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Livingston G, Huntley J, Liu KY, Costafreda SG, Selbæk G, Alladi S, Ames D, Banerjee S, Burns A, Brayne C, Fox NC, Ferri CP, Gitlin LN, Howard R, Kales HC, Kivimäki M, Larson EB, Nakasujja N, Rockwood K, Samus Q, Shirai K, Singh-Manoux A, Schneider LS, Walsh S, Yao Y, Sommerlad A, Mukadam N. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet 2024; 404:572-628. [PMID: 39096926 DOI: 10.1016/s0140-6736(24)01296-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 04/08/2024] [Accepted: 06/16/2024] [Indexed: 08/05/2024]
Affiliation(s)
- Gill Livingston
- Division of Psychiatry, University College London, London, UK; Camden and Islington NHS Foundation Trust, London, UK.
| | - Jonathan Huntley
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Kathy Y Liu
- Division of Psychiatry, University College London, London, UK
| | - Sergi G Costafreda
- Division of Psychiatry, University College London, London, UK; Camden and Islington NHS Foundation Trust, London, UK
| | - Geir Selbæk
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway; Geriatric Department, Oslo University Hospital, Oslo, Norway
| | - Suvarna Alladi
- National Institute of Mental Health and Neurosciences, Bangalore, India
| | - David Ames
- National Ageing Research Institute, Melbourne, VIC, Australia; University of Melbourne Academic Unit for Psychiatry of Old Age, Melbourne, VIC, Australia
| | - Sube Banerjee
- Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | | | - Carol Brayne
- Cambridge Public Health, University of Cambridge, Cambridge, UK
| | - Nick C Fox
- The Dementia Research Centre, Department of Neurodegenerative Disease, University College London, London, UK
| | - Cleusa P Ferri
- Health Technology Assessment Unit, Hospital Alemão Oswaldo Cruz, São Paulo, Brazil; Department of Psychiatry, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Laura N Gitlin
- College of Nursing and Health Professions, AgeWell Collaboratory, Drexel University, Philadelphia, PA, USA
| | - Robert Howard
- Division of Psychiatry, University College London, London, UK; Camden and Islington NHS Foundation Trust, London, UK
| | - Helen C Kales
- Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California, Sacramento, CA, USA
| | - Mika Kivimäki
- Division of Psychiatry, University College London, London, UK; Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Eric B Larson
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Noeline Nakasujja
- Department of Psychiatry College of Health Sciences, Makerere University College of Health Sciences, Makerere University, Kampala City, Uganda
| | - Kenneth Rockwood
- Centre for the Health Care of Elderly People, Geriatric Medicine, Dalhousie University, Halifax, NS, Canada
| | - Quincy Samus
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins Bayview, Johns Hopkins University, Baltimore, MD, USA
| | - Kokoro Shirai
- Graduate School of Social and Environmental Medicine, Osaka University, Osaka, Japan
| | - Archana Singh-Manoux
- Division of Psychiatry, University College London, London, UK; Université Paris Cité, Inserm U1153, Paris, France
| | - Lon S Schneider
- Department of Psychiatry and the Behavioural Sciences and Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Sebastian Walsh
- Cambridge Public Health, University of Cambridge, Cambridge, UK
| | - Yao Yao
- China Center for Health Development Studies, School of Public Health, Peking University, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Andrew Sommerlad
- Division of Psychiatry, University College London, London, UK; Camden and Islington NHS Foundation Trust, London, UK
| | - Naaheed Mukadam
- Division of Psychiatry, University College London, London, UK; Camden and Islington NHS Foundation Trust, London, UK
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Damme KSF, Ristanovic I, Mittal VA. Reduced hippocampal volume unmasks distinct impacts of cumulative adverse childhood events (ACEs) on psychotic-like experiences in late childhood and early adolescence. Psychoneuroendocrinology 2024; 169:107149. [PMID: 39128397 DOI: 10.1016/j.psyneuen.2024.107149] [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: 03/25/2024] [Revised: 06/17/2024] [Accepted: 07/25/2024] [Indexed: 08/13/2024]
Abstract
Stress is associated with increased vulnerability to psychosis, yet the mechanisms that contribute to these effects are poorly understood. Substantial literature has linked reduced hippocampal volume to both psychosis risk and early life stress. However, less work has explored the direct and indirect effects of stress on psychosis through the hippocampus in preclinical samples- when vulnerability for psychosis is accumulating. The current paper leverages the Adolescent Brain Cognitive Development (ABCD) Study sample to examine whether objective psychosocial stressors, specifically adverse childhood experiences (ACE), are linked to vulnerability for psychosis, measured by psychotic-like experiences (PLE) severity, in late childhood and early adolescence, both directly and indirectly through the deleterious effects of stress on the hippocampus. Baseline data from 11,728 individuals included previously examined and validated items to assess ACE exposure, hippocampal volume, and PLE severity - a developmentally appropriate metric of risk for psychosis. Objective psychosocial stress exposure in childhood was associated with elevated PLE severity during the transition from childhood to adolescence. Hippocampal volume was significantly reduced in individuals with greater PLE severity and greater childhood stress exposure compared to peers with low symptoms or low stress exposure. These findings are consistent with a hippocampal vulnerability model of psychosis risk. Stress exposure may cumulatively impact hippocampal volume and may also reflect a direct pathway of psychosis risk. Objective psychosocial stress should be considered as a treatment target that may impact neurodevelopment and psychosis risk.
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Affiliation(s)
- Katherine S F Damme
- Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA; Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Innovations in Developmental Sciences (DevSci), Northwestern University, Evanston and Chicago, IL, USA; Department of Psychiatry, Northwestern University, Chicago, IL, USA.
| | - Ivanka Ristanovic
- Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Innovations in Developmental Sciences (DevSci), Northwestern University, Evanston and Chicago, IL, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Innovations in Developmental Sciences (DevSci), Northwestern University, Evanston and Chicago, IL, USA; Department of Psychiatry, Northwestern University, Chicago, IL, USA; Medical Social Sciences, Northwestern University, Chicago, IL, USA; Institute for Policy Research (IPR), Northwestern University, Chicago, IL, USA
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3
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de Filippis R, Carbone EA, Rania M, Aloi M, Segura-Garcia C, De Fazio P. Applying a clinical staging model in patients affected by schizophrenia spectrum disorder. Front Psychiatry 2024; 15:1387913. [PMID: 39081534 PMCID: PMC11287066 DOI: 10.3389/fpsyt.2024.1387913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 06/24/2024] [Indexed: 08/02/2024] Open
Abstract
Background Clinical staging, already widespread in medicine, represents a new frontier in psychiatry. Our goal was to convert the existing theoretical staging model for schizophrenia into a feasible tool to have a timely assessment of patients' health status applicable in any psychiatric facility. Methods We assessed the empirical soundness of a staging model for schizophrenia spectrum disorders (SSDs), primarily centered on their current status. This model delineated six sequential stages (1, 2A, 2B, 3A, 3B, and 4) based on factors like symptom recurrence, persistence, and progression, including functional decline. Our analysis involved data from 137 individuals affected by SSDs. We examined 22 baseline variables, 23 construct-related variables, and 31 potentially modifiable clinical variables. Results The latter stages demonstrated significantly poorer outcomes compared to the early stages across various measures, indicating medium to large effect sizes and a dose-response pattern. This pattern confirmed the validity of the model. Notably, stages 2 and 3A exhibited pronounced differences in comparison to other stages, although variables from each validation category also distinguished between consecutive stages, particularly 3A and beyond. Conclusion Baseline predictors, such as familial predisposition to schizophrenia, neurodevelopmental impairment, childhood adversities, treatment delay, negative symptoms, neurological impairment, and inadequate early response to treatment, independently largely explained the staging variance. The clinical staging model, grounded in the extended course of psychosis, exhibited sound validity and feasibility, even without the use of biological or neuroimaging markers, which could greatly improve the sensitivity of the model. These findings provide insights into stage indicators and predictors of clinical stages from the onset of psychosis.
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Affiliation(s)
- Renato de Filippis
- Psychiatry Unit, Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Elvira Anna Carbone
- Psychiatry Unit, Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Marianna Rania
- Outpatient Unit for Clinical Research and Treatment of Eating Disorders, University Hospital Renato Dulbecco, Catanzaro, Italy
| | - Matteo Aloi
- Psychiatry Unit, Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Cristina Segura-Garcia
- Psychiatry Unit, Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Pasquale De Fazio
- Psychiatry Unit, Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
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Lu H, Li J, Chan SSM, Ma SL, Mok VCT, Shi L, Mak ADP, Lam LCW. Predictive values of pre-treatment brain age models to rTMS effects in neurocognitive disorder with depression: Secondary analysis of a randomised sham-controlled clinical trial. DIALOGUES IN CLINICAL NEUROSCIENCE 2024; 26:38-52. [PMID: 38963341 PMCID: PMC11225634 DOI: 10.1080/19585969.2024.2373075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 06/21/2024] [Indexed: 07/05/2024]
Abstract
INTRODUCTION One major challenge in developing personalised repetitive transcranial magnetic stimulation (rTMS) is that the treatment responses exhibited high inter-individual variations. Brain morphometry might contribute to these variations. This study sought to determine whether individual's brain morphometry could predict the rTMS responders and remitters. METHODS This was a secondary analysis of data from a randomised clinical trial that included fifty-five patients over the age of 60 with both comorbid depression and neurocognitive disorder. Based on magnetic resonance imaging scans, estimated brain age was calculated with morphometric features using a support vector machine. Brain-predicted age difference (brain-PAD) was computed as the difference between brain age and chronological age. RESULTS The rTMS responders and remitters had younger brain age. Every additional year of brain-PAD decreased the odds of relieving depressive symptoms by ∼25.7% in responders (Odd ratio [OR] = 0.743, p = .045) and by ∼39.5% in remitters (OR = 0.605, p = .022) in active rTMS group. Using brain-PAD score as a feature, responder-nonresponder classification accuracies of 85% (3rd week) and 84% (12th week), respectively were achieved. CONCLUSION In elderly patients, younger brain age appears to be associated with better treatment responses to active rTMS. Pre-treatment brain age models informed by morphometry might be used as an indicator to stratify suitable patients for rTMS treatment. TRIAL REGISTRATION ClinicalTrials.gov Identifier: ChiCTR-IOR-16008191.
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Affiliation(s)
- Hanna Lu
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jing Li
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Sandra Sau Man Chan
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Suk Ling Ma
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Vincent Chung Tong Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Arthur Dun-Ping Mak
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Linda Chiu Wa Lam
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
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Liu Y, Li X, Yang M, Ding Y, Ji M. Screening indicators to evaluate the clinical significance of drug-drug interactions in polypharmacy among older adults with psychiatric disorders: a delphi study. BMC Psychiatry 2024; 24:417. [PMID: 38834965 PMCID: PMC11151475 DOI: 10.1186/s12888-024-05872-3] [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: 01/06/2024] [Accepted: 05/28/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Polypharmacy is common in older adults with psychiatric disorders, but no consensus has reached about the reliable indicators evaluating the benefits and risks of drug-drug interactions (DDIs) in polypharmacy. We aimed to identify indicators suitable for evaluating the clinical significance of DDIs in polypharmacy in older adults with psychiatric disorders. METHODS The online tools were used to distribute and collect the questionnaires. The Delphi method was applied to analyze experts' opinions. The degree of authority and coordination of experts were analyzed using the coefficient of variation, coefficient of coordination, expert's judgment factor, familiarity with the study content factor, and Kendall coordination coefficient. Statistical analysis was conducted using the IBM SPSS® Statistics Package version 26.0. RESULTS After three rounds of expert consultation, five primary and eleven secondary indicators were identified. The primary "pharmacodynamic indicator" included "severity of adverse drug reactions", "duration of adverse drug reaction", "symptom relief", "time to onset of symptomatic relief", "number of days in hospital", and "duration of medication". The secondary "pharmacokinetic indicator" contained "dosage administered" and "dosing intervals". The primary "patient tolerance indicator" contained one secondary indicator of "patient tolerability". The primary indicator "patient adherence" contained one secondary indicator of "patient adherence to medication". The primary indicator "cost of drug combination" contained one secondary indicator of "readmission". These indicators were used to determine the clinical significance of DDIs during polypharmacy. CONCLUSIONS The clinical significance of drug combinations should be taken into account when polypharmacy is used in the elderly. The five primary indicators and eleven secondary indicators might be preferred to evaluate their risks and benefits. Medication management in this population requires a multidisciplinary team, in which nurses play a key role. Future research should focus on how to establish efficient multidisciplinary team workflows and use functional factors to assess DDIs in polypharmacy for psychiatric disorders.
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Affiliation(s)
- Yu Liu
- Department of Nursing, Kangda College of Nanjing Medical University, 88 Chunhui Road, Huaguoshan Avenue, Haizhou District, Lianyungang, Jiangsu Province, 222000, China
| | - Xuefeng Li
- Department of Geriatrics, The First People's Hospital of Lianyungang, No. 6 East Zhenhua Road, Haizhou District, Lianyungang, Jiangsu Province, 222061, China
| | - Man Yang
- Department of Psychiatry, The Fourth People's Hospital of Lianyungang, Affiliated to Kangda College, Nanjing Medical University, No. 316 Jiefang East Road, Haizhou District, Lianyungang, Jiangsu Province, 222000, China
| | - Yaping Ding
- Department of Fundamental and Community Nursing, School of Nursing, Nanjing Medical University, 101 Longmian Avenue, Jiangning District, Nanjing, Jiangsu Province, 211166, China.
| | - Minghui Ji
- Department of Fundamental and Community Nursing, School of Nursing, Nanjing Medical University, 101 Longmian Avenue, Jiangning District, Nanjing, Jiangsu Province, 211166, China.
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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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Salisbury DF, Wulf BM, Seebold D, Coffman BA, Curtis MT, Karim HT. Predicted Brain Age in First-Episode Psychosis: Association with Inexpressivity. Brain Sci 2024; 14:532. [PMID: 38928532 PMCID: PMC11201938 DOI: 10.3390/brainsci14060532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 05/17/2024] [Accepted: 05/17/2024] [Indexed: 06/28/2024] Open
Abstract
Accelerated brain aging is a possible mechanism of pathology in schizophrenia. Advances in MRI-based brain development algorithms allow for the calculation of predicted brain age (PBA) for individuals. Here, we assessed PBA in 70 first-episode schizophrenia-spectrum individuals (FESz) and 76 matched healthy neurotypical comparison individuals (HC) to determine if FESz showed advanced aging proximal to psychosis onset and whether PBA was associated with neurocognitive, social functioning, or symptom severity measures. PBA was calculated with BrainAgeR (v2.1) from T1-weighted MR scans. There were no differences in the PBAs between groups. After controlling for actual age, a "younger" PBA was associated with higher vocabulary scores among all individuals, while an "older" PBA was associated with more severe negative symptom "Inexpressivity" component scores among FESz. Female participants in both groups had an elevated PBA relative to male participants. These results suggest that a relatively younger brain age is associated with a better semantic memory performance. There is no evidence for accelerated aging in FESz with a late adolescent/early adult onset. Despite a normative PBA, FESz with a greater residual PBA showed impairments in a cluster of negative symptoms, which may indicate some underlying age-related pathology proximal to psychosis onset. Although a period of accelerated aging cannot be ruled out with disease course, it does not occur at the time of the first episode.
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Affiliation(s)
- Dean F. Salisbury
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Brian M. Wulf
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Dylan Seebold
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Brian A. Coffman
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Mark T. Curtis
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Helmet T. Karim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
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Valdes-Hernandez PA, Johnson AJ, Montesino-Goicolea S, Nodarse CL, Bashyam V, Davatzikos C, Fillingim RB, Cruz-Almeida Y. Accelerated Brain Aging Mediates the Association Between Psychological Profiles and Clinical Pain in Knee Osteoarthritis. THE JOURNAL OF PAIN 2024; 25:104423. [PMID: 37952863 PMCID: PMC11144298 DOI: 10.1016/j.jpain.2023.11.006] [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: 03/28/2023] [Revised: 10/12/2023] [Accepted: 11/02/2023] [Indexed: 11/14/2023]
Abstract
Chronic pain is driven by factors across the biopsychosocial spectrum. Previously, we demonstrated that magnetic resonance images (MRI)-based brain-predicted age differences (brain-PAD: brain-predicted age minus chronological age) were significantly associated with pain severity in individuals with chronic knee pain. We also previously identified four distinct, replicable, multidimensional psychological profiles significantly associated with clinical pain. The brain aging-psychological characteristics interface in persons with chronic pain promises elucidating factors contributing to their poor health outcomes, yet this relationship is barely understood. That is why we examined the interplay between the psychological profiles in participants having chronic knee pain impacting function, brain-PAD, and clinical pain severity. Controlling for demographics and MRI scanner, we compared the brain-PAD among psychological profiles at baseline (n = 164) and over two years (n = 90). We also explored whether profile-related differences in pain severity were mediated by brain-PAD. Brain-PAD differed significantly between profiles (ANOVA's omnibus test, P = .039). Specifically, participants in the profile 3 group (high negative/low positive emotions) had an average brain-PAD ∼4 years higher than those in profile- (low somatic reactivity), with P = .047, Bonferroni-corrected, and than those in profile 2 (high coping), with P = .027, uncorrected. Repeated measures ANOVA revealed no significant change in profile-related brain-PAD differences over time, but there was a significant decrease in brain-PAD for profile 4 (high optimism/high positive affect), with P = .045. Moreover, profile-related differences in pain severity at baseline were partly explained by brain-PAD differences between profile 3 and 1, or 2; but brain-PAD did not significantly mediate the influence of variations in profiles on changes in pain severity over time. PERSPECTIVE: Accelerated brain aging could underlie the psychological-pain relationship, and psychological characteristics may predispose individuals with chronic knee pain to worse health outcomes via neuropsychological processes.
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Affiliation(s)
- Pedro A. Valdes-Hernandez
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - Alisa J. Johnson
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
| | - Soamy Montesino-Goicolea
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - Chavier Laffitte Nodarse
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - Vishnu Bashyam
- Center for Biomedical Image Computing & Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA
- Artificial Intelligence in Biomedical Imaging Lab (AIBIL), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing & Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Roger B. Fillingim
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
| | - Yenisel Cruz-Almeida
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
- Department of Neuroscience, College of Medicine, University of Florida, USA
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Hartmann S, Cearns M, Pantelis C, Dwyer D, Cavve B, Byrne E, Scott I, Yuen HP, Gao C, Allott K, Lin A, Wood SJ, Wigman JTW, Amminger GP, McGorry PD, Yung AR, Nelson B, Clark SR. Combining Clinical With Cognitive or Magnetic Resonance Imaging Data for Predicting Transition to Psychosis in Ultra High-Risk Patients: Data From the PACE 400 Cohort. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:417-428. [PMID: 38052267 DOI: 10.1016/j.bpsc.2023.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/19/2023] [Accepted: 11/26/2023] [Indexed: 12/07/2023]
Abstract
BACKGROUND Multimodal modeling that combines biological and clinical data shows promise in predicting transition to psychosis in individuals who are at ultra-high risk. Individuals who transition to psychosis are known to have deficits at baseline in cognitive function and reductions in gray matter volume in multiple brain regions identified by magnetic resonance imaging. METHODS In this study, we used Cox proportional hazards regression models to assess the additive predictive value of each modality-cognition, cortical structure information, and the neuroanatomical measure of brain age gap-to a previously developed clinical model using functioning and duration of symptoms prior to service entry as predictors in the Personal Assessment and Crisis Evaluation (PACE) 400 cohort. The PACE 400 study is a well-characterized cohort of Australian youths who were identified as ultra-high risk of transitioning to psychosis using the Comprehensive Assessment of At Risk Mental States (CAARMS) and followed for up to 18 years; it contains clinical data (from N = 416 participants), cognitive data (n = 213), and magnetic resonance imaging cortical parameters extracted using FreeSurfer (n = 231). RESULTS The results showed that neuroimaging, brain age gap, and cognition added marginal predictive information to the previously developed clinical model (fraction of new information: neuroimaging 0%-12%, brain age gap 7%, cognition 0%-16%). CONCLUSIONS In summary, adding a second modality to a clinical risk model predicting the onset of a psychotic disorder in the PACE 400 cohort showed little improvement in the fit of the model for long-term prediction of transition to psychosis.
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Affiliation(s)
- Simon Hartmann
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, South Australia, Australia; Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia.
| | - Micah Cearns
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, South Australia, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton South, Melbourne, Victoria, Australia; Western Centre for Health Research & Education, Western Hospital Sunshine, The University of Melbourne, St. Albans, Victoria, Australia
| | - Dominic Dwyer
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Blake Cavve
- Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia
| | - Enda Byrne
- Child Health Research Center, The University of Queensland, Brisbane, Queensland, Australia
| | - Isabelle Scott
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Hok Pan Yuen
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Caroline Gao
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kelly Allott
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Ashleigh Lin
- Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia
| | - Stephen J Wood
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia; School of Psychology, The University of Birmingham, Birmingham, England, United Kingdom
| | - Johanna T W Wigman
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - G Paul Amminger
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Patrick D McGorry
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Alison R Yung
- Institute for Mental and Physical Health and Clinical Translation, Deakin University, Melbourne, Victoria, Australia
| | - Barnaby Nelson
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Scott R Clark
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, South Australia, Australia
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10
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Arakelyan A, Avagyan S, Kurnosov A, Mkrtchyan T, Mkrtchyan G, Zakharyan R, Mayilyan KR, Binder H. Temporal changes of gene expression in health, schizophrenia, bipolar disorder, and major depressive disorder. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:19. [PMID: 38368435 PMCID: PMC10874418 DOI: 10.1038/s41537-024-00443-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 02/02/2024] [Indexed: 02/19/2024]
Abstract
The molecular events underlying the development, manifestation, and course of schizophrenia, bipolar disorder, and major depressive disorder span from embryonic life to advanced age. However, little is known about the early dynamics of gene expression in these disorders due to their relatively late manifestation. To address this, we conducted a secondary analysis of post-mortem prefrontal cortex datasets using bioinformatics and machine learning techniques to identify differentially expressed gene modules associated with aging and the diseases, determine their time-perturbation points, and assess enrichment with expression quantitative trait loci (eQTL) genes. Our findings revealed early, mid, and late deregulation of expression of functional gene modules involved in neurodevelopment, plasticity, homeostasis, and immune response. This supports the hypothesis that multiple hits throughout life contribute to disease manifestation rather than a single early-life event. Moreover, the time-perturbed functional gene modules were associated with genetic loci affecting gene expression, highlighting the role of genetic factors in gene expression dynamics and the development of disease phenotypes. Our findings emphasize the importance of investigating time-dependent perturbations in gene expression before the age of onset in elucidating the molecular mechanisms of psychiatric disorders.
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Affiliation(s)
- Arsen Arakelyan
- Institute of Molecular Biology NAS RA, Yerevan, Armenia.
- Armenian Bioinformatics Institute, Yerevan, Armenia.
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, Yerevan, Armenia.
| | | | | | - Tigran Mkrtchyan
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, Yerevan, Armenia
| | | | - Roksana Zakharyan
- Institute of Molecular Biology NAS RA, Yerevan, Armenia
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, Yerevan, Armenia
| | - Karine R Mayilyan
- Institute of Molecular Biology NAS RA, Yerevan, Armenia
- Department of Therapeutics, Faculty of General Medicine, University of Traditional Medicine, Yerevan, Armenia
| | - Hans Binder
- Armenian Bioinformatics Institute, Yerevan, Armenia
- Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
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11
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Long Y, Li X, Cao H, Zhang M, Lu B, Huang Y, Liu M, Xu M, Liu Z, Yan C, Sui J, Ouyang X, Zhou X. Common and distinct functional brain network abnormalities in adolescent, early-middle adult, and late adult major depressive disorders. Psychol Med 2024; 54:582-591. [PMID: 37553976 DOI: 10.1017/s0033291723002234] [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: 08/10/2023]
Abstract
BACKGROUND The age-related heterogeneity in major depressive disorder (MDD) has received significant attention. However, the neural mechanisms underlying such heterogeneity still need further investigation. This study aimed to explore the common and distinct functional brain abnormalities across different age groups of MDD patients from a large-sample, multicenter analysis. METHODS The analyzed sample consisted of a total of 1238 individuals including 617 MDD patients (108 adolescents, 12-17 years old; 411 early-middle adults, 18-54 years old; and 98 late adults, > = 55 years old) and 621 demographically matched healthy controls (60 adolescents, 449 early-middle adults, and 112 late adults). MDD-related abnormalities in brain functional connectivity (FC) patterns were investigated in each age group separately and using the whole pooled sample, respectively. RESULTS We found shared FC reductions among the sensorimotor, visual, and auditory networks across all three age groups of MDD patients. Furthermore, adolescent patients uniquely exhibited increased sensorimotor-subcortical FC; early-middle adult patients uniquely exhibited decreased visual-subcortical FC; and late adult patients uniquely exhibited wide FC reductions within the subcortical, default-mode, cingulo-opercular, and attention networks. Analysis of covariance models using the whole pooled sample further revealed: (1) significant main effects of age group on FCs within most brain networks, suggesting that they are decreased with aging; and (2) a significant age group × MDD diagnosis interaction on FC within the default-mode network, which may be reflective of an accelerated aging-related decline in default-mode FCs. CONCLUSIONS To summarize, these findings may deepen our understanding of the age-related biological and clinical heterogeneity in MDD.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xuemei Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hengyi Cao
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Manqi Zhang
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Bing Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yang Huang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mengqi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ming Xu
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhening Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Chaogan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Jing Sui
- IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xuan Ouyang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xinyu Zhou
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Yang Z, Zhao W, Linli Z, Guo S, Feng J. Associations between polygenic risk scores and accelerated brain ageing in smokers. Psychol Med 2023; 53:7785-7794. [PMID: 37555321 PMCID: PMC10755245 DOI: 10.1017/s0033291723001812] [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: 01/10/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 08/10/2023]
Abstract
BACKGROUND Smoking contributes to a variety of neurodegenerative diseases and neurobiological abnormalities, suggesting that smoking is associated with accelerated brain aging. However, the neurobiological mechanisms affected by smoking, and whether they are genetically influenced, remain to be investigated. METHODS Using structural magnetic resonance imaging data from the UK Biobank (n = 33 293), a brain age predictor was trained on non-smoking healthy groups and tested on smokers to obtain the BrainAge Gap (BAG). The cumulative effect of multiple common genetic variants associated with smoking was then calculated to acquire a polygenic risk score (PRS). The relationship between PRS, BAG, total gray matter volume (tGMV), and smoking parameters was explored and further genes included in the PRS were annotated to identify potential molecular mechanisms affected by smoking. RESULTS The BrainAge in smokers was predicted with very high accuracy (r = 0.725, MAE = 4.16). Smokers had a greater BAG (Cohen's d = 0.074, p < 0.0001) and higher PRS (Cohen's d = 0.63, p < 0.0001) than non-smokers. A higher PRS was associated with increased amount of smoking, mediated by BAG and tGMV. Several neurotransmitters and ion channel pathways were enriched in the group of smoking-related genes involved in addiction, brain synaptic plasticity, and some neurological disorders. CONCLUSION By using a simplified single indicator of the entire brain (BAG) in combination with the PRS, this study highlights the greater BAG in smokers and its linkage with genes and smoking behavior, providing insight into the neurobiological underpinnings and potential features of smoking-related aging.
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Affiliation(s)
- Zeyu Yang
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Zeqiang Linli
- School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou, 510006, P.R.China
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Jianfeng Feng
- Centre for Computational Systems Biology, Fudan University, Shanghai 200433, P.R.China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, England
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Rodriguez FS, Grabe HJ, Frenzel S, Klinger-König J, Bülow R, Völzke H, Hoffmann W. Association Between Psychosocial Stress and Brain Aging: Results of the Population-Based Cohort Study of Health in Pomerania (SHIP). J Neuropsychiatry Clin Neurosci 2023; 36:110-117. [PMID: 37849313 DOI: 10.1176/appi.neuropsych.20230020] [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] [Indexed: 10/19/2023]
Abstract
OBJECTIVE Recent studies suggest that psychosocial factors can have an impact on brain health. Yet, it is unclear whether psychosocial stress affects aging of the brain. The aim of the study was to investigate the association between psychosocial stress and brain aging. METHODS Data from the German population-based cohort Study of Health in Pomerania (N=991; age range 20-78 years) were used to calculate a total psychosocial stress score by combining subscores from five domains: stress related to the living situation, the occupational situation, the social situation, danger experiences, and emotions. Associations with brain aging, indicated by an MRI-derived score quantifying age-related brain atrophy, were estimated by using regression models adjusted for age, gender, education, diabetes, problematic alcohol consumption, smoking, and hypertension. RESULTS The relative risk ratio for advanced brain aging was 1.21 (95% CI=1.04-1.41) for stress related to emotions in fully adjusted models. The interactions between stress related to emotions and mental health symptoms were also significantly associated with advanced brain aging. The association between higher total psychosocial stress and brain aging was not statistically significant. CONCLUSIONS These findings highlight that high stress related to emotions is associated with advanced brain aging. To protect brain health in older age, more research is needed to explore the role of emotional distress.
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Affiliation(s)
- Francisca S Rodriguez
- German Center for Neurodegenerative Diseases, Rostock/Greifswald, Germany (Rodriguez, Grabe, Hoffmann); Department of Psychiatry and Psychotherapy (Grabe, Frenzel, Klinger-König), Institute for Diagnostic Radiology and Neuroradiology (Bülow), and Institute for Community Medicine (Völzke, Hoffmann), University Medicine Greifswald, Greifswald, Germany
| | - Hans J Grabe
- German Center for Neurodegenerative Diseases, Rostock/Greifswald, Germany (Rodriguez, Grabe, Hoffmann); Department of Psychiatry and Psychotherapy (Grabe, Frenzel, Klinger-König), Institute for Diagnostic Radiology and Neuroradiology (Bülow), and Institute for Community Medicine (Völzke, Hoffmann), University Medicine Greifswald, Greifswald, Germany
| | - Stefan Frenzel
- German Center for Neurodegenerative Diseases, Rostock/Greifswald, Germany (Rodriguez, Grabe, Hoffmann); Department of Psychiatry and Psychotherapy (Grabe, Frenzel, Klinger-König), Institute for Diagnostic Radiology and Neuroradiology (Bülow), and Institute for Community Medicine (Völzke, Hoffmann), University Medicine Greifswald, Greifswald, Germany
| | - Johanna Klinger-König
- German Center for Neurodegenerative Diseases, Rostock/Greifswald, Germany (Rodriguez, Grabe, Hoffmann); Department of Psychiatry and Psychotherapy (Grabe, Frenzel, Klinger-König), Institute for Diagnostic Radiology and Neuroradiology (Bülow), and Institute for Community Medicine (Völzke, Hoffmann), University Medicine Greifswald, Greifswald, Germany
| | - Robin Bülow
- German Center for Neurodegenerative Diseases, Rostock/Greifswald, Germany (Rodriguez, Grabe, Hoffmann); Department of Psychiatry and Psychotherapy (Grabe, Frenzel, Klinger-König), Institute for Diagnostic Radiology and Neuroradiology (Bülow), and Institute for Community Medicine (Völzke, Hoffmann), University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- German Center for Neurodegenerative Diseases, Rostock/Greifswald, Germany (Rodriguez, Grabe, Hoffmann); Department of Psychiatry and Psychotherapy (Grabe, Frenzel, Klinger-König), Institute for Diagnostic Radiology and Neuroradiology (Bülow), and Institute for Community Medicine (Völzke, Hoffmann), University Medicine Greifswald, Greifswald, Germany
| | - Wolfgang Hoffmann
- German Center for Neurodegenerative Diseases, Rostock/Greifswald, Germany (Rodriguez, Grabe, Hoffmann); Department of Psychiatry and Psychotherapy (Grabe, Frenzel, Klinger-König), Institute for Diagnostic Radiology and Neuroradiology (Bülow), and Institute for Community Medicine (Völzke, Hoffmann), University Medicine Greifswald, Greifswald, Germany
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14
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Navarro-González R, García-Azorín D, Guerrero-Peral ÁL, Planchuelo-Gómez Á, Aja-Fernández S, de Luis-García R. Increased MRI-based Brain Age in chronic migraine patients. J Headache Pain 2023; 24:133. [PMID: 37798720 PMCID: PMC10557155 DOI: 10.1186/s10194-023-01670-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/22/2023] [Indexed: 10/07/2023] Open
Abstract
INTRODUCTION Neuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants. METHODS We trained a machine learning model to predict Brain Age from 2,771 T1-weighted magnetic resonance imaging scans of healthy subjects. The processing pipeline included the automatic segmentation of the images, the extraction of 1,479 imaging features (both morphological and intensity-based), harmonization, feature selection and training inside a 10-fold cross-validation scheme. Separate models based only on morphological and intensity features were also trained, and all the Brain Age models were later applied to a discovery cohort composed of 247 subjects, divided into healthy controls (HC, n=82), episodic migraine (EM, n=91), and chronic migraine patients (CM, n=74). RESULTS CM patients showed an increased Brain Age Gap compared to HC (4.16 vs -0.56 years, P=0.01). A smaller Brain Age Gap was found for EM patients, not reaching statistical significance (1.21 vs -0.56 years, P=0.19). No associations were found between the Brain Age Gap and headache or migraine frequency, or duration of the disease. Brain imaging features that have previously been associated with migraine were among the main drivers of the differences in the predicted age. Also, the separate analysis using only morphological or intensity-based features revealed different patterns in the Brain Age biomarker in patients with migraine. CONCLUSION The brain-predicted age has shown to be a sensitive biomarker of CM patients and can help reveal distinct aging patterns in migraine.
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Affiliation(s)
| | - David García-Azorín
- Headache Unit, Department of Neurology, Hospital Clínico Universitario de Valladolid, Valladolid, Spain.
- Department of Medicine, Universidad de Valladolid, Valladolid, Spain.
| | - Ángel L Guerrero-Peral
- Headache Unit, Department of Neurology, Hospital Clínico Universitario de Valladolid, Valladolid, Spain
- Department of Medicine, Universidad de Valladolid, Valladolid, Spain
| | - Álvaro Planchuelo-Gómez
- Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
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15
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Okagbue HI, Ijezie OA, Ugwoke PO, Adeyemi-Kayode TM, Jonathan O. Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases. Heliyon 2023; 9:e19422. [PMID: 37674848 PMCID: PMC10477489 DOI: 10.1016/j.heliyon.2023.e19422] [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: 11/21/2022] [Revised: 08/04/2023] [Accepted: 08/22/2023] [Indexed: 09/08/2023] Open
Abstract
Psychotic disorder diseases (PDD) or mental illnesses are group of illnesses that affect the minds and impair the cognitive ability, retard emotional ability and obstruct the process of communication and relationship with others and are characterized by delusions, hallucinations and disoriented or disordered pattern of thinking. Prognosis of PDD is not sufficient because of the nature of the diseases and as such adequate form of diagnosis is required to detect, manage and treat the illness. This paper applied the single-label classification (SLC) machine learning approach in mining of electronic health records of people with PDD in Nigeria using eleven independent (demographic) variables and five PDD as target variables. The five PDDs are Insomnia, Schizophrenia, Minimal Brain dysfunction (MBD), which is also known as Attention-Deficit/Hyperactivity Disorder (ADHD), Vascular Dementia (VD) and Bipolar Disorder (BD). The aim of using SLC is that it would be easier to detect some PDDs that are related to each other without the loss of information, which is a plus over multi-label classification (MLC). ReliefF algorithm was used at each experiment to precipitate the order of importance of the independent variables and redundant variables were excluded from the analysis. The order of the variables in feature selection was matched with feature importance after the classifications and quantified using the Spearman rank correlation coefficient. The data was divided into: 70% for training and 30% for testing. Four new performance metrics adapted from the root mean square (RMSE) were proposed and used to measure the differences between the performance results of the 10 Machine learning models in terms of the training and testing and secondly, feature and without feature selection. The new metrics are close to zero which is an indication that the use of feature selection and cross validation may not greatly affects the accuracy of the SLC. When the PDDs are included as predictors for classifying others, there was a tremendous improvement as revealed by the four new metrics for classification accuracy (CA), precision and recall. Analysis of variance showed the four different metrics differs significantly for classification accuracy (CA) and precision. However, there were no significant difference between the CA and precision when the duo are compared together across the four evaluation metrics at p value less than 0.05.
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Affiliation(s)
| | - Ogochukwu A. Ijezie
- Faculty of Science and Technology, Bournemouth University, Poole, BH12 5BB, UK
| | - Paulinus O. Ugwoke
- Department of Computer Science, University of Nigeria, Nsukka, Nigeria
- Digital Bridge Institute, International Centre for Information & Communications Technology Studies, Abuja, Nigeria
| | | | - Oluranti Jonathan
- Department of Computer & Information Sciences, Covenant University, Ota, Nigeria
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Abé C, Liberg B, Klahn AL, Petrovic P, Landén M. Mania-related effects on structural brain changes in bipolar disorder - a narrative review of the evidence. Mol Psychiatry 2023; 28:2674-2682. [PMID: 37147390 PMCID: PMC10615759 DOI: 10.1038/s41380-023-02073-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 04/12/2023] [Accepted: 04/14/2023] [Indexed: 05/07/2023]
Abstract
Cross-sectional neuroimaging studies show that bipolar disorder is associated with structural brain abnormalities, predominantly observed in prefrontal and temporal cortex, cingulate gyrus, and subcortical regions. However, longitudinal studies are needed to elucidate whether these abnormalities presage disease onset or are consequences of disease processes, and to identify potential contributing factors. Here, we narratively review and summarize longitudinal structural magnetic resonance imaging studies that relate imaging outcomes to manic episodes. First, we conclude that longitudinal brain imaging studies suggest an association of bipolar disorder with aberrant brain changes, including both deviant decreases and increases in morphometric measures. Second, we conclude that manic episodes have been related to accelerated cortical volume and thickness decreases, with the most consistent findings occurring in prefrontal brain areas. Importantly, evidence also suggests that in contrast to healthy controls, who in general show age-related cortical decline, brain metrics remain stable or increase during euthymic periods in bipolar disorder patients, potentially reflecting structural recovering mechanisms. The findings stress the importance of preventing manic episodes. We further propose a model of prefrontal cortical trajectories in relation to the occurrence of manic episodes. Finally, we discuss potential mechanisms at play, remaining limitations, and future directions.
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Affiliation(s)
- Christoph Abé
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Quantify Research, Stockholm, Sweden
| | - Benny Liberg
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Anna Luisa Klahn
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Predrag Petrovic
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Cognitive and Computational Neuropsychiatry, Karolinska Institutet, Stockholm, Sweden
- Center for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Landén
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
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Xia Y, Sun H, Hua L, Dai Z, Wang X, Tang H, Han Y, Du Y, Zhou H, Zou H, Yao Z, Lu Q. Spontaneous beta power, motor-related beta power and cortical thickness in major depressive disorder with psychomotor disturbance. Neuroimage Clin 2023; 38:103433. [PMID: 37216848 PMCID: PMC10209543 DOI: 10.1016/j.nicl.2023.103433] [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: 04/06/2023] [Revised: 05/05/2023] [Accepted: 05/11/2023] [Indexed: 05/24/2023]
Abstract
INTRODUCTION The psychomotor disturbance is a common symptom in patients with major depressive disorder (MDD). The neurological mechanisms of psychomotor disturbance are intricate, involving alterations in the structure and function of motor-related regions. However, the relationship among changes in the spontaneous activity, motor-related activity, local cortical thickness, and psychomotor function remains unclear. METHOD A total of 140 patients with MDD and 68 healthy controls performed a simple right-hand visuomotor task during magnetoencephalography (MEG) scanning. All patients were divided into two groups according to the presence of psychomotor slowing. Spontaneous beta power, movement-related beta desynchronization (MRBD), absolute beta power during movement and cortical characteristics in the bilateral primary motor cortex were compared using general linear models with the group as a fixed effect and age as a covariate. Finally, the moderated mediation model was tested to examine the relationship between brain metrics with group differences and psychomotor performance. RESULTS The patients with psychomotor slowing showed higher spontaneous beta power, movement-related beta desynchronization and absolute beta power during movement than patients without psychomotor slowing. Compared with the other two groups, significant decreases were found in cortical thickness of the left primary motor cortex in patients with psychomotor slowing. Our moderated mediation model showed that the increased spontaneous beta power indirectly affected impaired psychomotor performance by abnormal MRBD, and the indirect effects were moderated by cortical thickness. CONCLUSION These results suggest that patients with MDD have aberrant cortical beta activity at rest and during movement, combined with abnormal cortical thickness, contributing to the psychomotor disturbance observed in this patient population.
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Affiliation(s)
- Yi Xia
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Hao Sun
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China
| | - Lingling Hua
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing 210096, China
| | - Xiaoqin Wang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Hao Tang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Yinglin Han
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Yishan Du
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Hongliang Zhou
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Haowen Zou
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China
| | - Zhijian Yao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing 210096, China.
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Jirsaraie RJ, Gorelik AJ, Gatavins MM, Engemann DA, Bogdan R, Barch DM, Sotiras A. A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility. PATTERNS (NEW YORK, N.Y.) 2023; 4:100712. [PMID: 37123443 PMCID: PMC10140612 DOI: 10.1016/j.patter.2023.100712] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Brain aging is a complex, multifaceted process that can be challenging to model in ways that are accurate and clinically useful. One of the most common approaches has been to apply machine learning to neuroimaging data with the goal of predicting age in a data-driven manner. Building on initial brain age studies that were derived solely from T1-weighted scans (i.e., unimodal), recent studies have incorporated features across multiple imaging modalities (i.e., "multimodal"). In this systematic review, we show that unimodal and multimodal models have distinct advantages. Multimodal models are the most accurate and sensitive to differences in chronic brain disorders. In contrast, unimodal models from functional magnetic resonance imaging were most sensitive to differences across a broad array of phenotypes. Altogether, multimodal imaging has provided us valuable insight for improving the accuracy of brain age models, but there is still much untapped potential with regard to achieving widespread clinical utility.
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Affiliation(s)
- Robert J. Jirsaraie
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Aaron J. Gorelik
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Martins M. Gatavins
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Undergraduate Neuroscience Program, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Denis A. Engemann
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche, Ltd., Basel, Switzerland
- Université Paris-Saclay, Inria, CEA, Palaiseau, France
| | - 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
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Corresponding author
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Peralta V, de Jalón EG, Moreno-Izco L, Peralta D, Janda L, Sánchez-Torres AM, Cuesta MJ. A clinical staging model of psychotic disorders based on a long-term follow-up of first-admission psychosis: A validation study. Psychiatry Res 2023; 322:115109. [PMID: 36841052 DOI: 10.1016/j.psychres.2023.115109] [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: 06/10/2022] [Revised: 02/10/2023] [Accepted: 02/12/2023] [Indexed: 02/16/2023]
Abstract
We examined the empirical validity of a staging model of psychotic disorders primarily based on their long-term course. The model distinguished 6 consecutive stages (2A, 2B, 3A, 3B, 4A, 4B) based on symptom recurrence, persistence and progression, such as functional decline. We analyzed data from 243 participants with first-admission psychosis who were followed-up for a mean of 20.9 years and assessed for 22 baseline variables, 23 construct-related variables and 31 outcome variables. Later stages scored significantly poorer than early stages on most validators by showing generally medium to large effect sizes and a dose-response pattern, thus confirming the validity of the model. For each set of validators, differences between consecutive stages were especially evident for stages 2 and 3A, although many variables from each validation realm also differentiated between the consecutive stages 3A and above. Baseline predictors including familial load of schizophrenia, neurodevelopmental impairment, childhood adversity, treatment delay, negative symptoms, neurological impairment and poor early response to treatment, independently accounted for 49.9% of the variance of staging. A staging model of psychosis based primarily on its long-term course has sound construct, outcome and predictive validity, which may inform about stage indicators and predictors of clinical stages from psychosis onset.
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Affiliation(s)
- Victor Peralta
- Mental Health Department, Servicio Navarro de Salud, Pamplona, Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain.
| | - Elena García de Jalón
- Mental Health Department, Servicio Navarro de Salud, Pamplona, Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Lucía Moreno-Izco
- Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain; Department of Psychiatry, Complejo Hospitalario de Navarra, Pamplona, Spain
| | - David Peralta
- Mental Health Department, Servicio Navarro de Salud, Pamplona, Spain
| | - Lucía Janda
- Mental Health Department, Servicio Navarro de Salud, Pamplona, Spain
| | - Ana M Sánchez-Torres
- Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain; Department of Psychiatry, Complejo Hospitalario de Navarra, Pamplona, Spain
| | - Manuel J Cuesta
- Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain; Department of Psychiatry, Complejo Hospitalario de Navarra, Pamplona, Spain
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20
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Investigating brain aging trajectory deviations in different brain regions of individuals with schizophrenia using multimodal magnetic resonance imaging and brain-age prediction: a multicenter study. Transl Psychiatry 2023; 13:82. [PMID: 36882419 PMCID: PMC9992684 DOI: 10.1038/s41398-023-02379-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/09/2023] Open
Abstract
Although many studies on brain-age prediction in patients with schizophrenia have been reported recently, none has predicted brain age based on different neuroimaging modalities and different brain regions in these patients. Here, we constructed brain-age prediction models with multimodal MRI and examined the deviations of aging trajectories in different brain regions of participants with schizophrenia recruited from multiple centers. The data of 230 healthy controls (HCs) were used for model training. Next, we investigated the differences in brain age gaps between participants with schizophrenia and HCs from two independent cohorts. A Gaussian process regression algorithm with fivefold cross-validation was used to train 90, 90, and 48 models for gray matter (GM), functional connectivity (FC), and fractional anisotropy (FA) maps in the training dataset, respectively. The brain age gaps in different brain regions for all participants were calculated, and the differences in brain age gaps between the two groups were examined. Our results showed that most GM regions in participants with schizophrenia in both cohorts exhibited accelerated aging, particularly in the frontal lobe, temporal lobe, and insula. The parts of the white matter tracts, including the cerebrum and cerebellum, indicated deviations in aging trajectories in participants with schizophrenia. However, no accelerated brain aging was noted in the FC maps. The accelerated aging in 22 GM regions and 10 white matter tracts in schizophrenia potentially exacerbates with disease progression. In individuals with schizophrenia, different brain regions demonstrate dynamic deviations of brain aging trajectories. Our findings provided more insights into schizophrenia neuropathology.
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21
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Constantinides C, Han LKM, Alloza C, Antonucci LA, Arango C, Ayesa-Arriola R, Banaj N, Bertolino A, Borgwardt S, Bruggemann J, Bustillo J, Bykhovski O, Calhoun V, Carr V, Catts S, Chung YC, Crespo-Facorro B, Díaz-Caneja CM, Donohoe G, Plessis SD, Edmond J, Ehrlich S, Emsley R, Eyler LT, Fuentes-Claramonte P, Georgiadis F, Green M, Guerrero-Pedraza A, Ha M, Hahn T, Henskens FA, Holleran L, Homan S, Homan P, Jahanshad N, Janssen J, Ji E, Kaiser S, Kaleda V, Kim M, Kim WS, Kirschner M, Kochunov P, Kwak YB, Kwon JS, Lebedeva I, Liu J, Mitchie P, Michielse S, Mothersill D, Mowry B, de la Foz VOG, Pantelis C, Pergola G, Piras F, Pomarol-Clotet E, Preda A, Quidé Y, Rasser PE, Rootes-Murdy K, Salvador R, Sangiuliano M, Sarró S, Schall U, Schmidt A, Scott RJ, Selvaggi P, Sim K, Skoch A, Spalletta G, Spaniel F, Thomopoulos SI, Tomecek D, Tomyshev AS, Tordesillas-Gutiérrez D, van Amelsvoort T, Vázquez-Bourgon J, Vecchio D, Voineskos A, Weickert CS, Weickert T, Thompson PM, Schmaal L, van Erp TGM, Turner J, Cole JH, Dima D, Walton E. Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium. Mol Psychiatry 2023; 28:1201-1209. [PMID: 36494461 PMCID: PMC10005935 DOI: 10.1038/s41380-022-01897-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 10/14/2022] [Accepted: 11/17/2022] [Indexed: 12/13/2022]
Abstract
Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.
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Affiliation(s)
| | - Laura K M Han
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
- Orygen, Parkville, VIC, Australia
- Department of Psychiatry, Amsterdam University Medical Centers, Vrije Universiteit and GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Clara Alloza
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, School of Medicine, Universidad Complutense, Madrid, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
| | - Linda Antonella Antonucci
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians Universität-Munich, Munich, Germany
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, School of Medicine, Universidad Complutense, Madrid, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
| | - Rosa Ayesa-Arriola
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
- Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Alessandro Bertolino
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Stefan Borgwardt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- Department of Psychiatry, Psychosomatics and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Jason Bruggemann
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Oleg Bykhovski
- Department of Psychiatry, Psychiatric University Hospital (UPK), University of Basel, Basel, Switzerland
- Division of Addiction Medicine, Centre Hospitalier des Quatre Villes, St. Cloud, France
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Vaughan Carr
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
- Department of Psychiatry, Monash University, Clayton, VIC, Australia
| | - Stanley Catts
- School of Medicine, University of Queensland, Herston, QLD, Australia
| | - Young-Chul Chung
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Benedicto Crespo-Facorro
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
- Hospital Universitario Virgen del Rocío, IBiS-CSIC, Universidad de Sevilla, Seville, Spain
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, School of Medicine, Universidad Complutense, Madrid, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
| | - Gary Donohoe
- Centre for Neuroimaging and Cognitive Genomics (NICOG), School of Psychology, National University of Ireland Galway, Galway, Ireland
| | - Stefan Du Plessis
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
- Stellenbosch University Genomics of Brain Disorders Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - Jesse Edmond
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Germany
| | - Robin Emsley
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- Desert-Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, CA, USA
| | - Paola Fuentes-Claramonte
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Catalonia, Spain
| | - Foivos Georgiadis
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Melissa Green
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
| | - Amalia Guerrero-Pedraza
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Catalonia, Spain
- Hospital Benito Menni CASM, Sant Boi de Llobregat, Catalonia, Spain
| | - Minji Ha
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Frans A Henskens
- School of Medicine & Public Health, The University of Newcastle, Newcastle, NSW, Australia
- Priority Research Centre for Health Behaviour, The University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Laurena Holleran
- Centre for Neuroimaging and Cognitive Genomics (NICOG), School of Psychology, National University of Ireland Galway, Galway, Ireland
| | - Stephanie Homan
- Psychiatric University Hospital Zurich, Zurich, Switzerland
- Department of Experimental Psychopathology and Psychotherapy, University of Zurich, Zurich, Switzerland
| | - Philipp Homan
- Psychiatric University Hospital Zurich, Zurich, Switzerland
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Joost Janssen
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, School of Medicine, Universidad Complutense, Madrid, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
| | - Ellen Ji
- Psychiatric University Hospital Zurich, Zurich, Switzerland
| | - Stefan Kaiser
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | | | - Minah Kim
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
| | - Woo-Sung Kim
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Matthias Kirschner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yoo Bin Kwak
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - Jun Soo Kwon
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
| | | | - Jingyu Liu
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Patricia Mitchie
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- School of Psychological Sciences, University of Newcastle, Callaghan, NSW, Australia
| | - Stijn Michielse
- Department of Neurosurgery, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - David Mothersill
- Centre for Neuroimaging and Cognitive Genomics (NICOG), School of Psychology, National University of Ireland Galway, Galway, Ireland
- Department of Psychology, School of Business, National College of Ireland, Dublin, Ireland
| | - Bryan Mowry
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- The Queensland Centre for Mental Health Research, The University of Queensland, Brisbane, QLD, Australia
| | - Víctor Ortiz-García de la Foz
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
- Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Carlton South, VIC, Australia
- Florey Institute of Neuroscience & Mental Health, Parkville, VIC, Australia
| | - Giulio Pergola
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Edith Pomarol-Clotet
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Catalonia, Spain
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Yann Quidé
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
- School of Psychology, University of New South Wales, Sydney, NSW, Australia
| | - Paul E Rasser
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- Priority Centre for Brain & Mental Health Research, The University of Newcastle, Newcastle, NSW, Australia
| | - Kelly Rootes-Murdy
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Raymond Salvador
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Catalonia, Spain
| | - Marina Sangiuliano
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Salvador Sarró
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Catalonia, Spain
| | - Ulrich Schall
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- Priority Centre for Brain & Mental Health Research, The University of Newcastle, Newcastle, NSW, Australia
| | - André Schmidt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Rodney J Scott
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Pierluigi Selvaggi
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Antonin Skoch
- National Institute of Mental Health, Klecany, Czech Republic
- MR unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Filip Spaniel
- National Institute of Mental Health, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - David Tomecek
- National Institute of Mental Health, Klecany, Czech Republic
- Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | | | - Diana Tordesillas-Gutiérrez
- Department of Radiology, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute IDIVAL, Santander, Spain
- Advanced Computation and e-Science, Instituto de Física de Cantabria CSIC, Santander, Spain
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands
| | - Javier Vázquez-Bourgon
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Spain
- Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Aristotle Voineskos
- Campbell Family Mental Health Research Institute, CAMH, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Cynthia S Weickert
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Thomas Weickert
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - Jessica Turner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Dementia Research Centre, Queen Square, Institute of Neurology, University College London, London, UK
| | - Danai Dima
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Psychology, School of Arts and Social Sciences, City, University of London, London, UK
| | - Esther Walton
- Department of Psychology, University of Bath, Bath, UK.
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22
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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: 8] [Impact Index Per Article: 8.0] [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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23
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Advanced brain ageing in adult psychopathology: A systematic review and meta-analysis of structural MRI studies. J Psychiatr Res 2023; 157:180-191. [PMID: 36473289 DOI: 10.1016/j.jpsychires.2022.11.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/14/2022] [Accepted: 11/12/2022] [Indexed: 11/16/2022]
Abstract
Evidence suggests that psychopathology is associated with an advanced brain ageing process, typically mapped using machine learning models that predict an individual's age based on structural neuroimaging data. The brain predicted age difference (brain-PAD) captures the deviation of brain age from chronological age. Substantial heterogeneity between studies has introduced uncertainty regarding the magnitude of the brain-PAD in adult psychopathology. The present meta-analysis aimed to quantify structural MRI-based brain-PAD in adult psychotic and mood disorders, while addressing possible sources of heterogeneity related to diagnosis subtypes, segmentation method, age and sex. Clinical factors influencing brain ageing in axis 1 psychiatric disorders were systematically reviewed. Thirty-three studies were included for review. A random-effects meta-analysis revealed a brain-PAD of +3.12 (standard error = 0.49) years in psychotic disorders (n = 16 studies), +2.04 (0.10) years in bipolar disorder (n = 5), and +0.90 (0.20) years in major depression (n = 7). An exploratory meta-analysis found a brain-PAD of +1.57 (0.67) in first episode psychosis (n = 4), which was smaller than that observed in psychosis and schizophrenia (n = 10, +3.87 (0.61)). Patient mean age significantly explained heterogeneity in effect size estimates in psychotic disorders, but not mood disorders. The systematic review determined that clinical factors, such as higher symptom severity, may be associated with a larger brain-PAD in psychopathology. In conclusion, larger structural MRI-based brain-PAD was confirmed in adult psychopathology. Preliminary evidence was obtained that brain ageing is greater in those with prolonged duration of psychotic disorders. Accentuated brain ageing may underlie the cognitive difficulties experienced by some patients, and may be progressive in nature.
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24
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Lorenzo EC, Kuchel GA, Kuo CL, Moffitt TE, Diniz BS. Major depression and the biological hallmarks of aging. Ageing Res Rev 2023; 83:101805. [PMID: 36410621 PMCID: PMC9772222 DOI: 10.1016/j.arr.2022.101805] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/10/2022] [Accepted: 11/17/2022] [Indexed: 11/23/2022]
Abstract
Major depressive disorder (MDD) is characterized by psychological and physiological manifestations contributing to the disease severity and outcome. In recent years, several lines of evidence have suggested that individuals with MDD have an elevated risk of age-related adverse outcomes across the lifespan. This review provided evidence of a significant overlap between the biological abnormalities in MDD and biological changes commonly observed during the aging process (i.e., hallmarks of biological aging). Based on such evidence, we formulate a mechanistic model showing how abnormalities in the hallmarks of biological aging can be a common denominator and mediate the elevated risk of age-related health outcomes commonly observed in MDD. Finally, we proposed a roadmap for novel studies to investigate the intersection between the biology of aging and MDD, including the use of geroscience-guided interventions, such as senolytics, to delay or improve major depression by targeting biological aging.
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Affiliation(s)
- Erica C Lorenzo
- UConn Center on Aging, University of Connecticut Health Center, Farmington, CT, USA
| | - George A Kuchel
- UConn Center on Aging, University of Connecticut Health Center, Farmington, CT, USA
| | - Chia-Ling Kuo
- Department of Public Health Sciences, University of Connecticut Health Center, Farmington, CT, USA
| | - Terrie E Moffitt
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA; Social, Genetic, and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, and Neuroscience, Kings College London, London, United Kingdom; PROMENTA Center, University of Oslo, Oslo, Norway
| | - Breno S Diniz
- UConn Center on Aging, University of Connecticut Health Center, Farmington, CT, USA.
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25
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Depression, aging, and immunity: implications for COVID-19 vaccine immunogenicity. Immun Ageing 2022; 19:32. [PMID: 35836263 PMCID: PMC9281075 DOI: 10.1186/s12979-022-00288-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 07/06/2022] [Indexed: 11/26/2022]
Abstract
The aging process can have detrimental effects on the immune system rendering the elderly more susceptible to infectious disease and less responsive to vaccination. Major depressive disorder (MDD) has been hypothesized to show characteristics of accelerated biological aging. This raises the possibility that depressed individuals will show some overlap with elderly populations with respect to their immune response to infection and vaccination. Here we provide an umbrella review of this literature in the context of the SARS CoV-2 pandemic. On balance, the available data do indeed suggest that depression is a risk factor for both adverse outcomes following COVID-19 infection and for reduced COVID-19 vaccine immunogenicity. We conclude that MDD (and other major psychiatric disorders) should be recognized as vulnerable populations that receive priority for vaccination along with other at-risk groups.
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26
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Han LK, Dinga R, Leenings R, Hahn T, Cole JH, Aftanas LI, Amod AR, Besteher B, Colle R, Corruble E, Couvy-Duchesne B, Danilenko KV, Fuentes-Claramonte P, Gonul AS, Gotlib IH, Goya-Maldonado R, Groenewold NA, Hamilton P, Ichikawa N, Ipser JC, Itai E, Koopowitz SM, Li M, Okada G, Okamoto Y, Churikova OS, Osipov EA, Penninx BW, Pomarol-Clotet E, Rodríguez-Cano E, Sacchet MD, Shinzato H, Sim K, Stein DJ, Uyar-Demir A, Veltman DJ, Schmaal L. A large-scale ENIGMA multisite replication study of brain age in depression. NEUROIMAGE: REPORTS 2022. [DOI: 10.1016/j.ynirp.2022.100149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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27
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Abstract
Bipolar disorder (BD) is a severe mental illness associated with alterations in brain organization. Neuroimaging studies have generated a large body of knowledge regarding brain morphological and functional abnormalities in BD. Current advances in the field have focussed on the need for more precise neuroimaging biomarkers. Here we present a selective overview of precision neuroimaging biomarkers for BD, focussing on personalized metrics and novel neuroimaging methods aiming to provide mechanistic insights into the brain alterations associated with BD. The evidence presented covers (a) machine learning techniques applied to neuroimaging data to differentiate patients with BD from healthy individuals or other clinical groups; (b) the 'brain-age-gap-estimation (brainAGE), which is an individualized measure of brain health; (c) diffusional kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI) and Positron Emission Tomography (PET) techniques that open new opportunities to measure microstructural changes in neurite/synaptic integrity and function.
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Affiliation(s)
- Delfina Janiri
- Department of Psychiatry, Fondazione Policlinico Universitario 'Agostino Gemelli' IRCCS, Roma, Italy.,Department of Psychiatry and Neurology, Sapienza University of Rome, Roma, Italy
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver British Columbia, Canada
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28
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Squassina A, Meloni A, Congiu D, Bosganas P, Patrinos GP, Lin R, Turecki G, Severino G, Ardau R, Chillotti C, Pisanu C. Analysis on in vitro effect of lithium on telomere length in lymphoblastoid cell lines from bipolar disorder patients with different clinical response to long-term lithium treatment. Hum Genomics 2022; 16:45. [PMID: 36253798 PMCID: PMC9575289 DOI: 10.1186/s40246-022-00418-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 09/30/2022] [Indexed: 11/26/2022] Open
Abstract
Background It has been suggested that bipolar disorder (BD) is associated with clinical and biological features of accelerated aging. In our previous studies, we showed that long-term lithium treatment was correlated with longer leukocyte telomere length (LTL) in BD patients. A recent study explored the role of TL in BD using patients-derived lymphoblastoid cell lines (LCLs), showing that baseline TL was shorter in BD compared to controls and that lithium in vitro increased TL but only in BD. Here, we used the same cell system (LCLs) to explore if a 7-day treatment protocol with lithium chloride (LiCl) 1 mM was able to highlight differences in TL between BD patients clinically responders (Li-R; n = 15) or non-responders (Li-NR; n = 15) to lithium, and if BD differed from non-psychiatric controls (HC; n = 15).
Results There was no difference in TL between BD patients and HC. Moreover, LiCl did not influence TL in the overall sample, and there was no difference between diagnostic or clinical response groups. Likewise, LiCl did not affect TL in neural precursor cells from healthy donors. Conclusions Our findings suggest that a 7-day lithium treatment protocol and the use of LCLs might not represent a suitable approach to deepen our understanding on the role of altered telomere dynamics in BD as previously suggested by studies in vivo. Supplementary Information The online version contains supplementary material available at 10.1186/s40246-022-00418-8.
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Affiliation(s)
- Alessio Squassina
- Laboratory of Pharmacogenomics, Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Sp 8 Sestu-Monserrato, Km 0.700, Mosnerrato, 09042, Cagliari, Italy.
| | - Anna Meloni
- Laboratory of Pharmacogenomics, Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Sp 8 Sestu-Monserrato, Km 0.700, Mosnerrato, 09042, Cagliari, Italy
| | - Donatella Congiu
- Laboratory of Pharmacogenomics, Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Sp 8 Sestu-Monserrato, Km 0.700, Mosnerrato, 09042, Cagliari, Italy
| | - Panagiotis Bosganas
- Laboratory of Pharmacogenomics and Individualized Therapy, School of Health Sciences, Department of Pharmacy, University of Patras, Patras, Greece
| | - George P Patrinos
- Laboratory of Pharmacogenomics and Individualized Therapy, School of Health Sciences, Department of Pharmacy, University of Patras, Patras, Greece.,College of Medicine and Health Sciences, Department of Genetics and Genomics, United Arab Emirates University, Al-Ain, Abu Dhabi, UAE.,Zayed Center for Health Sciences, United Arab Emirates University, Al-Ain, Abu Dhabi, UAE
| | - Rixing Lin
- McGill Group for Suicide Studies, Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.,Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Gustavo Turecki
- McGill Group for Suicide Studies, Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.,Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Giovanni Severino
- Laboratory of Pharmacogenomics, Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Sp 8 Sestu-Monserrato, Km 0.700, Mosnerrato, 09042, Cagliari, Italy
| | - Raffaella Ardau
- Unit of Clinical Pharmacology, University Hospital Agency of Cagliari, Cagliari, Italy
| | - Caterina Chillotti
- Unit of Clinical Pharmacology, University Hospital Agency of Cagliari, Cagliari, Italy
| | - Claudia Pisanu
- Laboratory of Pharmacogenomics, Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Sp 8 Sestu-Monserrato, Km 0.700, Mosnerrato, 09042, Cagliari, Italy.
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29
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Lima CNC, Suchting R, Scaini G, Cuellar VA, Favero-Campbell AD, Walss-Bass C, Soares JC, Quevedo J, Fries GR. Epigenetic GrimAge acceleration and cognitive impairment in bipolar disorder. Eur Neuropsychopharmacol 2022; 62:10-21. [PMID: 35810614 PMCID: PMC9427697 DOI: 10.1016/j.euroneuro.2022.06.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/14/2022] [Accepted: 06/16/2022] [Indexed: 12/20/2022]
Abstract
Bipolar disorder (BD) has been previously associated with clinical signs of premature aging, including accelerated epigenetic aging in blood and brain, and a steeper age-related decline in cognitive function. However, the clinical drivers and cognitive correlates of epigenetic aging in BD are still unknown. We aimed to investigate the relationship between multiple measures of epigenetic aging acceleration with clinical, functioning, and cognitive outcomes in patients with BD and controls. Blood genome-wide DNA methylation levels were measured in BD patients (n = 153) and matched healthy controls (n = 50) with the Infinium MethylationEPIC BeadChip (Illumina). Epigenetic age estimates were calculated using an online tool, including the recently developed lifespan predictor GrimAge, and analyzed with generalized linear models controlling for demographic variables and blood cell proportions. BD was significantly associated with greater GrimAge acceleration (AgeAccelGrim, β=0.197, p = 0.009), and significant group-dependent interactions were found between AgeAccelGrim and blood cell proportions (CD4+ T-lymphocytes, monocytes, granulocytes, and B-cells). Within patients, higher AgeAccelGrim was associated with worse cognitive function in multiple domains (short-term affective memory (β=-0.078, p = 0.030), short-term non-affective memory (β=-0.088, p = 0.018), inhibition (β=0.064, p = 0.046), and problem solving (β=-0.067, p = 0.034)), age of first diagnosis with any mood disorder (β=-0.076, p = 0.039) or BD (β=-0.102, p = 0.016), as well as with current non-smoking status (β=-0.392, p < 0.001). Overall, our findings support the contribution of epigenetic factors to the aging-related cognitive decline and premature mortality reported in BD patients, with an important driving effect of smoking in this population.
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Affiliation(s)
- Camila N C Lima
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX
| | - Robert Suchting
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX; Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX
| | - Giselli Scaini
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX; Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX
| | - Valeria A Cuellar
- Center of Excellence on Mood Disorders, Faillace Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX
| | - Alexandra Del Favero-Campbell
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX
| | - Consuelo Walss-Bass
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX; Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX
| | - Jair C Soares
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX; Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX; Center of Excellence on Mood Disorders, Faillace Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX
| | - Joao Quevedo
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX; Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX; Center of Excellence on Mood Disorders, Faillace Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX; Translational Psychiatry Laboratory, Graduate Program in Health Sciences, University of Southern Santa Catarina (UNESC), Criciúma, SC, Brazil
| | - Gabriel R Fries
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX; Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX; Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX.
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30
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Rajashekar N, Blumberg HP, Villa LM. Neuroimaging Studies of Brain Structure in Older Adults with Bipolar Disorder: A Review. JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2022; 7:e220006. [PMID: 36092855 PMCID: PMC9453888 DOI: 10.20900/jpbs.20220006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Bipolar disorder (BD) is a common mood disorder that can have severe consequences during later life, including suffering and impairment due to mood and cognitive symptoms, elevated risk for dementia and an especially high risk for suicide. Greater understanding of the brain circuitry differences involved in older adults with BD (OABD) in later life and their relationship to aging processes is required to improve outcomes of OABD. The current literature on gray and white matter findings, from high resolution structural and diffusion-weighted magnetic resonance imaging (MRI) studies, has shown that BD in younger age groups is associated with gray matter reductions within cortical and subcortical brain regions that subserve emotion processing and regulation, as well as reduced structural integrity of white matter tracts connecting these brain regions. While fewer neuroimaging studies have focused on OABD, it does appear that many of the structural brain differences found in younger samples are present in OABD. There is also initial suggestion that there are additional brain differences, for at least a subset of OABD, that may result from more pronounced gray and white matter declines with age that may contribute to adverse outcomes. Preclinical and clinical data supporting neuro-plastic and -protective effects of mood-stabilizing medications, suggest that treatments may reverse and/or prevent the progression of brain changes thereby reducing symptoms. Future neuroimaging research implementing longitudinal designs, and large-scale, multi-site initiatives with detailed clinical and treatment data, holds promise for reducing suffering, cognitive dysfunction and suicide in OABD.
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Affiliation(s)
- Niroop Rajashekar
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Hilary P. Blumberg
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT 06519, USA
| | - Luca M. Villa
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
- Department of Psychiatry, University of Oxford, Oxford, OX37JX, UK
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