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Kim WS, Heo DW, Maeng J, Shen J, Tsogt U, Odkhuu S, Zhang X, Cheraghi S, Kim SW, Ham BJ, Rami FZ, Sui J, Kang CY, Suk HI, Chung YC. Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders. Schizophr Bull 2024; 50:804-814. [PMID: 38085061 PMCID: PMC11283195 DOI: 10.1093/schbul/sbad167] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/29/2024]
Abstract
BACKGROUND AND HYPOTHESIS The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PAD in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging (sMRI). STUDY DESIGN We employed a convolutional network-based regression (SFCNR), and compared its performance with models based on three machine learning (ML) algorithms. We pretrained the SFCNR with sMRI data of 7590 healthy controls (HCs) selected from the UK Biobank. The parameters of the pretrained model were transferred to the next training phase with a new set of HCs (n = 541). The brain-PAD was analyzed in independent HCs (n = 209) and patients (n = 233). Correlations between the brain-PAD and clinical measures were investigated. STUDY RESULTS The SFCNR model outperformed three commonly used ML models. Advanced brain aging was observed in patients with SCZ, FE-SSDs, and TRS compared to HCs. A significant difference in brain-PAD was observed between FE-SSDs and TRS with ridge regression but not with the SFCNR model. Chlorpromazine equivalent dose and cognitive function were correlated with the brain-PAD in SCZ and FE-SSDs. CONCLUSIONS Our findings indicate that there is advanced brain aging in patients with SCZ and higher brain-PAD in SCZ can be used as a surrogate marker for cognitive dysfunction. These findings warrant further investigations on the causes of advanced brain age in SCZ. In addition, possible psychosocial and pharmacological interventions targeting brain health should be considered in early-stage SCZ patients with advanced brain age.
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Affiliation(s)
- 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
| | - Da-Woon Heo
- Department of Artificial Intelligence, Korea University, Seoul, Korea
| | - Junyeong Maeng
- Department of Artificial Intelligence, Korea University, Seoul, Korea
| | - Jie Shen
- 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
- Department of Psychiatry, Yanbian University, Medical School, Yanji, China
| | - Uyanga Tsogt
- 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
| | - Soyolsaikhan Odkhuu
- 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
| | - Xuefeng Zhang
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Sahar Cheraghi
- 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
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Fatima Zahra Rami
- 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
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Chae Yeong Kang
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Heung-Il Suk
- Department of Artificial Intelligence, Korea University, Seoul, Korea
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - 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
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Vakli P, Weiss B, Rozmann D, Erőss G, Nárai Á, Hermann P, Vidnyánszky Z. The effect of head motion on brain age prediction using deep convolutional neural networks. Neuroimage 2024; 294:120646. [PMID: 38750907 DOI: 10.1016/j.neuroimage.2024.120646] [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: 04/16/2024] [Revised: 05/10/2024] [Accepted: 05/12/2024] [Indexed: 05/23/2024] Open
Abstract
Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age-referred to as brain-predicted age difference (brain-PAD)-is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.
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Affiliation(s)
- Pál Vakli
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary.
| | - Béla Weiss
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary; Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Óbuda University, Budapest 1034, Hungary.
| | - Dorina Rozmann
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - György Erőss
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Ádám Nárai
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary; Doctoral School of Biology and Sportbiology, Institute of Biology, Faculty of Sciences, University of Pécs, Pécs 7624, Hungary
| | - Petra Hermann
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary.
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Panikratova YR, Tomyshev AS, Abdullina EG, Rodionov GI, Arkhipov AY, Tikhonov DV, Bozhko OV, Kaleda VG, Strelets VB, Lebedeva IS. Resting-state functional connectivity correlates of brain structural aging in schizophrenia. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01837-5. [PMID: 38914851 DOI: 10.1007/s00406-024-01837-5] [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] [Received: 11/07/2023] [Accepted: 05/27/2024] [Indexed: 06/26/2024]
Abstract
A large body of research has shown that schizophrenia patients demonstrate increased brain structural aging. Although this process may be coupled with aberrant changes in intrinsic functional architecture of the brain, they remain understudied. We hypothesized that there are brain regions whose whole-brain functional connectivity at rest is differently associated with brain structural aging in schizophrenia patients compared to healthy controls. Eighty-four male schizophrenia patients and eighty-six male healthy controls underwent structural MRI and resting-state fMRI. The brain-predicted age difference (b-PAD) was a measure of brain structural aging. Resting-state fMRI was applied to obtain global correlation (GCOR) maps comprising voxelwise values of the strength and sign of functional connectivity of a given voxel with the rest of the brain. Schizophrenia patients had higher b-PAD compared to controls (mean between-group difference + 2.9 years). Greater b-PAD in schizophrenia patients, compared to controls, was associated with lower whole-brain functional connectivity of a region in frontal orbital cortex, inferior frontal gyrus, Heschl's Gyrus, plana temporale and polare, insula, and opercular cortices of the right hemisphere (rFTI). According to post hoc seed-based correlation analysis, decrease of functional connectivity with the posterior cingulate gyrus, left superior temporal cortices, as well as right angular gyrus/superior lateral occipital cortex has mainly driven the results. Lower functional connectivity of the rFTI was related to worse verbal working memory and language production. Our findings demonstrate that well-established frontotemporal functional abnormalities in schizophrenia are related to increased brain structural aging.
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Affiliation(s)
| | | | | | - Georgiy I Rodionov
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, Moscow, Russia
| | - Andrey Yu Arkhipov
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, Moscow, Russia
| | | | | | | | - Valeria B Strelets
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, Moscow, Russia
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Wang M, Wei M, Wang L, Song J, Rominger A, Shi K, Jiang J. Tau Protein Accumulation Trajectory-Based Brain Age Prediction in the Alzheimer's Disease Continuum. Brain Sci 2024; 14:575. [PMID: 38928575 PMCID: PMC11201453 DOI: 10.3390/brainsci14060575] [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: 05/02/2024] [Revised: 05/22/2024] [Accepted: 05/25/2024] [Indexed: 06/28/2024] Open
Abstract
Clinical cognitive advancement within the Alzheimer's disease (AD) continuum is intimately connected with sustained accumulation of tau protein pathology. The biological brain age and its gap show great potential for pathological risk and disease severity. In the present study, we applied multivariable linear support vector regression to train a normative brain age prediction model using tau brain images. We further assessed the predicted biological brain age and its gap for patients within the AD continuum. In the AD continuum, evaluated pathologic tau binding was found in the inferior temporal, parietal-temporal junction, precuneus/posterior cingulate, dorsal frontal, occipital, and inferior-medial temporal cortices. The biological brain age gaps of patients within the AD continuum were notably higher than those of the normal controls (p < 0.0001). Significant positive correlations were observed between the brain age gap and global tau protein accumulation levels for mild cognitive impairment (r = 0.726, p < 0.001), AD (r = 0.845, p < 0.001), and AD continuum (r = 0.797, p < 0.001). The pathologic tau-based age gap was significantly linked to neuropsychological scores. The proposed pathologic tau-based biological brain age model could track the tau protein accumulation trajectory of cognitive impairment and further provide a comprehensive quantification index for the tau accumulation risk.
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Affiliation(s)
- Min Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Min Wei
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, China
| | - Luyao Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Jun Song
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
- Computer Aided Medical Procedures, School of Computation, Information and Technology, Technical University of Munich, 85748 Munich, Germany
| | - Jiehui Jiang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
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Baran B, Lee EE. Age-Related Changes in Sleep and Its Implications for Cognitive Decline in Aging Persons With Schizophrenia: A Critical Review. Schizophr Bull 2024:sbae059. [PMID: 38713085 DOI: 10.1093/schbul/sbae059] [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: 05/08/2024]
Abstract
BACKGROUND AND HYPOTHESIS Cognitive impairment is a core feature of schizophrenia that worsens with aging and interferes with quality of life. Recent work identifies sleep as an actionable target to alleviate cognitive deficits. Cardinal non-rapid eye movement (NREM) sleep oscillations such as sleep spindles and slow oscillations are critical for cognition. People living with schizophrenia (PLWS) and their first-degree relatives have a specific reduction in sleep spindles and an abnormality in their temporal coordination with slow oscillations that predict impaired memory consolidation. While NREM oscillatory activity is reduced in typical aging, it is not known how further disruption in these oscillations contributes to cognitive decline in older PLWS. Another understudied risk factor for cognitive deficits among older PLWS is obstructive sleep apnea (OSA) which may contribute to cognitive decline. STUDY DESIGN We conducted a narrative review to examine the published literature on aging, OSA, and NREM sleep oscillations in PLWS. STUDY RESULTS Spindles are propagated via thalamocortical feedback loops, and this circuitry shows abnormal hyperconnectivity in schizophrenia as revealed by structural and functional MRI studies. While the risk and severity of OSA increase with age, older PLWS are particularly vulnerable to OSA-related cognitive deficits because OSA is often underdiagnosed and undertreated, and OSA adds further damage to the circuitry that generates NREM sleep oscillations. CONCLUSIONS We highlight the critical need to study NREM sleep in older PWLS and propose that identifying and treating OSA in older PLWS will provide an avenue to potentially mitigate and prevent cognitive decline.
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Affiliation(s)
- Bengi Baran
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, USA
- Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Ellen E Lee
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Desert-Pacific Mental Illness Research Education and Clinical Center, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
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6
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Dines M, Kes M, Ailán D, Cetkovich-Bakmas M, Born C, Grunze H. Bipolar disorders and schizophrenia: discrete disorders? Front Psychiatry 2024; 15:1352250. [PMID: 38745778 PMCID: PMC11091416 DOI: 10.3389/fpsyt.2024.1352250] [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: 12/07/2023] [Accepted: 04/16/2024] [Indexed: 05/16/2024] Open
Abstract
Background With similarities in heritability, neurobiology and symptomatology, the question has been raised whether schizophrenia and bipolar disorder are truly distinctive disorders or belong to a continuum. This narrative review summarizes common and distinctive findings from genetics, neuroimaging, cognition and clinical course that may help to solve this ethiopathogenetic puzzle. Methods The authors conducted a literature search for papers listed in PubMed and Google Scholar, using the search terms "schizophrenia" and "bipolar disorder" combined with different terms such as "genes", "neuroimaging studies", "phenomenology differences", "cognition", "epidemiology". Articles were considered for inclusion if they were written in English or Spanish, published as full articles, if they compared subjects with schizophrenia and bipolar disorder, or subjects with either disorder with healthy controls, addressing differences between groups. Results Several findings support the hypothesis that schizophrenia and bipolar disorder are discrete disorders, yet some overlapping of findings exists. The evidence for heritability of both SZ and BD is obvious, as well as the environmental impact on individual manifestations of both disorders. Neuroimaging studies support subtle differences between disorders, it appears to be rather a pattern of irregularities than an unequivocally unique finding distinguishing schizophrenia from bipolar disorder. The cognitive profile displays differences between disorders in certain domains, such as premorbid intellectual functioning and executive functions. Finally, the timing and trajectory of cognitive impairment in both disorders also differs. Conclusion The question whether SZ and BD belong to a continuum or are separate disorders remains a challenge for further research. Currently, our research tools may be not precise enough to carve out distinctive, unique and undisputable differences between SZ and BD, but current evidence favors separate disorders. Given that differences are subtle, a way to overcome diagnostic uncertainties in the future could be the application of artificial intelligence based on BigData. Limitations Despite the detailed search, this article is not a full and complete review of all available studies on the topic. The search and selection of papers was also limited to articles in English and Spanish. Selection of papers and conclusions may be biased by the personal view and clinical experience of the authors.
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Affiliation(s)
- Micaela Dines
- Department of Psychiatry, Instituto de Neurología Cognitiva (INECO), Buenos Aires, Argentina
- Department of Psychiatry, Instituto de Neurociencia Cognitiva y Traslacional (Consejo Nacional de Investigaciones Científicas y Técnicas - Fundación INECO - Universidad Favaloro), Buenos Aires, Argentina
| | - Mariana Kes
- Department of Psychiatry, Instituto de Neurología Cognitiva (INECO), Buenos Aires, Argentina
- Department of Psychiatry, Instituto de Neurociencia Cognitiva y Traslacional (Consejo Nacional de Investigaciones Científicas y Técnicas - Fundación INECO - Universidad Favaloro), Buenos Aires, Argentina
| | - Delfina Ailán
- Department of Psychiatry, Instituto de Neurología Cognitiva (INECO), Buenos Aires, Argentina
- Department of Psychiatry, Instituto de Neurociencia Cognitiva y Traslacional (Consejo Nacional de Investigaciones Científicas y Técnicas - Fundación INECO - Universidad Favaloro), Buenos Aires, Argentina
| | - Marcelo Cetkovich-Bakmas
- Department of Psychiatry, Instituto de Neurología Cognitiva (INECO), Buenos Aires, Argentina
- Department of Psychiatry, Instituto de Neurociencia Cognitiva y Traslacional (Consejo Nacional de Investigaciones Científicas y Técnicas - Fundación INECO - Universidad Favaloro), Buenos Aires, Argentina
| | - Christoph Born
- Department of Psychiatry, Psychiatrie Schwäbisch Hall, Ringstraße, Germany
- Department of Psychiatry, Paracelsus Medical University, Nuremberg, Germany
| | - Heinz Grunze
- Department of Psychiatry, Psychiatrie Schwäbisch Hall, Ringstraße, Germany
- Department of Psychiatry, Paracelsus Medical University, Nuremberg, Germany
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van der Markt A, Klumpers U, Dols A, Korten N, Boks MP, Ophoff RA, Beekman A, Kupka R, van Haren NEM, Schnack H. Accelerated brain aging as a biomarker for staging in bipolar disorder: an exploratory study. Psychol Med 2024; 54:1016-1025. [PMID: 37749940 DOI: 10.1017/s0033291723002829] [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: 09/27/2023]
Abstract
BACKGROUND Two established staging models outline the longitudinal progression in bipolar disorder (BD) based on episode recurrence or inter-episodic functioning. However, underlying neurobiological mechanisms and corresponding biomarkers remain unexplored. This study aimed to investigate if global and (sub)cortical brain structures, along with brain-predicted age difference (brain-PAD) reflect illness progression as conceptualized in these staging models, potentially identifying brain-PAD as a biomarker for BD staging. METHODS In total, 199 subjects with bipolar-I-disorder and 226 control subjects from the Dutch Bipolar Cohort with a high-quality T1-weighted magnetic resonance imaging scan were analyzed. Global and (sub)cortical brain measures and brain-PAD (the difference between biological and chronological age) were estimated. Associations between individual brain measures and the stages of both staging models were explored. RESULTS A higher brain-PAD (higher biological age than chronological age) correlated with an increased likelihood of being in a higher stage of the inter-episodic functioning model, but not in the model based on number of mood episodes. However, after correcting for the confounding factors lithium-use and comorbid anxiety, the association lost significance. Global and (sub)cortical brain measures showed no significant association with the stages. CONCLUSIONS These results suggest that brain-PAD may be associated with illness progression as defined by impaired inter-episodic functioning. Nevertheless, the significance of this association changed after considering lithium-use and comorbid anxiety disorders. Further research is required to disentangle the intricate relationship between brain-PAD, illness stages, and lithium intake or anxiety disorders. This study provides a foundation for potentially using brain-PAD as a biomarker for illness progression.
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Affiliation(s)
- Afra van der Markt
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- GGZ inGeest Specialized Mental Health Care, Amsterdam, The Netherlands
- Mental Health, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ursula Klumpers
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress, Amsterdam, The Netherlands
| | - Annemiek Dols
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, The Netherlands
| | - Nicole Korten
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- GGZ inGeest Specialized Mental Health Care, Amsterdam, The Netherlands
| | - Marco P Boks
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, The Netherlands
| | - Roel A Ophoff
- Department of Psychiatry and Biobehavioral Science, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Aartjan Beekman
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- GGZ inGeest Specialized Mental Health Care, Amsterdam, The Netherlands
- Mental Health, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ralph Kupka
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- GGZ inGeest Specialized Mental Health Care, Amsterdam, The Netherlands
- Mental Health, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Neeltje E M van Haren
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands
- Erasmus Medical Center - Sophia, Child and Adolescent Psychiatry and Psychology, Rotterdam, The Netherlands
| | - Hugo Schnack
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, The Netherlands
- Department of Languages, Literature and Communication, Faculty of Humanities, Utrecht University, Utrecht, The Netherlands
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Constantinides C, Baltramonaityte V, Caramaschi D, Han LKM, Lancaster TM, Zammit S, Freeman TP, Walton E. Assessing the association between global structural brain age and polygenic risk for schizophrenia in early adulthood: A recall-by-genotype study. Cortex 2024; 172:1-13. [PMID: 38154374 DOI: 10.1016/j.cortex.2023.11.015] [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: 04/28/2023] [Revised: 09/22/2023] [Accepted: 11/23/2023] [Indexed: 12/30/2023]
Abstract
Neuroimaging studies consistently show advanced brain age in schizophrenia, suggesting that brain structure is often 'older' than expected at a given chronological age. Whether advanced brain age is linked to genetic liability for schizophrenia remains unclear. In this pre-registered secondary data analysis, we utilised a recall-by-genotype approach applied to a population-based subsample from the Avon Longitudinal Study of Parents and Children to assess brain age differences between young adults aged 21-24 years with relatively high (n = 96) and low (n = 93) polygenic risk for schizophrenia (SCZ-PRS). A global index of brain age (or brain-predicted age) was estimated using a publicly available machine learning model previously trained on a combination of region-wise gray-matter measures, including cortical thickness, surface area and subcortical volumes derived from T1-weighted magnetic resonance imaging (MRI) scans. We found no difference in mean brain-PAD (the difference between brain-predicted age and chronological age) between the high- and low-SCZ-PRS groups, controlling for the effects of sex and age at time of scanning (b = -.21; 95% CI -2.00, 1.58; p = .82; Cohen's d = -.034; partial R2 = .00029). These findings do not support an association between SCZ-PRS and brain-PAD based on global age-related structural brain patterns, suggesting that brain age may not be a vulnerability marker of common genetic risk for SCZ. Future studies with larger samples and multimodal brain age measures could further investigate global or localised effects of SCZ-PRS.
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Affiliation(s)
| | | | - Doretta Caramaschi
- Department of Psychology, Faculty of Health and Life Sciences, University of Exeter, UK
| | - Laura K M Han
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia; Orygen, Parkville, Australia
| | | | - Stanley Zammit
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK; Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom P Freeman
- Addiction and Mental Health Group (AIM), Department of Psychology, University of Bath, UK
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Kalc P, Dahnke R, Hoffstaedter F, Gaser C. BrainAGE: Revisited and reframed machine learning workflow. Hum Brain Mapp 2024; 45:e26632. [PMID: 38379519 PMCID: PMC10879910 DOI: 10.1002/hbm.26632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 01/23/2024] [Accepted: 02/05/2024] [Indexed: 02/22/2024] Open
Abstract
Since the introduction of the BrainAGE method, novel machine learning methods for brain age prediction have continued to emerge. The idea of estimating the chronological age from magnetic resonance images proved to be an interesting field of research due to the relative simplicity of its interpretation and its potential use as a biomarker of brain health. We revised our previous BrainAGE approach, originally utilising relevance vector regression (RVR), and substituted it with Gaussian process regression (GPR), which enables more stable processing of larger datasets, such as the UK Biobank (UKB). In addition, we extended the global BrainAGE approach to regional BrainAGE, providing spatially specific scores for five brain lobes per hemisphere. We tested the performance of the new algorithms under several different conditions and investigated their validity on the ADNI and schizophrenia samples, as well as on a synthetic dataset of neocortical thinning. The results show an improved performance of the reframed global model on the UKB sample with a mean absolute error (MAE) of less than 2 years and a significant difference in BrainAGE between healthy participants and patients with Alzheimer's disease and schizophrenia. Moreover, the workings of the algorithm show meaningful effects for a simulated neocortical atrophy dataset. The regional BrainAGE model performed well on two clinical samples, showing disease-specific patterns for different levels of impairment. The results demonstrate that the new improved algorithms provide reliable and valid brain age estimations.
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Affiliation(s)
- Polona Kalc
- Structural Brain Mapping Group, Department of NeurologyJena University HospitalJenaGermany
| | - Robert Dahnke
- Structural Brain Mapping Group, Department of NeurologyJena University HospitalJenaGermany
- Department of Psychiatry and PsychotherapyJena University HospitalJenaGermany
| | - Felix Hoffstaedter
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine, Brain and Behaviour (INM‐7)JülichGermany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University DüsseldorfDüsseldorfGermany
| | - Christian Gaser
- Structural Brain Mapping Group, Department of NeurologyJena University HospitalJenaGermany
- Department of Psychiatry and PsychotherapyJena University HospitalJenaGermany
- German Center for Mental Health (DZPG)Jena‐Halle‐MagdeburgGermany
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Diniz BS, Seitz-Holland J, Sehgal R, Kasamoto J, Higgins-Chen AT, Lenze E. Geroscience-Centric Perspective for Geriatric Psychiatry: Integrating Aging Biology With Geriatric Mental Health Research. Am J Geriatr Psychiatry 2024; 32:1-16. [PMID: 37845116 PMCID: PMC10841054 DOI: 10.1016/j.jagp.2023.09.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/30/2023] [Accepted: 09/14/2023] [Indexed: 10/18/2023]
Abstract
The geroscience hypothesis asserts that physiological aging is caused by a small number of biological pathways. Despite the explosion of geroscience research over the past couple of decades, the research on how serious mental illnesses (SMI) affects the biological aging processes is still in its infancy. In this review, we aim to provide a critical appraisal of the emerging literature focusing on how we measure biological aging systematically, and in the brain and how SMIs affect biological aging measures in older adults. We will also review recent developments in the field of cellular senescence and potential targets for interventions for SMIs in older adults, based on the geroscience hypothesis.
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Affiliation(s)
- Breno S Diniz
- UConn Center on Aging & Department of Psychiatry (BSD), School of Medicine, University of Connecticut Health Center, Farmington, CT.
| | - Johanna Seitz-Holland
- Department of Psychiatry (JSH), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry (JSH), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Raghav Sehgal
- Program in Computational Biology and Bioinformatics (RS, JK), Yale University, New Haven, CT
| | - Jessica Kasamoto
- Program in Computational Biology and Bioinformatics (RS, JK), Yale University, New Haven, CT
| | - Albert T Higgins-Chen
- Department of Psychiatry (ATHC), Yale University School of Medicine, New Haven, CT; Department of Pathology (ATHC), Yale University School of Medicine, New Haven, CT
| | - Eric Lenze
- Department of Psychiatry (EL), School of Medicine, Washington University at St. Louis, St. Louis, MO
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11
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Schinz D, Schmitz-Koep B, Tahedl M, Teckenberg T, Schultz V, Schulz J, Zimmer C, Sorg C, Gaser C, Hedderich DM. Lower cortical thickness and increased brain aging in adults with cocaine use disorder. Front Psychiatry 2023; 14:1266770. [PMID: 38025412 PMCID: PMC10679447 DOI: 10.3389/fpsyt.2023.1266770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Background Cocaine use disorder (CUD) is a global health issue with severe behavioral and cognitive sequelae. While previous evidence suggests a variety of structural and age-related brain changes in CUD, the impact on both, cortical thickness and brain age measures remains unclear. Methods Derived from a publicly available data set (SUDMEX_CONN), 74 CUD patients and 62 matched healthy controls underwent brain MRI and behavioral-clinical assessment. We determined cortical thickness by surface-based morphometry using CAT12 and Brain Age Gap Estimate (BrainAGE) via relevance vector regression. Associations between structural brain changes and behavioral-clinical variables of patients with CUD were investigated by correlation analyses. Results We found significantly lower cortical thickness in bilateral prefrontal cortices, posterior cingulate cortices, and the temporoparietal junction and significantly increased BrainAGE in patients with CUD [mean (SD) = 1.97 (±3.53)] compared to healthy controls (p < 0.001, Cohen's d = 0.58). Increased BrainAGE was associated with longer cocaine abuse duration. Conclusion Results demonstrate structural brain abnormalities in CUD, particularly lower cortical thickness in association cortices and dose-dependent, increased brain age.
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Affiliation(s)
- David Schinz
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen- (FAU), Nürnberg, Germany
| | - Benita Schmitz-Koep
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Marlene Tahedl
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Timo Teckenberg
- Digital Management & Transformation, SRH Fernhochschule - The Mobile University, Riedlingen, Germany
| | - Vivian Schultz
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Julia Schulz
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christian Sorg
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Psychiatry, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Department of Neurology, Jena University Hospital, Jena, Germany
- German Center for Mental Health (DZPG), Site Jena-Magdeburg-Halle, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Jena-Magdeburg-Halle, Germany
| | - Dennis M. Hedderich
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
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12
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Choi US, Park JY, Lee JJ, Choi KY, Won S, Lee KH. Predicting mild cognitive impairments from cognitively normal brains using a novel brain age estimation model based on structural magnetic resonance imaging. Cereb Cortex 2023; 33:10858-10866. [PMID: 37718166 DOI: 10.1093/cercor/bhad331] [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: 05/27/2023] [Revised: 08/20/2023] [Accepted: 08/22/2023] [Indexed: 09/19/2023] Open
Abstract
Brain age prediction is a practical method used to quantify brain aging and detect neurodegenerative diseases such as Alzheimer's disease (AD). However, very few studies have considered brain age prediction as a biomarker for the conversion of cognitively normal (CN) to mild cognitive impairment (MCI). In this study, we developed a novel brain age prediction model using brain volume and cortical thickness features. We calculated an acceleration of brain age (ABA) derived from the suggested model to estimate different diagnostic groups (CN, MCI, and AD) and to classify CN to MCI and MCI to AD conversion groups. We observed a strong association between ABA and the 3 diagnostic groups. Additionally, the classification models for CN to MCI conversion and MCI to AD conversion exhibited acceptable and robust performances, with area under the curve values of 0.66 and 0.76, respectively. We believe that our proposed model provides a reliable estimate of brain age for elderly individuals and can identify those at risk of progressing from CN to MCI. This model has great potential to reveal a diagnosis associated with a change in cognitive decline.
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Affiliation(s)
- Uk-Su Choi
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation, Daegu 41061, Republic of Korea
| | - Jun Young Park
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Republic of Korea
- Neurozen Inc., Seoul 06168, Republic of Korea
| | - Jang Jae Lee
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
| | - Kyu Yeong Choi
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
| | - Sungho Won
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Republic of Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Republic of Korea
- Institute of Health and Environment, Seoul National University, Seoul 08826, Republic of Korea
| | - Kun Ho Lee
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Department of Biomedical Sciences, Chosun University, Gwangju 61452, Republic of Korea
- Korea Brain Research Institute, Daegu 41061, Republic of Korea
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13
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Garcia Condado J, Cortes JM. NeuropsychBrainAge: A biomarker for conversion from mild cognitive impairment to Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12493. [PMID: 37908437 PMCID: PMC10614125 DOI: 10.1002/dad2.12493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 08/21/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
INTRODUCTION BrainAge models based on neuroimaging data have diagnostic classification power but have replicability issues due to site and patient variability. BrainAge models trained on neuropsychological tests could help distinguish stable mild cognitive impairment (sMCI) from progressive MCI (pMCI) to Alzheimer's disease (AD). METHODS A linear regressor BrainAge model was trained on healthy controls using neuropsychological tests and neuroimaging features separately. The BrainAge delta, predicted age minus chronological age, was used to distinguish between sMCI and pMCI. RESULTS The cross-validated area under the receiver-operating characteristic (ROC) curve for sMCI versus pMCI was 0.91 for neuropsychological features in contrast to 0.68 for neuroimaging features. The BrainAge delta was correlated with the time to conversion, the time taken for a pMCI subject to convert to AD. DISCUSSION The BrainAge delta from neuropsychological tests is a good biomarker to distinguish between sMCI and pMCI. Other neurological and psychiatric disorders could be studied using this strategy. Highlights BrainAge models based on neuropsychological tests outperform models based on neuroimaging features when distinguishing between stable mild cognitive impairment (sMCI) from progressive MCI (pMCI) to Alzheimer's disease (AD).The combination of neuropsychological tests with neuroimaging features does not lead to an improvement in sMCI versus pMCI classification compared to using neuropsychological tests on their own.BrainAge delta of both neuroimaging and neuropsychological models was correlated with the time to conversion, the time taken for a pMCI subject to convert to AD.
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Affiliation(s)
- Jorge Garcia Condado
- Computational Neuroimaging LaboratoryBiobizkaia Health Research InstituteBarakaldo, BizkaiaSpain
- Biomedical Research Doctorate ProgramUniversity of the Basque CountryLeioa, BizkaiaSpain
| | - Jesus M. Cortes
- Computational Neuroimaging LaboratoryBiobizkaia Health Research InstituteBarakaldo, BizkaiaSpain
- Department of Cell Biology and HistologyUniversity of the Basque CountryLeioa, BizkaiaSpain
- IKERBASQUE Basque Foundation for ScienceBilbaoSpain
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14
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Walhovd KB, Lövden M, Fjell AM. Timing of lifespan influences on brain and cognition. Trends Cogn Sci 2023; 27:901-915. [PMID: 37563042 DOI: 10.1016/j.tics.2023.07.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/04/2023] [Accepted: 07/04/2023] [Indexed: 08/12/2023]
Abstract
Modifiable risk and protective factors for boosting brain and cognitive development and preventing neurodegeneration and cognitive decline are embraced in neuroimaging studies. We call for sobriety regarding the timing and quantity of such influences on brain and cognition. Individual differences in the level of brain and cognition, many of which present already at birth and early in development, appear stable, larger, and more pervasive than differences in change across the lifespan. Incorporating early-life factors, including genetics, and investigating both level and change will reduce the risk of ascribing undue importance and causality to proximate factors in adulthood and older age. This has implications for both mechanistic understanding and prevention.
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Affiliation(s)
- Kristine B Walhovd
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Norway; Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
| | - Martin Lövden
- Department of Psychology, University of Gothenburg, Gothenburg, Sweden
| | - Anders M Fjell
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Norway; Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
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15
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Domagała A, Domagała L, Kopiś-Posiej N, Harciarek M, Krukow P. Differentiation of the retinal morphology aging trajectories in schizophrenia and their associations with cognitive dysfunctions. Front Psychiatry 2023; 14:1207608. [PMID: 37539329 PMCID: PMC10396397 DOI: 10.3389/fpsyt.2023.1207608] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/30/2023] [Indexed: 08/05/2023] Open
Abstract
Previous studies evaluating the morphology of the selected retinal layers in schizophrenia showed abnormalities regarding macular thickness, retinal nerve fiber layer (RNLF), and ganglion cell complex (GCC). Concurrently, accumulating neuroimaging results suggest that structural alterations of the brain in this disease might be an effect of accelerated aging. Referring to these findings, we aimed to determine whether the thinning of the retinal layers assessed with the optic coherence tomography (OCT) in a group of schizophrenia patients (n = 60) presents a significant age-related decrease exceeding potential changes noted in the control group (n = 61). Samples of patients and controls were divided into three age subgroups, namely, younger, middle-aged, and older participants. OCT outcomes, such as macular thickness and volume, macular RNFL, peripapillary RNFL, and GCC, were analyzed concerning a diagnosis status (controls vs. patients) and age subgroups. Additionally, associations between retinal parameters, age, and selected cognitive functions were evaluated. post-hoc tests revealed that macular thickness and volume in patients undergo significant age-dependent thinning, which was not observed in the control group. Regression analyses confirmed the association between macular morphology and age. Selected speed-dependent cognitive functions in patients decreased significantly with age, and these features were also significantly associated with some OCT outcomes also after controlling for antipsychotic treatment. Our results suggest that reduced measures of retinal structure detected in schizophrenia may be an effect of accelerated aging; however, further research is needed using computational solutions derived from brain imaging studies based on large datasets covering representatives of all age groups.
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Affiliation(s)
- Adam Domagała
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland
| | - Lucyna Domagała
- Non-Public Health Facility “OKO-MED”, Sandomierz, Sandomierz County, Poland
| | - Natalia Kopiś-Posiej
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland
| | - Michał Harciarek
- Department of Neuropsychology, Institute of Psychology, University of Gdańsk, Gdansk, Poland
| | - Paweł Krukow
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland
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16
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Beheshti I. Cocaine Destroys Gray Matter Brain Cells and Accelerates Brain Aging. BIOLOGY 2023; 12:biology12050752. [PMID: 37237564 DOI: 10.3390/biology12050752] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/16/2023] [Accepted: 05/18/2023] [Indexed: 05/28/2023]
Abstract
Introduction: Cocaine use disorder (CUD) is a substance use disorder characterized by a strong desire to obtain, consume, and misuse cocaine. Little is known about how cocaine affects the structure of the brain. In this study, we first investigated the anatomical brain changes in individuals with CUD compared to their matched healthy controls, and then explored whether these anatomical brain abnormalities contribute to considerably accelerated brain aging among this population. Methods: At the first stage, we used anatomical magnetic resonance imaging (MRI) data, voxel-based morphometry (VBM), and deformation-based morphometry techniques to uncover the morphological and macroscopic anatomical brain changes in 74 CUD patients compared to 62 age- and sex-matched healthy controls (HCs) obtained from the SUDMEX CONN dataset, the Mexican MRI dataset of patients with CUD. Then, we computed brain-predicted age difference (i.e., brain-PAD: the brain-predicted age minus the actual age) in CUD and HC groups using a robust brain age estimation framework. Using a multiple regression analysis, we also investigated the regional gray matter (GM) and white matter (WM) changes associated with the brain-PAD. Results: Using a whole-brain VBM analysis, we observed widespread gray matter atrophy in CUD patients located in the temporal lobe, frontal lobe, insula, middle frontal gyrus, superior frontal gyrus, rectal gyrus, and limbic lobe regions compared to the HCs. In contrast, we did not observe any swelling in the GM, changes in the WM, or local brain tissue atrophy or expansion between the CUD and HC groups. Furthermore, we found a significantly higher brain-PAD in CUD patients compared to matched HCs (mean difference = 2.62 years, Cohen's d = 0.54; t-test = 3.16, p = 0.002). The regression analysis showed significant negative changes in GM volume associated with brain-PAD in the CUD group, particularly in the limbic lobe, subcallosal gyrus, cingulate gyrus, and anterior cingulate regions. Discussion: The results of our investigation reveal that chronic cocaine use is linked to significant changes in gray matter, which hasten the process of structural brain aging in individuals who use the drug. These findings offer valuable insights into the impact of cocaine on the composition of the brain.
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Affiliation(s)
- Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, MB R3E 3J7, Canada
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17
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Holton KM, Chan SY, Brockmeier AJ, Öngür D, Hall MH. Exploring the influence of functional architecture on cortical thickness networks in early psychosis - a longitudinal study. Neuroimage 2023; 274:120127. [PMID: 37086876 DOI: 10.1016/j.neuroimage.2023.120127] [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: 02/25/2023] [Revised: 04/17/2023] [Accepted: 04/19/2023] [Indexed: 04/24/2023] Open
Abstract
Cortical thickness reductions differ between individuals with psychotic disorders and comparison subjects even in early stages of illness. Whether these reductions covary as expected by functional network membership or simply by spatial proximity has not been fully elucidated. Through orthonormal projective non-negative matrix factorization, cortical thickness measurements in functionally-annotated regions from MRI scans of early-stage psychosis and matched healthy controls were reduced in dimensionality into features capturing positive covariance. Rather than matching the functional networks, the covarying regions in each feature displayed a more localized spatial organization. With Bayesian belief networks, the covarying regions per feature were arranged into a network topology to visualize the dependency structure and identify key driving regions. The features demonstrated diagnosis-specific differences in cortical thickness distributions per feature, identifying reduction-vulnerable spatial regions. Differences in key cortical thickness features between psychosis and control groups were delineated, as well as those between affective and non-affective psychosis. Clustering of the participants, stratified by diagnosis and clinical variables, characterized the clinical traits that define the cortical thickness patterns. Longitudinal follow-up revealed that in select clusters with low baseline cortical thickness, clinical traits improved over time. Our study represents a novel effort to characterize brain structure in relation to functional networks in healthy and clinical populations and to map patterns of cortical thickness alterations among ESP patients onto clinical variables for a better understanding of brain pathophysiology.
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Affiliation(s)
- Kristina M Holton
- Computational Neural Information Engineering Lab, University of Delaware, 139 The Green, Newark, DE 19716.
| | - Shi Yu Chan
- Psychosis Neurobiology Laboratory, McLean Hospital, 115 Mill St, Belmont, MA 02478; Division of Psychotic Disorders, McLean Hospital, 115 Mill St, Belmont, MA 02478
| | - Austin J Brockmeier
- Computational Neural Information Engineering Lab, University of Delaware, 139 The Green, Newark, DE 19716
| | - Dost Öngür
- Department of Psychiatry, Harvard Medical School, 25 Shattuck St, Boston, MA 02115; Division of Psychotic Disorders, McLean Hospital, 115 Mill St, Belmont, MA 02478
| | - Mei-Hua Hall
- Psychosis Neurobiology Laboratory, McLean Hospital, 115 Mill St, Belmont, MA 02478; Department of Psychiatry, Harvard Medical School, 25 Shattuck St, Boston, MA 02115; Division of Psychotic Disorders, McLean Hospital, 115 Mill St, Belmont, MA 02478.
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18
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Fingelkurts AA, Fingelkurts AA. Turning Back the Clock: A Retrospective Single-Blind Study on Brain Age Change in Response to Nutraceuticals Supplementation vs. Lifestyle Modifications. Brain Sci 2023; 13:brainsci13030520. [PMID: 36979330 PMCID: PMC10046544 DOI: 10.3390/brainsci13030520] [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: 02/20/2023] [Revised: 03/17/2023] [Accepted: 03/19/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND There is a growing consensus that chronological age (CA) is not an accurate indicator of the aging process and that biological age (BA) instead is a better measure of an individual's risk of age-related outcomes and a more accurate predictor of mortality than actual CA. In this context, BA measures the "true" age, which is an integrated result of an individual's level of damage accumulation across all levels of biological organization, along with preserved resources. The BA is plastic and depends upon epigenetics. Brain state is an important factor contributing to health- and lifespan. METHODS AND OBJECTIVE Quantitative electroencephalography (qEEG)-derived brain BA (BBA) is a suitable and promising measure of brain aging. In the present study, we aimed to show that BBA can be decelerated or even reversed in humans (N = 89) by using customized programs of nutraceutical compounds or lifestyle changes (mean duration = 13 months). RESULTS We observed that BBA was younger than CA in both groups at the end of the intervention. Furthermore, the BBA of the participants in the nutraceuticals group was 2.83 years younger at the endpoint of the intervention compared with their BBA score at the beginning of the intervention, while the BBA of the participants in the lifestyle group was only 0.02 years younger at the end of the intervention. These results were accompanied by improvements in mental-physical health comorbidities in both groups. The pre-intervention BBA score and the sex of the participants were considered confounding factors and analyzed separately. CONCLUSIONS Overall, the obtained results support the feasibility of the goal of this study and also provide the first robust evidence that halting and reversal of brain aging are possible in humans within a reasonable (practical) timeframe of approximately one year.
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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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20
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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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21
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Peterson JA, Johnson A, Nordarse CL, Huo Z, Cole J, Fillingim RB, Cruz-Almeida Y. Brain predicted age difference mediates pain impact on physical performance in community dwelling middle to older aged adults. Geriatr Nurs 2023; 50:181-187. [PMID: 36787663 PMCID: PMC10360023 DOI: 10.1016/j.gerinurse.2023.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 02/16/2023]
Abstract
The purpose of the study was to examine associations between physical performance and brain aging in individuals with knee pain and whether the association between pain and physical performance is mediated by brain aging. Participants (n=202) with low impact knee pain (n=111), high impact knee pain (n=60) and pain-free controls (n=31) completed self-reported pain, magnetic resonance imaging (MRI), and a Short Physical Performance Battery (SPPB) that included balance, walking, and sit to stand tasks. Brain predicted age difference, calculated using machine learning from MRI images, significantly mediated the relationships between walking and knee pain impact (CI: -0.124; -0.013), walking and pain-severity (CI: -0.008; -0.001), total SPPB score and knee pain impact (CI: -0.232; -0.025), and total SPPB scores and pain-severity (CI: -0.019; -0.001). Brain-aging begins to explain the association between pain and physical performance, especially walking. This study supports the idea that a brain aging prediction can be calculated from shorter duration MRI sequences and possibly implemented in a clinical setting to be used to identify individuals with pain who are at risk for accelerated brain atrophy and increased likelihood of disability.
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Affiliation(s)
- Jessica A Peterson
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA; Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL 32610, USA
| | - Alisa Johnson
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA; Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL 32610, USA
| | - Chavier Laffitte Nordarse
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA; Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL 32610, USA
| | - Zhiguang Huo
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - James Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK; Psychology and Neuroscience, King's College London, Institute of Psychiatry, London, UK; Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Roger B Fillingim
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA; Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL 32610, USA
| | - Yenisel Cruz-Almeida
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA; Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL 32610, USA.
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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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Zhu JD, Tsai SJ, Lin CP, Lee YJ, Yang AC. Predicting aging trajectories of decline in brain volume, cortical thickness and fractional anisotropy in schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:1. [PMID: 36596800 PMCID: PMC9810255 DOI: 10.1038/s41537-022-00325-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 12/20/2022] [Indexed: 01/05/2023]
Abstract
Brain-age prediction is a novel approach to assessing deviated brain aging trajectories in different diseases. However, most studies have used an average brain age gap (BAG) of individuals with schizophrenia of different illness durations for comparison with healthy participants. Therefore, this study investigated whether declined brain structures as reflected by BAGs may be present in schizophrenia in terms of brain volume, cortical thickness, and fractional anisotropy across different illness durations. We used brain volume, cortical thickness, and fractional anisotropy as features to train three models from the training dataset. Three models were applied to predict brain ages in the hold-out test and schizophrenia datasets and calculate BAGs. We divided the schizophrenia dataset into multiple groups based on the illness duration using a sliding time window approach for ANCOVA analysis. The brain volume and cortical thickness models revealed that, in comparison with healthy controls, individuals with schizophrenia had larger BAGs across different illness durations, whereas the BAG in terms of fractional anisotropy did not differ from that of healthy controls after disease onset. Moreover, the BAG at the initial stage of schizophrenia was the largest in the cortical thickness model. In contrast, the BAG from approximately two decades after disease onset was the largest in the brain volume model. Our findings suggest that schizophrenia differentially affects the decline of different brain structures during the disease course. Moreover, different trends of decline in thickness and volume-based measures suggest a differential decline in dimensions of brain structure throughout the course of schizophrenia.
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Affiliation(s)
- Jun-Ding Zhu
- grid.260539.b0000 0001 2059 7017Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shih-Jen Tsai
- grid.260539.b0000 0001 2059 7017Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan ,grid.278247.c0000 0004 0604 5314Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Ching-Po Lin
- grid.260539.b0000 0001 2059 7017Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Ju Lee
- grid.28665.3f0000 0001 2287 1366Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Albert C. Yang
- grid.260539.b0000 0001 2059 7017Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan ,grid.260539.b0000 0001 2059 7017Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan ,grid.278247.c0000 0004 0604 5314Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
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24
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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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Matziorinis AM, Gaser C, Koelsch S. Is musical engagement enough to keep the brain young? Brain Struct Funct 2023; 228:577-588. [PMID: 36574049 PMCID: PMC9945036 DOI: 10.1007/s00429-022-02602-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 12/08/2022] [Indexed: 12/28/2022]
Abstract
Music-making and engagement in music-related activities have shown procognitive benefits for healthy and pathological populations, suggesting reductions in brain aging. A previous brain aging study, using Brain Age Gap Estimation (BrainAGE), showed that professional and amateur-musicians had younger appearing brains than non-musicians. Our study sought to replicate those findings and analyze if musical training or active musical engagement was necessary to produce an age-decelerating effect in a cohort of healthy individuals. We scanned 125 healthy controls and investigated if musician status, and if musical behaviors, namely active engagement (AE) and musical training (MT) [as measured using the Goldsmiths Musical Sophistication Index (Gold-MSI)], had effects on brain aging. Our findings suggest that musician status is not related to BrainAGE score, although involvement in current physical activity is. Although neither MT nor AE subscales of the Gold-MSI are predictive for BrainAGE scores, dispositional resilience, namely the ability to deal with challenge, is related to both musical behaviors and sensitivity to musical pleasure. While the study failed to replicate the findings in a previous brain aging study, musical training and active musical engagement are related to the resilience factor of challenge. This finding may reveal how such musical behaviors can potentially strengthen the brain's resilience to age, which may tap into a type of neurocognitive reserve.
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Affiliation(s)
- Anna Maria Matziorinis
- Department of Biological and Medical Psychology, University of Bergen, Jonas Lies Vei 91, 5009, Bergen, Norway.
| | - Christian Gaser
- Department of Neurology, Jena University Hospital, Am Klinikum 1, 07747, Jena, Germany
| | - Stefan Koelsch
- Department of Biological and Medical Psychology, University of Bergen, Jonas Lies Vei 91, 5009, Bergen, Norway
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Abram SV, Roach BJ, Hua JPY, Han LKM, Mathalon DH, Ford JM, Fryer SL. Advanced brain age correlates with greater rumination and less mindfulness in schizophrenia. Neuroimage Clin 2023; 37:103301. [PMID: 36586360 PMCID: PMC9830317 DOI: 10.1016/j.nicl.2022.103301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/05/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Individual variation in brain aging trajectories is linked with several physical and mental health outcomes. Greater stress levels, worry, and rumination correspond with advanced brain age, while other individual characteristics, like mindfulness, may be protective of brain health. Multiple lines of evidence point to advanced brain aging in schizophrenia (i.e., neural age estimate > chronological age). Whether psychological dimensions such as mindfulness, rumination, and perceived stress contribute to brain aging in schizophrenia is unknown. METHODS We estimated brain age from high-resolution anatomical scans in 54 healthy controls (HC) and 52 individuals with schizophrenia (SZ) and computed the brain predicted age difference (BrainAGE-diff), i.e., the delta between estimated brain age and chronological age. Emotional well-being summary scores were empirically derived to reflect individual differences in trait mindfulness, rumination, and perceived stress. Core analyses evaluated relationships between BrainAGE-diff and emotional well-being, testing for slopes differences across groups. RESULTS HC showed higher emotional well-being (greater mindfulness and less rumination/stress), relative to SZ. We observed a significant group difference in the relationship between BrainAge-diff and emotional well-being, explained by BrainAGE-diff negatively correlating with emotional well-being scores in SZ, and not in HC. That is, SZ with younger appearing brains (predicted age < chronological age) had emotional summary scores that were more like HC, a relationship that endured after accounting for several demographic and clinical variables. CONCLUSIONS These data reveal clinically relevant aspects of brain age heterogeneity among SZ and point to case-control differences in the relationship between advanced brain aging and emotional well-being.
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Affiliation(s)
- Samantha V Abram
- Sierra Pacific Mental Illness Research Education and Clinical Centers, San Francisco Veterans Affairs Medical Center, and the University of California, San Francisco, CA, United States; Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States; Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Brian J Roach
- Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States
| | - Jessica P Y Hua
- Sierra Pacific Mental Illness Research Education and Clinical Centers, San Francisco Veterans Affairs Medical Center, and the University of California, San Francisco, CA, United States; Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States; Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Laura K M Han
- Orygen, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Daniel H Mathalon
- Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States; Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Judith M Ford
- Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States; Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Susanna L Fryer
- Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States; Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States.
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Lu C, Li B, Zhang Q, Chen X, Pang Y, Lu F, Wu Y, Li M, He B, Chen H. An individual-level weighted artificial neural network method to improve the systematic bias in BrainAGE analysis. Cereb Cortex 2022; 33:6132-6138. [PMID: 36562996 DOI: 10.1093/cercor/bhac490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022] Open
Abstract
BrainAGE is a commonly used machine learning technique to measure the accelerated/delayed development pattern of human brain structure/function with neuropsychiatric disorders. However, recent studies have shown a systematic bias ("regression toward mean" effect) in the BrainAGE method, which indicates that the prediction error is not uniformly distributed across Chronological Ages: for the older individuals, the Brain Ages would be under-estimated but would be over-estimated for the younger individuals. In the present study, we propose an individual-level weighted artificial neural network method and apply it to simulation datasets (containing 5000 simulated subjects) and a real dataset (containing 135 subjects). Results show that compared with traditional machine learning methods, the individual-level weighted strategy can significantly reduce the "regression toward mean" effect, while the prediction performance can achieve the comparable level with traditional machine learning methods. Further analysis indicates that the sigmoid active function for artificial neural network shows better performance than the relu active function. The present study provides a novel strategy to reduce the "regression toward mean" effect of BrainAGE analysis, which is helpful to improve accuracy in exploring the atypical brain structure/function development pattern of neuropsychiatric disorders.
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Affiliation(s)
- Chunying Lu
- School of Medicine, Guizhou University, Jiaxiu Road, Huaxi District, Guiyang, Guizhou, 550025, PRChina
| | - Bowen Li
- School of Medicine, Guizhou University, Jiaxiu Road, Huaxi District, Guiyang, Guizhou, 550025, PRChina
| | - Qianyue Zhang
- School of Medicine, Guizhou University, Jiaxiu Road, Huaxi District, Guiyang, Guizhou, 550025, PRChina
| | - Xue Chen
- School of Medicine, Guizhou University, Jiaxiu Road, Huaxi District, Guiyang, Guizhou, 550025, PRChina
| | - Yajing Pang
- School of Electrical Engineering, Zhengzhou University, Sience Avenue, Gaoxin District, Zhengzhou, Henan, 450001, PRChina
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Yingmenkou Road, Jinniu District, Chengdu, Sichuan, 611731, PRChina
| | - Yifei Wu
- School of Medicine, Guizhou University, Jiaxiu Road, Huaxi District, Guiyang, Guizhou, 550025, PRChina
| | - Min Li
- School of Medicine, Guizhou University, Jiaxiu Road, Huaxi District, Guiyang, Guizhou, 550025, PRChina
| | - Bifang He
- School of Medicine, Guizhou University, Jiaxiu Road, Huaxi District, Guiyang, Guizhou, 550025, PRChina
| | - Heng Chen
- School of Medicine, Guizhou University, Jiaxiu Road, Huaxi District, Guiyang, Guizhou, 550025, PRChina
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Peterson JA, Strath LJ, Nodarse CL, Rani A, Huo Z, Meng L, Yoder S, Cole JH, Foster TC, Fillingim RB, Cruz-Almeida Y. Epigenetic Aging Mediates the Association between Pain Impact and Brain Aging in Middle to Older Age Individuals with Knee Pain. Epigenetics 2022; 17:2178-2187. [PMID: 35950599 PMCID: PMC9665126 DOI: 10.1080/15592294.2022.2111752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 08/05/2022] [Indexed: 02/01/2023] Open
Abstract
Chronic musculoskeletal pain is a health burden that may accelerate the aging process. Accelerated brain aging and epigenetic aging have separately been observed in those with chronic pain. However, it is unknown whether these biological markers of aging are associated with each other in those with chronic pain. We aimed to explore the association of epigenetic aging and brain aging in middle-to-older age individuals with varying degrees of knee pain. Participants (57.91 ± 8.04 y) with low impact knee pain (n = 95), high impact knee pain (n = 53), and pain-free controls (n = 26) completed self-reported pain, a blood draw, and an MRI scan. We used an epigenetic clock previously associated with knee pain (DNAmGrimAge), the subsequent difference of predicted epigenetic and brain age from chronological age (DNAmGrimAge-Difference and Brain-PAD, respectively). There was a significant main effect for pain impact group (F (2,167) = 3.847, P = 0.023, r o t a t i o n a l e n e r g y = 1 / 2 I ω 2 = 0.038, ANCOVA) on Brain-PAD and DNAmGrimAge-difference (F (2,167) = 6.800, P = 0.001, I = m k 2 = 0.075, ANCOVA) after controlling for covariates. DNAmGrimAge-Difference and Brain-PAD were modestly correlated (r =0.198; P =0.010). Exploratory analysis revealed that DNAmGrimAge-difference mediated GCPS pain impact, GCPS pain severity, and pain-related disability scores on Brain-PAD. Based upon the current study findings, we suggest that pain could be a driver for accelerated brain aging via epigenome interactions.
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Affiliation(s)
- Jessica A. Peterson
- Pain Research & Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL, USA
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, USA
| | - Larissa J. Strath
- Pain Research & Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL, USA
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, USA
| | - Chavier Laffitte Nodarse
- Pain Research & Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL, USA
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, USA
| | - Asha Rani
- Department of Neuroscience, McKnight Brain Institute, Gainesville, Florida, USA
| | - Zhiguang Huo
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Lingsong Meng
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Sean Yoder
- Molecular Genomics Core Facility, Moffit Cancer Center, Tampa, FL, USA
| | - James H. Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, England
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, England
| | - Thomas C. Foster
- Genetics and Genomics Program, University of Florida, Gainesville, FL, USA
| | - Roger B. Fillingim
- Pain Research & Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL, USA
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, USA
| | - Yenisel Cruz-Almeida
- Pain Research & Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL, USA
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, USA
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Gallucci J, Pomarol-Clotet E, Voineskos AN, Guerrero-Pedraza A, Alonso-Lana S, Vieta E, Salvador R, Hawco C. Longer illness duration is associated with greater individual variability in functional brain activity in Schizophrenia, but not bipolar disorder. Neuroimage Clin 2022; 36:103269. [PMID: 36451371 PMCID: PMC9723315 DOI: 10.1016/j.nicl.2022.103269] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/24/2022] [Accepted: 11/14/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Individuals with schizophrenia exhibit greater inter-patient variability in functional brain activity during neurocognitive task performance. Some studies have shown associations of age and illness duration with brain function; however, the association of these variables with variability in brain function activity is not known. In order to better understand the progressive effects of age and illness duration across disorders, we examined the relationship with individual variability in brain activity. METHODS Neuroimaging and behavioural data were extracted from harmonized datasets collectively including 212 control participants, 107 individuals with bipolar disorder, and 232 individuals with schizophrenia (total n = 551). Functional activity in response to an N-back working memory task (2-back vs 1-back) was examined. Individual variability was quantified via the correlational distance of fMRI activity between participants; mean correlational distance of one participant in relation to all others was defined as a 'variability score'. RESULTS Greater individual variability was found in the schizophrenia group compared to the bipolar disorder and control groups (p = 1.52e-09). Individual variability was significantly associated with aging (p = 0.027), however, this relationship was not different across diagnostic groups. In contrast, in the schizophrenia sample only, a longer illness duration was associated with increased variability (p = 0.027). CONCLUSION An increase in variability was observed in the schizophrenia group related to illness duration, beyond the effects of normal aging, implying illness-related deterioration of cognitive networks. This has clinical implications for considering long-term trajectories in schizophrenia and progressive neural and cognitive decline which may be amiable to novel treatments.
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Affiliation(s)
- Julia Gallucci
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Catalonia, Spain,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Catalonia, Spain
| | - Aristotle N. Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Amalia Guerrero-Pedraza
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Catalonia, Spain,Benito Menni Complex Assistencial en Salut Mental, Barcelona, Catalonia, Spain
| | - Silvia Alonso-Lana
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Catalonia, Spain,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Catalonia, Spain,Research Centre and Memory Clinic, Fundació ACE Institut Català de Neurociències Aplicades – Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Eduard Vieta
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Catalonia, Spain,Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, Barcelona, Catalonia, Spain
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Catalonia, Spain,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Catalonia, Spain
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada,Corresponding authors at: Centre for Addiction and Mental Health, 250 College Street, Toronto, ON, Spain.
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30
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Sone D, Beheshti I. Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry. J Pers Med 2022; 12:jpm12111850. [PMID: 36579560 PMCID: PMC9695293 DOI: 10.3390/jpm12111850] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/01/2022] [Accepted: 11/01/2022] [Indexed: 11/10/2022] Open
Abstract
It is now possible to estimate an individual's brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, Jikei University School of Medicine, Tokyo 105-8461, Japan
- Correspondence: ; Tel.: +81-03-3433
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB R3E 3P5, Canada
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31
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Tesli N, Bell C, Hjell G, Fischer-Vieler T, I Maximov I, Richard G, Tesli M, Melle I, Andreassen OA, Agartz I, Westlye LT, Friestad C, Haukvik UK, Rokicki J. The age of violence: Mapping brain age in psychosis and psychopathy. Neuroimage Clin 2022; 36:103181. [PMID: 36088844 PMCID: PMC9474919 DOI: 10.1016/j.nicl.2022.103181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 07/31/2022] [Accepted: 08/30/2022] [Indexed: 12/14/2022]
Abstract
Young chronological age is one of the strongest predictors for antisocial behaviour in the general population and for violent offending in individuals with psychotic disorders. An individual's age can be predicted with high accuracy using neuroimaging and machine-learning. The deviation between predicted and chronological age, i.e., brain age gap (BAG) has been suggested to reflect brain health, likely relating partly to neurodevelopmental and aging-related processes and specific disease mechanisms. Higher BAG has been demonstrated in patients with psychotic disorders. However, little is known about the brain-age in violent offenders with psychosis and the possible associations with psychopathy traits. We estimated brain-age in 782 male individuals using T1-weighted MRI scans. Three machine learning models (random forest, extreme gradient boosting with and without hyper parameter tuning) were first trained and tested on healthy controls (HC, n = 586). The obtained BAGs were compared between HC and age matched violent offenders with psychosis (PSY-V, n = 38), violent offenders without psychosis (NPV, n = 20) and non-violent psychosis patients (PSY-NV, n = 138). We ran additional comparisons between BAG of PSY-V and PSY-NV and associations with Positive and Negative Syndrome Scale (PANSS) total score as a measure of psychosis symptoms. Psychopathy traits in the violence groups were assessed with Psychopathy Checklist-revised (PCL-R) and investigated for associations with BAG. We found significantly higher BAG in PSY-V compared with HC (4.9 years, Cohen'sd = 0.87) and in PSY-NV compared with HC (2.7 years, d = 0.41). Total PCL-R scores were negatively associated with BAG in the violence groups (d = 1.17, p < 0.05). Additionally, there was a positive association between psychosis symptoms and BAG in the psychosis groups (d = 1.12, p < 0.05). While the significant BAG differences related to psychosis and not violence suggest larger BAG for psychosis, the negative associations between BAG and psychopathy suggest a complex interplay with psychopathy traits. This proof-of-concept application of brain age prediction in severe mental disorders with a history of violence and psychopathy traits should be tested and replicated in larger samples.
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Affiliation(s)
- Natalia Tesli
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Christina Bell
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Gabriela Hjell
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry, Østfold Hospital Trust, Graalum, Norway
| | - Thomas Fischer-Vieler
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Vestre Viken Hospital Trust, Drammen, Norway
| | - Ivan I Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Genevieve Richard
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Martin Tesli
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway; Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Ingrid Melle
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Adult Psychiatry, Institute of Clinical Medicine, University of Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Christine Friestad
- Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway; University College of Norwegian Correctional Service, Oslo, Norway
| | - Unn K Haukvik
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychiatry, Oslo University Hospital, Oslo, Norway; Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Jaroslav Rokicki
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway.
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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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33
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Klaus F, Nguyen TT, Thomas ML, Liou SC, Soontornniyomkij B, Mitchell K, Daly R, Sutherland AN, Jeste DV, Eyler LT. Peripheral inflammation levels associated with degree of advanced brain aging in schizophrenia. Front Psychiatry 2022; 13:966439. [PMID: 36032250 PMCID: PMC9412908 DOI: 10.3389/fpsyt.2022.966439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 07/26/2022] [Indexed: 11/23/2022] Open
Abstract
Brain structural abnormalities have been demonstrated in schizophrenia (SZ); these resemble those seen in typical aging, but are seen at younger ages. Furthermore, SZ is associated with accelerated global brain aging, as measured by brain structure-based brain predicted age difference (Brain-PAD). High heterogeneity exists in the degree of brain abnormalities in SZ, and individual differences may be related to levels of peripheral inflammation and may relate to cognitive deficits and negative symptoms. The goal of our study was to investigate the relationship between brain aging, peripheral inflammation, and symptoms of SZ. We hypothesized older brain-PAD in SZ vs. healthy comparison (HC) participants, as well as positive relationships of brain-PAD with peripheral inflammation markers and symptoms in SZ. We analyzed data from two cross-sectional studies in SZ (n = 26; M/F: 21/5) and HC (n = 28; 20/8) (22-64 years). Brain-PAD was calculated using a previously validated Gaussian process regression model applied to raw T1-weighted MRI data. Plasma levels of inflammatory biomarkers (CRP, Eotaxin, Fractalkine, IP10, IL6, IL10, ICAM1, IFNγ, MCP1, MIP1β, SAA, TNFα, VEGF, VCAM1) and cognitive and negative symptoms were assessed. We observed a higher brain-PAD in SZ vs. HC, and advanced brain age relative to chronological age was related to higher peripheral levels of TNFα in the overall group and in the SZ group; other inflammatory markers were not related to brain-PAD. Within the SZ group, we observed no association between cognitive or negative symptoms and brain-PAD. These results support our hypothesis of advanced brain aging in SZ. Furthermore, our findings on the relationship of the pro-inflammatory cytokine TNFα with higher brain-PAD of SZ are relevant to explain heterogeneity of brain ages in SZ, but we did not find strong evidence for cognitive or negative symptom relationships with brain-PAD.
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Affiliation(s)
- Federica Klaus
- Department of Psychiatry, UC San Diego, La Jolla, CA, United States
- VA San Diego Healthcare System, La Jolla, CA, United States
| | - Tanya T. Nguyen
- Department of Psychiatry, UC San Diego, La Jolla, CA, United States
- VA San Diego Healthcare System, La Jolla, CA, United States
| | - Michael L. Thomas
- Department of Psychology, Colorado State University, Fort Collins, CO, United States
| | - Sharon C. Liou
- Department of Psychiatry, UC San Diego, La Jolla, CA, United States
| | | | - Kyle Mitchell
- VA San Diego Healthcare System, La Jolla, CA, United States
| | - Rebecca Daly
- Department of Psychiatry, UC San Diego, La Jolla, CA, United States
- Sam and Rose Stein Institute for Research on Aging, University of California, San Diego, La Jolla, CA, United States
| | - Ashley N. Sutherland
- Department of Psychiatry, UC San Diego, La Jolla, CA, United States
- VA San Diego Healthcare System, La Jolla, CA, United States
| | - Dilip V. Jeste
- Department of Psychiatry, UC San Diego, La Jolla, CA, United States
- Sam and Rose Stein Institute for Research on Aging, University of California, San Diego, La Jolla, CA, United States
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Lisa T. Eyler
- Department of Psychiatry, UC San Diego, La Jolla, CA, United States
- VA San Diego Healthcare System, La Jolla, CA, United States
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Huisman SI, van der Boog ATJ, Cialdella F, Verhoeff JJC, David S. Quantifying the post-radiation accelerated brain aging rate in glioma patients with deep learning. Radiother Oncol 2022; 175:18-25. [PMID: 35963398 DOI: 10.1016/j.radonc.2022.08.002] [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: 02/21/2022] [Revised: 07/12/2022] [Accepted: 08/01/2022] [Indexed: 10/15/2022]
Abstract
BACKGROUND AND PURPOSE Changes of healthy appearing brain tissue after radiotherapy (RT) have been previously observed. Patients undergoing RT may have a higher risk of cognitive decline, leading to a reduced quality of life. The experienced tissue atrophy is similar to the effects of normal aging in healthy individuals. We propose a new way to quantify tissue changes after cranial RT as accelerated brain aging using the BrainAGE framework. MATERIALS AND METHODS BrainAGE was applied to longitudinal MRI scans of 32 glioma patients. Utilizing a pre-trained deep learning model, brain age is estimated for all patients' pre-radiotherapy planning and follow-up MRI scans to acquire a quantification of the changes occurring in the brain over time. Saliency maps were extracted from the model to spatially identify which areas of the brain the deep learning model weighs highest for predicting age. The predicted ages from the deep learning model were used in a linear mixed effects model to quantify aging of patients after RT. RESULTS The linear mixed effects model resulted in an accelerated aging rate of 2.78 years/year, a significant increase over a normal aging rate of 1 (p < 0.05, confidence interval = 2.54-3.02). Furthermore, the saliency maps showed numerous anatomically well-defined areas, e.g.: Heschl's gyrus among others, determined by the model as important for brain age prediction. CONCLUSION We found that patients undergoing RT are affected by significant post-radiation accelerated aging, with several anatomically well-defined areas contributing to this aging. The estimated brain age could provide a method for quantifying quality of life post-radiotherapy.
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Affiliation(s)
- Selena I Huisman
- Department of Radiation Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands.
| | | | - Fia Cialdella
- Department of Radiation Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands; Department of Medical Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands.
| | - Joost J C Verhoeff
- Department of Radiation Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands.
| | - Szabolcs David
- Department of Radiation Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands.
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Leonardsen EH, Peng H, Kaufmann T, Agartz I, Andreassen OA, Celius EG, Espeseth T, Harbo HF, Høgestøl EA, Lange AMD, Marquand AF, Vidal-Piñeiro D, Roe JM, Selbæk G, Sørensen Ø, Smith SM, Westlye LT, Wolfers T, Wang Y. Deep neural networks learn general and clinically relevant representations of the ageing brain. Neuroimage 2022; 256:119210. [PMID: 35462035 PMCID: PMC7614754 DOI: 10.1016/j.neuroimage.2022.119210] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/16/2022] [Accepted: 04/11/2022] [Indexed: 12/17/2022] Open
Abstract
The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data - the brain age delta - has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in data acquisition are vital. To this end, we compiled raw structural magnetic resonance images into one of the largest and most diverse datasets assembled (n=53542), and trained convolutional neural networks (CNNs) to predict age. We achieved state-of-the-art performance on unseen data from unknown scanners (n=2553), and showed that higher brain age delta is associated with diabetes, alcohol intake and smoking. Using transfer learning, the intermediate representations learned by our model complemented and partly outperformed brain age delta in predicting common brain disorders. Our work shows we can achieve generalizable and biologically plausible brain age predictions using CNNs trained on heterogeneous datasets, and transfer them to clinical use cases.
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Affiliation(s)
- Esten H Leonardsen
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Han Peng
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Germany
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Elisabeth Gulowsen Celius
- Department of Neurology, Oslo University Hospital, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Thomas Espeseth
- Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychology, Bjørknes University College, Oslo, Norway
| | - Hanne F Harbo
- Department of Neurology, Oslo University Hospital, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Einar A Høgestøl
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Neurology, Oslo University Hospital, Norway
| | - Ann-Marie de Lange
- Department of Psychology, University of Oslo, Oslo, Norway; LREN, Centre for Research in Neurosciences-Department of Clinical Neurosciences, CHUV and University of Lausanne, Lausanne, Switzerland; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Andre F Marquand
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
| | | | - James M Roe
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Geir Selbæk
- Norwegian National Advisory Unit on Aging and Health, Vestfold Hospital Trust, Tønsberg, Norway; Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Yunpeng Wang
- Department of Psychology, University of Oslo, Oslo, Norway
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Chen CL, Hwang TJ, Tung YH, Yang LY, Hsu YC, Liu CM, Lin YT, Hsieh MH, Liu CC, Chien YL, Hwu HG, Tseng WYI. Detection of advanced brain aging in schizophrenia and its structural underpinning by using normative brain age metrics. Neuroimage Clin 2022; 34:103003. [PMID: 35413648 PMCID: PMC9018160 DOI: 10.1016/j.nicl.2022.103003] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 03/24/2022] [Accepted: 04/04/2022] [Indexed: 11/28/2022]
Abstract
Novel metrics are proposed using the brain age paradigm with normative modeling. Normative brain age is validated to reveal advanced aging in schizophrenia. Men with schizophrenia have older brain age than women with the disorder. The brain age in white matter is positively associated with the negative symptom. The precuneus and uncinate fasciculus are markedly related to the advanced aging.
Conceptualizing mental disorders as deviations from normative functioning provides a statistical perspective for understanding the individual heterogeneity underlying psychiatric disorders. To broaden the understanding of the idiosyncrasy of brain aging in schizophrenia, we introduced an imaging-derived brain age paradigm combined with normative modeling as novel brain age metrics. We constructed brain age models based on GM, WM, and their combination (multimodality) features of 482 normal participants. The normalized predicted age difference (nPAD) was estimated in 147 individuals with schizophrenia and their 130 demographically matched controls through normative models of brain age metrics and compared between the groups. Regression analyses were also performed to investigate the associations of nPAD with illness duration, onset age, symptom severity, and intelligence quotient. Finally, regional contributions to advanced brain aging in schizophrenia were investigated. The results showed that the individuals exhibited significantly higher nPAD (P < 0.001), indicating advanced normative brain age than the normal controls in GM, WM, and multimodality models. The nPAD measure based on WM was positively associated with the negative symptom score (P = 0.009), and negatively associated with the intelligence quotient (P = 0.039) and onset age (P = 0.006). The imaging features that contributed to nPAD mostly involved the prefrontal, temporal, and parietal lobes, especially the precuneus and uncinate fasciculus. This study demonstrates that normative brain age metrics could detect advanced brain aging and associated clinical and neuroanatomical features in schizophrenia. The proposed nPAD measures may be useful to investigate aberrant brain aging in mental disorders and their brain-phenotype relationships.
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Affiliation(s)
- Chang-Le Chen
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Tzung-Jeng Hwang
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
| | - Yu-Hung Tung
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Li-Ying Yang
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan
| | | | - Chih-Min Liu
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
| | - Yi-Tin Lin
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
| | - Ming-Hsien Hsieh
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
| | - Chen-Chung Liu
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
| | - Yi-Ling Chien
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
| | - Hai-Gwo Hwu
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
| | - Wen-Yih Isaac Tseng
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan; AcroViz Inc., Taipei, Taiwan; Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan; Molecular Imaging Center, National Taiwan University, Taipei, Taiwan.
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Dias MDF, de Carvalho P, Duarte JV, Castelo-Branco M. Deformation Fields: A new source of information to predict Brain Age. J Neural Eng 2022; 19. [PMID: 35576908 DOI: 10.1088/1741-2552/ac7003] [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: 11/30/2021] [Accepted: 05/16/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The modelling of healthy ageing critically requires the identification of methods that detect subtle changes in this process. In the last few years multiple machine learning models have been proposed that learn age patterns from Magnetic Resonance Images (MRI). Current standard information sources rely on local volumetric information of brain tissues, namely white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF). Information about patterns of brain deformation remains underexplored. In this paper an assessment is performed to understand better the predictive value of the deformation fields. APPROACH A shallow approach was used to compare the predictive value of deformation fields with the brain tissues (GM, WM and CSF). Images were compressed into a lower dimension space using Principal Components Analysis and then, a Relevant Vector Regression (RVR) learned the age patterns from the components. A model was trained per modality (deformation fields, GM, WM and CSF) and the performance between the models was compared. To evaluate whether the deformation fields increased the predictive power of GM, a model fusion approach was explored in which the final estimator was an RVR. Each model was validated using a cross-validation approach and was also evaluated on an external dataset. MAIN RESULTS We found that models trained with deformation patterns have higher predictive value than the ones trained with WM or CSF. Furthermore, deformation fields had a significantly better performance on the test set and also yield the lower difference between the validation and test set. Moreover, the predictions based on the combination of deformation patterns with GM volume yields better results than GM volumetric information alone. SIGNIFICANCE These findings suggest that deformation fields have a higher predictive power than WM and CSF and are robustly invariant across a set of confounding variables. Therefore, deformation fields should be considered in BrainAge models.
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Affiliation(s)
- Maria de Fátima Dias
- CIBIT, ICNAS, University of Coimbra, Universidade de Coimbra, Pólo das Ciências da Saúde Azinhaga de, R. Santa Comba, Coimbra, 3000-548, PORTUGAL
| | - Paulo de Carvalho
- University of Coimbra, Pólo II, 3030-290, Coimbra, 3004-504, PORTUGAL
| | - João Valente Duarte
- CIBIT, ICNAS, University of Coimbra, Universidade de Coimbra, Pólo das Ciências da Saúde Azinhaga de, R. Santa Comba, Coimbra, 3000-548, PORTUGAL
| | - Miguel Castelo-Branco
- CIBIT, ICNAS, University of Coimbra, Universidade de Coimbra, Pólo das Ciências da Saúde Azinhaga de, R. Santa Comba, Coimbra, 3000-548, PORTUGAL
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Zanelli J, Reichenberg A, Sandin S, Morgan C, Dazzan P, Pilecka I, Marques TR, Morgan K, Young AH, Mollon J. Dynamic and Static Cognitive Deficits in Schizophrenia and Bipolar Disorder After the First Episode. Schizophr Bull 2022; 48:590-598. [PMID: 35064259 PMCID: PMC9077411 DOI: 10.1093/schbul/sbab150] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Few studies have comprehensively examined the profile of cognitive functioning in first episode psychosis patients throughout the lifespan, and from first episode to chronic stage. We assessed functioning in general and specific cognitive functions, comparing both schizophrenia (N = 64) and bipolar I (N = 19) patients to controls (N = 103). Participants were from a population-based, case-control study of first episode psychosis patients, who were followed prospectively up to 10 years post first admission. A cognitive battery was administered at baseline and follow-up. By combining longitudinal and cross-sectional data, we were able to examine the cognitive profile of patients and controls throughout the entire age range of our sample (16-65). Schizophrenia patients exhibited widespread declines in IQ, executive function, visual memory, language ability, and verbal knowledge. However, the ages at which these declines occurred differed between functions. Deficits in verbal memory, working memory, processing speed, and visuospatial ability, on the other hand, were present at the first episode, and remained relatively static thereafter. Bipolar I patients also showed declines in IQ, verbal knowledge, and language ability, albeit at different ages to schizophrenia patients and only in verbal functions. Deficits on measures of verbal memory, processing speed, and executive function remained relatively static. Thus, both schizophrenia and bipolar I patients experienced cognitive decline in general and specific functions after the first episode, but the age at which these declines occurred differed between disorder and function. Cognitive remediation efforts may be most fruitful when targeting individual functions during specific time periods throughout adulthood.
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Affiliation(s)
- Jolanta Zanelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Department of Psychiatry, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Abraham Reichenberg
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sven Sandin
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Craig Morgan
- Centre for Public Mental Health, Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Izabela Pilecka
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Tiago Reis Marques
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Kevin Morgan
- Department of Psychology, University of Westminster, London, UK
| | - Allan H Young
- Centre for Public Mental Health, Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Josephine Mollon
- Department of Psychiatry, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
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Demro C, Shen C, Hendrickson TJ, Arend JL, Disner SG, Sponheim SR. Advanced Brain-Age in Psychotic Psychopathology: Evidence for Transdiagnostic Neurodevelopmental Origins. Front Aging Neurosci 2022; 14:872867. [PMID: 35527740 PMCID: PMC9074783 DOI: 10.3389/fnagi.2022.872867] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/11/2022] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia is characterized by abnormal brain structure such as global reductions in gray matter volume. Machine learning models trained to estimate the age of brains from structural neuroimaging data consistently show advanced brain-age to be associated with schizophrenia. Yet, it is unclear whether advanced brain-age is specific to schizophrenia compared to other psychotic disorders, and whether evidence that brain structure is "older" than chronological age actually reflects neurodevelopmental rather than atrophic processes. It is also unknown whether advanced brain-age is associated with genetic liability for psychosis carried by biological relatives of people with schizophrenia. We used the Brain-Age Regression Analysis and Computation Utility Software (BARACUS) prediction model and calculated the residualized brain-age gap of 332 adults (163 individuals with psychotic disorders: 105 schizophrenia, 17 schizoaffective disorder, 41 bipolar I disorder with psychotic features; 103 first-degree biological relatives; 66 controls). The model estimated advanced brain-ages for people with psychosis in comparison to controls and relatives, with no differences among psychotic disorders or between relatives and controls. Specifically, the model revealed an enlarged brain-age gap for schizophrenia and bipolar disorder with psychotic features. Advanced brain-age was associated with lower cognitive and general functioning in the full sample. Among relatives, cognitive performance and schizotypal symptoms were related to brain-age gap, suggesting that advanced brain-age is associated with the subtle expressions associated with psychosis. Exploratory longitudinal analyses suggested that brain aging was not accelerated in individuals with a psychotic disorder. In sum, we found that people with psychotic disorders, irrespective of specific diagnosis or illness severity, show indications of non-progressive, advanced brain-age. These findings support a transdiagnostic, neurodevelopmental formulation of structural brain abnormalities in psychotic psychopathology.
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Affiliation(s)
- Caroline Demro
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | - Chen Shen
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | | | - Jessica L. Arend
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | - Seth G. Disner
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
- Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, United States
| | - Scott R. Sponheim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
- Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, United States
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Huang J, Ke P, Chen X, Li S, Zhou J, Xiong D, Huang Y, Li H, Ning Y, Duan X, Li X, Zhang W, Wu F, Wu K. Multimodal Magnetic Resonance Imaging Reveals Aberrant Brain Age Trajectory During Youth in Schizophrenia Patients. Front Aging Neurosci 2022; 14:823502. [PMID: 35309897 PMCID: PMC8929292 DOI: 10.3389/fnagi.2022.823502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 01/18/2022] [Indexed: 11/13/2022] Open
Abstract
Accelerated brain aging had been widely reported in patients with schizophrenia (SZ). However, brain aging trajectories in SZ patients have not been well-documented using three-modal magnetic resonance imaging (MRI) data. In this study, 138 schizophrenia patients and 205 normal controls aged 20–60 were included and multimodal MRI data were acquired for each individual, including structural MRI, resting state-functional MRI and diffusion tensor imaging. The brain age of each participant was estimated by features extracted from multimodal MRI data using linear multiple regression. The correlation between the brain age gap and chronological age in SZ patients was best fitted by a positive quadratic curve with a peak chronological age of 47.33 years. We used the peak to divide the subjects into a youth group and a middle age group. In the normal controls, brain age matched chronological age well for both the youth and middle age groups, but this was not the case for schizophrenia patients. More importantly, schizophrenia patients exhibited increased brain age in the youth group but not in the middle age group. In this study, we aimed to investigate brain aging trajectories in SZ patients using multimodal MRI data and revealed an aberrant brain age trajectory in young schizophrenia patients, providing new insights into the pathophysiological mechanisms of schizophrenia.
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Affiliation(s)
- Jiayuan Huang
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
| | - Pengfei Ke
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Xiaoyi Chen
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
| | - Shijia Li
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Jing Zhou
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Dongsheng Xiong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Yuanyuan Huang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Hehua Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Xujun Duan
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Wensheng Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- *Correspondence: Fengchun Wu,
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
- Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou, China
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Kai Wu,
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Angebrandt A, Abulseoud OA, Kisner M, Diazgranados N, Momenan R, Yang Y, Stein EA, Ross TJ. Dose-dependent relationship between social drinking and brain aging. Neurobiol Aging 2022; 111:71-81. [PMID: 34973470 PMCID: PMC8929531 DOI: 10.1016/j.neurobiolaging.2021.11.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 11/18/2021] [Accepted: 11/26/2021] [Indexed: 12/25/2022]
Abstract
Low-level alcohol consumption is commonly perceived as being inconsequential or even beneficial for overall health, with some reports suggesting that it may protect against dementia or cardiovascular risks. However, these potential benefits do not preclude the concurrent possibility of negative health outcomes related to alcohol consumption. To examine whether casual, non-heavy drinking is associated with premature brain aging, we utilized the Brain-Age Regression Analysis and Computational Utility Software package to predict brain age in a community sample of adults [n = 240, mean age 35.1 (±10.7) years, 48% male, 49% African American]. Accelerated brain aging was operationalized as the difference between predicted and chronological age ("brain age gap"). Multiple regression analysis revealed a significant association between previous 90-day alcohol consumption and brain age gap (β = 0.014, p = 0.023). We replicated these results in an independent cohort [n = 231 adults, mean age 34.3 (±11.1) years, 55% male, 28% African American: β = 0.014, p = 0.002]. Our results suggest that even low-level alcohol consumption is associated with premature brain aging. The clinical significance of these findings remains to be investigated.
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Affiliation(s)
- Alexanndra Angebrandt
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA
| | - Osama A. Abulseoud
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA,Department of Psychiatry and Psychology, Mayo Clinic, Phoenix, AZ, USA,Corresponding author at: Department of Psychiatry and Psychology, Mayo Clinic, 5777 E Mayo Blvd., Phoenix, AZ 85054, USA. Phone: 480-301-8297, Fax: 480-301-6258. (O.A. Abulseoud)
| | - Mallory Kisner
- Clinical NeuroImaging Research Core, Intramural Research Program, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | - Nancy Diazgranados
- Office of Clinical Director, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | - Reza Momenan
- Clinical NeuroImaging Research Core, Intramural Research Program, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | - Yihong Yang
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA
| | - Elliot A. Stein
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA
| | - Thomas J. Ross
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA,Corresponding author at: Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, 251 Bayview Blvd, Baltimore, MD 21244, USA. Phone 443-740-2645, Fax 443-740-2734. (T.J. Ross)
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Haas SS, Ge R, Sanford N, Modabbernia A, Reichenberg A, Whalley HC, Kahn RS, Frangou S. Accelerated Global and Local Brain Aging Differentiate Cognitively Impaired From Cognitively Spared Patients With Schizophrenia. Front Psychiatry 2022; 13:913470. [PMID: 35815015 PMCID: PMC9257006 DOI: 10.3389/fpsyt.2022.913470] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/30/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Accelerated aging has been proposed as a mechanism underlying the clinical and cognitive presentation of schizophrenia. The current study extends the field by examining both global and regional patterns of brain aging in schizophrenia, as inferred from brain structural data, and their association with cognitive and psychotic symptoms. METHODS Global and local brain-age-gap-estimates (G-brainAGE and L-brainAGE) were computed using a U-Net Model from T1-weighted structural neuroimaging data from 84 patients (aged 16-35 years) with early-stage schizophrenia (illness duration <5 years) and 1,169 healthy individuals (aged 16-37 years). Multidomain cognitive data from the patient sample were submitted to Heterogeneity through Discriminative Analysis (HYDRA) to identify cognitive clusters. RESULTS HYDRA classified patients into a cognitively impaired cluster (n = 69) and a cognitively spared cluster (n = 15). Compared to healthy individuals, G-brainAGE was significantly higher in the cognitively impaired cluster (+11.08 years) who also showed widespread elevation in L-brainAGE, with the highest deviance observed in frontal and temporal regions. The cognitively spared cluster showed a moderate increase in G-brainAGE (+8.94 years), and higher L-brainAGE localized in the anterior cingulate cortex. Psychotic symptom severity in both clusters showed a positive but non-significant association with G-brainAGE. DISCUSSION Accelerated aging in schizophrenia can be detected at the early disease stages and appears more closely associated with cognitive dysfunction rather than clinical symptoms. Future studies replicating our findings in multi-site cohorts with larger numbers of participants are warranted.
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Affiliation(s)
- Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Ruiyang Ge
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Nicole Sanford
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Amirhossein Modabbernia
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Abraham Reichenberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Heather C Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, United States.,Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
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43
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Ballester PL, Romano MT, de Azevedo Cardoso T, Hassel S, Strother SC, Kennedy SH, Frey BN. Brain age in mood and psychotic disorders: a systematic review and meta-analysis. Acta Psychiatr Scand 2022; 145:42-55. [PMID: 34510423 DOI: 10.1111/acps.13371] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 09/05/2021] [Accepted: 09/07/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To evaluate whether accelerated brain aging occurs in individuals with mood or psychotic disorders. METHODS A systematic review following PRISMA guidelines was conducted. A meta-analysis was then performed to assess neuroimaging-derived brain age gap in three independent groups: (1) schizophrenia and first-episode psychosis, (2) major depressive disorder, and (3) bipolar disorder. RESULTS A total of 18 papers were included. The random-effects model meta-analysis showed a significantly increased neuroimaging-derived brain age gap relative to age-matched controls for the three major psychiatric disorders, with schizophrenia (3.08; 95%CI [2.32; 3.85]; p < 0.01) presenting the largest effect, followed by bipolar disorder (1.93; [0.53; 3.34]; p < 0.01) and major depressive disorder (1.12; [0.41; 1.83]; p < 0.01). The brain age gap was larger in older compared to younger individuals. CONCLUSION Individuals with mood and psychotic disorders may undergo a process of accelerated brain aging reflected in patterns captured by neuroimaging data. The brain age gap tends to be more pronounced in older individuals, indicating a possible cumulative biological effect of illness burden.
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Affiliation(s)
- Pedro L Ballester
- Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada
| | - Maria T Romano
- Integrated Science Undergraduate Program, McMaster University, Hamilton, Ontario, Canada
| | - Taiane de Azevedo Cardoso
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Sidney H Kennedy
- Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada.,Mood Disorders Treatment and Research Centre, and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Ontario, Canada
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44
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Bøstrand SM, Vaher K, de Nooij L, Harris MA, Cole JH, Cox SR, Marioni RE, McCartney DL, Walker RM, McIntosh AM, Evans KL, Whalley HC, Wootton RE, Clarke TK. Associations between alcohol use and accelerated biological ageing. Addict Biol 2022; 27:e13100. [PMID: 34636470 PMCID: PMC7614236 DOI: 10.1111/adb.13100] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 08/31/2021] [Accepted: 09/13/2021] [Indexed: 12/12/2022]
Abstract
Harmful alcohol use is a leading cause of premature death and is associated with age-related disease. Biological ageing is highly variable between individuals and may deviate from chronological ageing, suggesting that biomarkers of biological ageing (derived from DNA methylation or brain structural measures) may be clinically relevant. Here, we investigated the relationships between alcohol phenotypes and both brain and DNA methylation age estimates. First, using data from UK Biobank and Generation Scotland, we tested the association between alcohol consumption (units/week) or hazardous use (Alcohol Use Disorders Identification Test [AUDIT] scores) and accelerated brain and epigenetic ageing in 20,258 and 8051 individuals, respectively. Second, we used Mendelian randomisation (MR) to test for a causal effect of alcohol consumption levels and alcohol use disorder (AUD) on biological ageing. Alcohol use showed a consistent positive association with higher predicted brain age (AUDIT-C: β = 0.053, p = 3.16 × 10-13 ; AUDIT-P: β = 0.052, p = 1.6 × 10-13 ; total AUDIT score: β = 0.062, p = 5.52 × 10-16 ; units/week: β = 0.078, p = 2.20 × 10-16 ), and two DNA methylation-based estimates of ageing, GrimAge (units/week: β = 0.053, p = 1.48 × 10-7 ) and PhenoAge (units/week: β = 0.077, p = 2.18x10-10 ). MR analyses revealed limited evidence for a causal effect of AUD on accelerated brain ageing (β = 0.118, p = 0.044). However, this result should be interpreted cautiously as the significant effect was driven by a single genetic variant. We found no evidence for a causal effect of alcohol consumption levels on accelerated biological ageing. Future studies investigating the mechanisms associating alcohol use with accelerated biological ageing are warranted.
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Affiliation(s)
- Sunniva M.K. Bøstrand
- Division of Psychiatry, Royal Edinburgh Hospital, The University of Edinburgh, Edinburgh, UK
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, The University of Edinburgh, Edinburgh, UK
| | - Kadi Vaher
- Division of Psychiatry, Royal Edinburgh Hospital, The University of Edinburgh, Edinburgh, UK
- MRC Centre for Reproductive Health, The Queen’s Medical Research Institute, The University of Edinburgh, Edinburgh, UK
| | - Laura de Nooij
- Division of Psychiatry, Royal Edinburgh Hospital, The University of Edinburgh, Edinburgh, UK
| | - Matthew A. Harris
- Division of Psychiatry, Royal Edinburgh Hospital, The University of Edinburgh, Edinburgh, UK
| | - James H. Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Simon R. Cox
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Riccardo E. Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, UK
| | - Daniel L. McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, UK
| | - Rosie M. Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Andrew M. McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, The University of Edinburgh, Edinburgh, UK
| | - Kathryn L. Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, UK
| | - Heather C. Whalley
- Division of Psychiatry, Royal Edinburgh Hospital, The University of Edinburgh, Edinburgh, UK
| | - Robyn E. Wootton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Toni-Kim Clarke
- Division of Psychiatry, Royal Edinburgh Hospital, The University of Edinburgh, Edinburgh, UK
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45
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Brusini I, MacNicol E, Kim E, Smedby Ö, Wang C, Westman E, Veronese M, Turkheimer F, Cash D. MRI-derived brain age as a biomarker of ageing in rats: validation using a healthy lifestyle intervention. Neurobiol Aging 2022; 109:204-215. [PMID: 34775211 DOI: 10.1016/j.neurobiolaging.2021.10.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 10/06/2021] [Accepted: 10/08/2021] [Indexed: 12/21/2022]
Abstract
The difference between brain age predicted from MRI and chronological age (the so-called BrainAGE) has been proposed as an ageing biomarker. We analyse its cross-species potential by testing it on rats undergoing an ageing modulation intervention. Our rat brain age prediction model combined Gaussian process regression with a classifier and achieved a mean absolute error (MAE) of 4.87 weeks using cross-validation on a longitudinal dataset of 31 normal ageing rats. It was then tested on two groups of 24 rats (MAE = 9.89 weeks, correlation coefficient = 0.86): controls vs. a group under long-term environmental enrichment and dietary restriction (EEDR). Using a linear mixed-effects model, BrainAGE was found to increase more slowly with chronological age in EEDR rats (p=0.015 for the interaction term). Cox regression showed that older BrainAGE at 5 months was associated with higher mortality risk (p=0.03). Our findings suggest that lifestyle-related prevention approaches may help to slow down brain ageing in rodents and the potential of BrainAGE as a predictor of age-related health outcomes.
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Affiliation(s)
- Irene Brusini
- Department of Biomedical Engineering and Health Systems,KTH Royal Institute of Technology, Stockholm, Sweden; Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden.
| | - Eilidh MacNicol
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Eugene Kim
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Örjan Smedby
- Department of Biomedical Engineering and Health Systems,KTH Royal Institute of Technology, Stockholm, Sweden
| | - Chunliang Wang
- Department of Biomedical Engineering and Health Systems,KTH Royal Institute of Technology, Stockholm, Sweden
| | - Eric Westman
- Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Mattia Veronese
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; Department of Information Engineering, University of Padua, Padua, Italy
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Diana Cash
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
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46
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Jawinski P, Markett S, Drewelies J, Düzel S, Demuth I, Steinhagen-Thiessen E, Wagner GG, Gerstorf D, Lindenberger U, Gaser C, Kühn S. Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II. Front Aging Neurosci 2022; 14:791222. [PMID: 35936763 PMCID: PMC9355695 DOI: 10.3389/fnagi.2022.791222] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 06/17/2022] [Indexed: 11/13/2022] Open
Abstract
From a biological perspective, humans differ in the speed they age, and this may manifest in both mental and physical health disparities. The discrepancy between an individual's biological and chronological age of the brain ("brain age gap") can be assessed by applying machine learning techniques to Magnetic Resonance Imaging (MRI) data. Here, we examined the links between brain age gap and a broad range of cognitive, affective, socioeconomic, lifestyle, and physical health variables in up to 335 adults of the Berlin Aging Study II. Brain age gap was assessed using a validated prediction model that we previously trained on MRI scans of 32,634 UK Biobank individuals. Our statistical analyses revealed overall stronger evidence for a link between higher brain age gap and less favorable health characteristics than expected under the null hypothesis of no effect, with 80% of the tested associations showing hypothesis-consistent effect directions and 23% reaching nominal significance. The most compelling support was observed for a cluster covering both cognitive performance variables (episodic memory, working memory, fluid intelligence, digit symbol substitution test) and socioeconomic variables (years of education and household income). Furthermore, we observed higher brain age gap to be associated with heavy episodic drinking, higher blood pressure, and higher blood glucose. In sum, our results point toward multifaceted links between brain age gap and human health. Understanding differences in biological brain aging may therefore have broad implications for future informed interventions to preserve mental and physical health in old age.
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Affiliation(s)
- Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Johanna Drewelies
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.,Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
| | - Sandra Düzel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Ilja Demuth
- Division of Lipid Metabolism, Department of Endocrinology and Metabolic Diseases, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BCRT-Berlin Institute of Health Center for Regenerative Therapies, Berlin, Germany
| | - Elisabeth Steinhagen-Thiessen
- Division of Lipid Metabolism, Department of Endocrinology and Metabolic Diseases, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Gert G Wagner
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,German Socio-Economic Panel Study (SOEP), Berlin, Germany.,Federal Institute for Population Research (BiB), Berlin, Germany
| | - Denis Gerstorf
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,German Socio-Economic Panel Study (SOEP), Berlin, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Christian Gaser
- Structural Brain Mapping Group, Department of Psychiatry and Neurology, Jena University Hospital, Jena, Germany
| | - Simone Kühn
- Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany.,Department of Psychiatry and Psychotherapy, University Clinic Hamburg Eppendorf, Hamburg, Germany
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Wang M, Ren Q, Shi Y, Shu H, Liu D, Gu L, Xie C, Zhang Z, Wu T, Wang Z. The effect of Alzheimer's disease risk factors on brain aging in normal Chineses: Cognitive aging and cognitive reserve. Neurosci Lett 2021; 771:136398. [PMID: 34923042 DOI: 10.1016/j.neulet.2021.136398] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 11/22/2021] [Accepted: 12/12/2021] [Indexed: 11/28/2022]
Abstract
Aging has been recognized as a major driving force of the Alzheimer's disease's (AD) progression, however, the relationship between brain aging and AD is still unclear. There is also a lack of studies investigating the influence of AD risk factors on brain aging in cognitively normal people. Here, the "Brain Age Gap Estimation" (BrainAGE) framework was applied to investigate the effects of AD risk factors on individual brain aging. Across a total of 165 cognitively normal elderly subjects, although no significant difference was observed in the BrainAGE scores among the three groups, AD risk dose (i.e., the number of AD risk factors) is tend to associated with an increased BrainAGE scores (high-risk > middle risk > low risk). Female exhibited more advanced brain aging (P = 0.004), and higher education years were associated with preserved brain aging (P < 0.001). APOE-ɛ4 (P = 0.846) and family history (FH) of dementia (P = 0.209) did not increase BrainAGE scores. When comparing 52 aMCI patients with 38 cognitively normal controls from ADNI dataset, aMCI patients showed significantly increased BrainAGE scores. BrainAGE scores were negatively correlated with CSF Aβ42 levels in the aMCI group (r = -0.275, P = 0.048). With an accuracy of 68.9%, BrainAGE outperformed APOE-ɛ4 and hippocampus gray matter volume (GMV) in predicting aMCI. In conclusion, AD is independently associated with structural changes in the brain that reflect advanced aging. Potentially, BrainAGE combined with APOE-ɛ4 and hippocampus GMV could be used as a pre-screening tool in early-stage AD.
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Affiliation(s)
- Mengxue Wang
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Qingguo Ren
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China.
| | - Yachen Shi
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Hao Shu
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China
| | - Duan Liu
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Lihua Gu
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China
| | - Chunming Xie
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China
| | - Zhijun Zhang
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China
| | - Tiange Wu
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Zan Wang
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China.
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48
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Xi YB, Wu XS, Cui LB, Bai LJ, Gan SQ, Jia XY, Li X, Xu YQ, Kang XW, Guo F, Yin H. Neuroimaging-based brain-age prediction of first-episode schizophrenia and the alteration of brain age after early medication. Br J Psychiatry 2021; 220:1-8. [PMID: 35049480 DOI: 10.1192/bjp.2021.169] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear. AIMS To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age. METHOD The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed. RESULTS The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = -0.326, P = 0.014). CONCLUSIONS The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia.
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Affiliation(s)
- Yi-Bin Xi
- Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), China; and Department of Radiology, Xijing Hospital, Fourth Military Medical University, China
| | - Xu-Sha Wu
- Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), China; and School of Medical Technology, Shaanxi University of Chinese Medicine, China
| | - Long-Biao Cui
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, China; and Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, China
| | - Li-Jun Bai
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, China
| | - Shuo-Qiu Gan
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, China
| | - Xiao-Yan Jia
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, China
| | - Xuan Li
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, China
| | - Yong-Qiang Xu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, China
| | - Xiao-Wei Kang
- Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), China
| | - Fan Guo
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, China
| | - Hong Yin
- Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), China; and Department of Radiology, Xijing Hospital, Fourth Military Medical University, China
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49
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Cheng J, Liu Z, Guan H, Wu Z, Zhu H, Jiang J, Wen W, Tao D, Liu T. Brain Age Estimation From MRI Using Cascade Networks With Ranking Loss. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3400-3412. [PMID: 34086565 DOI: 10.1109/tmi.2021.3085948] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Chronological age of healthy people is able to be predicted accurately using deep neural networks from neuroimaging data, and the predicted brain age could serve as a biomarker for detecting aging-related diseases. In this paper, a novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data. Compared with existing methods, TSAN has the following improvements. First, TSAN uses a two-stage cascade network architecture, where the first-stage network estimates a rough brain age, then the second-stage network estimates the brain age more accurately from the discretized brain age by the first-stage network. Second, to our knowledge, TSAN is the first work to apply novel ranking losses in brain age estimation, together with the traditional mean square error (MSE) loss. Third, densely connected paths are used to combine feature maps with different scales. The experiments with 6586 MRIs showed that TSAN could provide accurate brain age estimation, yielding mean absolute error (MAE) of 2.428 and Pearson's correlation coefficient (PCC) of 0.985, between the estimated and chronological ages. Furthermore, using the brain age gap between brain age and chronological age as a biomarker, Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) can be distinguished from healthy control (HC) subjects by support vector machine (SVM). Classification AUC in AD/HC and MCI/HC was 0.904 and 0.823, respectively. It showed that brain age gap is an effective biomarker associated with risk of dementia, and has potential for early-stage dementia risk screening. The codes and trained models have been released on GitHub: https://github.com/Milan-BUAA/TSAN-brain-age-estimation.
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Cotrena C, Branco LD, Ponsoni A, Shansis FM, Fonseca RP. Cognitive reserve may outperform age, mood and psychiatric comorbidities as a predictor of executive functioning in bipolar disorder and healthy adults. J Clin Exp Neuropsychol 2021; 43:611-622. [PMID: 34730064 DOI: 10.1080/13803395.2021.1981251] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Cognitive reserve plays a protective role against executive dysfunction in healthy adults and individuals with psychiatric illnesses such as bipolar disorder. However, the magnitude of the influence of cognitive reserve on specific executive functions (EFs), and its impact relative to variables such as depressive symptoms, age and psychiatric comorbidities, is unexplored. This study aimed to quantify the influence of cognitive reserve on specific EFs, and compare its impact with that of depressive symptoms, age and psychiatric comorbidities, in separate models for patients with bipolar disorder and healthy adults. METHOD This was a cross-sectional study of 121 adults with no mood disorders and 109 with bipolar disorder, all of whom underwent a comprehensive psychiatric assessment and evaluation of the EFs. Cognitive reserve was measured using years of education, IQ and reading and writing habits. The association between EFs and predictors (cognitive reserve, depressive symptoms, age and psychiatric comorbidities) was evaluated through structural equation modeling. Four models were constructed for each group independently (bipolar disorder and control), one each for working memory, verbal fluency, inhibition and flexibility, due to group differences in age and cognitive reserve. RESULTS Working memory, inhibition and flexibility were most significantly predicted by cognitive reserve and age. Verbal fluency was only predicted by cognitive reserve. Comorbidities and depressive symptoms were not significant in any of the models. Cognitive reserve had a positive influence on all EFs in models for patients with bipolar disorder and models for control participants. Age had a negative impact on three of the four EFs tested. CONCLUSION Fostering cognitive reserve through continued education and cognitively stimulating leisure activities may be an effective intervention for executive dysfunction in patients and non-patients alike. In some cases, the effects of these interventions may outweigh the negative cognitive impact of aging, depressive symptoms and psychiatric conditions.
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Affiliation(s)
- Charles Cotrena
- Graduate Department of Psychology, School of Health Sciences, Pontifical Catholic University of Rio Grande do Sul (PUCRS)
| | - Laura Damiani Branco
- Graduate Department of Psychology, School of Health Sciences, Pontifical Catholic University of Rio Grande do Sul (PUCRS)
| | - André Ponsoni
- Graduate Department of Psychology, School of Health Sciences, Pontifical Catholic University of Rio Grande do Sul (PUCRS)
| | | | - Rochele Paz Fonseca
- Graduate Department of Psychology, School of Health Sciences, Pontifical Catholic University of Rio Grande do Sul (PUCRS)
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