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Chan SY, Fitzgerald E, Ngoh ZM, Lee J, Chuah J, Chia JSM, Fortier MV, Tham EH, Zhou JH, Silveira PP, Meaney MJ, Tan AP. Examining the associations between microglia genetic capacity, early life exposures and white matter development at the level of the individual. Brain Behav Immun 2024; 119:781-791. [PMID: 38677627 DOI: 10.1016/j.bbi.2024.04.038] [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: 03/15/2024] [Revised: 04/17/2024] [Accepted: 04/23/2024] [Indexed: 04/29/2024] Open
Abstract
There are inter-individual differences in susceptibility to the influence of early life experiences for which the underlying neurobiological mechanisms are poorly understood. Microglia play a role in environmental surveillance and may influence individual susceptibility to environmental factors. As an index of neurodevelopment, we estimated individual slopes of mean white matter fractional anisotropy (WM-FA) across three time-points (age 4.5, 6.0, and 7.5 years) for 351 participants. Individual variation in microglia reactivity was derived from an expression-based polygenic score(ePGS) comprised of Single Nucleotide Polymorphisms (SNPs) functionally related to the expression of microglia-enriched genes.A higher ePGS denotes an increased genetic capacity for the expression of microglia-related genes, and thus may confer a greater capacity to respond to the early environment and to influence brain development. We hypothesized that this ePGS would associate with the WM-FA index of neurodevelopment and moderate the influence of early environmental factors.Our findings show sex dependency, where a significant association between WM-FA and microglia ePGS was only obtained for females.We then examined associations with perinatal factors known to decrease (optimal birth outcomes and familial conditions) or increase (systemic inflammation) the risk for later mental health problems.In females, individuals with high microglia ePGS showed a negative association between systemic inflammation and WM-FA and a positive association between more advantageous environmental conditions and WM-FA. The microglia ePGS in females thus accounted for variations in the influence of the quality of the early environment on WM-FA.Finally, WM-FA slopes mediated the association of microglia ePGS with interpersonal problems and social hostility in females. Our findings suggest the genetic capacity for microglia function as a potential factor underlying differential susceptibility to early life exposuresthrough influences on neurodevelopment.
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Affiliation(s)
- Shi Yu Chan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Eamon Fitzgerald
- Ludmer Centre for Neuroinformatics and Mental Health, McGill University, 1010 Rue Sherbrooke O, QC H3A 2R7, Canada; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, 6875 Bd LaSalle, QC H4H 1R3, Canada
| | - Zhen Ming Ngoh
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Janice Lee
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Jasmine Chuah
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Joanne S M Chia
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Marielle V Fortier
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore; Department of Diagnostic and Interventional Imaging, KK Women's and Children's Hospital, 100 Bukit Timah Rd, Singapore 229899, Singapore; Duke-NUS Medical School, 8 College Rd, Singapore 169857, Singapore
| | - Elizabeth H Tham
- Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore; Khoo Teck Puat-National University Children's Medical Institute, National University Health System (NUHS), 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Juan H Zhou
- Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
| | - Patricia P Silveira
- Ludmer Centre for Neuroinformatics and Mental Health, McGill University, 1010 Rue Sherbrooke O, QC H3A 2R7, Canada; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, 6875 Bd LaSalle, QC H4H 1R3, Canada; Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore
| | - Michael J Meaney
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, 6875 Bd LaSalle, QC H4H 1R3, Canada; Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore; Brain - Body Initiative Program, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Connexis North Tower, Singapore 138632, Singapore
| | - Ai Peng Tan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore; Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore; Brain - Body Initiative Program, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Connexis North Tower, Singapore 138632, Singapore; Department of Diagnostic Imaging, National University Health System, 1E Kent Ridge Rd, Singapore 119228, Singapore.
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Caspi A, Shireby G, Mill J, Moffitt TE, Sugden K, Hannon E. Accelerated Pace of Aging in Schizophrenia: Five Case-Control Studies. Biol Psychiatry 2024; 95:1038-1047. [PMID: 37924924 PMCID: PMC11063120 DOI: 10.1016/j.biopsych.2023.10.023] [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: 06/22/2023] [Revised: 09/29/2023] [Accepted: 10/21/2023] [Indexed: 11/06/2023]
Abstract
BACKGROUND Schizophrenia is associated with increased risk of developing multiple aging-related diseases, including metabolic, respiratory, and cardiovascular diseases, and Alzheimer's and related dementias, leading to the hypothesis that schizophrenia is accompanied by accelerated biological aging. This has been difficult to test because there is no widely accepted measure of biological aging. Epigenetic clocks are promising algorithms that are used to calculate biological age on the basis of information from combined cytosine-phosphate-guanine sites (CpGs) across the genome, but they have yielded inconsistent and often negative results about the association between schizophrenia and accelerated aging. Here, we tested the schizophrenia-aging hypothesis using a DNA methylation measure that is uniquely designed to predict an individual's rate of aging. METHODS We brought together 5 case-control datasets to calculate DunedinPACE (Pace of Aging Calculated from the Epigenome), a new measure trained on longitudinal data to detect differences between people in their pace of aging over time. Data were available from 1812 psychosis cases (schizophrenia or first-episode psychosis) and 1753 controls. Mean chronological age was 38.9 (SD = 13.6) years. RESULTS We observed consistent associations across datasets between schizophrenia and accelerated aging as measured by DunedinPACE. These associations were not attributable to tobacco smoking or clozapine medication. CONCLUSIONS Schizophrenia is accompanied by accelerated biological aging by midlife. This may explain the wide-ranging risk among people with schizophrenia for developing multiple different age-related physical diseases, including metabolic, respiratory, and cardiovascular diseases, and dementia. Measures of biological aging could prove valuable for assessing patients' risk for physical and cognitive decline and for evaluating intervention effectiveness.
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Affiliation(s)
- Avshalom Caspi
- Department of Psychology & Neuroscience, Duke University, Durham, North Carolina; Social, Genetic and Developmental Psychiatry, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, London, United Kingdom; PROMENTA, Department of Psychology, University of Oslo, Oslo, Norway.
| | - Gemma Shireby
- Centre of Longitudinal Studies, University College London, Exeter, United Kingdom
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
| | - Terrie E Moffitt
- Department of Psychology & Neuroscience, Duke University, Durham, North Carolina; Social, Genetic and Developmental Psychiatry, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, London, United Kingdom; PROMENTA, Department of Psychology, University of Oslo, Oslo, Norway
| | - Karen Sugden
- Department of Psychology & Neuroscience, Duke University, Durham, North Carolina
| | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
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3
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Hua JPY, Abram SV, Loewy RL, Stuart B, Fryer SL, Vinogradov S, Mathalon DH. Brain Age Gap in Early Illness Schizophrenia and the Clinical High-Risk Syndrome: Associations With Experiential Negative Symptoms and Conversion to Psychosis. Schizophr Bull 2024:sbae074. [PMID: 38815987 DOI: 10.1093/schbul/sbae074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
BACKGROUND AND HYPOTHESIS Brain development/aging is not uniform across individuals,spawning efforts to characterize brain age from a biological perspective to model the effects of disease and maladaptive life processes on the brain. The brain age gap represents the discrepancy between estimated brain biological age and chronological age (in this case, based on structural magnetic resonance imaging, MRI). Structural MRI studies report an increased brain age gap (biological age > chronological age) in schizophrenia, with a greater brain age gap related to greater negative symptom severity. Less is known regarding the nature of this gap early in schizophrenia (ESZ), if this gap represents a psychosis conversion biomarker in clinical high-risk (CHR-P) individuals, and how altered brain development and/or agingmap onto specific symptom facets. STUDY DESIGN Using structural MRI, we compared the brain age gap among CHR-P (n = 51), ESZ (n = 78), and unaffected comparison participants (UCP; n = 90), and examined associations with CHR-P psychosis conversion (CHR-P converters n = 10; CHR-P non-converters; n = 23) and positive and negative symptoms. STUDY RESULTS ESZ showed a greater brain age gap relative to UCP and CHR-P (Ps < .010). CHR-P individuals who converted to psychosis showed a greater brain age gap (P = .043) relative to CHR-P non-converters. A larger brain age gap in ESZ was associated with increased experiential (P = .008), but not expressive negative symptom severity. CONCLUSIONS Consistent with schizophrenia pathophysiological models positing abnormal brain maturation, results suggest abnormal brain development is present early in psychosis. An increased brain age gap may be especially relevant to motivational and functional deficits in schizophrenia.
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Affiliation(s)
- Jessica P Y Hua
- Sierra Pacific Mental Illness Research Education and Clinical Centers, San Francisco VA Medical Center, University of California, San Francisco, CA, USA
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Samantha V Abram
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Rachel L Loewy
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Barbara Stuart
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Susanna L Fryer
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sophia Vinogradov
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Daniel H Mathalon
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
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4
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Faris P, Pischedda D, Palesi F, D’Angelo E. New clues for the role of cerebellum in schizophrenia and the associated cognitive impairment. Front Cell Neurosci 2024; 18:1386583. [PMID: 38799988 PMCID: PMC11116653 DOI: 10.3389/fncel.2024.1386583] [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: 02/15/2024] [Accepted: 04/26/2024] [Indexed: 05/29/2024] Open
Abstract
Schizophrenia (SZ) is a complex neuropsychiatric disorder associated with severe cognitive dysfunction. Although research has mainly focused on forebrain abnormalities, emerging results support the involvement of the cerebellum in SZ physiopathology, particularly in Cognitive Impairment Associated with SZ (CIAS). Besides its role in motor learning and control, the cerebellum is implicated in cognition and emotion. Recent research suggests that structural and functional changes in the cerebellum are linked to deficits in various cognitive domains including attention, working memory, and decision-making. Moreover, cerebellar dysfunction is related to altered cerebellar circuit activities and connectivity with brain regions associated with cognitive processing. This review delves into the role of the cerebellum in CIAS. We initially consider the major forebrain alterations in CIAS, addressing impairments in neurotransmitter systems, synaptic plasticity, and connectivity. We then focus on recent findings showing that several mechanisms are also altered in the cerebellum and that cerebellar communication with the forebrain is impaired. This evidence implicates the cerebellum as a key component of circuits underpinning CIAS physiopathology. Further studies addressing cerebellar involvement in SZ and CIAS are warranted and might open new perspectives toward understanding the physiopathology and effective treatment of these disorders.
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Affiliation(s)
- Pawan Faris
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Doris Pischedda
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Digital Neuroscience Center, IRCCS Mondino Foundation, Pavia, Italy
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5
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Smith KA, Hardy A, Vinnikova A, Blease C, Milligan L, Hidalgo-Mazzei D, Lambe S, Marzano L, Uhlhaas PJ, Ostinelli EG, Anmella G, Zangani C, Aronica R, Dwyer B, Torous J, Cipriani A. Digital Mental Health for Schizophrenia and Other Severe Mental Illnesses: An International Consensus on Current Challenges and Potential Solutions. JMIR Ment Health 2024; 11:e57155. [PMID: 38717799 PMCID: PMC11112473 DOI: 10.2196/57155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/08/2024] [Accepted: 03/21/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Digital approaches may be helpful in augmenting care to address unmet mental health needs, particularly for schizophrenia and severe mental illness (SMI). OBJECTIVE An international multidisciplinary group was convened to reach a consensus on the challenges and potential solutions regarding collecting data, delivering treatment, and the ethical challenges in digital mental health approaches for schizophrenia and SMI. METHODS The consensus development panel method was used, with an in-person meeting of 2 groups: the expert group and the panel. Membership was multidisciplinary including those with lived experience, with equal participation at all stages and coproduction of the consensus outputs and summary. Relevant literature was shared in advance of the meeting, and a systematic search of the recent literature on digital mental health interventions for schizophrenia and psychosis was completed to ensure that the panel was informed before the meeting with the expert group. RESULTS Four broad areas of challenge and proposed solutions were identified: (1) user involvement for real coproduction; (2) new approaches to methodology in digital mental health, including agreed standards, data sharing, measuring harms, prevention strategies, and mechanistic research; (3) regulation and funding issues; and (4) implementation in real-world settings (including multidisciplinary collaboration, training, augmenting existing service provision, and social and population-focused approaches). Examples are provided with more detail on human-centered research design, lived experience perspectives, and biomedical ethics in digital mental health approaches for SMI. CONCLUSIONS The group agreed by consensus on a number of recommendations: (1) a new and improved approach to digital mental health research (with agreed reporting standards, data sharing, and shared protocols), (2) equal emphasis on social and population research as well as biological and psychological approaches, (3) meaningful collaborations across varied disciplines that have previously not worked closely together, (4) increased focus on the business model and product with planning and new funding structures across the whole development pathway, (5) increased focus and reporting on ethical issues and potential harms, and (6) organizational changes to allow for true communication and coproduction with those with lived experience of SMI. This study approach, combining an international expert meeting with patient and public involvement and engagement throughout the process, consensus methodology, discussion, and publication, is a helpful way to identify directions for future research and clinical implementation in rapidly evolving areas and can be combined with measurements of real-world clinical impact over time. Similar initiatives will be helpful in other areas of digital mental health and similarly fast-evolving fields to focus research and organizational change and effect improved real-world clinical implementation.
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Affiliation(s)
- Katharine A Smith
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom
| | - Amy Hardy
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London & Maudsley NHS Foundation Trust, London, United Kingdom
| | | | - Charlotte Blease
- Participatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Lea Milligan
- MQ Mental Health Research, London, United Kingdom
| | - Diego Hidalgo-Mazzei
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Sinéad Lambe
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Lisa Marzano
- School of Science and Technology, Middlesex University, London, United Kingdom
| | - Peter J Uhlhaas
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
- Department of Child and Adolescent Psychiatry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Edoardo G Ostinelli
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom
| | - Gerard Anmella
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Caroline Zangani
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom
| | - Rosario Aronica
- Psychiatry Unit, Department of Neurosciences and Mental Health, Ospedale Maggiore Policlinico Ca' Granda, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Milan, Italy
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Bridget Dwyer
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom
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Hanson JL, Adkins DJ, Bacas E, Zhou P. Examining the reliability of brain age algorithms under varying degrees of participant motion. Brain Inform 2024; 11:9. [PMID: 38573551 PMCID: PMC10994881 DOI: 10.1186/s40708-024-00223-0] [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: 09/06/2023] [Accepted: 03/18/2024] [Indexed: 04/05/2024] Open
Abstract
Brain age algorithms using data science and machine learning techniques show promise as biomarkers for neurodegenerative disorders and aging. However, head motion during MRI scanning may compromise image quality and influence brain age estimates. We examined the effects of motion on brain age predictions in adult participants with low, high, and no motion MRI scans (Original N = 148; Analytic N = 138). Five popular algorithms were tested: brainageR, DeepBrainNet, XGBoost, ENIGMA, and pyment. Evaluation metrics, intraclass correlations (ICCs), and Bland-Altman analyses assessed reliability across motion conditions. Linear mixed models quantified motion effects. Results demonstrated motion significantly impacted brain age estimates for some algorithms, with ICCs dropping as low as 0.609 and errors increasing up to 11.5 years for high motion scans. DeepBrainNet and pyment showed greatest robustness and reliability (ICCs = 0.956-0.965). XGBoost and brainageR had the largest errors (up to 13.5 RMSE) and bias with motion. Findings indicate motion artifacts influence brain age estimates in significant ways. Furthermore, our results suggest certain algorithms like DeepBrainNet and pyment may be preferable for deployment in populations where motion during MRI acquisition is likely. Further optimization and validation of brain age algorithms is critical to use brain age as a biomarker relevant for clinical outcomes.
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Affiliation(s)
- Jamie L Hanson
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA.
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Dorthea J Adkins
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
| | - Eva Bacas
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
| | - Peiran Zhou
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
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7
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Ling E, Nemesh J, Goldman M, Kamitaki N, Reed N, Handsaker RE, Genovese G, Vogelgsang JS, Gerges S, Kashin S, Ghosh S, Esposito JM, Morris K, Meyer D, Lutservitz A, Mullally CD, Wysoker A, Spina L, Neumann A, Hogan M, Ichihara K, Berretta S, McCarroll SA. A concerted neuron-astrocyte program declines in ageing and schizophrenia. Nature 2024; 627:604-611. [PMID: 38448582 PMCID: PMC10954558 DOI: 10.1038/s41586-024-07109-5] [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: 12/04/2022] [Accepted: 01/23/2024] [Indexed: 03/08/2024]
Abstract
Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA sequencing to analyse the prefrontal cortex of 191 human donors aged 22-97 years, including healthy individuals and people with schizophrenia. Latent-factor analysis of these data revealed that, in people whose cortical neurons more strongly expressed genes encoding synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the synaptic neuron and astrocyte program (SNAP). In schizophrenia and ageing-two conditions that involve declines in cognitive flexibility and plasticity1,2-cells divested from SNAP: astrocytes, glutamatergic (excitatory) neurons and GABAergic (inhibitory) neurons all showed reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy people of similar age, may underlie many aspects of normal human interindividual differences and may be an important point of convergence for multiple kinds of pathophysiology.
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Affiliation(s)
- Emi Ling
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
| | - James Nemesh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Melissa Goldman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Nolan Kamitaki
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Nora Reed
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Robert E Handsaker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Giulio Genovese
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Jonathan S Vogelgsang
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Sherif Gerges
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Seva Kashin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Sulagna Ghosh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | | | | | - Daniel Meyer
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Alyssa Lutservitz
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Christopher D Mullally
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Alec Wysoker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Liv Spina
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Anna Neumann
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Marina Hogan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Kiku Ichihara
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Sabina Berretta
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
- Program in Neuroscience, Harvard Medical School, Boston, MA, USA.
| | - Steven A McCarroll
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
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Pitrez PR, Monteiro LM, Borgogno O, Nissan X, Mertens J, Ferreira L. Cellular reprogramming as a tool to model human aging in a dish. Nat Commun 2024; 15:1816. [PMID: 38418829 PMCID: PMC10902382 DOI: 10.1038/s41467-024-46004-5] [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: 09/29/2023] [Accepted: 02/12/2024] [Indexed: 03/02/2024] Open
Abstract
The design of human model systems is highly relevant to unveil the underlying mechanisms of aging and to provide insights on potential interventions to extend human health and life span. In this perspective, we explore the potential of 2D or 3D culture models comprising human induced pluripotent stem cells and transdifferentiated cells obtained from aged or age-related disorder-affected donors to enhance our understanding of human aging and to catalyze the discovery of anti-aging interventions.
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Affiliation(s)
- Patricia R Pitrez
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, 3000-548, Coimbra, Portugal
| | - Luis M Monteiro
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, 3000-548, Coimbra, Portugal
- IIIUC-institute of Interdisciplinary Research, University of Coimbra, Casa Costa Alemão, Coimbra, 3030-789, Portugal
| | - Oliver Borgogno
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Laboratory of Genetics, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Xavier Nissan
- CECS, I-STEM, AFM, Institute for Stem Cell Therapy and Exploration of Monogenic diseases, Evry cedex, France
| | - Jerome Mertens
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA.
- Laboratory of Genetics, The Salk Institute for Biological Studies, La Jolla, CA, USA.
| | - Lino Ferreira
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal.
- Faculty of Medicine, University of Coimbra, 3000-548, Coimbra, Portugal.
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9
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Ling E, Nemesh J, Goldman M, Kamitaki N, Reed N, Handsaker RE, Genovese G, Vogelgsang JS, Gerges S, Kashin S, Ghosh S, Esposito JM, French K, Meyer D, Lutservitz A, Mullally CD, Wysoker A, Spina L, Neumann A, Hogan M, Ichihara K, Berretta S, McCarroll SA. Concerted neuron-astrocyte gene expression declines in aging and schizophrenia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.07.574148. [PMID: 38260461 PMCID: PMC10802483 DOI: 10.1101/2024.01.07.574148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a striking relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA-seq to analyze the prefrontal cortex of 191 human donors ages 22-97 years, including healthy individuals and persons with schizophrenia. Latent-factor analysis of these data revealed that in persons whose cortical neurons more strongly expressed genes for synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the Synaptic Neuron-and-Astrocyte Program (SNAP). In schizophrenia and aging - two conditions that involve declines in cognitive flexibility and plasticity 1,2 - cells had divested from SNAP: astrocytes, glutamatergic (excitatory) neurons, and GABAergic (inhibitory) neurons all reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy persons of similar age, may underlie many aspects of normal human interindividual differences and be an important point of convergence for multiple kinds of pathophysiology.
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Affiliation(s)
- Emi Ling
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - James Nemesh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Melissa Goldman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Nolan Kamitaki
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Nora Reed
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Robert E. Handsaker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Giulio Genovese
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Jonathan S. Vogelgsang
- McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
| | - Sherif Gerges
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Seva Kashin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Sulagna Ghosh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | | | | | - Daniel Meyer
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Alyssa Lutservitz
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Christopher D. Mullally
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Alec Wysoker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Liv Spina
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Anna Neumann
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Marina Hogan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Kiku Ichihara
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Sabina Berretta
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- Program in Neuroscience, Harvard Medical School, Boston, MA 02215, USA
| | - Steven A. McCarroll
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
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10
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Kozhemiako N, Jiang C, Sun Y, Guo Z, Chapman S, Gai G, Wang Z, Zhou L, Li S, Law RG, Wang LA, Mylonas D, Shen L, Murphy M, Qin S, Zhu W, Zhou Z, Stickgold R, Huang H, Tan S, Manoach DS, Wang J, Hall MH, Pan JQ, Purcell SM. A spectrum of altered non-rapid eye movement sleep in schizophrenia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.28.573548. [PMID: 38234726 PMCID: PMC10793442 DOI: 10.1101/2023.12.28.573548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Background Multiple facets of sleep neurophysiology, including electroencephalography (EEG) metrics such as non-rapid eye movement (NREM) spindles and slow oscillations (SO), are altered in individuals with schizophrenia (SCZ). However, beyond group-level analyses which treat all patients as a unitary set, the extent to which NREM deficits vary among patients is unclear, as are their relationships to other sources of heterogeneity including clinical factors, illness duration and ageing, cognitive profiles and medication regimens. Using newly collected high density sleep EEG data on 103 individuals with SCZ and 68 controls, we first sought to replicate our previously reported (Kozhemiako et. al, 2022) group-level mean differences between patients and controls (original N=130). Then in the combined sample (N=301 including 175 patients), we characterized patient-to-patient variability in NREM neurophysiology. Results We replicated all group-level mean differences and confirmed the high accuracy of our predictive model (Area Under the ROC Curve, AUC = 0.93 for diagnosis). Compared to controls, patients showed significantly increased between-individual variability across many (26%) sleep metrics, with patterns only partially recapitulating those for group-level mean differences. Although multiple clinical and cognitive factors were associated with NREM metrics including spindle density, collectively they did not account for much of the general increase in patient-to-patient variability. Medication regimen was a greater (albeit still partial) contributor to variability, although original group mean differences persisted after controlling for medications. Some sleep metrics including fast spindle density showed exaggerated age-related effects in SCZ, and patients exhibited older predicted biological ages based on an independent model of ageing and the sleep EEG. Conclusion We demonstrated robust and replicable alterations in sleep neurophysiology in individuals with SCZ and highlighted distinct patterns of effects contrasting between-group means versus within-group variances. We further documented and controlled for a major effect of medication use, and pointed to greater age-related change in NREM sleep in patients. That increased NREM heterogeneity was not explained by standard clinical or cognitive patient assessments suggests the sleep EEG provides novel, nonredundant information to support the goals of personalized medicine. Collectively, our results point to a spectrum of NREM sleep deficits among SCZ patients that can be measured objectively and at scale, and that may offer a unique window on the etiological and genetic diversity that underlies SCZ risk, treatment response and prognosis.
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Affiliation(s)
- Nataliia Kozhemiako
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School; Boston, USA
| | - Chenguang Jiang
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University; Wuxi, China
| | - Yifan Sun
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University; Wuxi, China
| | - Zhenglin Guo
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Boston, USA
| | - Sinéad Chapman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Boston, USA
| | - Guanchen Gai
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University; Wuxi, China
| | - Zhe Wang
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University; Wuxi, China
| | - Lin Zhou
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Boston, USA
| | - Shen Li
- Department of Psychiatry, McLean Hospital, Harvard Medical School; Boston, USA
| | - Robert G. Law
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School; Boston, USA
| | - Lei A. Wang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Boston, USA
| | - Dimitrios Mylonas
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School; Boston, USA
| | - Lu Shen
- Bio-X Institutes, Shanghai Jiao Tong University; Shanghai China
| | - Michael Murphy
- Department of Psychiatry, McLean Hospital, Harvard Medical School; Boston, USA
| | - Shengying Qin
- Bio-X Institutes, Shanghai Jiao Tong University; Shanghai China
| | - Wei Zhu
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University; Wuxi, China
| | - Zhenhe Zhou
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University; Wuxi, China
| | - Robert Stickgold
- Beth Israel Deaconess Medical Center; Boston, USA
- Department of Psychiatry, Harvard Medical School; Boston, USA
| | - Hailiang Huang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Boston, USA
- ATGU, MGH, Harvard Medical School; Boston, USA
| | - Shuping Tan
- Huilong Guan Hospital, Beijing University; Beijing China
| | - Dara S. Manoach
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School; Boston, USA
| | - Jun Wang
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University; Wuxi, China
| | - Mei-Hua Hall
- Department of Psychiatry, McLean Hospital, Harvard Medical School; Boston, USA
| | - Jen Q. Pan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Boston, USA
| | - Shaun M. Purcell
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School; Boston, USA
- Department of Psychiatry, Harvard Medical School; Boston, USA
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11
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Chan YH, Yew WC, Chew QH, Sim K, Rajapakse JC. Elucidating salient site-specific functional connectivity features and site-invariant biomarkers in schizophrenia via deep neural networks. Sci Rep 2023; 13:21047. [PMID: 38030699 PMCID: PMC10687079 DOI: 10.1038/s41598-023-48548-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 11/28/2023] [Indexed: 12/01/2023] Open
Abstract
Schizophrenia is a highly heterogeneous disorder and salient functional connectivity (FC) features have been observed to vary across study sites, warranting the need for methods that can differentiate between site-invariant FC biomarkers and site-specific salient FC features. We propose a technique named Semi-supervised learning with data HaRmonisation via Encoder-Decoder-classifier (SHRED) to examine these features from resting state functional magnetic resonance imaging scans gathered from four sites. Our approach involves an encoder-decoder-classifier architecture that simultaneously performs data harmonisation and semi-supervised learning (SSL) to deal with site differences and labelling inconsistencies across sites respectively. The minimisation of reconstruction loss from SSL was shown to improve model performance even within small datasets whilst data harmonisation often led to lower model generalisability, which was unaffected using the SHRED technique. We show that our proposed model produces site-invariant biomarkers, most notably the connection between transverse temporal gyrus and paracentral lobule. Site-specific salient FC features were also elucidated, especially implicating the paracentral lobule for our local dataset. Our examination of these salient FC features demonstrates how site-specific features and site-invariant biomarkers can be differentiated, which can deepen our understanding of the neurobiology of schizophrenia.
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Affiliation(s)
- Yi Hao Chan
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Wei Chee Yew
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Qian Hui Chew
- Research Division, Institute of Mental Health (IMH), Singapore, Singapore
| | - Kang Sim
- Research Division, Institute of Mental Health (IMH), Singapore, Singapore
- West Region, Institute of Mental Health (IMH), Singapore, Singapore
| | - Jagath C Rajapakse
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
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12
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Liu L, Qi X, Cheng S, Meng P, Yang X, Pan C, Zhang N, Chen Y, Li C, Zhang H, Zhang Z, Zhang J, Cheng B, Wen Y, Jia Y, Liu H, Zhang F. Epigenetic analysis suggests aberrant cerebellum brain aging in old-aged adults with autism spectrum disorder and schizophrenia. Mol Psychiatry 2023; 28:4867-4876. [PMID: 37612365 DOI: 10.1038/s41380-023-02233-6] [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: 11/13/2022] [Revised: 08/11/2023] [Accepted: 08/15/2023] [Indexed: 08/25/2023]
Abstract
The aberrant aging hypothesis of schizophrenia (SCZ) and autism spectrum disorder (ASD) has been proposed, and the DNA methylation (DNAm) clock, which is a cumulative evaluation of DNAm levels at age-related CpGs, could serve as a biological aging indicator. This study evaluated epigenetic brain aging of ASD and SCZ using Horvath's epigenetic clock, based on two public genome-wide DNA methylation datasets of post-mortem brain samples (NASD = 222; NSCZ = 142). Total subjects were further divided into subgroups by gender and age. The epigenetic age acceleration (AgeAccel) for each sample was calculated as the residual value resulting from the regression model and compared between groups. Results showed DNAm age has a strong correlation with chronological age in both datasets across multiple brain regions (P < 0.05). When divided into equally sized age groups, the AgeAccel of the cerebellum (CB) region from people over 45 years of age was greater compared to the control sample (AgeAccel of ASD vs control: 5.069 vs -6.249; P < 0.001). And a decelerated epigenetic aging process was observed in the CB region of individuals with SCZ aged 50-70 years (AgeAccel of SCZ vs control: -3.171 vs 2.418; P < 0.05). However, our results showed no significant difference in AgeAccel between ASD and control groups, and between SCZ and control groups in the total and gender-specific groups (P > 0.05). This study's results revealed some evidence for aberrant epigenetic CB brain aging in old-aged patients with ASD and SCZ, indicating a different pattern of CB aging in older adults with these two diseases. However, further studies of larger ASD and SCZ cohorts are necessary to make definitive conclusions on this observation.
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Affiliation(s)
- Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Xin Qi
- Precision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, P. R. China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Na Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Yujing Chen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Chune Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Huijie Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Zhen Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Jingxi Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Huan Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, P. R. China.
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13
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Meesters PD. New horizons in schizophrenia in older people. Age Ageing 2023; 52:afad161. [PMID: 37725971 DOI: 10.1093/ageing/afad161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Indexed: 09/21/2023] Open
Abstract
People aged 65 years and older will soon constitute more than a quarter of the total population with schizophrenia, challenging the existing systems of care. For a long time, research into schizophrenia in later life was very limited. However, recent years have seen an encouraging surge in novel and high-quality studies related to this stage of life. Older people with schizophrenia consist of those who had an early onset and aged with the disorder, and of a smaller but sizeable group with a late onset or a very late onset. With ageing, physical needs gain importance relative to psychiatric needs. Medical comorbidity contributes to a markedly higher mortality compared to the general population. In many persons, symptoms and functioning fluctuate with time, leading to deterioration in some but improvement in others. Of note, a substantial number of older people may experience subjective well-being in spite of ongoing symptoms and social impairments. The majority of individuals with schizophrenia reside in the community, but when institutionalization is required many are placed in residential or nursing homes where staff is often ill-equipped to address their complex needs. There is a clear need for implementation of new models of care in which mental health and general health systems cooperate. This review provides a state-of-the-art overview of current knowledge in late life schizophrenia and related disorders, with a focus on themes with clinical relevance.
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Affiliation(s)
- Paul D Meesters
- Department of Research and Education, Friesland Mental Health Services, Leeuwarden, The Netherlands
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14
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Domagała A, Domagała L, Kopiś-Posiej N, Harciarek M, Krukow P. Differentiation of the retinal morphology aging trajectories in schizophrenia and their associations with cognitive dysfunctions. Front Psychiatry 2023; 14:1207608. [PMID: 37539329 PMCID: PMC10396397 DOI: 10.3389/fpsyt.2023.1207608] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/30/2023] [Indexed: 08/05/2023] Open
Abstract
Previous studies evaluating the morphology of the selected retinal layers in schizophrenia showed abnormalities regarding macular thickness, retinal nerve fiber layer (RNLF), and ganglion cell complex (GCC). Concurrently, accumulating neuroimaging results suggest that structural alterations of the brain in this disease might be an effect of accelerated aging. Referring to these findings, we aimed to determine whether the thinning of the retinal layers assessed with the optic coherence tomography (OCT) in a group of schizophrenia patients (n = 60) presents a significant age-related decrease exceeding potential changes noted in the control group (n = 61). Samples of patients and controls were divided into three age subgroups, namely, younger, middle-aged, and older participants. OCT outcomes, such as macular thickness and volume, macular RNFL, peripapillary RNFL, and GCC, were analyzed concerning a diagnosis status (controls vs. patients) and age subgroups. Additionally, associations between retinal parameters, age, and selected cognitive functions were evaluated. post-hoc tests revealed that macular thickness and volume in patients undergo significant age-dependent thinning, which was not observed in the control group. Regression analyses confirmed the association between macular morphology and age. Selected speed-dependent cognitive functions in patients decreased significantly with age, and these features were also significantly associated with some OCT outcomes also after controlling for antipsychotic treatment. Our results suggest that reduced measures of retinal structure detected in schizophrenia may be an effect of accelerated aging; however, further research is needed using computational solutions derived from brain imaging studies based on large datasets covering representatives of all age groups.
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Affiliation(s)
- Adam Domagała
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland
| | - Lucyna Domagała
- Non-Public Health Facility “OKO-MED”, Sandomierz, Sandomierz County, Poland
| | - Natalia Kopiś-Posiej
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland
| | - Michał Harciarek
- Department of Neuropsychology, Institute of Psychology, University of Gdańsk, Gdansk, Poland
| | - Paweł Krukow
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland
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15
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Leonardsen EH, Vidal-Piñeiro D, Roe JM, Frei O, Shadrin AA, Iakunchykova O, de Lange AMG, Kaufmann T, Taschler B, Smith SM, Andreassen OA, Wolfers T, Westlye LT, Wang Y. Genetic architecture of brain age and its causal relations with brain and mental disorders. Mol Psychiatry 2023; 28:3111-3120. [PMID: 37165155 PMCID: PMC10615751 DOI: 10.1038/s41380-023-02087-y] [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: 01/05/2023] [Revised: 04/18/2023] [Accepted: 04/19/2023] [Indexed: 05/12/2023]
Abstract
The difference between chronological age and the apparent age of the brain estimated from brain imaging data-the brain age gap (BAG)-is widely considered a general indicator of brain health. Converging evidence supports that BAG is sensitive to an array of genetic and nongenetic traits and diseases, yet few studies have examined the genetic architecture and its corresponding causal relationships with common brain disorders. Here, we estimate BAG using state-of-the-art neural networks trained on brain scans from 53,542 individuals (age range 3-95 years). A genome-wide association analysis across 28,104 individuals (40-84 years) from the UK Biobank revealed eight independent genomic regions significantly associated with BAG (p < 5 × 10-8) implicating neurological, metabolic, and immunological pathways - among which seven are novel. No significant genetic correlations or causal relationships with BAG were found for Parkinson's disease, major depressive disorder, or schizophrenia, but two-sample Mendelian randomization indicated a causal influence of AD (p = 7.9 × 10-4) and bipolar disorder (p = 1.35 × 10-2) on BAG. These results emphasize the polygenic architecture of brain age and provide insights into the causal relationship between selected neurological and neuropsychiatric disorders and BAG.
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Affiliation(s)
- Esten H Leonardsen
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
| | - Didac Vidal-Piñeiro
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - James M Roe
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
| | - Alexey A Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Olena Iakunchykova
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Ann-Marie G de Lange
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, 1015, Lausanne, Switzerland
- Department of Psychiatry, University of Oxford, OX1 2JD, Oxford, UK
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72074, Tübingen, Germany
| | - Bernd Taschler
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, OX3 9DU, Oxford, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, OX3 9DU, Oxford, United Kingdom
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72074, Tübingen, Germany
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Yunpeng Wang
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway.
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16
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Adamowicz DH, Lee EE. Dementia among older people with schizophrenia: an update on recent studies. Curr Opin Psychiatry 2023; 36:150-155. [PMID: 36794983 PMCID: PMC10079629 DOI: 10.1097/yco.0000000000000861] [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] [Indexed: 02/17/2023]
Abstract
PURPOSE OF REVIEW This narrative review examines recently published research that examines the prevalence, underlying causes, and treatments for dementia among people with schizophrenia. RECENT FINDINGS People with schizophrenia have high rates of dementia, compared with the general population, and cognitive decline has been observed 14 years prior to onset of psychosis with accelerated decline in middle age. Underlying mechanisms of cognitive decline in schizophrenia include low cognitive reserve, accelerated cognitive aging, cerebrovascular disease and medication exposure. Although pharmacologic, psychosocial and lifestyle interventions show early promise for preventing and mitigating cognitive decline, few studies have been conducted in older people with schizophrenia. SUMMARY Recent evidence supports accelerated cognitive decline and brain changes in middle-aged and older people with schizophrenia, relative to the general population. More research in older people with schizophrenia is needed to tailor existing cognitive interventions and develop novel approaches for this vulnerable and high-risk group.
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Affiliation(s)
| | - Ellen E Lee
- Department of Psychiatry
- Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla
- Desert-Pacific Mental Illness Research Education and Clinical Center, Veterans Affairs San Diego Healthcare System, San Diego, California, USA
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17
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Ballester PL, Suh JS, Ho NCW, Liang L, Hassel S, Strother SC, Arnott SR, Minuzzi L, Sassi RB, Lam RW, Milev R, Müller DJ, Taylor VH, Kennedy SH, Reilly JP, Palaniyappan L, Dunlop K, Frey BN. Gray matter volume drives the brain age gap in schizophrenia: a SHAP study. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:3. [PMID: 36624107 PMCID: PMC9829754 DOI: 10.1038/s41537-022-00330-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 12/28/2022] [Indexed: 01/11/2023]
Abstract
Neuroimaging-based brain age is a biomarker that is generated by machine learning (ML) predictions. The brain age gap (BAG) is typically defined as the difference between the predicted brain age and chronological age. Studies have consistently reported a positive BAG in individuals with schizophrenia (SCZ). However, there is little understanding of which specific factors drive the ML-based brain age predictions, leading to limited biological interpretations of the BAG. We gathered data from three publicly available databases - COBRE, MCIC, and UCLA - and an additional dataset (TOPSY) of early-stage schizophrenia (82.5% untreated first-episode sample) and calculated brain age with pre-trained gradient-boosted trees. Then, we applied SHapley Additive Explanations (SHAP) to identify which brain features influence brain age predictions. We investigated the interaction between the SHAP score for each feature and group as a function of the BAG. These analyses identified total gray matter volume (group × SHAP interaction term β = 1.71 [0.53; 3.23]; pcorr < 0.03) as the feature that influences the BAG observed in SCZ among the brain features that are most predictive of brain age. Other brain features also presented differences in SHAP values between SCZ and HC, but they were not significantly associated with the BAG. We compared the findings with a non-psychotic depression dataset (CAN-BIND), where the interaction was not significant. This study has important implications for the understanding of brain age prediction models and the BAG in SCZ and, potentially, in other psychiatric disorders.
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Affiliation(s)
- Pedro L. Ballester
- grid.25073.330000 0004 1936 8227Neuroscience Graduate Program, McMaster University, Hamilton, ON Canada
| | - Jee Su Suh
- grid.25073.330000 0004 1936 8227Neuroscience Graduate Program, McMaster University, Hamilton, ON Canada
| | - Natalie C. W. Ho
- grid.17063.330000 0001 2157 2938Faculty of Arts & Science, University of Toronto, Toronto, ON Canada ,grid.415502.7Keenan Research Centre for Biomedical Science, Unity Health Toronto, Toronto, Canada
| | - Liangbing Liang
- grid.39381.300000 0004 1936 8884Graduate Program in Neuroscience, Western University, London, ON Canada ,grid.39381.300000 0004 1936 8884Robarts Research Institute, Western University, London, ON Canada
| | - Stefanie Hassel
- grid.22072.350000 0004 1936 7697Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB Canada
| | - Stephen C. Strother
- grid.17063.330000 0001 2157 2938Rotman Research Institute, Baycrest, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Institute of Medical Science, University of Toronto, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Medical Biophysics, University of Toronto, Toronto, ON Canada
| | - Stephen R. Arnott
- grid.17063.330000 0001 2157 2938Rotman Research Institute, Baycrest, Toronto, ON Canada
| | - Luciano Minuzzi
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, Hamilton, ON Canada ,grid.416721.70000 0001 0742 7355Women’s Health Concerns Clinic, St. Joseph’s Healthcare Hamilton, Hamilton, ON Canada
| | - Roberto B. Sassi
- grid.17091.3e0000 0001 2288 9830Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Raymond W. Lam
- grid.17091.3e0000 0001 2288 9830Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Roumen Milev
- grid.410356.50000 0004 1936 8331Departments of Psychiatry and Psychology, Queen’s University, and Providence Care, Kingston, ON Canada
| | - Daniel J. Müller
- grid.17063.330000 0001 2157 2938Department of Psychiatry, University of Toronto, Toronto, ON Canada ,grid.155956.b0000 0000 8793 5925Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON Canada
| | - Valerie H. Taylor
- grid.22072.350000 0004 1936 7697Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB Canada
| | - Sidney H. Kennedy
- grid.17063.330000 0001 2157 2938Institute of Medical Science, University of Toronto, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Psychiatry, University of Toronto, Toronto, ON Canada ,grid.231844.80000 0004 0474 0428Centre for Mental Health, University Health Network, Toronto, ON Canada ,grid.231844.80000 0004 0474 0428Krembil Research Institute, University Health Network, Toronto, ON Canada ,grid.415502.7Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON Canada
| | - James P. Reilly
- grid.25073.330000 0004 1936 8227Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON Canada
| | - Lena Palaniyappan
- grid.39381.300000 0004 1936 8884Robarts Research Institute, Western University, London, ON Canada ,grid.39381.300000 0004 1936 8884Department of Medical Biophysics, Western University, London, ON Canada ,grid.415847.b0000 0001 0556 2414Lawson Health Research Institute, London, ON Canada ,grid.39381.300000 0004 1936 8884Department of Psychiatry, Western University, London, ON Canada ,grid.14709.3b0000 0004 1936 8649Department of Psychiatry, Douglas Mental Health University Institute, McGill, Douglas, QC Canada
| | - Katharine Dunlop
- grid.415502.7Keenan Research Centre for Biomedical Science, Unity Health Toronto, Toronto, Canada ,grid.17063.330000 0001 2157 2938Institute of Medical Science, University of Toronto, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Psychiatry, University of Toronto, Toronto, ON Canada ,Centre for Depression & Suicide Studies, Unity Health Toronto, Toronto, ON Canada
| | - Benicio N. Frey
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, Hamilton, ON Canada ,grid.416721.70000 0001 0742 7355Women’s Health Concerns Clinic, St. Joseph’s Healthcare Hamilton, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON Canada
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18
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Seeman MV. Subjective Overview of Accelerated Aging in Schizophrenia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:737. [PMID: 36613059 PMCID: PMC9819113 DOI: 10.3390/ijerph20010737] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
Schizophrenia, like many other human diseases, particularly neuropsychiatric diseases, shows evidence of accelerated brain aging. The molecular nature of the process of aging is unknown but several potential indicators have been used in research. The concept of accelerated aging in schizophrenia took hold in 2008 and its timing, pace, determinants and deterrents have been increasingly examined since. The present overview of the field is brief and selective, based on diverse studies, expert opinions and successive reviews. Current thinking is that the timing of age acceleration in schizophrenia can occur at different time periods of the lifespan in different individuals, and that antipsychotics may be preventive. The majority opinion is that the cognitive decline and premature death often seen in schizophrenia are, in principle, preventable.
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Affiliation(s)
- Mary V Seeman
- Department of Psychiatry, University of Toronto, 260 Heath St. West, Suite #605, Toronto, ON M5P 3L6, Canada
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