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Sun L, Zhao T, Liang X, Xia M, Li Q, Liao X, Gong G, Wang Q, Pang C, Yu Q, Bi Y, Chen P, Chen R, Chen Y, Chen T, Cheng J, Cheng Y, Cui Z, Dai Z, Deng Y, Ding Y, Dong Q, Duan D, Gao JH, Gong Q, Han Y, Han Z, Huang CC, Huang R, Huo R, Li L, Lin CP, Lin Q, Liu B, Liu C, Liu N, Liu Y, Liu Y, Lu J, Ma L, Men W, Qin S, Qiu J, Qiu S, Si T, Tan S, Tang Y, Tao S, Wang D, Wang F, Wang J, Wang P, Wang X, Wang Y, Wei D, Wu Y, Xie P, Xu X, Xu Y, Xu Z, Yang L, Yuan H, Zeng Z, Zhang H, Zhang X, Zhao G, Zheng Y, Zhong S, He Y. Functional connectome through the human life span. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.12.557193. [PMID: 37745373 PMCID: PMC10515818 DOI: 10.1101/2023.09.12.557193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimer's disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.
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Affiliation(s)
- Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Qian Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chenxuan Pang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qian Yu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Taolin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, China
| | - Zhengjia Dai
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yao Deng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuyin Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dingna Duan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Zaizhu Han
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Ruiwang Huang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ran Huo
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Lingjiang Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Ching-Po Lin
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, China
- Department of Education and Research, Taipei City Hospital, Taipei, China
| | - Qixiang Lin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Bangshan Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Chao Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ningyu Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ying Liu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yong Liu
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Jing Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Tianmei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji’nan, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiali Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yankun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Peng Xie
- Chongqing Key Laboratory of Neurobiology, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuehua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhilei Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Liyuan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Zilong Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Haibo Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yanting Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Suyu Zhong
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | | | | | | | | | | | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
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2
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Grasso-Cladera A, Bremer M, Ladouce S, Parada F. A systematic review of mobile brain/body imaging studies using the P300 event-related potentials to investigate cognition beyond the laboratory. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2024:10.3758/s13415-024-01190-z. [PMID: 38834886 DOI: 10.3758/s13415-024-01190-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/15/2024] [Indexed: 06/06/2024]
Abstract
The P300 ERP component, related to the onset of task-relevant or infrequent stimuli, has been widely used in the Mobile Brain/Body Imaging (MoBI) literature. This systematic review evaluates the quality and breadth of P300 MoBI studies, revealing a maturing field with well-designed research yet grappling with standardization and global representation challenges. While affirming the reliability of measuring P300 ERP components in mobile settings, the review identifies significant hurdles in standardizing data cleaning and processing techniques, impacting comparability and reproducibility. Geographical disparities emerge, with studies predominantly in the Global North and a dearth of research from the Global South, emphasizing the need for broader inclusivity to counter the WEIRD bias in psychology. Collaborative projects and mobile EEG systems showcase the feasibility of reaching diverse populations, which is essential to advance precision psychiatry and to integrate varied data streams. Methodologically, a trend toward ecological validity is noted, shifting from lab-based to real-world settings with portable EEG system advancements. Future hardware developments are expected to balance signal quality and sensor intrusiveness, enriching data collection in everyday contexts. Innovative methodologies reflect a move toward more natural experimental settings, prompting critical questions about the applicability of traditional ERP markers, such as the P300 outside structured paradigms. The review concludes by highlighting the crucial role of integrating mobile technologies, physiological sensors, and machine learning to advance cognitive neuroscience. It advocates for an operational definition of ecological validity to bridge the gap between controlled experiments and the complexity of embodied cognitive experiences, enhancing both theoretical understanding and practical application in study design.
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Affiliation(s)
| | - Marko Bremer
- Facultad de Psicología, Centro de Estudios en Neurociencia Humana y Neuropsicología (CENHN), Diego Portales University, Santiago, Chile
- Facultad de Psicología, Programa de Magíster en Neurociencia Social, Diego Portales University, Santiago, Chile
| | - Simon Ladouce
- Department Brain and Cognition, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Francisco Parada
- Facultad de Psicología, Centro de Estudios en Neurociencia Humana y Neuropsicología (CENHN), Diego Portales University, Santiago, Chile.
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3
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Jiang R, Noble S, Rosenblatt M, Dai W, Ye J, Liu S, Qi S, Calhoun VD, Sui J, Scheinost D. The brain structure, inflammatory, and genetic mechanisms mediate the association between physical frailty and depression. Nat Commun 2024; 15:4411. [PMID: 38782943 PMCID: PMC11116547 DOI: 10.1038/s41467-024-48827-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024] Open
Abstract
Cross-sectional studies have demonstrated strong associations between physical frailty and depression. However, the evidence from prospective studies is limited. Here, we analyze data of 352,277 participants from UK Biobank with 12.25-year follow-up. Compared with non-frail individuals, pre-frail and frail individuals have increased risk for incident depression independent of many putative confounds. Altogether, pre-frail and frail individuals account for 20.58% and 13.16% of depression cases by population attributable fraction analyses. Higher risks are observed in males and individuals younger than 65 years than their counterparts. Mendelian randomization analyses support a potential causal effect of frailty on depression. Associations are also observed between inflammatory markers, brain volumes, and incident depression. Moreover, these regional brain volumes and three inflammatory markers-C-reactive protein, neutrophils, and leukocytes-significantly mediate associations between frailty and depression. Given the scarcity of curative treatment for depression and the high disease burden, identifying potential modifiable risk factors of depression, such as frailty, is needed.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA.
| | - Stephanie Noble
- Department of Psychology, Northeastern University, Boston, MA, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Center for Cognitive and Brain Health, Northeastern University, Boston, MA, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Wei Dai
- Department of Biostatistics, Yale University, New Haven, CT, 06520, USA
| | - Jean Ye
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06520, USA
| | - Shu Liu
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, 30303, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, 30303, USA
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06520, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
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4
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Öngür D. JAMA Psychiatry-The Year in Review, 2023. JAMA Psychiatry 2024; 81:435-436. [PMID: 38506797 DOI: 10.1001/jamapsychiatry.2024.0138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Affiliation(s)
- Dost Öngür
- Editor, JAMA Psychiatry
- McLean Hospital, Harvard Medical School, Belmont, Massachusetts
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5
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Chaudhari AS. AI in osteoarthritis: Illuminating the meandering path forward. Osteoarthritis Cartilage 2024; 32:227-228. [PMID: 38013138 DOI: 10.1016/j.joca.2023.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/13/2023] [Accepted: 11/21/2023] [Indexed: 11/29/2023]
Affiliation(s)
- Akshay S Chaudhari
- Department of Radiology, Stanford University, 1201 Welch Road P269, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
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Lathe J, Silverwood RJ, Hughes AD, Patalay P. Examining how well economic evaluations capture the value of mental health. Lancet Psychiatry 2024; 11:221-230. [PMID: 38281493 DOI: 10.1016/s2215-0366(23)00436-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 12/21/2023] [Accepted: 12/27/2023] [Indexed: 01/30/2024]
Abstract
Health economics evidence informs health-care decision making, but the field has historically paid insufficient attention to mental health. Economic evaluations in health should define an appropriate scope for benefits and costs and how to value them. This Health Policy provides an overview of these processes and considers to what extent they capture the value of mental health. We suggest that although current practices are both transparent and justifiable, they have distinct limitations from the perspective of mental health. Most social value judgements, such as the exclusion of interindividual outcomes and intersectoral costs, diminish the value of improving mental health, and this reduction in value might be disproportionate compared with other types of health. Economic analyses might have disadvantaged interventions that improve mental health compared with physical health, but research is required to test the size of such differential effects and any subsequent effect on decision-making systems such as health technology assessment systems. Collaboration between health economics and the mental health sciences is crucial for achieving mental-physical health parity in evaluative frameworks and, ultimately, improving population mental health.
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Affiliation(s)
- James Lathe
- MRC Unit for Lifelong Health and Ageing, Department of Population Science and Experimental Medicine, Faculty of Population Health Sciences, University College London, London, UK.
| | - Richard J Silverwood
- Centre for Longitudinal Studies, Social Research Institute, Institute of Education, Faculty of Education and Society, University College London, London, UK
| | - Alun D Hughes
- MRC Unit for Lifelong Health and Ageing, Department of Population Science and Experimental Medicine, Faculty of Population Health Sciences, University College London, London, UK
| | - Praveetha Patalay
- MRC Unit for Lifelong Health and Ageing, Department of Population Science and Experimental Medicine, Faculty of Population Health Sciences, University College London, London, UK; Centre for Longitudinal Studies, Social Research Institute, Institute of Education, Faculty of Education and Society, University College London, London, UK
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7
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Cheng L, Wu H, Cai X, Zhang Y, Yu S, Hou Y, Yin Z, Yan Q, Wang Q, Sun T, Wang G, Yuan Y, Zhang X, Hao H, Zheng X. A Gpr35-tuned gut microbe-brain metabolic axis regulates depressive-like behavior. Cell Host Microbe 2024; 32:227-243.e6. [PMID: 38198925 DOI: 10.1016/j.chom.2023.12.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/29/2023] [Accepted: 12/13/2023] [Indexed: 01/12/2024]
Abstract
Gene-environment interactions shape behavior and susceptibility to depression. However, little is known about the signaling pathways integrating genetic and environmental inputs to impact neurobehavioral outcomes. We report that gut G-protein-coupled receptor, Gpr35, engages a microbe-to-brain metabolic pathway to modulate neuronal plasticity and depressive behavior in mice. Psychological stress decreases intestinal epithelial Gpr35, genetic deletion of which induces depressive-like behavior in a microbiome-dependent manner. Gpr35-/- mice and individuals with depression have increased Parabacteroides distasonis, and its colonization to wild-type mice induces depression. Gpr35-/- and Parabacteroides distasonis-colonized mice show reduced indole-3-carboxaldehyde (IAld) and increased indole-3-lactate (ILA), which are produced from opposing branches along the bacterial catabolic pathway of tryptophan. IAld and ILA counteractively modulate neuroplasticity in the nucleus accumbens, a brain region linked to depression. IAld supplementation produces anti-depressant effects in mice with stress or gut epithelial Gpr35 deficiency. Together, these findings elucidate a gut microbe-brain signaling mechanism that underlies susceptibility to depression.
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Affiliation(s)
- Lingsha Cheng
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Haoqian Wu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Xiaoying Cai
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Youying Zhang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Siqi Yu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Yuanlong Hou
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Zhe Yin
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Qingyuan Yan
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Qiong Wang
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China
| | - Taipeng Sun
- Department of Psychosomatics and Psychiatry, Southeast University Affiliated Zhongda Hospital, Nanjing 210009, China
| | - Guangji Wang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, Southeast University Affiliated Zhongda Hospital, Nanjing 210009, China.
| | - Xueli Zhang
- Department of Pharmacy, Southeast University Affiliated Zhongda Hospital, Nanjing 210009, China.
| | - Haiping Hao
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China.
| | - Xiao Zheng
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China.
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8
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Diniz BS, Seitz-Holland J, Sehgal R, Kasamoto J, Higgins-Chen AT, Lenze E. Geroscience-Centric Perspective for Geriatric Psychiatry: Integrating Aging Biology With Geriatric Mental Health Research. Am J Geriatr Psychiatry 2024; 32:1-16. [PMID: 37845116 PMCID: PMC10841054 DOI: 10.1016/j.jagp.2023.09.014] [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: 07/20/2023] [Revised: 08/30/2023] [Accepted: 09/14/2023] [Indexed: 10/18/2023]
Abstract
The geroscience hypothesis asserts that physiological aging is caused by a small number of biological pathways. Despite the explosion of geroscience research over the past couple of decades, the research on how serious mental illnesses (SMI) affects the biological aging processes is still in its infancy. In this review, we aim to provide a critical appraisal of the emerging literature focusing on how we measure biological aging systematically, and in the brain and how SMIs affect biological aging measures in older adults. We will also review recent developments in the field of cellular senescence and potential targets for interventions for SMIs in older adults, based on the geroscience hypothesis.
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Affiliation(s)
- Breno S Diniz
- UConn Center on Aging & Department of Psychiatry (BSD), School of Medicine, University of Connecticut Health Center, Farmington, CT.
| | - Johanna Seitz-Holland
- Department of Psychiatry (JSH), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry (JSH), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Raghav Sehgal
- Program in Computational Biology and Bioinformatics (RS, JK), Yale University, New Haven, CT
| | - Jessica Kasamoto
- Program in Computational Biology and Bioinformatics (RS, JK), Yale University, New Haven, CT
| | - Albert T Higgins-Chen
- Department of Psychiatry (ATHC), Yale University School of Medicine, New Haven, CT; Department of Pathology (ATHC), Yale University School of Medicine, New Haven, CT
| | - Eric Lenze
- Department of Psychiatry (EL), School of Medicine, Washington University at St. Louis, St. Louis, MO
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Augustin N, Bendau A, Heuer S, Kaminski J, Ströhle A. Resistance Training in Depression. DEUTSCHES ARZTEBLATT INTERNATIONAL 2023; 120:757-762. [PMID: 37656468 PMCID: PMC10745562 DOI: 10.3238/arztebl.m2023.0196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 08/11/2023] [Accepted: 08/11/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND More than 320 million people around the world suffer from depression. Physical activity and sports are effective treatment strategies. Endurance training has already been intensively studied, but any potential antidepressant effect of resistance training is unknown at present, nor is it clear whether this could yield any relevant benefit in clinical use. METHODS The PubMed database was selectively searched for recent studies and review articles concerning the use, efficacy, and safety of resistance training in persons with depressive symptoms and diagnosed depression. RESULTS Two meta-analyses revealed that resistance training alleviated depressive symptoms with a low to moderate effect size (0.39-0.66). Resistance training in patients with diagnosed depression was studied in seven randomized controlled trials, in which the duration of the intervention ranged from eight weeks to eight months. In six of these trials, the depressive symptoms were reduced. In one trial, a persistent benefit was seen in the resistance-training group at 26 months of follow-up (adherence, 33%). Moreover, resistance training improved strength, quality of life, and quality of sleep. No serious adverse events occurred; this indicates that resistance training in depression is safe. CONCLUSION Resistance training seems to have an antidepressant effect. Open questions remain concerning its effects in different age groups, as well as the optimal training parameters. Further high-quality trials will be needed to document the effect of resistance training more conclusively and to enable the formulation of treatment recommendations.
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Affiliation(s)
- Noah Augustin
- Department of Psychiatry and Psychotherapy at the Charité Campus Mitte
| | - Antonia Bendau
- Department of Psychiatry and Psychotherapy at the Charité Campus Mitte
- HMU Health and -Medical University Potsdam
| | - Selina Heuer
- Department of Psychiatry and Psychotherapy at the Charité Campus Mitte
| | - Jan Kaminski
- Department of Psychiatry and Psychotherapy at the Charité Campus Mitte
| | - Andreas Ströhle
- Department of Psychiatry and Psychotherapy at the Charité Campus Mitte
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10
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Perry BI, Mitchell C, Holt RI, Shiers D, Chew-Graham CA. Lester positive cardiometabolic resource update: improving cardiometabolic outcomes in people with severe mental illness. Br J Gen Pract 2023; 73:488-489. [PMID: 37884375 PMCID: PMC10617973 DOI: 10.3399/bjgp23x735273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2023] Open
Affiliation(s)
- Benjamin I Perry
- Department of Psychiatry, University of Cambridge; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge
| | | | - Richard Ig Holt
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton
| | - David Shiers
- School of Medicine, Keele University, Keele; Division of Psychology and Mental Health, University of Manchester; Psychosis Research Unit, Greater Manchester Mental Health NHS Trust, Manchester
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11
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Blose BA, Lai A, Crosta C, Thompson JL, Silverstein SM. Retinal Neurodegeneration as a Potential Biomarker of Accelerated Aging in Schizophrenia Spectrum Disorders. Schizophr Bull 2023; 49:1316-1324. [PMID: 37459382 PMCID: PMC10483469 DOI: 10.1093/schbul/sbad102] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
BACKGROUND AND HYPOTHESES Several biological markers are believed to reflect accelerated aging in schizophrenia spectrum disorders; however, retinal neural changes have not yet been explored as potential CNS biomarkers of accelerated aging in this population. The aim of this study was to determine whether retinal neural layer thinning is more strongly related to age in schizophrenia and schizoaffective disorder patients (SZ) than in a psychiatrically healthy control group (CON). STUDY DESIGN Schizophrenia (n = 60) and CON participants (n = 69) underwent spectral domain optical coherence tomography (OCT) scans to examine the following variables in both eyes: retinal nerve fiber layer (RNFL) thickness, macula central subfield (CSF) thickness, macula volume, ganglion cell layer-inner plexiform layer (GCL-IPL) thickness, optic cup volume, and cup-to-disc ratio. Eleven participants in each group had diabetes or hypertension. STUDY RESULTS Significant negative relationships between age and RNFL thickness, macula volume, and GCL-IPL thickness were observed in the SZ group, while no significant relationships were observed in the CON group. However, many of the findings in the SZ group lost significance when participants with diabetes/hypertension were removed from analyses. A notable exception to this was that the age × SZ interaction accounted for a unique proportion of variance in GCL-IPL thinning over and above the effect of diabetes/hypertension. CONCLUSIONS The results suggest that retinal atrophy occurs at an increased rate in schizophrenia spectrum disorders, potentially reflecting accelerated aging inherent to these conditions, with considerable contributions from systemic medical diseases closely linked to this population.
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Affiliation(s)
- Brittany A Blose
- Department of Psychology, University of Rochester, Rochester, NY, USA
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
| | - Adriann Lai
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
- University Behavioral Health Care, Rutgers University, Piscataway, NJ, USA
| | - Christen Crosta
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, USA
| | - Judy L Thompson
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
- Department of Psychiatric Rehabilitation and Counseling Professions, Rutgers University, Piscataway, NJ, USA
| | - Steven M Silverstein
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
- University Behavioral Health Care, Rutgers University, Piscataway, NJ, USA
- Department of Psychiatry, Rutgers University, Piscataway, NJ, USA
- Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, USA
- Department of Ophthalmology, University of Rochester Medical Center, Rochester, NY, USA
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Shindo R, Tanifuji T, Okazaki S, Otsuka I, Shirai T, Mouri K, Horai T, Hishimoto A. Accelerated epigenetic aging and decreased natural killer cells based on DNA methylation in patients with untreated major depressive disorder. NPJ AGING 2023; 9:19. [PMID: 37673891 PMCID: PMC10482893 DOI: 10.1038/s41514-023-00117-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 06/29/2023] [Indexed: 09/08/2023]
Abstract
Major depressive disorder (MDD) is known to cause significant disability. Genome-wide DNA methylation (DNAm) profiles can be used to estimate biological aging and as epigenetic clocks. However, information on epigenetic clocks reported in MDD patients is inconsistent. Since antidepressants are likely confounders, we evaluated biological aging using various DNAm-based predictors in patients with MDD who had never received depression medication. A publicly available dataset consisting of whole blood samples from untreated MDD patients (n = 40) and controls (n = 40) was used. We analyzed five epigenetic clocks (HorvathAge, HannumAge, SkinBloodAge, PhenoAge, and GrimAge), DNAm-based telomere length (DNAmTL), and DNAm-based age-related plasma proteins (GrimAge components), as well as DNAm-based white blood cell composition. The results indicate that patients with untreated MDD were significantly associated with epigenetic aging acceleration in HannumAge and GrimAge. Furthermore, a decrease in natural killer cells, based on DNAm, was observed in patients with untreated MDD.
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Affiliation(s)
- Ryota Shindo
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Takaki Tanifuji
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Satoshi Okazaki
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan.
| | - Ikuo Otsuka
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Toshiyuki Shirai
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Kentaro Mouri
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Tadasu Horai
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Akitoyo Hishimoto
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
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Hoemann K, Wormwood JB, Barrett LF, Quigley KS. Multimodal, Idiographic Ambulatory Sensing Will Transform our Understanding of Emotion. AFFECTIVE SCIENCE 2023; 4:480-486. [PMID: 37744967 PMCID: PMC10513989 DOI: 10.1007/s42761-023-00206-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 07/17/2023] [Indexed: 09/26/2023]
Abstract
Emotions are inherently complex - situated inside the brain while being influenced by conditions inside the body and outside in the world - resulting in substantial variation in experience. Most studies, however, are not designed to sufficiently sample this variation. In this paper, we discuss what could be discovered if emotion were systematically studied within persons 'in the wild', using biologically-triggered experience sampling: a multimodal and deeply idiographic approach to ambulatory sensing that links body and mind across contexts and over time. We outline the rationale for this approach, discuss challenges to its implementation and widespread adoption, and set out opportunities for innovation afforded by emerging technologies. Implementing these innovations will enrich method and theory at the frontier of affective science, propelling the contextually situated study of emotion into the future.
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Affiliation(s)
- Katie Hoemann
- Department of Psychology, KU Leuven, Tiensestraat 102, Box 3727, 3000 Leuven, BE Belgium
| | - Jolie B. Wormwood
- Department of Psychology, University of New Hampshire, Durham, NH USA
| | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, MA USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Cambridge, MA USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA USA
| | - Karen S. Quigley
- Department of Psychology, Northeastern University, Boston, MA USA
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Ibanez A, Zimmer ER. Time to synergize mental health with brain health. NATURE. MENTAL HEALTH 2023; 1:441-443. [PMID: 38867916 PMCID: PMC11168413 DOI: 10.1038/s44220-023-00086-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Affiliation(s)
- Agustin Ibanez
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, US
- Trinity College Dublin (TCD), Dublin, Ireland
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Eduardo R. Zimmer
- Department of Pharmacology, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Graduate Program in Biological Sciences: Biochemistry (PPGBioq) and Pharmacology and Therapeutics (PPGFT), UFRGS, Porto Alegre, Brazil
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, Canada
- Brain Institute of Rio Grande do Sul, Pontíficia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
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