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Dearman A, Bao Y, Schalkwyk L, Kumari M. Serum proteomic correlates of mental health symptoms in a representative UK population sample. Brain Behav Immun Health 2025; 44:100947. [PMID: 39911945 PMCID: PMC11795072 DOI: 10.1016/j.bbih.2025.100947] [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: 02/13/2024] [Revised: 10/24/2024] [Accepted: 01/13/2025] [Indexed: 02/07/2025] Open
Abstract
Poor mental health constitutes a public health crisis due to its high prevalence, unmet need and its mechanistic heterogeneity. A comprehensive understanding of the biological correlates of poor mental health in the population could enhance epidemiological research and eventually help guide treatment strategies. The human bloodstream contains many proteins, several of which have been linked to diagnosed mental health conditions but not to population mental health symptoms, however recent technological advances have made this possible. Here we perform exploratory factor analyses of 184 proteins from two panels (cardiometabolic and neurology-related) measured using proximity extension assays from Understanding Society (the UK Household Longitudinal Study; UKHLS). Data reduction results in 28 factors that explain 55-59% of the variance per panel. We perform multiple linear regressions in up to 5304 participants using two mental health symptom-based outcomes: psychological distress assessed with the general health questionnaire (GHQ-12) and mental health functioning assessed with the 12-Item Short Form Survey, Mental Component Summary (SF12-MCS) using the proteomic factors as explanatory variables and adjusting for demographic covariates. We use backward selection to discard non-significant proteomic factors from the models. Ten factors are independently associated with population mental health symptoms, three of which are immune-related (immunometabolism, immune cell-mediated processes, acute phase processes), three brain-related (neurodevelopment, synaptic processes, neuroprotective processes), two proteolysis-related (proteolysis & the kynurenine pathway, haemostasis & proteolysis), growth factors & muscle, and oxidative stress & the cytoskeleton. Associations partially overlap across the two outcomes, and a sensitivity analysis excluding people taking antidepressants or other central nervous system medications suggestively implicates some of the factors in treatment-resistant poor mental health. Our findings replicate those of case-control studies and expand these to underlie mental health symptomatology in the adult population. More work is needed to understand the direction of causality in these associations.
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Affiliation(s)
- Anna Dearman
- Institute for Social and Economic Research, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UK
| | - Yanchun Bao
- School of Mathematics, Statistics and Actuarial Science (SMSAS), University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UK
| | - Leonard Schalkwyk
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UK
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UK
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2
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Jirsaraie RJ, Gatavins MM, Pines AR, Kandala S, Bijsterbosch JD, Marek S, Bogdan R, Barch DM, Sotiras A. Mapping the neurodevelopmental predictors of psychopathology. Mol Psychiatry 2025; 30:478-488. [PMID: 39107582 DOI: 10.1038/s41380-024-02682-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 07/13/2024] [Accepted: 07/22/2024] [Indexed: 08/10/2024]
Abstract
Neuroimaging research has uncovered a multitude of neural abnormalities associated with psychopathology, but few prediction-based studies have been conducted during adolescence, and even fewer used neurobiological features that were extracted across multiple neuroimaging modalities. This gap in the literature is critical, as deriving accurate brain-based models of psychopathology is an essential step towards understanding key neural mechanisms and identifying high-risk individuals. As such, we trained adaptive tree-boosting algorithms on multimodal neuroimaging features from the Lifespan Human Connectome Developmental (HCP-D) sample that contained 956 participants between the ages of 8 to 22 years old. Our feature space consisted of 1037 anatomical, 1090 functional, and 192 diffusion MRI features, which were used to derive models that separately predicted internalizing symptoms, externalizing symptoms, and the general psychopathology factor. We found that multimodal models were the most accurate, but all brain-based models of psychopathology yielded out-of-sample predictions that were weakly correlated with actual symptoms (r2 < 0.15). White matter microstructural properties, including orientation dispersion indices and intracellular volume fractions, were the most predictive of general psychopathology, followed by cortical thickness and functional connectivity. Spatially, the most predictive features of general psychopathology were primarily localized within the default mode and dorsal attention networks. These results were mostly consistent across all dimensions of psychopathology, except orientation dispersion indices and the default mode network were not as heavily weighted in the prediction of internalizing and externalizing symptoms. Taken with prior literature, it appears that neurobiological features are an important part of the equation for predicting psychopathology but relying exclusively on neural markers is clearly not sufficient, especially among adolescent samples with subclinical symptoms. Consequently, risk factor models of psychopathology may benefit from incorporating additional sources of information that have also been shown to explain individual differences, such as psychosocial factors, environmental stressors, and genetic vulnerabilities.
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Affiliation(s)
- Robert J Jirsaraie
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Martins M Gatavins
- Lifespan Brain Institute, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam R Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Sridhar Kandala
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Scott Marek
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- AI for Health Institute, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
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3
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Shui X, Xu H, Tan S, Zhang D. Depression Recognition Using Daily Wearable-Derived Physiological Data. SENSORS (BASEL, SWITZERLAND) 2025; 25:567. [PMID: 39860935 PMCID: PMC11768625 DOI: 10.3390/s25020567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2024] [Revised: 01/15/2025] [Accepted: 01/17/2025] [Indexed: 01/27/2025]
Abstract
The objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life contexts. Compared to other objective measurement methods, wearables offer the potential for continuous, unobtrusive monitoring, which can capture subtle physiological changes indicative of depressive states. The present study leverages multimodal wristband devices to collect data from fifty-eight participants clinically diagnosed with depression during their normal daytime activities over six hours. Data collected include pulse wave, skin conductance, and triaxial acceleration. For comparison, we also utilized data from fifty-eight matched healthy controls from a publicly available dataset, collected using the same devices over equivalent durations. Our aim was to identify depressive individuals through the analysis of multimodal physiological measurements derived from wearable devices in daily life scenarios. We extracted static features such as the mean, variance, skewness, and kurtosis of physiological indicators like heart rate, skin conductance, and acceleration, as well as autoregressive coefficients of these signals reflecting the temporal dynamics. Utilizing a Random Forest algorithm, we distinguished depressive and non-depressive individuals with varying classification accuracies on data aggregated over 6 h, 2 h, 30 min, and 5 min segments, as 90.0%, 84.7%, 80.1%, and 76.0%, respectively. Our results demonstrate the feasibility of using daily wearable-derived physiological data for depression recognition. The achieved classification accuracies suggest that this approach could be integrated into clinical settings for the early detection and monitoring of depressive symptoms. Future work will explore the potential of these methods for personalized interventions and real-time monitoring, offering a promising avenue for enhancing mental health care through the integration of wearable technology.
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Affiliation(s)
- Xinyu Shui
- Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China
| | - Hao Xu
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Dan Zhang
- Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China
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4
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Azzam M, Xu Z, Liu R, Li L, Meng Soh K, Challagundla KB, Wan S, Wang J. A review of artificial intelligence-based brain age estimation and its applications for related diseases. Brief Funct Genomics 2025; 24:elae042. [PMID: 39436320 PMCID: PMC11735757 DOI: 10.1093/bfgp/elae042] [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: 07/30/2024] [Revised: 10/02/2024] [Accepted: 10/12/2024] [Indexed: 10/23/2024] Open
Abstract
The study of brain age has emerged over the past decade, aiming to estimate a person's age based on brain imaging scans. Ideally, predicted brain age should match chronological age in healthy individuals. However, brain structure and function change in the presence of brain-related diseases. Consequently, brain age also changes in affected individuals, making the brain age gap (BAG)-the difference between brain age and chronological age-a potential biomarker for brain health, early screening, and identifying age-related cognitive decline and disorders. With the recent successes of artificial intelligence in healthcare, it is essential to track the latest advancements and highlight promising directions. This review paper presents recent machine learning techniques used in brain age estimation (BAE) studies. Typically, BAE models involve developing a machine learning regression model to capture age-related variations in brain structure from imaging scans of healthy individuals and automatically predict brain age for new subjects. The process also involves estimating BAG as a measure of brain health. While we discuss recent clinical applications of BAE methods, we also review studies of biological age that can be integrated into BAE research. Finally, we point out the current limitations of BAE's studies.
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Affiliation(s)
- Mohamed Azzam
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States
- Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
| | - Ziyang Xu
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Ruobing Liu
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Lie Li
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Kah Meng Soh
- Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Kishore B Challagundla
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Shibiao Wan
- Department of Genetics, Cell Biology and Anatomy, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Jieqiong Wang
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States
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5
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Jamadar SD, Behler A, Deery H, Breakspear M. The metabolic costs of cognition. Trends Cogn Sci 2025:S1364-6613(24)00319-X. [PMID: 39809687 DOI: 10.1016/j.tics.2024.11.010] [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: 07/22/2024] [Revised: 11/18/2024] [Accepted: 11/22/2024] [Indexed: 01/16/2025]
Abstract
Cognition and behavior are emergent properties of brain systems that seek to maximize complex and adaptive behaviors while minimizing energy utilization. Different species reconcile this trade-off in different ways, but in humans the outcome is biased towards complex behaviors and hence relatively high energy use. However, even in energy-intensive brains, numerous parsimonious processes operate to optimize energy use. We review how this balance manifests in both homeostatic processes and task-associated cognition. We also consider the perturbations and disruptions of metabolism in neurocognitive diseases.
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Affiliation(s)
- Sharna D Jamadar
- School of Psychological Sciences, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Victoria, Australia; Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia.
| | - Anna Behler
- School of Psychological Sciences, College of Engineering, Science, and the Environment, University of Newcastle, Newcastle, New South Wales, Australia
| | - Hamish Deery
- School of Psychological Sciences, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Victoria, Australia; Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Michael Breakspear
- School of Psychological Sciences, College of Engineering, Science, and the Environment, University of Newcastle, Newcastle, New South Wales, Australia; School of Public Health and Medicine, College of Medicine, Health and Wellbeing, University of Newcastle, Newcastle, New South Wales, Australia
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Kim EY, Kim J, Jeong JH, Jang J, Kang N, Seo J, Park YE, Park J, Jeong H, Ahn YM, Kim YS, Lee D, Kim SH. Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics. Schizophr Res 2025; 275:146-155. [PMID: 39731846 DOI: 10.1016/j.schres.2024.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 12/09/2024] [Accepted: 12/22/2024] [Indexed: 12/30/2024]
Abstract
Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from 299 patients with schizophrenia from three multicenter, open-label, non-comparative clinical trials. For prediction of treatment response at weeks 4, 8, and 24, psychopathology (both objective and subjective symptoms), sociodemographic and clinical factors, functional outcomes, attitude toward medication, and metabolic characteristics were evaluated. Various ML techniques were applied. The highest area under the curve (AUC) at weeks 4, 8 and 24 was 0.711, 0.664 and 0.678 with extreme gradient boosting, respectively. Notably, our findings indicate that BMI and attitude toward medication play a pivotal role in predicting treatment responses at all-time points. Other salient features for weeks 4 and 8 included psychosocial functioning, negative symptoms, subjective symptoms like psychoticism and hostility, and the level of prolactin. For week 24, positive symptoms, depression, education level and duration of illness were also important. This study introduced a precise clinical model for predicting schizophrenia treatment outcomes using multiple readily accessible predictors. The findings underscore the significance of metabolic parameters and subjective traits.
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Affiliation(s)
- Eun Young Kim
- Department of Psychiatry, Seoul National University Health Service Center, Seoul, Republic of Korea; Department of Human Systems Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jayoun Kim
- Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jae Hoon Jeong
- Department of Psychiatry, Nowon Eulji University Hospital, Seoul, Republic of Korea
| | - Jinhyeok Jang
- Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Nuree Kang
- Department of Psychiatry, Gyeongsang National University Hospital, Jinju, Republic of Korea
| | - Jieun Seo
- Department of Statistics, Ewha Womans University, Seoul, Republic of Korea
| | - Young Eun Park
- Department of Statistics, Ewha Womans University, Seoul, Republic of Korea
| | - Jiae Park
- Department of Statistics, Ewha Womans University, Seoul, Republic of Korea
| | - Hyunsu Jeong
- Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yong Min Ahn
- Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yong Sik Kim
- Department of Psychiatry, Nowon Eulji University Hospital, Seoul, Republic of Korea
| | - Donghwan Lee
- Department of Statistics, Ewha Womans University, Seoul, Republic of Korea.
| | - Se Hyun Kim
- Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
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7
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Koutsouleris N, Khuntia AT, Popovic D, Sarisik E, Buciuman MO, Pedersen ML, Westlye LT, Andreassen O, Meyer-Lindenberg A, Kambeitz J, Salokangas R, Hietala J, Bertolino A, Borgwardt S, Brambilla P, Upthegrove R, Wood S, Lencer R, Meisenzahl E, Falkai P, Schwarz E, Wiegand A. BMIgap: a new tool to quantify transdiagnostic brain signatures of current and future weight. RESEARCH SQUARE 2024:rs.3.rs-5259910. [PMID: 39711565 PMCID: PMC11661285 DOI: 10.21203/rs.3.rs-5259910/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2024]
Abstract
Understanding the neurobiological underpinnings of weight gain could reduce excess mortality and improve long-term trajectories of psychiatric disorders. We used support-vector machines and whole-brain voxel-wise grey matter volume to generate and validate a BMI predictor in healthy individuals (N = 1504) and applied it to individuals with schizophrenia (SCZ,N = 146), clinical high-risk states for psychosis (CHR,N = 213) and recent-onset depression (ROD,N = 200). We computed BMIgap (BMIpredicted-BMImeasured), interrogated its brain-level overlaps with SCZ and explored whether BMIgap predicted weight gain at 1- and 2-year follow-up. SCZ (BMIgap = 1.05kg/m2) and CHR individuals (BMIgap = 0.51 kg/m2) showed increased and ROD individuals (BMIgap=-0.82 kg/m2) decreased BMIgap. Shared brain patterns of BMI and SCZ were linked to illness duration, disease onset, and hospitalization frequency. Higher BMIgap predicted future weight gain, particularly in younger ROD individuals, and at 2-year follow-up. Therefore, we propose BMIgap as a potential brain-derived measure to stratify at-risk individuals and deliver tailored interventions for better metabolic risk control.
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Affiliation(s)
| | | | | | | | | | | | | | - Ole Andreassen
- Oslo University Hospital & Institute of Clinical Medicine, University of Oslo
| | | | - Joseph Kambeitz
- Faculty of Medicine and University Hospital University of Cologne Cologne
| | | | - Jarmo Hietala
- Department of Psychiatry, University of Turku and Turku University Hospital, Finland
| | - Alessandro Bertolino
- Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
| | | | | | | | | | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy and Center for Brain, Behaviour and Metabolism, University of Lübeck
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8
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Huguet G, Renne T, Poulain C, Dubuc A, Kumar K, Kazem S, Engchuan W, Shanta O, Douard E, Proulx C, Jean-Louis M, Saci Z, Mollon J, Schultz LM, Knowles EEM, Cox SR, Porteous D, Davies G, Redmond P, Harris SE, Schumann G, Dumas G, Labbe A, Pausova Z, Paus T, Scherer SW, Sebat J, Almasy L, Glahn DC, Jacquemont S. Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes. CELL GENOMICS 2024; 4:100721. [PMID: 39667348 PMCID: PMC11701252 DOI: 10.1016/j.xgen.2024.100721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 07/30/2024] [Accepted: 11/13/2024] [Indexed: 12/14/2024]
Abstract
Copy-number variants (CNVs) that increase the risk for neurodevelopmental disorders also affect cognitive ability. However, such CNVs remain challenging to study due to their scarcity, limiting our understanding of gene-dosage-sensitive biological processes linked to cognitive ability. We performed a genome-wide association study (GWAS) in 258,292 individuals, which identified-for the first time-a duplication at 2q12.3 associated with higher cognitive performance. We developed a functional-burden analysis, which tested the association between cognition and CNVs disrupting 6,502 gene sets biologically defined across tissues, cell types, and ontologies. Among those, 864 gene sets were associated with cognition, and effect sizes of deletion and duplication were negatively correlated. The latter suggested that functions across all biological processes were sensitive to either deletions (e.g., subcortical regions, postsynaptic) or duplications (e.g., cerebral cortex, presynaptic). Associations between non-brain tissues and cognition were driven partly by constrained genes, which may shed light on medical comorbidities in neurodevelopmental disorders.
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Affiliation(s)
- Guillaume Huguet
- Centre Hospitalier Universitaire Sainte-Justine Research Center, Montreal, QC, Canada.
| | - Thomas Renne
- Centre Hospitalier Universitaire Sainte-Justine Research Center, Montreal, QC, Canada; Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, QC, Canada
| | - Cécile Poulain
- Centre Hospitalier Universitaire Sainte-Justine Research Center, Montreal, QC, Canada; Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, QC, Canada
| | - Alma Dubuc
- École Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Kuldeep Kumar
- Centre Hospitalier Universitaire Sainte-Justine Research Center, Montreal, QC, Canada
| | - Sayeh Kazem
- Centre Hospitalier Universitaire Sainte-Justine Research Center, Montreal, QC, Canada; Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, QC, Canada
| | - Worrawat Engchuan
- The Hospital for Sick Children, Genetics and Genome Biology, Toronto, ON, Canada; The Hospital for Sick Children, The Centre for Applied Genomics, Toronto, ON, Canada
| | - Omar Shanta
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Elise Douard
- Centre Hospitalier Universitaire Sainte-Justine Research Center, Montreal, QC, Canada
| | - Catherine Proulx
- Centre Hospitalier Universitaire Sainte-Justine Research Center, Montreal, QC, Canada
| | - Martineau Jean-Louis
- Centre Hospitalier Universitaire Sainte-Justine Research Center, Montreal, QC, Canada
| | - Zohra Saci
- Centre Hospitalier Universitaire Sainte-Justine Research Center, Montreal, QC, Canada
| | - Josephine Mollon
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Laura M Schultz
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Emma E M Knowles
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - David Porteous
- Lothian Birth Cohorts, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh EH8 9JZ, UK; Medical Genetics Section, Centre for Genomic & Experimental Medicine, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK; Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Gail Davies
- Lothian Birth Cohorts, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Paul Redmond
- Lothian Birth Cohorts, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Sarah E Harris
- Lothian Birth Cohorts, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Gunter Schumann
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Guillaume Dumas
- Centre Hospitalier Universitaire Sainte-Justine Research Center, Montreal, QC, Canada; Mila - Quebec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Aurélie Labbe
- Département de Sciences de la Décision, HEC Montreal, Montreal, QC, Canada
| | - Zdenka Pausova
- Research Institute of the Hospital for Sick Children, Toronto, ON, Canada; Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada; ECOGENE-21, Chicoutimi, QC, Canada
| | - Tomas Paus
- Centre Hospitalier Universitaire Sainte-Justine Research Center, Montreal, QC, Canada; Department of Psychiatry and Addictology, Department of Neuroscience, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Stephen W Scherer
- The Hospital for Sick Children, Genetics and Genome Biology, Toronto, ON, Canada; The Hospital for Sick Children, The Centre for Applied Genomics, Toronto, ON, Canada; McLaughlin Centre and Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Jonathan Sebat
- University of California, San Diego, Department of Psychiatry, Department of Cellular & Molecular Medicine, Beyster Center of Psychiatric Genomics, San Diego, CA, USA
| | - Laura Almasy
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Sébastien Jacquemont
- Centre Hospitalier Universitaire Sainte-Justine Research Center, Montreal, QC, Canada; Department of Pediatrics, Université de Montréal, Montreal, QC, Canada.
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9
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Brosch K, Dhamala E. Influences of sex and gender on the associations between risk and protective factors, brain, and behavior. Biol Sex Differ 2024; 15:97. [PMID: 39593154 PMCID: PMC11590223 DOI: 10.1186/s13293-024-00674-4] [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: 01/31/2024] [Accepted: 11/12/2024] [Indexed: 11/28/2024] Open
Abstract
Risk and protective factors for psychiatric illnesses are linked to distinct structural and functional changes in the brain. Further, the prevalence of these factors varies across sexes and genders, yet the distinct and joint effects of sex and gender in this context have not been extensively characterized. This suggests that risk and protective factors may map onto the brain and uniquely influence individuals across sexes and genders. Here, we review how specific risk (childhood maltreatment, the COVID-19 pandemic, experiences of racism), and protective factors (social support and psychological resilience) distinctly influence the brain across sexes and genders. We also discuss the role of sex and gender in the compounding effects of risk factors and in the interdependent influences of risk and protective factors. As such, we call on researchers to consider sex and gender when researching risk and protective factors for psychiatric illnesses, and we provide concrete recommendations on how to account for them in future research. Considering protective factors alongside risk factors in research and acknowledging sex and gender differences will enable us to establish sex- and gender-specific brain-behavior relationships. This will subsequently inform the development of targeted prevention and intervention strategies for psychiatric illnesses, which have been lacking. To achieve sex and gender equality in mental health, acknowledging and researching potential differences will lead to a better understanding of men and women, males and females, and the factors that make them more vulnerable or resilient to psychopathology.
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Affiliation(s)
- Katharina Brosch
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA.
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA.
| | - Elvisha Dhamala
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA.
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA.
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Uniondale, NY, USA.
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10
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Roell L, Fischer T, Keeser D, Papazov B, Lembeck M, Papazova I, Greska D, Muenz S, Schneider-Axmann T, Sykorova E, Thieme CE, Vogel BO, Mohnke S, Huppertz C, Roeh A, Keller-Varady K, Malchow B, Stoecklein S, Ertl-Wagner B, Henkel K, Wolfarth B, Tantchik W, Walter H, Hirjak D, Schmitt A, Hasan A, Meyer-Lindenberg A, Falkai P, Maurus I. Effects of aerobic exercise on hippocampal formation volume in people with schizophrenia - a systematic review and meta-analysis with original data from a randomized-controlled trial. Psychol Med 2024:1-12. [PMID: 39552395 DOI: 10.1017/s0033291724001867] [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] [Indexed: 11/19/2024]
Abstract
BACKGROUND The hippocampal formation represents a key region in the pathophysiology of schizophrenia. Aerobic exercise poses a promising add-on treatment to potentially counteract structural impairments of the hippocampal formation and associated symptomatic burden. However, current evidence regarding exercise effects on the hippocampal formation in schizophrenia is largely heterogeneous. Therefore, we conducted a systematic review and meta-analysis to assess the impact of aerobic exercise on total hippocampal formation volume. Additionally, we used data from a recent multicenter randomized-controlled trial to examine the effects of aerobic exercise on hippocampal formation subfield volumes and their respective clinical implications. METHODS The meta-analysis comprised six studies that investigated the influence of aerobic exercise on total hippocampal formation volume compared to a control condition with a total of 186 people with schizophrenia (100 male, 86 female), while original data from 29 patients (20 male, 9 female) was considered to explore effects of six months of aerobic exercise on hippocampal formation subfield volumes. RESULTS Our meta-analysis did not demonstrate a significant effect of aerobic exercise on total hippocampal formation volume in people with schizophrenia (g = 0.33 [-0.12 to 0.77]), p = 0.15), but our original data suggested significant volume increases in certain hippocampal subfields, namely the cornu ammonis and dentate gyrus. CONCLUSIONS Driven by the necessity of better understanding the pathophysiology of schizophrenia, the present work underlines the importance to focus on hippocampal formation subfields and to characterize subgroups of patients that show neuroplastic responses to aerobic exercise accompanied by corresponding clinical improvements.
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Affiliation(s)
- Lukas Roell
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- Neuroimaging Core Unit Munich (NICUM), LMU University Hospital, LMU Munich, Munich, Germany
| | - Tim Fischer
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
| | - Daniel Keeser
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- Neuroimaging Core Unit Munich (NICUM), LMU University Hospital, LMU Munich, Munich, Germany
- Munich Center for Neurosciences (MCN), LMU Munich, Munich, Germany
| | - Boris Papazov
- Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Moritz Lembeck
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
| | - Irina Papazova
- Department of Psychiatry, Psychotherapy and Psychosomatics of the University Augsburg, Medical Faculty, University of Augsburg, Bezirkskrankenhaus Augsburg, Augsburg, Germany
| | - David Greska
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
| | - Susanne Muenz
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
| | - Thomas Schneider-Axmann
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
| | - Eliska Sykorova
- Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Heidelberg, Germany
| | - Cristina E Thieme
- Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Heidelberg, Germany
| | - Bob O Vogel
- Department of Psychiatry and Psychotherapy, University Hospital Charité Berlin, Berlin, Germany
| | - Sebastian Mohnke
- Department of Psychiatry and Psychotherapy, University Hospital Charité Berlin, Berlin, Germany
| | - Charlotte Huppertz
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Astrid Roeh
- Department of Psychiatry, Psychotherapy and Psychosomatics of the University Augsburg, Medical Faculty, University of Augsburg, Bezirkskrankenhaus Augsburg, Augsburg, Germany
| | - Katriona Keller-Varady
- Department of Rehabilitation and Sports Medicine, Hannover Medical School, Hannover, Germany
| | - Berend Malchow
- Department of Psychiatry and Psychotherapy, University Hospital Göttingen, Göttingen, Germany
| | - Sophia Stoecklein
- Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Birgit Ertl-Wagner
- Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany
- Division of Neuroradiology, Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Karsten Henkel
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Bernd Wolfarth
- Department of Sports Medicine, University Hospital Charité Berlin, Berlin, Germany
| | - Wladimir Tantchik
- Department of Psychiatry and Psychotherapy, University Hospital Charité Berlin, Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, University Hospital Charité Berlin, Berlin, Germany
| | - Dusan Hirjak
- Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Heidelberg, Germany
- German Center for Mental Health (DZPG), partner site Mannheim/Heidelberg/Ulm, Germany
| | - Andrea Schmitt
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- Laboratory of Neuroscience (LIM27), Institute of Psychiatry, University of Sao Paulo, São Paulo, Brazil
- Max Planck Institute of Psychiatry, Munich, Germany
- German Center for Mental Health (DZPG), partner site Munich/Augsburg, Germany
| | - Alkomiet Hasan
- Department of Psychiatry, Psychotherapy and Psychosomatics of the University Augsburg, Medical Faculty, University of Augsburg, Bezirkskrankenhaus Augsburg, Augsburg, Germany
- German Center for Mental Health (DZPG), partner site Munich/Augsburg, Germany
| | - Andreas Meyer-Lindenberg
- Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Heidelberg, Germany
- German Center for Mental Health (DZPG), partner site Mannheim/Heidelberg/Ulm, Germany
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
- German Center for Mental Health (DZPG), partner site Munich/Augsburg, Germany
| | - Isabel Maurus
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
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11
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Barth B, Arcego DM, de Mendonça Filho EJ, de Lima RMS, Parent C, Dalmaz C, Portella AK, Pokhvisneva I, Meaney MJ, Silveira PP. Striatal dopamine gene network moderates the effect of early adversity on the risk for adult psychiatric and cardiometabolic comorbidity. Sci Rep 2024; 14:27349. [PMID: 39521843 PMCID: PMC11550826 DOI: 10.1038/s41598-024-78465-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2024] [Accepted: 10/31/2024] [Indexed: 11/16/2024] Open
Abstract
Cardiometabolic and psychiatric disorders often co-exist and share common early life risk factors, such as low birth weight. However, the biological pathways linking early adversity to adult cardiometabolic/psychiatric comorbidity remain unknown. Dopamine (DA) neurotransmission in the striatum is sensitive to early adversity and influences the development of both cardiometabolic and psychiatric diseases. Here we show that a co-expression based polygenic score (ePGS) reflecting individual variations in the expression of the striatal dopamine transporter gene (SLC6A3) network significantly interacts with birth weight to predict psychiatric and cardiometabolic comorbidities in both adults (UK Biobank, N = 225,972) and adolescents (ALSPAC, N = 1188). Decreased birth weight is associated with an increased risk for psychiatric and cardiometabolic comorbidities, but the effect is dependent on a striatal SLC6A3 ePGS, that reflects individual variation in gene expression of genes coexpressed with the SLC6A3 gene in the striatum. Neuroanatomical analyses revealed that SNPs from the striatum SLC6A3 ePGS were significantly associated with prefrontal cortex gray matter density, suggesting a neuroanatomical basis for the link between early adversity and psychiatric and cardiometabolic comorbidity. Our study reveals that psychiatric and cardiometabolic diseases share common developmental pathways and underlying neurobiological mechanisms that includes dopamine signaling in the striatum.
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Affiliation(s)
- Barbara Barth
- Integrated Program in Neurosciences, McGill University, Montreal, QC, Canada
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montreal, QC, Canada
| | - Danusa Mar Arcego
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montreal, QC, Canada
| | - Euclides José de Mendonça Filho
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montreal, QC, Canada
- Department of Psychiatry, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
| | - Randriely Merscher Sobreira de Lima
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montreal, QC, Canada
- Department of Psychiatry, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
| | - Carine Parent
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Carla Dalmaz
- Programa de Pós-Graduação em Neurociências, Instituto de Ciências Básicas da Saúde (ICBS), Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | | | - Irina Pokhvisneva
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Michael J Meaney
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Department of Psychiatry, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
- Translational Neuroscience Programme, Singapore Institute for Clinical Sciences, Agency for Science, Technology & Research, Singapore, Singapore
| | - Patricia Pelufo Silveira
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montreal, QC, Canada.
- Department of Psychiatry, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.
- Department of Paediatrics, Yong Lin Loon School of Medicine, National University of Singapore, Singapore, Singapore.
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12
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Szabo A, O'Connell KS, Akkouh IA, Ueland T, Sønderby IE, Hope S, Røe AB, Dønnum MS, Sjaastad I, Steen NE, Ueland T, Sæther LS, Osete JR, Andreassen OA, Nærland T, Djurovic S. Elevated levels of peripheral and central nervous system immune markers reflect innate immune dysregulation in autism spectrum disorder. Psychiatry Res 2024; 342:116245. [PMID: 39481220 DOI: 10.1016/j.psychres.2024.116245] [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: 09/24/2023] [Revised: 07/30/2024] [Accepted: 10/23/2024] [Indexed: 11/02/2024]
Abstract
BACKGROUND Evidence suggests dysregulated immune functions in the pathophysiology of Autism spectrum disorder (ASD), although specific immune mechanisms are yet to be identified. METHODS We assessed circulating levels of 25 immune/neuroinflammatory markers in a large ASD sample (n = 151) and matched controls (n = 72) using linear models. In addition, we performed global brain transcriptomics analyses of relevant immune-related genes. We also assessed the expression and function of factors and pathway elements of the inflammasome system in peripheral blood mononuclear cells (PBMC) isolated from ASD and controls using in vitro methods. RESULTS We found higher circulating levels of IL-18 and adhesion factors (ICAM-1, MADCAM1) in individuals with ASD relative to controls. Consistent with this, brain levels of ICAM1 mRNA were also higher in ASD compared to controls. Furthermore, we found higher expression/activity of Caspase-1 and the inflammasome sensor NLRP3 in PBMCs in ASD, both at baseline and following inflammatory challenge. This corresponded with higher levels of secreted IL-18, IL-1β, and IL-8, as well as increased expression of adhesion factors following inflammasome activation in ASD PBMC cultures. Inhibition of the NLRP3-inflammasome rescued the observed immune phenotype in ASD in vitro. CONCLUSION Our results suggest a role for inflammasome dysregulation in ASD pathophysiology.
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Affiliation(s)
- Attila Szabo
- K.G. Jebsen Center for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
| | - Kevin S O'Connell
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ibrahim A Akkouh
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Medical Genetics, Oslo University Hospital, building 25, Kirkeveien 166, Oslo 0450, Norway
| | - Thor Ueland
- Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway; Thrombosis Research Center (TREC), Division of Internal Medicine, University Hospital of North Norway, Tromsø, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ida E Sønderby
- K.G. Jebsen Center for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Medical Genetics, Oslo University Hospital, building 25, Kirkeveien 166, Oslo 0450, Norway
| | - Sigrun Hope
- K.G. Jebsen Center for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Neurohabilitation, Oslo University Hospital, Oslo, Norway; Department of Rare Disorders and Disabilities, Nevsom, Oslo University Hospital, Oslo, Norway
| | - Anne B Røe
- St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Monica S Dønnum
- Department of Adult Habilitation, Akershus University Hospital, Oslo, Norway
| | - Ingrid Sjaastad
- Department of Child and Adolescent Psychiatry, Vestre Viken Hospital Trust, Norway
| | - Nils Eiel Steen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Torill Ueland
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Linn Sofie Sæther
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Jordi Requena Osete
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Medical Genetics, Oslo University Hospital, building 25, Kirkeveien 166, Oslo 0450, Norway
| | - Ole A Andreassen
- K.G. Jebsen Center for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Terje Nærland
- K.G. Jebsen Center for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Rare Disorders and Disabilities, Nevsom, Oslo University Hospital, Oslo, Norway
| | - Srdjan Djurovic
- K.G. Jebsen Center for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Medical Genetics, Oslo University Hospital, building 25, Kirkeveien 166, Oslo 0450, Norway; Department of Clinical Science, NORMENT, University of Bergen, Bergen, Norway.
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13
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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; 24:631-659. [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] [MESH Headings] [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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14
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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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15
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Böttcher E, Schreiber LS, Wozniak D, Scheller E, Schmidt FM, Pelz JO. Impaired Modulation of the Autonomic Nervous System in Adult Patients with Major Depressive Disorder. Biomedicines 2024; 12:1268. [PMID: 38927475 PMCID: PMC11201748 DOI: 10.3390/biomedicines12061268] [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: 04/30/2024] [Revised: 05/24/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Patients with major depressive disorder (MDD) have an increased risk for cardiac events. This is partly attributed to a disbalance of the autonomic nervous system (ANS) indicated by a reduced vagal tone and a (relative) sympathetic hyperactivity. However, in most studies, heart rate variability (HRV) was only examined while resting. So far, it remains unclear whether the dysbalance of the ANS in patients with MDD is restricted to resting or whether it is also evident during sympathetic and parasympathetic activation. The aim of this study was to compare the responses of the ANS to challenges that stimulated the sympathetic and, respectively, the parasympathetic nervous systems in patients with MDD. Forty-six patients with MDD (female 27 (58.7%), mean age 44 ± 17 years) and 46 healthy controls (female 26 (56.5%), mean age 44 ± 20 years) underwent measurement of time- and frequency-dependent domains of HRV at rest, while standing (sympathetic challenge), and during slow-paced breathing (SPB, vagal, i.e., parasympathetic challenge). Patients with MDD showed a higher heart rate, a reduced HRV, and a diminished vagal tone during resting, standing, and SPB compared to controls. Patients with MDD and controls responded similarly to sympathetic and vagal activation. However, the extent of modulation of the ANS was impaired in patients with MDD, who showed a reduced decrease in the vagal tone but also a reduced increase in sympathetic activity when switching from resting to standing. Assessing changes in the ANS during sympathetic and vagal activation via respective challenges might serve as a future biomarker and help to allocate patients with MDD to therapies like HRV biofeedback and psychotherapy that were recently found to modulate the vagal tone.
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Affiliation(s)
- Elise Böttcher
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Semmelweisstraße 10, 04103 Leipzig, Germany
| | - Lisa Sofie Schreiber
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Semmelweisstraße 10, 04103 Leipzig, Germany
| | - David Wozniak
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Semmelweisstraße 10, 04103 Leipzig, Germany
| | - Erik Scheller
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Semmelweisstraße 10, 04103 Leipzig, Germany
| | - Frank M. Schmidt
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Semmelweisstraße 10, 04103 Leipzig, Germany
| | - Johann Otto Pelz
- Department of Neurology, University Hospital Leipzig, Liebigstraße 20, 04103 Leipzig, Germany
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16
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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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17
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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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18
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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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19
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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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20
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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: 4] [Impact Index Per Article: 4.0] [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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21
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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: 3] [Impact Index Per Article: 3.0] [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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22
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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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23
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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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24
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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: 1.0] [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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25
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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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26
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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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27
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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: 22] [Impact Index Per Article: 11.0] [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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