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Ibanez A, Kringelbach ML, Deco G. A synergetic turn in cognitive neuroscience of brain diseases. Trends Cogn Sci 2024; 28:319-338. [PMID: 38246816 DOI: 10.1016/j.tics.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/15/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024]
Abstract
Despite significant improvements in our understanding of brain diseases, many barriers remain. Cognitive neuroscience faces four major challenges: complex structure-function associations; disease phenotype heterogeneity; the lack of transdiagnostic models; and oversimplified cognitive approaches restricted to the laboratory. Here, we propose a synergetics framework that can help to perform the necessary dimensionality reduction of complex interactions between the brain, body, and environment. The key solutions include low-dimensional spatiotemporal hierarchies for brain-structure associations, whole-brain modeling to handle phenotype diversity, model integration of shared transdiagnostic pathophysiological pathways, and naturalistic frameworks balancing experimental control and ecological validity. Creating whole-brain models with reduced manifolds combined with ecological measures can improve our understanding of brain disease and help identify novel interventions. Synergetics provides an integrated framework for future progress in clinical and cognitive neuroscience, pushing the boundaries of brain health and disease toward more mature, naturalistic approaches.
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Affiliation(s)
- Agustin Ibanez
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile; Global Brain Health Institute (GBHI), University California San Francisco (UCSF), San Francisco, CA, USA; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Department of Psychiatry, University of Oxford, Oxford, UK.
| | - Morten L Kringelbach
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain.
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2
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Mahroo A, Konstandin S, Günther M. Blood-Brain Barrier Permeability to Water Measured Using Multiple Echo Time Arterial Spin Labeling MRI in the Aging Human Brain. J Magn Reson Imaging 2024; 59:1269-1282. [PMID: 37337979 DOI: 10.1002/jmri.28874] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 06/06/2023] [Accepted: 06/06/2023] [Indexed: 06/21/2023] Open
Abstract
BACKGROUND The blood-brain barrier (BBB) plays a vital role in maintaining brain homeostasis, but the integrity of this barrier deteriorates slowly with aging. Noninvasive water exchange magnetic resonance imaging (MRI) methods may identify changes in the BBB occurring with healthy aging. PURPOSE To investigate age-related changes in the BBB permeability to water using multiple-echo-time (multi-TE) arterial spin labeling (ASL) MRI. STUDY TYPE Prospective, cohort. POPULATION Two groups of healthy humans-older group (≥50 years, mean age = 56 ± 4 years, N = 13, females = 5) and younger group (≤20 years, mean age = 18 ± 1, N = 13, females = 7). FIELD STRENGTH/SEQUENCE A 3T, multi-TE Hadamard pCASL with 3D Gradient and Spin Echo (GRASE) readout. ASSESSMENT Two different approaches of variable complexity were applied. A physiologically informed biophysical model with a higher complexity estimating time ( T ex ) taken by the labeled water to move across the BBB and a simpler model of triexponential decay measuring tissue transition rate ( k lin ) . STATISTICS Two-tailed unpaired Student t-test, Pearson's correlation coefficient and effect size. P < 0.05 was considered significant. RESULTS Older volunteers showed significant differences of 36% lower T ex , 29% lower cerebral perfusion, 17% pronged arterial transit time and 22% shorter intra-voxel transit time compared to the younger volunteers. Tissue fraction ( f EV ) at the earliest TI = 1600 msec was significantly higher in the older group, which contributed to a significantly lower k lin compared to the younger group. f EV at TI = 1600 msec showed significant negative correlation with T ex (r = -0.80), and k lin and T ex showed significant positive correlation (r = 0.73). DATA CONCLUSIONS Both approaches of Multi-TE ASL imaging showed sensitivity to detect age-related changes in the BBB permeability. High tissue fractions at the earliest TI and short T ex in the older volunteers indicate that the BBB permeability increased with age. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Amnah Mahroo
- Imaging Physics, Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Simon Konstandin
- Imaging Physics, Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- mediri GmbH, Heidelberg, Germany
| | - Matthias Günther
- Imaging Physics, Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- mediri GmbH, Heidelberg, Germany
- MR-Imaging and Spectroscopy, University of Bremen, Bremen, Germany
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3
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de Ruiter MB, Deardorff RL, Blommaert J, Chen BT, Dumas JA, Schagen SB, Sunaert S, Wang L, Cimprich B, Peltier S, Dittus K, Newhouse PA, Silverman DH, Schroyen G, Deprez S, Saykin AJ, McDonald BC. Brain gray matter reduction and premature brain aging after breast cancer chemotherapy: a longitudinal multicenter data pooling analysis. Brain Imaging Behav 2023; 17:507-518. [PMID: 37256494 PMCID: PMC10652222 DOI: 10.1007/s11682-023-00781-7] [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] [Accepted: 04/29/2023] [Indexed: 06/01/2023]
Abstract
Brain gray matter (GM) reductions have been reported after breast cancer chemotherapy, typically in small and/or cross-sectional cohorts, most commonly using voxel-based morphometry (VBM). There has been little examination of approaches such as deformation-based morphometry (DBM), machine-learning-based brain aging metrics, or the relationship of clinical and demographic risk factors to GM reduction. This international data pooling study begins to address these questions. Participants included breast cancer patients treated with (CT+, n = 183) and without (CT-, n = 155) chemotherapy and noncancer controls (NC, n = 145), scanned pre- and post-chemotherapy or comparable intervals. VBM and DBM examined GM volume. Estimated brain aging was compared to chronological aging. Correlation analyses examined associations between VBM, DBM, and brain age, and between neuroimaging outcomes, baseline age, and time since chemotherapy completion. CT+ showed longitudinal GM volume reductions, primarily in frontal regions, with a broader spatial extent on DBM than VBM. CT- showed smaller clusters of GM reduction using both methods. Predicted brain aging was significantly greater in CT+ than NC, and older baseline age correlated with greater brain aging. Time since chemotherapy negatively correlated with brain aging and annual GM loss. This large-scale data pooling analysis confirmed findings of frontal lobe GM reduction after breast cancer chemotherapy. Milder changes were evident in patients not receiving chemotherapy. CT+ also demonstrated premature brain aging relative to NC, particularly at older age, but showed evidence for at least partial GM recovery over time. When validated in future studies, such knowledge could assist in weighing the risks and benefits of treatment strategies.
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Affiliation(s)
- Michiel B de Ruiter
- Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Rachael L Deardorff
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, and Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jeroen Blommaert
- Department of Oncology, KU Leuven, Leuven, Belgium and Research Foundation Flanders (FWO), Brussels, Belgium
| | - Bihong T Chen
- City of Hope National Medical Center, Duarte, CA, USA
| | | | - Sanne B Schagen
- Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Stefan Sunaert
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Lei Wang
- Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | | | | | - Kim Dittus
- University of Vermont Cancer Center, University of Vermont, Burlington, VT, USA
| | - Paul A Newhouse
- Center for Cognitive Medicine, Vanderbilt University Medical Center and Geriatric Research Educational and Clinical Center, Tennessee Valley VA Health System, Nashville, TN, USA
| | | | - Gwen Schroyen
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Leuven Cancer Institute, KU Leuven, Leuven, Belgium
| | - Sabine Deprez
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Leuven Cancer Institute, KU Leuven, Leuven, Belgium
| | - Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, and Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Brenna C McDonald
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, and Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA.
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Biondo F, Jewell A, Pritchard M, Aarsland D, Steves CJ, Mueller C, Cole JH. Brain-age is associated with progression to dementia in memory clinic patients. Neuroimage Clin 2022; 36:103175. [PMID: 36087560 PMCID: PMC9467894 DOI: 10.1016/j.nicl.2022.103175] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 07/30/2022] [Accepted: 08/27/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Biomarkers for the early detection of dementia risk hold promise for better disease monitoring and targeted interventions. However, most biomarker studies, particularly in neuroimaging, have analysed artificially 'clean' research groups, free from comorbidities, erroneous referrals, contraindications and from a narrow sociodemographic pool. Such biases mean that neuroimaging samples are often unrepresentative of the target population for dementia risk (e.g., people referred to a memory clinic), limiting the generalisation of these studies to real-world clinical settings. To facilitate better translation from research to the clinic, datasets that are more representative of dementia patient groups are warranted. METHODS We analysed T1-weighted MRI scans from a real-world setting of patients referred to UK memory clinic services (n = 1140; 60.2 % female and mean [SD] age of 70.0[10.8] years) to derive 'brain-age'. Brain-age is an index of age-related brain health based on quantitative analysis of structural neuroimaging, largely reflecting brain atrophy. Brain-predicted age difference (brain-PAD) was calculated as brain-age minus chronological age. We determined which patients went on to develop dementia between three months and 7.8 years after neuroimaging assessment (n = 476) using linkage to electronic health records. RESULTS Survival analysis, using Cox regression, indicated a 3 % increased risk of dementia per brain-PAD year (hazard ratio [95 % CI] = 1.03 [1.02,1.04], p < 0.0001), adjusted for baseline age, age2, sex, Mini Mental State Examination (MMSE) score and normalised brain volume. In sensitivity analyses, brain-PAD remained significant when time-to-dementia was at least 3 years (hazard ratio [95 % CI] = 1.06 [1.02, 1.09], p = 0.0006), or when baseline MMSE score ≥ 27 (hazard ratio [95 % CI] = 1.03 [1.01, 1.05], p = 0.0006). CONCLUSIONS Memory clinic patients with older-appearing brains are more likely to receive a subsequent dementia diagnosis. Potentially, brain-age could aid decision-making during initial memory clinic assessment to improve early detection of dementia. Even when neuroimaging assessment was more than 3 years prior to diagnosis and when cognitive functioning was not clearly impaired, brain-age still proved informative. These real-world results support the use of quantitative neuroimaging biomarkers like brain-age in memory clinics.
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Affiliation(s)
- Francesca Biondo
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, WC1V 6LJ, UK.
| | | | | | - Dag Aarsland
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK; Centre for Age-Related Research, Stavanger University Hospital, Stavanger, Norway
| | - Claire J Steves
- Department of Ageing and Health, Guy's and St Thomas' NHS Foundation Trust, SE1 7EH, UK; Department of Twin Research and Genetic Epidemiology, King's College London, SE1 7EH, UK
| | - Christoph Mueller
- South London and Maudsley NHS Foundation Trust, UK; Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK
| | - James H Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, WC1V 6LJ, UK; Dementia Research Centre, Institute of Neurology, University College London, WC1N 3AR, UK.
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Bliss ES, Wong RHX, Howe PRC, Mills DE. Benefits of exercise training on cerebrovascular and cognitive function in ageing. J Cereb Blood Flow Metab 2021; 41:447-470. [PMID: 32954902 PMCID: PMC7907999 DOI: 10.1177/0271678x20957807] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Derangements in cerebrovascular structure and function can impair cognitive performance throughout ageing and in cardiometabolic disease states, thus increasing dementia risk. Modifiable lifestyle factors that cause a decline in cardiometabolic health, such as physical inactivity, exacerbate these changes beyond those that are associated with normal ageing. The purpose of this review was to examine cerebrovascular, cognitive and neuroanatomical adaptations to ageing and the potential benefits of exercise training on these outcomes in adults 50 years or older. We systematically searched for cross-sectional or intervention studies that included exercise (aerobic, resistance or multimodal) and its effect on cerebrovascular function, cognition and neuroanatomical adaptations in this age demographic. The included studies were tabulated and described narratively. Aerobic exercise training was the predominant focus of the studies identified; there were limited studies exploring the effects of resistance exercise training and multimodal training on cerebrovascular function and cognition. Collectively, the evidence indicated that exercise can improve cerebrovascular function, cognition and neuroplasticity through areas of the brain associated with executive function and memory in adults 50 years or older, irrespective of their health status. However, more research is required to ascertain the mechanisms of action.
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Affiliation(s)
- Edward S Bliss
- Respiratory and Exercise Physiology Research Group, School of
Health and Wellbeing, University of Southern Queensland, Ipswich, Queensland,
Australia
- Edward S Bliss, School of Health and
Wellbeing, University of Southern Queensland, Toowoomba Campus, West St,
Toowoomba QLD 4350, Australia.
| | - Rachel HX Wong
- Centre for Health, Informatics, and Economic Research, Institute
for Resilient Regions, University of Southern Queensland, Ipswich, Queensland,
Australia
- School of Biomedical Sciences and Pharmacy, Clinical Nutrition
Research Centre, University of Newcastle, Callaghan, New South Wales,
Australia
| | - Peter RC Howe
- Centre for Health, Informatics, and Economic Research, Institute
for Resilient Regions, University of Southern Queensland, Ipswich, Queensland,
Australia
- School of Biomedical Sciences and Pharmacy, Clinical Nutrition
Research Centre, University of Newcastle, Callaghan, New South Wales,
Australia
- Allied Health and Human Performance, University of South
Australia, Adelaide, South Australia, Australia
| | - Dean E Mills
- Respiratory and Exercise Physiology Research Group, School of
Health and Wellbeing, University of Southern Queensland, Ipswich, Queensland,
Australia
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6
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Generalization of diffusion magnetic resonance imaging–based brain age prediction model through transfer learning. Neuroimage 2020; 217:116831. [DOI: 10.1016/j.neuroimage.2020.116831] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 03/18/2020] [Accepted: 03/19/2020] [Indexed: 11/23/2022] Open
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Wall J, Xie H, Wang X. Interaction of Sleep and Cortical Structural Maintenance From an Individual Person Microlongitudinal Perspective and Implications for Precision Medicine Research. Front Neurosci 2020; 14:769. [PMID: 32848551 PMCID: PMC7411006 DOI: 10.3389/fnins.2020.00769] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 06/30/2020] [Indexed: 12/18/2022] Open
Abstract
Sleep and maintenance of brain structure are essential for the continuity of a person's cognitive/mental health. Interestingly, whether normal structural maintenance of the brain and sleep continuously interact in some way over day-week-month times has never been assessed at an individual-person level. This study used unconventional microlongitudinal sampling, structural magnetic resonance imaging, and n-of-1 analyses to assess normal interactions between fluctuations in the structural maintenance of cerebral cortical thickness and sleep duration for day, week, and multi-week intervals over a 6-month period in a healthy adult man. Correlation and time series analyses provided indications of "if-then," i.e., "if" this preceded "then" this followed, sleep-to-thickness maintenance and thickness maintenance-to-sleep bidirectional inverse interactions. Inverse interaction patterns were characterized by concepts of graded influences across nights, bilaterally positive relationships, continuity across successive weeks, and longer delayed/prolonged effects in the thickness maintenance-to-sleep than sleep-to-thickness maintenance direction. These interactions are proposed to involve normal circadian/allostatic/homeostatic mechanisms that continuously influence, and are influenced by, cortical substrate remodeling/turnover and sleep/wake cycle. Understanding interactions of individual person "-omics" is becoming a central interest in precision medicine research. The present n-of-1 findings contribute to this interest and have implications for precision medicine research use of a person's cortical structural and sleep "-omics" to optimize the continuous maintenance of that individual's cortical structure, sleep, and cognitive/mental health.
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Affiliation(s)
- John Wall
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
| | - Hong Xie
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
| | - Xin Wang
- Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
- Department of Psychiatry, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
- Department of Radiology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States
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8
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Moore JH, Raghavachari N. Artificial Intelligence Based Approaches to Identify Molecular Determinants of Exceptional Health and Life Span-An Interdisciplinary Workshop at the National Institute on Aging. Front Artif Intell 2019; 2:12. [PMID: 33733101 PMCID: PMC7861312 DOI: 10.3389/frai.2019.00012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 07/08/2019] [Indexed: 01/01/2023] Open
Abstract
Artificial intelligence (AI) has emerged as a powerful approach for integrated analysis of the rapidly growing volume of multi-omics data, including many research and clinical tasks such as prediction of disease risk and identification of potential therapeutic targets. However, the potential for AI to facilitate the identification of factors contributing to human exceptional health and life span and their translation into novel interventions for enhancing health and life span has not yet been realized. As researchers on aging acquire large scale data both in human cohorts and model organisms, emerging opportunities exist for the application of AI approaches to untangle the complex physiologic process(es) that modulate health and life span. It is expected that efficient and novel data mining tools that could unravel molecular mechanisms and causal pathways associated with exceptional health and life span could accelerate the discovery of novel therapeutics for healthy aging. Keeping this in mind, the National Institute on Aging (NIA) convened an interdisciplinary workshop titled “Contributions of Artificial Intelligence to Research on Determinants and Modulation of Health Span and Life Span” in August 2018. The workshop involved experts in the fields of aging, comparative biology, cardiology, cancer, and computational science/AI who brainstormed ideas on how AI can be leveraged for the analyses of large-scale data sets from human epidemiological studies and animal/model organisms to close the current knowledge gaps in processes that drive exceptional life and health span. This report summarizes the discussions and recommendations from the workshop on future application of AI approaches to advance our understanding of human health and life span.
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Affiliation(s)
- Jason H Moore
- University of Pennsylvania, Philadelphia, PA, United States
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Han LKM, Verhoeven JE, Tyrka AR, Penninx BWJH, Wolkowitz OM, Månsson KNT, Lindqvist D, Boks MP, Révész D, Mellon SH, Picard M. Accelerating research on biological aging and mental health: Current challenges and future directions. Psychoneuroendocrinology 2019; 106:293-311. [PMID: 31154264 PMCID: PMC6589133 DOI: 10.1016/j.psyneuen.2019.04.004] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 01/22/2019] [Accepted: 04/02/2019] [Indexed: 12/13/2022]
Abstract
Aging is associated with complex biological changes that can be accelerated, slowed, or even temporarily reversed by biological and non-biological factors. This article focuses on the link between biological aging, psychological stressors, and mental illness. Rather than comprehensively reviewing this rapidly expanding field, we highlight challenges in this area of research and propose potential strategies to accelerate progress in this field. This effort requires the interaction of scientists across disciplines - including biology, psychiatry, psychology, and epidemiology; and across levels of analysis that emphasize different outcome measures - functional capacity, physiological, cellular, and molecular. Dialogues across disciplines and levels of analysis naturally lead to new opportunities for discovery but also to stimulating challenges. Some important challenges consist of 1) establishing the best objective and predictive biological age indicators or combinations of indicators, 2) identifying the basis for inter-individual differences in the rate of biological aging, and 3) examining to what extent interventions can delay, halt or temporarily reverse aging trajectories. Discovering how psychological states influence biological aging, and vice versa, has the potential to create novel and exciting opportunities for healthcare and possibly yield insights into the fundamental mechanisms that drive human aging.
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Affiliation(s)
- Laura KM Han
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Public Health Research Institute, Oldenaller 1, The Netherlands,Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Josine E Verhoeven
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Public Health Research Institute, Oldenaller 1, The Netherlands
| | - Audrey R Tyrka
- Butler Hospital and the Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Brenda WJH Penninx
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Public Health Research Institute, Oldenaller 1, The Netherlands,Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Owen M Wolkowitz
- Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, School of Medicine, San Francisco, CA, USA
| | - Kristoffer NT Månsson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden,Department of Psychology, Stockholm University, Stockholm, Sweden,Department of Psychology, Uppsala University, Uppsala, Sweden
| | - Daniel Lindqvist
- Faculty of Medicine, Department of Clinical Sciences, Psychiatry, Lund University, Lund, Sweden,Department of Psychiatry, University of California San Francisco (UCSF) School of Medicine, San Francisco, CA, USA,Psychiatric Clinic, Lund, Division of Psychiatry, Lund, Sweden
| | - Marco P Boks
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, The Netherlands
| | - Dóra Révész
- Center of Research on Psychology in Somatic diseases (CoRPS), Department of Medical and Clinical Psychology, Tilburg University, Tilburg, The Netherlands
| | - Synthia H Mellon
- Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, School of Medicine, San Francisco, CA, USA
| | - Martin Picard
- Department of Psychiatry, Division of Behavioral Medicine, Columbia University Medical Center, New York, NY, USA; Department of Neurology, H. Houston Merritt Center, Columbia Translational Neuroscience Initiative, Columbia University Medical Center, New York, NY, USA; Columbia Aging Center, Columbia University, New York, NY, USA.
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10
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Gialluisi A, Di Castelnuovo A, Donati MB, de Gaetano G, Iacoviello L. Machine Learning Approaches for the Estimation of Biological Aging: The Road Ahead for Population Studies. Front Med (Lausanne) 2019; 6:146. [PMID: 31338367 PMCID: PMC6626911 DOI: 10.3389/fmed.2019.00146] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 06/10/2019] [Indexed: 12/21/2022] Open
Abstract
In recent years, different machine learning algorithms have been developed for the estimation of Biological Age (BA), defined as the hypothetical underlying age of an organism. BA can be computed based on different circulating and non-circulating biomarkers. In this perspective, identifying biomarkers with a prominent influence on BA and developing reliable models for its estimation is of fundamental importance for monitoring healthy aging, and could provide new tools to screen health status and the risk of clinical events in the general population. Here, we briefly review the different machine learning (ML) approaches used for BA estimation, focusing on those methods with potential application to the Moli-sani study, a prospective population-based cohort study of 24,325 subjects (35–99 years). In particular, we discuss the potential of BA estimation based on blood biomarkers, which likely represents the easiest and most immediate way to compute organismal BA. Similarly, we describe ML methods for the estimation of brain age based on structural neuroimaging features. For each method, we discuss the relation with epidemiological variables (e.g., mortality), genetic and environmental factors, and common age-related diseases (e.g., Alzheimer disease), to examine the potential as aging biomarker in the general population. Finally, we hypothesize new approaches for BA estimation, both at the single organ and at the whole organism level. Overall, here we trace the road ahead in the Big Data era for our and other prospective general population cohorts, presenting ways to exploit the notable amount of data available nowadays.
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Affiliation(s)
| | | | | | | | - Licia Iacoviello
- Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli, Italy.,Department of Medicine and Surgery, University of Insubria, Varese, Italy
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