporated in the network theory, and what kind of explanations of psychiatric disorders can such an "extended" network theory provide? The aim of this article is to critically examine what explanatory strategies the network theory that includes both symptoms and environmental factors can accommodate. We first analyze how proponents of the network theory conceptualize the relations between symptoms and between symptoms and environmental factors. Their claims suggest that the network theory could provide insight into the causal mechanisms underlying psychiatric disorders. We assess these claims in light of network analysis, Woodward's interventionist theory, and mechanistic explanation, and show that they can only be satisfied with additional assumptions and requirements. Then, we examine their claim that network characteristics may explain the dynamics of psychiatric disorders by means of a topological explanatory strategy. We argue that the network theory could accommodate topological explanations of symptom networks, but we also point out that this poses some difficulties. Finally, we suggest that a multilayer network account of psychiatric disorders might allow for the integration of symptoms and non-symptom factors related to psychiatric disorders and could accommodate both causal/mechanistic and topological explanations.
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Affiliation(s)
- Nina S de Boer
- Department of Philosophy, Radboud University, Nijmegen, Netherlands
| | - Leon C de Bruin
- Department of Philosophy, Radboud University, Nijmegen, Netherlands.,Department of Philosophy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam University Medical Centers (Location VUmc), Amsterdam, Netherlands
| | - Gerrit Glas
- Department of Philosophy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Department of Anatomy and Neurosciences, Amsterdam University Medical Centers (Location VUmc), Amsterdam, Netherlands
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31
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Viol K, Schiepek G, Kronbichler M, Hartl A, Grafetstätter C, Strasser P, Kastinger A, Schöller H, Reiter EM, Said-Yürekli S, Kronbichler L, Kravanja-Spannberger B, Stöger-Schmidinger B, Hütt MT, Aichhorn W, Aas B. Multi-level assessment of obsessive-compulsive disorder (OCD) reveals relations between neural and neurochemical levels. BMC Psychiatry 2020; 20:559. [PMID: 33238940 PMCID: PMC7687999 DOI: 10.1186/s12888-020-02913-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 10/11/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND While considerable progress has been made in exploring the psychological, the neural, and the neurochemical dimensions of OCD separately, their interplay is still an open question, especially their changes during psychotherapy. METHODS Seventeen patients were assessed at these three levels by psychological questionnaires, fMRI, and venipuncture before and after inpatient psychotherapy. Seventeen controls were scanned at comparable time intervals. First, pre/post treatment changes were investigated for all three levels separately: symptom severity, whole-brain and regional activity, and the concentrations of cortisol, serotonin, dopamine, brain-derived neurotrophic factor (BDNF), and immunological parameters (IL-6, IL-10, TNFα). Second, stepwise linear modeling was used to find relations between the variables of the levels. RESULTS The obsessive-compulsive, depressive, and overall symptom severity was significantly reduced after psychotherapy. At the neural level, the activity in the anterior cingulate cortex (ACC), in frontal regions, in the precuneus, and in the putamen had significantly decreased. No significant changes were found on the neurochemical level. When connecting the levels, a highly significant model was found that explains the decrease in neural activity of the putamen by increases of the concentrations of cortisol, IL-6, and dopamine. CONCLUSION Multivariate approaches offer insight on the influences that the different levels of the psychiatric disorder OCD have on each other. More research and adapted models are needed.
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Affiliation(s)
- Kathrin Viol
- grid.21604.310000 0004 0523 5263Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Ignaz-Harrer-Strasse 79, 5020 Salzburg, Austria ,grid.21604.310000 0004 0523 5263Department of Psychosomatics and Inpatient Psychotherapy, University Hospital for Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
| | - Günter Schiepek
- Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Ignaz-Harrer-Strasse 79, 5020, Salzburg, Austria. .,Department of Psychosomatics and Inpatient Psychotherapy, University Hospital for Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria. .,Department of Psychology, Ludwig Maximilians University, Munich, Germany.
| | - Martin Kronbichler
- grid.7039.d0000000110156330Centre for Cognitive Neuroscience and Department of Psychology, Paris Lodron University of Salzburg, Salzburg, Austria ,grid.21604.310000 0004 0523 5263Neuroscience Institute, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria
| | - Arnulf Hartl
- grid.21604.310000 0004 0523 5263Institute for Ecomedicine, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria
| | - Carina Grafetstätter
- grid.21604.310000 0004 0523 5263Institute for Ecomedicine, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria
| | - Peter Strasser
- grid.21604.310000 0004 0523 5263Institute of Biochemical Diagnostic, Paracelsus Medical University, Salzburg, Austria
| | - Anna Kastinger
- grid.21604.310000 0004 0523 5263Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Ignaz-Harrer-Strasse 79, 5020 Salzburg, Austria ,grid.21604.310000 0004 0523 5263Department of Psychosomatics and Inpatient Psychotherapy, University Hospital for Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
| | - Helmut Schöller
- grid.21604.310000 0004 0523 5263Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Ignaz-Harrer-Strasse 79, 5020 Salzburg, Austria ,grid.21604.310000 0004 0523 5263Department of Psychosomatics and Inpatient Psychotherapy, University Hospital for Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
| | - Eva-Maria Reiter
- grid.21604.310000 0004 0523 5263Department for Radiotherapy and Radio-Oncology, Christian-Doppler University Hospital of the Paracelsus Medical University, Salzburg, Austria
| | - Sarah Said-Yürekli
- grid.7039.d0000000110156330Centre for Cognitive Neuroscience and Department of Psychology, Paris Lodron University of Salzburg, Salzburg, Austria ,grid.21604.310000 0004 0523 5263Neuroscience Institute, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria ,grid.21604.310000 0004 0523 5263Department for Neurology, Christian-Doppler University Hospital of the Paracelsus Medical University, Salzburg, Austria
| | - Lisa Kronbichler
- grid.7039.d0000000110156330Centre for Cognitive Neuroscience and Department of Psychology, Paris Lodron University of Salzburg, Salzburg, Austria ,grid.21604.310000 0004 0523 5263Neuroscience Institute, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria
| | - Brigitte Kravanja-Spannberger
- grid.21604.310000 0004 0523 5263Department of Psychosomatics and Inpatient Psychotherapy, University Hospital for Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
| | - Barbara Stöger-Schmidinger
- grid.21604.310000 0004 0523 5263Department of Psychosomatics and Inpatient Psychotherapy, University Hospital for Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
| | - Marc-Thorsten Hütt
- grid.15078.3b0000 0000 9397 8745Department of Life Sciences and Chemistry, Jacobs University, Bremen, Germany
| | - Wolfgang Aichhorn
- grid.21604.310000 0004 0523 5263Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Ignaz-Harrer-Strasse 79, 5020 Salzburg, Austria ,grid.21604.310000 0004 0523 5263Department of Psychosomatics and Inpatient Psychotherapy, University Hospital for Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
| | - Benjamin Aas
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Ludwig Maximilians University, Munich, Germany ,grid.5252.00000 0004 1936 973XFaculty of Psychology and Educational Sciences, LMU Munich, Munich, Germany
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32
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Martens L, Kroemer NB, Teckentrup V, Colic L, Palomero-Gallagher N, Li M, Walter M. Localized Prediction of Glutamate from Whole-Brain Functional Connectivity of the Pregenual Anterior Cingulate Cortex. J Neurosci 2020; 40:9028-9042. [PMID: 33046545 PMCID: PMC7673009 DOI: 10.1523/jneurosci.0897-20.2020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 07/14/2020] [Accepted: 09/04/2020] [Indexed: 11/21/2022] Open
Abstract
Local measures of neurotransmitters provide crucial insights into neurobiological changes underlying altered functional connectivity in psychiatric disorders. However, noninvasive neuroimaging techniques such as magnetic resonance spectroscopy (MRS) may cover anatomically and functionally distinct areas, such as p32 and p24 of the pregenual anterior cingulate cortex (pgACC). Here, we aimed to overcome this low spatial specificity of MRS by predicting local glutamate and GABA based on functional characteristics and neuroanatomy in a sample of 88 human participants (35 females), using complementary machine learning approaches. Functional connectivity profiles of pgACC area p32 predicted pgACC glutamate better than chance (R2 = 0.324) and explained more variance compared with area p24 using both elastic net and partial least-squares regression. In contrast, GABA could not be robustly predicted. To summarize, machine learning helps exploit the high resolution of fMRI to improve the interpretation of local neurometabolism. Our augmented multimodal imaging analysis can deliver novel insights into neurobiology by using complementary information.SIGNIFICANCE STATEMENT Magnetic resonance spectroscopy (MRS) measures local glutamate and GABA noninvasively. However, conventional MRS requires large voxels compared with fMRI, because of its inherently low signal-to-noise ratio. Consequently, a single MRS voxel may cover areas with distinct cytoarchitecture. In the largest multimodal 7 tesla machine learning study to date, we overcome this limitation by capitalizing on the spatial resolution of fMRI to predict local neurotransmitters in the PFC. Critically, we found that prefrontal glutamate could be robustly and exclusively predicted from the functional connectivity fingerprint of one of two anatomically and functionally defined areas that form the pregenual anterior cingulate cortex. Our approach provides greater spatial specificity on neurotransmitter levels, potentially improving the understanding of altered functional connectivity in mental disorders.
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Affiliation(s)
- Louise Martens
- Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany
| | - Nils B Kroemer
- Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany
| | - Vanessa Teckentrup
- Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany
| | - Lejla Colic
- Clinical Affective Neuroimaging Laboratory, 39120 Magdeburg, Germany
- Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut 06511
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425 Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany
- C. and O. Vogt Institute for Brain Research, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Meng Li
- Department of Psychiatry and Psychotherapy, Jena University Hospital, 07743 Jena, Germany
| | - Martin Walter
- Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany
- Clinical Affective Neuroimaging Laboratory, 39120 Magdeburg, Germany
- Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, Jena University Hospital, 07743 Jena, Germany
- Center for Behavioral Brain Sciences, 39106 Magdeburg, Germany
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33
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Mizen LAM, Stanfield AC. Demystifying neuroscience laboratory techniques used to investigate single-gene disorders. BJPSYCH ADVANCES 2020. [DOI: 10.1192/bja.2020.30] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
SUMMARYThere is considerable work being carried out in neuroscientific laboratories to delineate the mechanisms underlying single-gene disorders, particularly those related to intellectual disability and autism spectrum disorder. Many clinicians will have little if any direct experience of this type of work and so find the procedures and terminology difficult to understand. This article describes some of the laboratory techniques used and their increasing relevance to clinical practice. It is pitched for clinicians with little or no laboratory science background.
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34
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Renz AF, Lee J, Tybrandt K, Brzezinski M, Lorenzo DA, Cerra Cheraka M, Lee J, Helmchen F, Vörös J, Lewis CM. Opto-E-Dura: A Soft, Stretchable ECoG Array for Multimodal, Multiscale Neuroscience. Adv Healthc Mater 2020; 9:e2000814. [PMID: 32691992 DOI: 10.1002/adhm.202000814] [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: 05/13/2020] [Revised: 06/30/2020] [Indexed: 11/07/2022]
Abstract
Soft, stretchable materials hold great promise for the fabrication of biomedical devices due to their capacity to integrate gracefully with and conform to biological tissues. Conformal devices are of particular interest in the development of brain interfaces where rigid structures can lead to tissue damage and loss of signal quality over the lifetime of the implant. Interfaces to study brain function and dysfunction increasingly require multimodal access in order to facilitate measurement of diverse physiological signals that span the disparate temporal and spatial scales of brain dynamics. Here the Opto-e-Dura, a soft, stretchable, 16-channel electrocorticography array that is optically transparent is presented. Its compatibility with diverse optical and electrical readouts is demonstrated enabling multimodal studies that bridge spatial and temporal scales. The device is chronically stable for weeks, compatible with wide-field and 2-photon calcium imaging and permits the repeated insertion of penetrating multielectrode arrays. As the variety of sensors and effectors realizable on soft, stretchable substrates expands, similar devices that provide large-scale, multimodal access to the brain will continue to improve fundamental understanding of brain function.
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Affiliation(s)
- Aline F. Renz
- Institute for Biomedical Engineering ETH Zurich Zurich 8092 Switzerland
| | - Jihyun Lee
- Institute for Biomedical Engineering ETH Zurich Zurich 8092 Switzerland
| | - Klas Tybrandt
- Institute for Biomedical Engineering ETH Zurich Zurich 8092 Switzerland
- Laboratory of Organic Electronics Department of Science and Technology Linköping University Norrköping 60174 Sweden
| | - Maciej Brzezinski
- Institute for Biomedical Engineering ETH Zurich Zurich 8092 Switzerland
| | - Dayra A. Lorenzo
- Laboratory of Neural Circuit Dynamics Brain Research Institute University of Zurich Zurich 8057 Switzerland
- Neuroscience Center Zurich University and ETH Zurich Zurich 8057 Switzerland
| | | | - Jaehong Lee
- Institute for Biomedical Engineering ETH Zurich Zurich 8092 Switzerland
| | - Fritjof Helmchen
- Laboratory of Neural Circuit Dynamics Brain Research Institute University of Zurich Zurich 8057 Switzerland
- Neuroscience Center Zurich University and ETH Zurich Zurich 8057 Switzerland
| | - Janos Vörös
- Institute for Biomedical Engineering ETH Zurich Zurich 8092 Switzerland
- Neuroscience Center Zurich University and ETH Zurich Zurich 8057 Switzerland
| | - Christopher M. Lewis
- Laboratory of Neural Circuit Dynamics Brain Research Institute University of Zurich Zurich 8057 Switzerland
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35
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van den Heuvel MP, Scholtens LH, de Lange SC, Pijnenburg R, Cahn W, van Haren NEM, Sommer IE, Bozzali M, Koch K, Boks MP, Repple J, Pievani M, Li L, Preuss TM, Rilling JK. Evolutionary modifications in human brain connectivity associated with schizophrenia. Brain 2020; 142:3991-4002. [PMID: 31724729 PMCID: PMC6906591 DOI: 10.1093/brain/awz330] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 08/13/2019] [Accepted: 09/05/2019] [Indexed: 12/21/2022] Open
Abstract
The genetic basis and human-specific character of schizophrenia has led to the hypothesis that human brain evolution may have played a role in the development of the disorder. We examined schizophrenia-related changes in brain connectivity in the context of evolutionary changes in human brain wiring by comparing in vivo neuroimaging data from humans and chimpanzees, one of our closest living evolutionary relatives and a species with which we share a very recent common ancestor. We contrasted the connectome layout between the chimpanzee and human brain and compared differences with the pattern of schizophrenia-related changes in brain connectivity as observed in patients. We show evidence of evolutionary modifications of human brain connectivity to significantly overlap with the cortical pattern of schizophrenia-related dysconnectivity (P < 0.001, permutation testing). We validated these effects in three additional, independent schizophrenia datasets. We further assessed the specificity of effects by examining brain dysconnectivity patterns in seven other psychiatric and neurological brain disorders (including, among others, major depressive disorder and obsessive-compulsive disorder, arguably characterized by behavioural symptoms that are less specific to humans), which showed no such associations with modifications of human brain connectivity. Comparisons of brain connectivity across humans, chimpanzee and macaques further suggest that features of connectivity that evolved in the human lineage showed the strongest association to the disorder, that is, brain circuits potentially related to human evolutionary specializations. Taken together, our findings suggest that human-specific features of connectome organization may be enriched for changes in brain connectivity related to schizophrenia. Modifications in human brain connectivity in service of higher order brain functions may have potentially also rendered the brain vulnerable to brain dysfunction.
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Affiliation(s)
- Martijn P van den Heuvel
- Connectome Lab, Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands.,Department of Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Lianne H Scholtens
- Connectome Lab, Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Siemon C de Lange
- Connectome Lab, Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Rory Pijnenburg
- Connectome Lab, Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Wiepke Cahn
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Neeltje E M van Haren
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands.,Department of Child and Adolescent Psychiatry, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Iris E Sommer
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands.,Department of Neuroscience and Department of Psychiatry, University Medical Center Groningen, The Netherlands
| | - Marco Bozzali
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, East Sussex, UK.,Neuroimaging Laboratory, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Kathrin Koch
- Department of Neuroradiology and TUM-Neuroimaging Center (TUM-NIC), School of Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,Graduate School of Systemic Neurosciences GSN, Ludwig-Maximilians-Universität, Biocenter, Munich, Germany
| | - Marco P Boks
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Jonathan Repple
- Department of Psychiatry, University of Muenster, Muenster, Germany
| | - Michela Pievani
- Lab Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Longchuan Li
- Marcus Autism Center, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA, USA
| | - Todd M Preuss
- Division of Neuropharmacology and Neurologic Diseases, Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA.,Center for Translational Social Neuroscience, Emory University, Atlanta, GA, USA.,Center for Behavioral Neuroscience, Atlanta, GA, USA
| | - James K Rilling
- Center for Translational Social Neuroscience, Emory University, Atlanta, GA, USA.,Center for Behavioral Neuroscience, Atlanta, GA, USA.,Department of Anthropology, Emory University, 1557 Dickey Drive, Atlanta, GA 30322, USA.,Department of Psychiatry and Behavioral Sciences, Emory University, 201 Dowman Drive, Atlanta, GA 30322, USA.,Division of Developmental and Cognitive Neuroscience, Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA
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36
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McCutcheon RA, Krystal JH, Howes OD. Dopamine and glutamate in schizophrenia: biology, symptoms and treatment. World Psychiatry 2020; 19:15-33. [PMID: 31922684 PMCID: PMC6953551 DOI: 10.1002/wps.20693] [Citation(s) in RCA: 275] [Impact Index Per Article: 68.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Glutamate and dopamine systems play distinct roles in terms of neuronal signalling, yet both have been proposed to contribute significantly to the pathophysiology of schizophrenia. In this paper we assess research that has implicated both systems in the aetiology of this disorder. We examine evidence from post-mortem, preclinical, pharmacological and in vivo neuroimaging studies. Pharmacological and preclinical studies implicate both systems, and in vivo imaging of the dopamine system has consistently identified elevated striatal dopamine synthesis and release capacity in schizophrenia. Imaging of the glutamate system and other aspects of research on the dopamine system have produced less consistent findings, potentially due to methodological limitations and the heterogeneity of the disorder. Converging evidence indicates that genetic and environmental risk factors for schizophrenia underlie disruption of glutamatergic and dopaminergic function. However, while genetic influences may directly underlie glutamatergic dysfunction, few genetic risk variants directly implicate the dopamine system, indicating that aberrant dopamine signalling is likely to be predominantly due to other factors. We discuss the neural circuits through which the two systems interact, and how their disruption may cause psychotic symptoms. We also discuss mechanisms through which existing treatments operate, and how recent research has highlighted opportunities for the development of novel pharmacological therapies. Finally, we consider outstanding questions for the field, including what remains unknown regarding the nature of glutamate and dopamine function in schizophrenia, and what needs to be achieved to make progress in developing new treatments.
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Affiliation(s)
- Robert A McCutcheon
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital, London, UK
- South London and Maudsley Foundation NHS Trust, Maudsley Hospital, London, UK
| | - John H Krystal
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- VA National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Oliver D Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital, London, UK
- South London and Maudsley Foundation NHS Trust, Maudsley Hospital, London, UK
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37
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Wei Y, de Lange SC, Scholtens LH, Watanabe K, Ardesch DJ, Jansen PR, Savage JE, Li L, Preuss TM, Rilling JK, Posthuma D, van den Heuvel MP. Genetic mapping and evolutionary analysis of human-expanded cognitive networks. Nat Commun 2019; 10:4839. [PMID: 31649260 PMCID: PMC6813316 DOI: 10.1038/s41467-019-12764-8] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 09/24/2019] [Indexed: 01/04/2023] Open
Abstract
Cognitive brain networks such as the default-mode network (DMN), frontoparietal network, and salience network, are key functional networks of the human brain. Here we show that the rapid evolutionary cortical expansion of cognitive networks in the human brain, and most pronounced the DMN, runs parallel with high expression of human-accelerated genes (HAR genes). Using comparative transcriptomics analysis, we present that HAR genes are differentially more expressed in higher-order cognitive networks in humans compared to chimpanzees and macaques and that genes with high expression in the DMN are involved in synapse and dendrite formation. Moreover, HAR and DMN genes show significant associations with individual variations in DMN functional activity, intelligence, sociability, and mental conditions such as schizophrenia and autism. Our results suggest that the expansion of higher-order functional networks subserving increasing cognitive properties has been an important locus of genetic changes in recent human brain evolution.
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Affiliation(s)
- Yongbin Wei
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Siemon C de Lange
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Lianne H Scholtens
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Kyoko Watanabe
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Dirk Jan Ardesch
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Philip R Jansen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychiatry, Erasmus Medical Center, 3015 GD, Rotterdam, The Netherlands
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Longchuan Li
- Marcus Autism Center, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Todd M Preuss
- Division of Neuropharmacology and Neurologic Diseases, Emory University, Atlanta, GA, 30322, USA
- Center for Translational Social Neuroscience, Emory University, Atlanta, GA, 30322, USA
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, 30322, USA
| | - James K Rilling
- Center for Translational Social Neuroscience, Emory University, Atlanta, GA, 30322, USA
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, 30322, USA
- Department of Anthropology, Emory University, Atlanta, GA, 30322, USA
- Center for Behavioral Neuroscience, Emory University, Atlanta, GA, 30322, USA
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, 30322, USA
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Amsterdam UMC, 1081 HV, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands.
- Department of Clinical Genetics, Amsterdam Neuroscience, Amsterdam UMC, 1081 HV, Amsterdam, The Netherlands.
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