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Liu C, Cai Q, Gou Y, Liu Y, Kang M, Hui J, Zhou R, Shi P, Wang B, Zhang F. Association of accelerated biological aging with brain volumes: A cross-sectional study. J Affect Disord 2024; 364:188-193. [PMID: 39147148 DOI: 10.1016/j.jad.2024.08.078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 07/10/2024] [Accepted: 08/12/2024] [Indexed: 08/17/2024]
Abstract
BACKGROUND Multiple epidemiological studies have observed the connection between aging and brain volumes. The concept of accelerated biological aging (BA) is more powerful for observing the degree of aging of an individual than chronologic age (CA). The objective of this study is to explore the relationship between BA and brain volumes. METHODS BA was measured from clinical traits using two blood-chemistry algorithms, the Klemera-Doubal method (KDM) and the PhenoAge. The two age acceleration biomarkers were calculated by the residuals from regressing CA, termed "KDM-acceleration" and "PhenoAge-acceleration". Brain volumes were from brain magnetic resonance imaging (MRI) data. After adjustment for confounding factors, general linear regression models were used to examine associations between KDM-acceleration and PhenoAge-acceleration and brain volumes, respectively. Additionally, we stratified participants by sex, age, and the four quartiles of the Townsend Deprivation Index (TDI) for extra subgroup analysis. RESULTS 14,725 participants with available information were enrolled. After full adjustment, we observed negative associations between KDM-acceleration and brain volumes, such as gray matter (β = -0.029), white matter (β = -0.021), gray and white matter (β = -0.026), and hippocampus (β = -0.011 for left and β = -0.014 for right). There were also negative associations between PhenoAge-acceleration and brain volumes, such as white matter (β = -0.008), gray and white matter (β = -0.010), thalamus (β = -0.012 for left and β = -0.012 for right). In the subgroup analysis stratified by sex, age, and the four quartiles of TDI, the association between KDM-acceleration and PhenoAge-acceleration and brain volumes still existed. In subgroup analyses, the variation in associations suggests that socioeconomic and biological factors may differentially influence brain aging. CONCLUSIONS Our research indicated that more advanced BA was associated with less brain tissue.
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Affiliation(s)
- Chen Liu
- Key Laboratory of Environment and Endemic Diseases of National Health Commission of China, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Qingqing Cai
- Key Laboratory of Environment and Endemic Diseases of National Health Commission of China, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yifan Gou
- Key Laboratory of Environment and Endemic Diseases of National Health Commission of China, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Ye Liu
- Key Laboratory of Environment and Endemic Diseases of National Health Commission of China, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Meijuan Kang
- Key Laboratory of Environment and Endemic Diseases of National Health Commission of China, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Jingni Hui
- Key Laboratory of Environment and Endemic Diseases of National Health Commission of China, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Ruixue Zhou
- Key Laboratory of Environment and Endemic Diseases of National Health Commission of China, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Panxing Shi
- Key Laboratory of Environment and Endemic Diseases of National Health Commission of China, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Bingyi Wang
- Key Laboratory of Environment and Endemic Diseases of National Health Commission of China, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Feng Zhang
- Key Laboratory of Environment and Endemic Diseases of National Health Commission of China, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China.
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2
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Wilkinson J, Curry OS, Mitchell BL, Bates T. Modular morals: Mapping the organization of the moral brain. Brain Cogn 2024; 180:106201. [PMID: 39173228 DOI: 10.1016/j.bandc.2024.106201] [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: 12/20/2023] [Revised: 06/10/2024] [Accepted: 06/16/2024] [Indexed: 08/24/2024]
Abstract
Is morality the product of multiple domain-specific psychological mechanisms, or one domain-general mechanism? Previous research suggests that morality consists of a range of solutions to the problems of cooperation recurrent in human social life. This theory of 'morality as cooperation' suggests that there are (at least) seven specific moral domains: family values, group loyalty, reciprocity, heroism, deference, fairness and property rights. However, it is unclear how these types of morality are implemented at the neuroanatomical level. The possibilities are that morality is (1) the product of multiple distinct domain-specific adaptations for cooperation, (2) the product of a single domain-general adaptation which learns a range of moral rules, or (3) the product of some combination of domain-specific and domain-general adaptations. To distinguish between these possibilities, we first conducted an anatomical likelihood estimation meta-analysis of previous studies investigating the relationship between these seven moral domains and neuroanatomy. This meta-analysis provided evidence for a combination of specific and general adaptations. Next, we investigated the relationship between the seven types of morality - as measured by the Morality as Cooperation Questionnaire (Relevance) - and grey matter volume in a large neuroimaging (n = 607) sample. No associations between moral values and grey matter volume survived whole-brain exploratory testing. We conclude that whatever combination of mechanisms are responsible for morality, either they are not neuroanatomically localised, or else their localisation is not manifested in grey matter volume. Future research should employ phylogenetically informed a priori predictions, as well as alternative measures of morality and of brain function.
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Affiliation(s)
- James Wilkinson
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; School of Business and Economics, Maastricht University, Maastricht, the Netherlands.
| | - Oliver Scott Curry
- School of Anthropology and Museum Ethnography, University of Oxford, Oxford, United Kingdom
| | - Brittany L Mitchell
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Timothy Bates
- Centre for Cognitive Ageing and Cognitive Epidemiology Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, United Kingdom
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3
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Treacy C, Campbell AJ, Anijärv TE, Lagopoulos J, Hermens DF, Andrews SC, Levenstein JM. Structural brain correlates of sustained attention in healthy ageing: Cross-sectional findings from the LEISURE study. Neurobiol Aging 2024; 144:93-103. [PMID: 39298870 DOI: 10.1016/j.neurobiolaging.2024.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 09/04/2024] [Accepted: 09/16/2024] [Indexed: 09/22/2024]
Abstract
Sustained attention is important for maintaining cognitive function and autonomy during ageing, yet older people often show reductions in this domain. The role of the underlying neurobiology is not yet well understood, with most neuroimaging studies primarily focused on fMRI. Here, we utilise sMRI to investigate the relationships between age, structural brain volumes and sustained attention performance. Eighty-nine healthy older adults (50-84 years, Mage 65.5 (SD=8.4) years, 74 f) underwent MRI brain scanning and completed two sustained attention tasks: a rapid visual information processing (RVP) task and sustained attention to response task (SART). Independent hierarchical linear regressions demonstrated that greater volumes of white matter hyperintensities (WMH) were associated with worse RVP_A' performance, whereas greater grey matter volumes were associated with better RVP_A' performance. Further, greater cerebral white matter volumes were associated with better SART_d' performance. Importantly, mediation analyses revealed that both grey and white matter volumes completely mediated the relationship between ageing and sustained attention. These results explain disparate attentional findings in older adults, highlighting the intervening role of brain structure.
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Affiliation(s)
- Ciara Treacy
- Thompson Institute, University of the Sunshine Coast, Birtinya, QLD, Australia.
| | - Alicia J Campbell
- Thompson Institute, University of the Sunshine Coast, Birtinya, QLD, Australia
| | - Toomas Erik Anijärv
- Thompson Institute, University of the Sunshine Coast, Birtinya, QLD, Australia; Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
| | - Jim Lagopoulos
- Thompson Institute, University of the Sunshine Coast, Birtinya, QLD, Australia; Thompson Brain and Mind Healthcare, Birtinya, QLD, Australia
| | - Daniel F Hermens
- Thompson Institute, University of the Sunshine Coast, Birtinya, QLD, Australia
| | - Sophie C Andrews
- Thompson Institute, University of the Sunshine Coast, Birtinya, QLD, Australia
| | - Jacob M Levenstein
- Thompson Institute, University of the Sunshine Coast, Birtinya, QLD, Australia
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4
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Zivadinov R, Tranquille A, Reeves JA, Dwyer MG, Bergsland N. Brain atrophy assessment in multiple sclerosis: technical- and subject-related barriers for translation to real-world application in individual subjects. Expert Rev Neurother 2024:1-16. [PMID: 39233336 DOI: 10.1080/14737175.2024.2398484] [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: 06/05/2024] [Accepted: 08/27/2024] [Indexed: 09/06/2024]
Abstract
INTRODUCTION Brain atrophy is a well-established MRI outcome for predicting clinical progression and monitoring treatment response in persons with multiple sclerosis (pwMS) at the group level. Despite the important progress made, the translation of brain atrophy assessment into clinical practice faces several challenges. AREAS COVERED In this review, the authors discuss technical- and subject-related barriers for implementing brain atrophy assessment as part of the clinical routine at the individual level. Substantial progress has been made to understand and mitigate technical barriers behind MRI acquisition. Numerous research and commercial segmentation techniques for volume estimation are available and technically validated, but their clinical value has not been fully established. A systematic assessment of subject-related barriers, which include genetic, environmental, biological, lifestyle, comorbidity, and aging confounders, is critical for the interpretation of brain atrophy measures at the individual subject level. Educating both medical providers and pwMS will help better clarify the benefits and limitations of assessing brain atrophy for disease monitoring and prognosis. EXPERT OPINION Integrating brain atrophy assessment into clinical practice for pwMS requires overcoming technical and subject-related challenges. Advances in MRI standardization, artificial intelligence, and clinician education will facilitate this process, improving disease management and potentially reducing long-term healthcare costs.
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Affiliation(s)
- Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
- Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ashley Tranquille
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Jack A Reeves
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
- Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
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5
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Voits T, DeLuca V, Hao J, Elin K, Abutalebi J, Duñabeitia JA, Berglund G, Gabrielsen A, Rook J, Thomsen H, Waagen P, Rothman J. Degree of multilingual engagement modulates resting state oscillatory activity across the lifespan. Neurobiol Aging 2024; 140:70-80. [PMID: 38735176 DOI: 10.1016/j.neurobiolaging.2024.04.009] [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: 01/05/2024] [Revised: 03/18/2024] [Accepted: 04/19/2024] [Indexed: 05/14/2024]
Abstract
Multilingualism has been demonstrated to lead to a more favorable trajectory of neurocognitive aging, yet our understanding of its effect on neurocognition across the lifespan remains limited. We collected resting state EEG recordings from a sample of multilingual individuals across a wide age range. Additionally, we obtained data on participant multilingual language use patterns alongside other known lifestyle enrichment factors. Language experience was operationalized via a modified multilingual diversity (MLD) score. Generalized additive modeling was employed to examine the effects and interactions of age and MLD on resting state oscillatory power and coherence. The data suggest an independent modulatory effect of individualized multilingual engagement on age-related differences in whole brain resting state power across alpha and theta bands, and an interaction between age and MLD on resting state coherence in alpha, theta, and low beta. These results provide evidence of multilingual engagement as an independent correlational factor related to differences in resting state EEG power, consistent with the claim that multilingualism can serve as a protective factor in neurocognitive aging.
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Affiliation(s)
- Toms Voits
- Department of Psychology, University of Gothenburg, Gothenburg, Sweden; UiT the Arctic University of Norway, Tromsø, Norway.
| | | | - Jiuzhou Hao
- UiT the Arctic University of Norway, Tromsø, Norway
| | - Kirill Elin
- UiT the Arctic University of Norway, Tromsø, Norway
| | - Jubin Abutalebi
- UiT the Arctic University of Norway, Tromsø, Norway; Centre for Neurolinguistics and Psycholinguistics (CNPL), Vita-Salute San Raffaele University, Milan, Italy
| | - Jon Andoni Duñabeitia
- UiT the Arctic University of Norway, Tromsø, Norway; Universidad Nebrija Research Center in Cognition (CINC), Nebrija University, Madrid, Spain
| | | | | | - Janine Rook
- Department of Applied Linguistics, University of Groningen, Groningen, the Netherlands
| | - Hilde Thomsen
- UiT the Arctic University of Norway, Tromsø, Norway; Université Côte d'Azur, Nice, France
| | | | - Jason Rothman
- UiT the Arctic University of Norway, Tromsø, Norway; Universidad Nebrija Research Center in Cognition (CINC), Nebrija University, Madrid, Spain
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6
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Pihlaja S, Jääskeläinen E, Heikkilä L, Hintsanen M. Associations of lipids in adolescence and adulthood with self- and other-directed compassion in adulthood. Scand J Psychol 2024. [PMID: 39013837 DOI: 10.1111/sjop.13052] [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: 11/08/2023] [Revised: 05/31/2024] [Accepted: 06/12/2024] [Indexed: 07/18/2024]
Abstract
Self- and other-directed compassion have been linked with better mental and physical health but research on factors contributing to their development is scarce. Previous studies indicate a possible causal relationship of lipids with personality and socioemotional functioning. As an extension to earlier research, in the present study we examine whether lipids assessed in adolescence and adulthood are associated with self-compassion and other-directed compassion in adulthood. The study utilizes data on lipids from two follow-ups in the Northern Finland Birth Cohort 1986 at ages 15-16 and 33-35. In the latter follow-up also self-compassion and other-directed compassion were assessed with the self-compassion scale - short form and the subscale for compassion in the dispositional positive emotions scale, respectively. The sample for the cross-sectional associations of lipids in adulthood with the compassion variables in adulthood includes 1,459 participants, whereas the sample for the longitudinal associations of lipids in adolescence and the compassion variables in adulthood consists of 1,509 participants. The associations were examined with hierarchical linear regression (lipids as continuous variables) and univariate general linear model (lipids as categorical variables). The results suggest that in women, high-density lipoprotein (HDL) cholesterol in adolescence is associated with high empathic concern (a component of other-directed compassion) in adulthood. The results show further that, in women, an HDL cholesterol level above 1.2 mmol/L in adulthood is associated with high other-directed compassion and empathic concern in adulthood. The present study provides tentative evidence that biological factors such as lipids might play a role in the development of empathic concern and other-directed compassion.
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Affiliation(s)
- Sofia Pihlaja
- Research Center of Psychology, Faculty of Education and Psychology, University of Oulu, Oulu, Finland
| | - Erika Jääskeläinen
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Psychiatry, Oulu University Hospital, Oulu, Finland
| | - Laura Heikkilä
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Medical Research Center Oulu, Oulu University Hospital, University of Oulu, Oulu, Finland
- Department of Sports and Exercise Medicine, Oulu Deaconess Institute Foundation sr, Oulu, Finland
| | - Mirka Hintsanen
- Research Center of Psychology, Faculty of Education and Psychology, University of Oulu, Oulu, Finland
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7
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Shi R, Xiang S, Jia T, Robbins TW, Kang J, Banaschewski T, Barker GJ, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Paus T, Poustka L, Hohmann S, Millenet S, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Lin X, Sahakian BJ, Feng J. Investigating grey matter volumetric trajectories through the lifespan at the individual level. Nat Commun 2024; 15:5954. [PMID: 39009591 PMCID: PMC11251262 DOI: 10.1038/s41467-024-50305-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 07/04/2024] [Indexed: 07/17/2024] Open
Abstract
Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to limited large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages, and the neurobiological basis underlying individual heterogeneity remains poorly understood. Here we identify, using the IMAGEN adolescent cohort followed up over 9 years (14-23 y), three groups of adolescents characterized by distinct developmental patterns of whole-brain gray matter volume (GMV). Group 1 show continuously decreasing GMV associated with higher neurocognitive performances than the other two groups during adolescence. Group 2 exhibit a slower rate of GMV decrease and lower neurocognitive performances compared with Group 1, which was associated with epigenetic differences and greater environmental burden. Group 3 show increasing GMV and lower baseline neurocognitive performances due to a genetic variation. Using the UK Biobank, we show these differences may be attenuated in mid-to-late adulthood. Our study reveals clusters of adolescent neurodevelopment based on GMV and the potential long-term impact.
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Grants
- R01 DA049238 NIDA NIH HHS
- R01 MH085772 NIMH NIH HHS
- R56 AG058854 NIA NIH HHS
- U54 EB020403 NIBIB NIH HHS
- National Key R&D Program of China (No.2023YFE0199700 [to X.L.])
- the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1 [to S.D.]), the National Institutes of Health (NIH) funded ENIGMA (grants 5U54EB020403-05 and 1R56AG058854-01 [to S.D.])
- NSFC grant 82150710554 and environMENTAL grant. Further support was provided by grants from: - the ANR (ANR-12-SAMA-0004, AAPG2019 - GeBra [to J.-L.M.]), the Eranet Neuron (AF12-NEUR0008-01 - WM2NA; and ANR-18-NEUR00002-01 - ADORe [to J.-L.M.]), the Fondation de France (00081242 [to J.-L.M.]), the Fondation pour la Recherche Médicale (DPA20140629802 [to J.-L.M.]), the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA [to J.-L.M.]), Paris Sud University IDEX 2012 [to J.-L.M.]
- the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant [to M.-L.P.M.]), the Fondation de l’Avenir (grant AP-RM-17-013 [to M.-L.P.M.])
- the Fédération pour la Recherche sur le Cerveau; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797 [to R.W.])
- the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT- 2007-037286 [to G.S.]), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313 [to G.S.]), Human Brain Project (HBP SGA 2, 785907, and HBP SGA 3, 945539 [to G.S.]), the Medical Research Council Grant 'c-VEDA’ (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1 [to G.S.]), the National Institute of Health (NIH) (R01DA049238 [to G.S.], A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers), the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152; 01EV0711 [to G.S.]; Forschungsnetz AERIAL 01EE1406A, 01EE1406B; Forschungsnetz IMAC-Mind 01GL1745B [to G.S.]), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2, SFB 940, TRR 265, NE 1383/14-1 [to G.S.])
- National Key R&D Program of China (No.2019YFA0709502 [to J.F.], No.2018YFC1312904 [to J.F.]),No.2019YFA0709502 [to J.F.], No.2018YFC1312904 [to J.F.]), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01 [to J.F.], ZJ Lab [to J.F.], and Shanghai Center for Brain Science and Brain-Inspired Technology [to J.F.]), the 111 Project (No.B18015 [to J.F.])
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Affiliation(s)
- Runye Shi
- School of Data Science, Fudan University, Shanghai, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, China
- School of Psychology, University of Southampton, Southampton, UK
| | - Trevor W Robbins
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie", Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie", Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Department of Child and Adolescent Psychiatry, AP-HP, Sorbonne Université, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie", Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein Kiel University, Kiel, Germany
| | | | - Tomáš Paus
- Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, QC, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, ON, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, China
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Xiaolei Lin
- School of Data Science, Fudan University, Shanghai, China.
- Huashan Institute of Medicine, Huashan Hospital affiliated to Fudan University, Shanghai, China.
| | - Barbara J Sahakian
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK.
| | - Jianfeng Feng
- School of Data Science, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, UK.
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8
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Rahmani F, Batson RD, Zimmerman A, Reddigari S, Bigler ED, Lanning SC, Ilasa E, Grafman JH, Lu H, Lin AP, Raji CA. Rate of abnormalities in quantitative MR neuroimaging of persons with chronic traumatic brain injury. BMC Neurol 2024; 24:235. [PMID: 38969967 PMCID: PMC11225195 DOI: 10.1186/s12883-024-03745-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 06/26/2024] [Indexed: 07/07/2024] Open
Abstract
BACKGROUND Mild traumatic brain injury (mTBI) can result in lasting brain damage that is often too subtle to detect by qualitative visual inspection on conventional MR imaging. Although a number of FDA-cleared MR neuroimaging tools have demonstrated changes associated with mTBI, they are still under-utilized in clinical practice. METHODS We investigated a group of 65 individuals with predominantly mTBI (60 mTBI, 48 due to motor-vehicle collision, mean age 47 ± 13 years, 27 men and 38 women) with MR neuroimaging performed in a median of 37 months post-injury. We evaluated abnormalities in brain volumetry including analysis of left-right asymmetry by quantitative volumetric analysis, cerebral perfusion by pseudo-continuous arterial spin labeling (PCASL), white matter microstructure by diffusion tensor imaging (DTI), and neurometabolites via magnetic resonance spectroscopy (MRS). RESULTS All participants demonstrated atrophy in at least one lobar structure or increased lateral ventricular volume. The globus pallidi and cerebellar grey matter were most likely to demonstrate atrophy and asymmetry. Perfusion imaging revealed significant reductions of cerebral blood flow in both occipital and right frontoparietal regions. Diffusion abnormalities were relatively less common though a subset analysis of participants with higher resolution DTI demonstrated additional abnormalities. All participants showed abnormal levels on at least one brain metabolite, most commonly in choline and N-acetylaspartate. CONCLUSION We demonstrate the presence of coup-contrecoup perfusion injury patterns, widespread atrophy, regional brain volume asymmetry, and metabolic aberrations as sensitive markers of chronic mTBI sequelae. Our findings expand the historic focus on quantitative imaging of mTBI with DTI by highlighting the complementary importance of volumetry, arterial spin labeling perfusion and magnetic resonance spectroscopy neurometabolite analyses in the evaluation of chronic mTBI.
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Affiliation(s)
- Farzaneh Rahmani
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Richard D Batson
- Endocrine & Brain Injury Research Alliance, Neurevolution Medicine, PLLC, NUNM Helfgott Research Institute, Portland, Oregon, USA
| | | | | | - Erin D Bigler
- Department of Neurology, Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | | | | | - Jordan H Grafman
- Departments of Physical Medicine & Rehabilitation, Neurology, Cognitive Neurology and Alzheimer's Center, Department of Psychiatry, Feinberg School of Medicine, Department of Psychology, Weinberg College of Arts and Sciences, Northwestern University, Chicago, IL, USA
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alexander P Lin
- Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Cyrus A Raji
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA.
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9
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Penalver-Andres JA, Buetler KA, Koenig T, Müri RM, Marchal-Crespo L. Resting-State Functional Networks Correlate with Motor Performance in a Complex Visuomotor Task: An EEG Microstate Pilot Study on Healthy Individuals. Brain Topogr 2024; 37:590-607. [PMID: 36566448 PMCID: PMC11199229 DOI: 10.1007/s10548-022-00934-9] [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] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 12/05/2022] [Indexed: 12/26/2022]
Abstract
Developing motor and cognitive skills is needed to achieve expert (motor) performance or functional recovery from a neurological condition, e.g., after stroke. While extensive practice plays an essential role in the acquisition of good motor performance, it is still unknown whether certain person-specific traits may predetermine the rate of motor learning. In particular, learners' functional brain organisation might play an important role in appropriately performing motor tasks. In this paper, we aimed to study how two critical cognitive brain networks-the Attention Network (AN) and the Default Mode Network (DMN)-affect the posterior motor performance in a complex visuomotor task: virtual surfing. We hypothesised that the preactivation of the AN would affect how participants divert their attention towards external stimuli, resulting in robust motor performance. Conversely, the excessive involvement of the DMN-linked to internally diverted attention and mind-wandering-would be detrimental for posterior motor performance. We extracted seven widely accepted microstates-representing participants mind states at rest-out of the Electroencephalography (EEG) resting-state recordings of 36 healthy volunteers, prior to execution of the virtual surfing task. By correlating neural biomarkers (microstates) and motor behavioural metrics, we confirmed that the preactivation of the posterior DMN was correlated with poor posterior performance in the motor task. However, we only found a non-significant association between AN preactivation and the posterior motor performance. In this EEG study, we propose the preactivation of the posterior DMN-imaged using EEG microstates-as a neural trait related to poor posterior motor performance. Our findings suggest that the role of the executive control system is to preserve an homeostasis between the AN and the DMN. Therefore, neurofeedback-based downregulation of DMN preactivation could help optimise motor training.
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Affiliation(s)
- Joaquin A Penalver-Andres
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
- Psychosomatic Medicine, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
| | - Karin A Buetler
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - René M Müri
- Perception and Eye Movement Laboratory, Department of Biomedical Research (DBMR) and Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Laura Marchal-Crespo
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Cognitive Robotics, Delft University of Technology, Delft, The Netherlands
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10
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Grevet LT, Teixeira DS, Pan PM, Jackowski AP, Zugman A, Miguel EC, Rohde LA, Salum GA. The association between duration of breastfeeding and the trajectory of brain development from childhood to young adulthood: an 8-year longitudinal study. Eur Child Adolesc Psychiatry 2024; 33:1863-1873. [PMID: 37650992 DOI: 10.1007/s00787-023-02283-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 08/14/2023] [Indexed: 09/01/2023]
Abstract
Breastfeeding has been associated with several short- and long-term health benefits, including positive cognitive and behavioral outcomes. However, the impact of breastfeeding on structural brain development over time remains unclear. We aimed to assess the association between breastfeeding duration in childhood and the developmental trajectory of overall cortical thickness, cortical area, and total intracranial volume during the transition from childhood to early adulthood. Participants included 670 children and adolescents with 1326 MRI scans acquired over 8 years from the Brazilian High-Risk Cohort for Mental Conditions (BHRCS). Breastfeeding was assessed using a questionnaire answered by the parents. Brain measures were estimated using MRI T1-weighted images at three time points, with 3-year intervals. Data were evaluated using generalized additive models adjusted for multiple confounders. We found that a longer breastfeeding duration was directly associated with higher global cortical thickness in the left (edf = 1.0, F = 6.07, p = 0.01) and right (edf = 1.0, F = 4.70, p = 0.03) hemispheres. For the total intracranial volume, we found an interaction between duration of breastfeeding and developmental stage (edf = 1.0, F = 6.81, p = 0.009). No association was found between breastfeeding duration and brain area. Our study suggests that the duration of breastfeeding impacts overall cortical thickness and the development of total brain volume, but not area. This study adds to the evidence on the potential impact of breastfeeding on brain development and provides relevant insights into the mechanisms by which breastfeeding might confer cognitive and mental health benefits.
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Affiliation(s)
- Laura Tietzmann Grevet
- Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), School of Medicine, Avenida Ipiranga, 6681-Partenon, Porto Alegre, Rio Grande do Sul, 90619-900, Brazil.
| | - Danielle Soares Teixeira
- Hospital de Clínicas de Porto Alegre (HCPA), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
| | - Pedro Mario Pan
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
- Universidade Federal de São Paulo (UNIFESP), Iterdisciplinary Lab for Clinical Neurosciences (LiNC), São Paulo, SP, Brazil
| | - Andrea Parolin Jackowski
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
- Universidade Federal de São Paulo (UNIFESP), Iterdisciplinary Lab for Clinical Neurosciences (LiNC), São Paulo, SP, Brazil
| | - André Zugman
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
- Universidade Federal de São Paulo (UNIFESP), Iterdisciplinary Lab for Clinical Neurosciences (LiNC), São Paulo, SP, Brazil
| | - Euripedes Constantino Miguel
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
- Department and Institute of Psychiatry, Universidade de São Paulo (USP), São Paulo, SP, Brazil
| | - Luis Augusto Rohde
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
- ADHD Outpatient Program and Developmental Psychiatry Program, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Giovanni Abrahão Salum
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
- Child Mind Institute, New York, NY, USA
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11
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Escalante YY, Adams JN, Yassa MA, Janssen N. Age-related constraints on the spatial geometry of the brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594753. [PMID: 38798452 PMCID: PMC11118588 DOI: 10.1101/2024.05.17.594753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Age-related structural brain changes may be better captured by assessing complex spatial geometric differences rather than isolated changes to individual regions. We applied a novel analytic method to quantify age-related changes to the spatial anatomy of the brain by measuring expansion and compression of global brain shape and the distance between cross-hemisphere homologous regions. To test how global brain shape and regional distances are affected by aging, we analyzed 2,603 structural MRIs (range: 30-97 years). Increasing age was associated with global shape expansion across inferior-anterior gradients, global compression across superior-posterior gradients, and regional expansion between frontotemporal homologues. Specific patterns of global and regional expansion and compression were further associated with clinical impairment and distinctly related to deficits in various cognitive domains. These findings suggest that changes to the complex spatial anatomy and geometry of the aging brain may be associated with reduced efficiency and cognitive dysfunction in older adults.
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12
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Farrher E, Grinberg F, Khechiashvili T, Neuner I, Konrad K, Shah NJ. Spatiotemporal Patterns of White Matter Maturation after Pre-Adolescence: A Diffusion Kurtosis Imaging Study. Brain Sci 2024; 14:495. [PMID: 38790472 PMCID: PMC11119177 DOI: 10.3390/brainsci14050495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Diffusion tensor imaging (DTI) enables the assessment of changes in brain tissue microstructure during maturation and ageing. In general, patterns of cerebral maturation and decline render non-monotonic lifespan trajectories of DTI metrics with age, and, importantly, the rate of microstructural changes is heterochronous for various white matter fibres. Recent studies have demonstrated that diffusion kurtosis imaging (DKI) metrics are more sensitive to microstructural changes during ageing compared to those of DTI. In a previous work, we demonstrated that the Cohen's d of mean diffusional kurtosis (dMK) represents a useful biomarker for quantifying maturation heterochronicity. However, some inferences on the maturation grades of different fibre types, such as association, projection, and commissural, were of a preliminary nature due to the insufficient number of fibres considered. Hence, the purpose of this follow-up work was to further explore the heterochronicity of microstructural maturation between pre-adolescence and middle adulthood based on DTI and DKI metrics. Using the effect size of the between-group parametric changes and Cohen's d, we observed that all commissural fibres achieved the highest level of maturity, followed by the majority of projection fibres, while the majority of association fibres were the least matured. We also demonstrated that dMK strongly correlates with the maxima or minima of the lifespan curves of DTI metrics. Furthermore, our results provide substantial evidence for the existence of spatial gradients in the timing of white matter maturation. In conclusion, our data suggest that DKI provides useful biomarkers for the investigation of maturation spatial heterogeneity and heterochronicity.
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Affiliation(s)
- Ezequiel Farrher
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
| | - Farida Grinberg
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany
| | - Tamara Khechiashvili
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
| | - Irene Neuner
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
| | - Kerstin Konrad
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry and Psychotherapy, RWTH Aachen University, 52074 Aachen, Germany
- Institute of Neuroscience and Medicine 3, INM-3, Forschungszentrum Jülich, 52425 Jülich, Germany
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, 52425 Jülich, Germany
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13
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Tao X, Zhu Z, Wang L, Li C, Sun L, Wang W, Gong W. Biomarkers of Aging and Relevant Evaluation Techniques: A Comprehensive Review. Aging Dis 2024; 15:977-1005. [PMID: 37611906 PMCID: PMC11081160 DOI: 10.14336/ad.2023.00808-1] [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: 06/03/2023] [Accepted: 08/08/2023] [Indexed: 08/25/2023] Open
Abstract
The risk of developing chronic illnesses and disabilities is increasing with age. To predict and prevent aging, biomarkers relevant to the aging process must be identified. This paper reviews the known molecular, cellular, and physiological biomarkers of aging. Moreover, we discuss the currently available technologies for identifying these biomarkers, and their applications and potential in aging research. We hope that this review will stimulate further research and innovation in this emerging and fast-growing field.
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Affiliation(s)
- Xue Tao
- Department of Research, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
| | - Ziman Zhu
- Beijing Rehabilitation Medicine Academy, Capital Medical University, Beijing, China.
| | - Liguo Wang
- Key Laboratory of Protein Sciences, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China.
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.
| | - Liwei Sun
- School of Biomedical Engineering, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.
| | - Wei Wang
- Department of Rehabilitation Radiology, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
| | - Weijun Gong
- Department of Neurological Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
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14
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Jakimovski D, Dorn RP, Regno MD, Bartnik A, Bergsland N, Ramanathan M, Dwyer MG, Benedict RHB, Zivadinov R, Szigeti K. Human restricted CHRFAM7A gene increases brain efficiency. Front Neurosci 2024; 18:1359028. [PMID: 38711941 PMCID: PMC11070550 DOI: 10.3389/fnins.2024.1359028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 04/12/2024] [Indexed: 05/08/2024] Open
Abstract
Introduction CHRFAM7A, a uniquely human fusion gene, has been associated with neuropsychiatric disorders including Alzheimer's disease, schizophrenia, anxiety, and attention deficit disorder. Understanding the physiological function of CHRFAM7A in the human brain is the first step to uncovering its role in disease. CHRFAM7A was identified as a potent modulator of intracellular calcium and an upstream regulator of Rac1 leading to actin cytoskeleton reorganization and a switch from filopodia to lamellipodia implicating a more efficient neuronal structure. We performed a neurocognitive-MRI correlation exploratory study on 46 normal human subjects to explore the effect of CHRFAM7A on human brain. Methods Dual locus specific genotyping of CHRFAM7A was performed on genomic DNA to determine copy number (TaqMan assay) and orientation (capillary sequencing) of the CHRFAM7A alleles. As only the direct allele is expressed at the protein level and affects α7 nAChR function, direct allele carriers and non-carriers are compared for neuropsychological and MRI measures. Subjects underwent neuropsychological testing to measure motor (Timed 25-foot walk test, 9-hole peg test), cognitive processing speed (Symbol Digit Modalities Test), Learning and memory (California Verbal Learning Test immediate and delayed recall, Brief Visuospatial Memory Test-Revised immediate and delayed recall) and Beck Depression Inventory-Fast Screen, Fatigue Severity Scale. All subjects underwent MRI scanning on the same 3 T GE scanner using the same protocol. Global and tissue-specific volumes were determined using validated cross-sectional algorithms including FSL's Structural Image Evaluation, using Normalization, of Atrophy (SIENAX) and FSL's Integrated Registration and Segmentation Tool (FIRST) on lesion-inpainted images. The cognitive tests were age and years of education-adjusted using analysis of covariance (ANCOVA). Age-adjusted analysis of covariance (ANCOVA) was performed on the MRI data. Results CHRFAM7A direct allele carrier and non-carrier groups included 33 and 13 individuals, respectively. Demographic variables (age and years of education) were comparable. CHRFAM7A direct allele carriers demonstrated an upward shift in cognitive performance including cognitive processing speed, learning and memory, reaching statistical significance in visual immediate recall (FDR corrected p = 0.018). The shift in cognitive performance was associated with smaller whole brain volume (uncorrected p = 0.046) and lower connectivity by resting state functional MRI in the visual network (FDR corrected p = 0.027) accentuating the cognitive findings. Conclusion These data suggest that direct allele carriers harbor a more efficient brain consistent with the cellular biology of actin cytoskeleton and synaptic gain of function. Further larger human studies of cognitive measures correlated with MRI and functional imaging are needed to decipher the impact of CHRFAM7A on brain function.
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Affiliation(s)
- Dejan Jakimovski
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Ryu P. Dorn
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Megan Del Regno
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Alexander Bartnik
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Niels Bergsland
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Murali Ramanathan
- Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Michael G. Dwyer
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
- Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Ralph H. B. Benedict
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Robert Zivadinov
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
- Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Kinga Szigeti
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
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15
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Ghosh A, Singh S, S. M, Jagtap T, Issac TG. Music and the aging brain - Exploring the role of long-term Carnatic music training on cognition and gray matter volumes. J Neurosci Rural Pract 2024; 15:327-333. [PMID: 38746502 PMCID: PMC11090532 DOI: 10.25259/jnrp_605_2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/14/2024] [Indexed: 05/16/2024] Open
Abstract
Objectives Aging is a natural process and is often associated with an increased incidence of cognitive impairment. Physical exercise, diet, and leisure activities (music, dance, and art) are some of the lifestyle factors that contribute to healthy aging. The present study aims to explore the differences in cognitive functioning between aging individuals involved in musical activity throughout their lifetime and the ones who were not. Materials and Methods Fifty-one healthy elderly individuals (50-80 years of age) residing in an urban locality were selected for the study from the Tata Longitudinal Study of Aging cohort. Participants were divided into two groups: Active musicians trained in Carnatic music for more than five years (n = 18) and age-matched non-musicians (n = 33). Addenbrooke cognitive examination-III, Hindi mental status examination, and trail-making test-B (TMT-B) were used to assess cognitive functioning. A Generalized Linear Regression Model was performed including covariates such as gender, age, and years of education. We also looked at the available brain magnetic resonance imaging data of a subset of our study population to inspect the volumetric differences between musicians and non-musicians. Results Our results showed that musicians had significantly better visuospatial abilities as compared to non-musicians (P = 0.043). Musicians (130.89 ± 45.16 s) also took less time to complete the TMT-B task than non-musicians (148.73 ± 39.65 s), although it was not a statistically significant difference (P =0.150). In addition, brain imaging data suggested that musicians had increased gray matter volumes in the right precuneus, right post-central gyrus, right medial and superior frontal gyrus, right orbital gyrus, left middle temporal gyrus, left cuneus, left fusiform gyrus, and bilateral cingulate gyrus. Conclusion Our findings are indicative of music being an important attribute in improving cognitive reserve and predicting cognitive resilience. These findings pave the way to explore the utility of non-pharmacological interventions, such as Music Therapy (especially Carnatic music in the Indian context), as a potential factor for improving cognitive reserve in elderly individuals.
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Affiliation(s)
- Aishwarya Ghosh
- Centre for Brain Research, Indian Institute of Science, Bengaluru, Karnataka, India
| | - Sadhana Singh
- Centre for Brain Research, Indian Institute of Science, Bengaluru, Karnataka, India
| | - Monisha S.
- Centre for Brain Research, Indian Institute of Science, Bengaluru, Karnataka, India
| | - Tejaswini Jagtap
- Centre for Brain Research, Indian Institute of Science, Bengaluru, Karnataka, India
| | - Thomas Gregor Issac
- Centre for Brain Research, Indian Institute of Science, Bengaluru, Karnataka, India
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16
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Shao J, Qin J, Wang H, Sun Y, Zhang W, Wang X, Wang T, Xue L, Yao Z, Lu Q. Capturing the Individual Deviations From Normative Models of Brain Structure for Depression Diagnosis and Treatment. Biol Psychiatry 2024; 95:403-413. [PMID: 37579934 DOI: 10.1016/j.biopsych.2023.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/20/2023] [Accepted: 08/03/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND The high heterogeneity of depression prevents us from obtaining reproducible and definite anatomical maps of brain structural changes associated with the disorder, which limits the individualized diagnosis and treatment of patients. In this study, we investigated the clinical issues related to depression according to individual deviations from normative ranges of gray matter volume. METHODS We enrolled 1092 participants, including 187 patients with depression and 905 healthy control participants. Structural magnetic resonance imaging data of healthy control participants from the Human Connectome Project (n = 510) and REST-meta-MDD Project (n = 229) were used to establish a normative model across the life span in adults 18 to 65 years old for each brain region. Deviations from the normative range for 187 patients and 166 healthy control participants recruited from two local hospitals were captured as normative probability maps, which were used to identify the disease risk and treatment-related latent factors. RESULTS In contrast to case-control results, our normative modeling approach revealed highly individualized patterns of anatomic abnormalities in depressed patients (less than 11% extreme deviation overlapping for any regions). Based on our classification framework, models trained with individual normative probability maps (area under the receiver operating characteristic curve range, 0.7146-0.7836) showed better performance than models trained with original gray matter volume values (area under the receiver operating characteristic curve range, 0.6800-0.7036), which was verified in an independent external test set. Furthermore, different latent brain structural factors in relation to antidepressant treatment were revealed by a Bayesian model based on normative probability maps, suggesting distinct treatment response and inclination. CONCLUSIONS Capturing personalized deviations from a normative range could help in understanding the heterogeneous neurobiology of depression and thus guide clinical diagnosis and treatment of depression.
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Affiliation(s)
- Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China; Key Laboratory of Child Development and Learning Science, Ministry of Education, Southeast University, Nanjing, China
| | - Jiaolong Qin
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Huan Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China; Key Laboratory of Child Development and Learning Science, Ministry of Education, Southeast University, Nanjing, China
| | - Yurong Sun
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China; Key Laboratory of Child Development and Learning Science, Ministry of Education, Southeast University, Nanjing, China
| | - Wei Zhang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China; Key Laboratory of Child Development and Learning Science, Ministry of Education, Southeast University, Nanjing, China
| | - Xinyi Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China; Key Laboratory of Child Development and Learning Science, Ministry of Education, Southeast University, Nanjing, China
| | - Ting Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China; Key Laboratory of Child Development and Learning Science, Ministry of Education, Southeast University, Nanjing, China
| | - Li Xue
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China; Key Laboratory of Child Development and Learning Science, Ministry of Education, Southeast University, Nanjing, China
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China; Key Laboratory of Child Development and Learning Science, Ministry of Education, Southeast University, Nanjing, China.
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17
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Davidson TL, Stevenson RJ. Vulnerability of the Hippocampus to Insults: Links to Blood-Brain Barrier Dysfunction. Int J Mol Sci 2024; 25:1991. [PMID: 38396670 PMCID: PMC10888241 DOI: 10.3390/ijms25041991] [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: 01/03/2024] [Revised: 01/25/2024] [Accepted: 01/29/2024] [Indexed: 02/25/2024] Open
Abstract
The hippocampus is a critical brain substrate for learning and memory; events that harm the hippocampus can seriously impair mental and behavioral functioning. Hippocampal pathophysiologies have been identified as potential causes and effects of a remarkably diverse array of medical diseases, psychological disorders, and environmental sources of damage. It may be that the hippocampus is more vulnerable than other brain areas to insults that are related to these conditions. One purpose of this review is to assess the vulnerability of the hippocampus to the most prevalent types of insults in multiple biomedical domains (i.e., neuroactive pathogens, neurotoxins, neurological conditions, trauma, aging, neurodegenerative disease, acquired brain injury, mental health conditions, endocrine disorders, developmental disabilities, nutrition) and to evaluate whether these insults affect the hippocampus first and more prominently compared to other brain loci. A second purpose is to consider the role of hippocampal blood-brain barrier (BBB) breakdown in either causing or worsening the harmful effects of each insult. Recent research suggests that the hippocampal BBB is more fragile compared to other brain areas and may also be more prone to the disruption of the transport mechanisms that act to maintain the internal milieu. Moreover, a compromised BBB could be a factor that is common to many different types of insults. Our analysis indicates that the hippocampus is more vulnerable to insults compared to other parts of the brain, and that developing interventions that protect the hippocampal BBB may help to prevent or ameliorate the harmful effects of many insults on memory and cognition.
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Affiliation(s)
- Terry L. Davidson
- Department of Neuroscience, Center for Neuroscience and Behavior, American University, 4400 Massachusetts Avenue, NW, Washington, DC 20016, USA
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18
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da Silva SP, de Castro CCM, Rabelo LN, Engelberth RC, Fernández-Calvo B, Fiuza FP. Neuropathological and sociodemographic factors associated with the cortical amyloid load in aging and Alzheimer's disease. GeroScience 2024; 46:621-643. [PMID: 37870702 PMCID: PMC10828279 DOI: 10.1007/s11357-023-00982-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 10/10/2023] [Indexed: 10/24/2023] Open
Abstract
Alzheimer's disease (AD) is the leading cause of dementia and is characterized by a progressive decline in cognitive abilities. A pathological hallmark of AD is a region-specific accumulation of the amyloid-beta protein (Aβ). Here, we explored the association between regional Aβ deposition, sociodemographic, and local biochemical factors. We quantified the Aβ burden in postmortem cortical samples from parietal (PCx) and temporal (TCx) regions of 27 cognitively unimpaired (CU) and 15 AD donors, aged 78-100 + years. Histological images of Aβ immunohistochemistry and local concentrations of pathological and inflammatory proteins were obtained at the "Aging, Dementia and TBI Study" open database. We used the area fraction fractionator stereological methodology to quantify the Aβ burden in the gray and white matter within each cortical region. We found higher Aβ burdens in the TCx of AD octogenarians compared to CU ones. We also found higher Aβ loads in the PCx of AD nonagenarians than in AD octogenarians. Moreover, AD women exhibited increased Aβ deposition compared to CU women. Interestingly, we observed a negative correlation between education years and Aβ burden in the white matter of both cortices in CU samples. In AD brains, the Aβ40, Aβ42, and pTau181 isoforms of Aβ and Tau proteins were positively correlated with the Aβ burden. Additionally, in the TCx of AD donors, the proinflammatory cytokine TNFα showed a positive correlation with the Aβ load. These novel findings contribute to understanding the interplay between sociodemographic characteristics, local inflammatory signaling, and the development of AD-related pathology in the cerebral cortex.
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Affiliation(s)
- Sayonara P da Silva
- Graduate Program in Neuroengineering, Edmond and Lily Safra International Institute of Neuroscience, Santos Dumont Institute, Macaíba, RN, 59280-000, Brazil
| | - Carla C M de Castro
- Graduate Program in Neuroengineering, Edmond and Lily Safra International Institute of Neuroscience, Santos Dumont Institute, Macaíba, RN, 59280-000, Brazil
| | - Lívia N Rabelo
- Graduate Program in Neuroengineering, Edmond and Lily Safra International Institute of Neuroscience, Santos Dumont Institute, Macaíba, RN, 59280-000, Brazil
- Laboratory of Neurochemical Studies, Department of Physiology and Behavior, Biosciences Center, Federal University of Rio Grande Do Norte, Natal, Brazil
| | - Rovena C Engelberth
- Laboratory of Neurochemical Studies, Department of Physiology and Behavior, Biosciences Center, Federal University of Rio Grande Do Norte, Natal, Brazil
| | - Bernardino Fernández-Calvo
- Department of Psychology, University of Córdoba, Córdoba, Spain
- Maimonides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain
- Reina Sofia University Hospital, Córdoba, Spain
- Department of Psychology, Federal University of Paraíba, João Pessoa, Brazil
| | - Felipe P Fiuza
- Graduate Program in Neuroengineering, Edmond and Lily Safra International Institute of Neuroscience, Santos Dumont Institute, Macaíba, RN, 59280-000, Brazil.
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19
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Wu Y, Chen Y, Yang Y, Lin C, Su S, Zhao J, Wu S, Wu G, Liu H, Liu X, Yang Z, Zhang J, Huang B. Predicting brain age using partition modeling strategy and atlas-based attentional enhancement in the Chinese population. Cereb Cortex 2024; 34:bhae030. [PMID: 38342684 DOI: 10.1093/cercor/bhae030] [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: 11/13/2023] [Revised: 01/13/2024] [Accepted: 01/15/2024] [Indexed: 02/13/2024] Open
Abstract
As a biomarker of human brain health during development, brain age is estimated based on subtle differences in brain structure from those under typical developmental. Magnetic resonance imaging (MRI) is a routine diagnostic method in neuroimaging. Brain age prediction based on MRI has been widely studied. However, few studies based on Chinese population have been reported. This study aimed to construct a brain age predictive model for the Chinese population across its lifespan. We developed a partition prediction method based on transfer learning and atlas attention enhancement. The participants were separated into four age groups, and a deep learning model was trained for each group to identify the brain regions most critical for brain age prediction. The Atlas attention-enhancement method was also used to help the models focus only on critical brain regions. The proposed method was validated using 354 participants from domestic datasets. For prediction performance in the testing sets, the mean absolute error was 2.218 ± 1.801 years, and the Pearson correlation coefficient (r) was 0.969, exceeding previous results for wide-range brain age prediction. In conclusion, the proposed method could provide brain age estimation to assist in assessing the status of brain health.
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Affiliation(s)
- Yingtong Wu
- Medical AI Lab, School of Biomedical Engineering, Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, Guangdong Province, China
| | - Yingqian Chen
- Department of Radiology, the First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Second Road, Guangzhou 510080, Guangdong Province, China
| | - Yang Yang
- Department of Radiology, Suining Central Hospital, 127 Desheng West Road, Suining 629099, Sichuan Province, China
- Medical Imaging Center of Guizhou Province, Department of Radiology, The Affiliated Hospital of Zunyi Medical University, 149 Dalian Road, Zunyi 563000, Guizhou Province, China
| | - Chuxuan Lin
- Medical AI Lab, School of Biomedical Engineering, Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
| | - Shu Su
- Department of Radiology, the First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Second Road, Guangzhou 510080, Guangdong Province, China
| | - Jing Zhao
- Department of Radiology, the First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Second Road, Guangzhou 510080, Guangdong Province, China
| | - Songxiong Wu
- Radiology Department, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
| | - Guangyao Wu
- Radiology Department, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
| | - Heng Liu
- Medical Imaging Center of Guizhou Province, Department of Radiology, The Affiliated Hospital of Zunyi Medical University, 149 Dalian Road, Zunyi 563000, Guizhou Province, China
| | - Xia Liu
- Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, 1080 Cuizhu Road, Shenzhen 518118, Guangdong Province, China
| | - Zhiyun Yang
- Department of Radiology, the First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Second Road, Guangzhou 510080, Guangdong Province, China
| | - Jian Zhang
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, 1068 Xueyuan Avenue, Shenzhen 518055, Guangdong Province, China
- School of Pharmaceutical Sciences, Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
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20
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Baranyi G, Williamson L, Feng Z, Tomlinson S, Vieno M, Dibben C. Early life PM 2.5 exposure, childhood cognitive ability and mortality between age 11 and 86: A record-linkage life-course study from Scotland. ENVIRONMENTAL RESEARCH 2023; 238:117021. [PMID: 37659643 DOI: 10.1016/j.envres.2023.117021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 08/18/2023] [Accepted: 08/29/2023] [Indexed: 09/04/2023]
Abstract
BACKGROUND Living in areas with high air pollution concentrations is associated with all-cause and cause-specific mortality. Exposure in sensitive developmental periods might be long-lasting but studies with very long follow-up are rare, and mediating pathways between early life exposure and life-course mortality are not fully understood. METHODS Data were drawn from the Scottish Longitudinal Study Birth Cohort of 1936, a representative record-linkage study comprising 5% of the Scottish population born in 1936. Participants had valid age 11 cognitive ability test scores along with linked mortality data until age 86. Fine particle (PM2.5) concentrations estimated with the EMEP4UK atmospheric chemistry transport model were linked to participants' residential address derived from the National Identity Register in 1939 (age 3). Confounder-adjusted Cox regression estimated associations between PM2.5 and mortality; regression-based causal mediation analysis explored mediation through childhood cognitive ability. RESULTS The final sample consisted of 2734 individuals with 1608 deaths registered during the 1,833,517 person-months at risk follow-up time. Higher early life PM2.5 exposure increased the risk of all-cause mortality (HR = 1.03, 95% CI: 1.01-1.04 per 10 μg m-3 increment), associations were stronger for mortality between age 65 and 86. PM2.5 increased the risk of cancer-related mortality (HR = 1.05, 95% CI: 1.02-1.08), especially for lung cancer among females (HR = 1.11, 95% CI: 1.02-1.21), but not for cardiovascular and respiratory diseases. Higher PM2.5 in early life (≥50 μg m-3) was associated with lower childhood cognitive ability, which, in turn, increased the risk of all-cause mortality and mediated 25% of the total associations. CONCLUSIONS In our life-course study with 75-year of continuous mortality records, we found that exposure to air pollution in early life was associated with higher mortality in late adulthood, and that childhood cognitive ability partly mediated this relationship. Findings suggest that past air pollution concentrations will likely impact health and longevity for decades to come.
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Affiliation(s)
- Gergő Baranyi
- Centre for Research on Environment, Society and Health, School of Geosciences, The University of Edinburgh, Edinburgh, UK.
| | - Lee Williamson
- Centre for Research on Environment, Society and Health, School of Geosciences, The University of Edinburgh, Edinburgh, UK; Longitudinal Studies Centre - Scotland, School of GeoSciences, The University of Edinburgh, Edinburgh, UK
| | - Zhiqiang Feng
- Centre for Research on Environment, Society and Health, School of Geosciences, The University of Edinburgh, Edinburgh, UK
| | - Sam Tomlinson
- UK Centre for Ecology & Hydrology, Library Ave, Bailrigg, Lancaster, UK
| | - Massimo Vieno
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, UK
| | - Chris Dibben
- Centre for Research on Environment, Society and Health, School of Geosciences, The University of Edinburgh, Edinburgh, UK
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21
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Roca V, Kuchcinski G, Pruvo JP, Manouvriez D, Leclerc X, Lopes R. A three-dimensional deep learning model for inter-site harmonization of structural MR images of the brain: Extensive validation with a multicenter dataset. Heliyon 2023; 9:e22647. [PMID: 38107313 PMCID: PMC10724680 DOI: 10.1016/j.heliyon.2023.e22647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/03/2023] [Accepted: 11/15/2023] [Indexed: 12/19/2023] Open
Abstract
In multicenter MRI studies, pooling the imaging data can introduce site-related variabilities and can therefore bias the subsequent analyses. To harmonize the intensity distributions of brain MR images in a multicenter dataset, unsupervised deep learning methods can be employed. Here, we developed a model based on cycle-consistent adversarial networks for the harmonization of T1-weighted brain MR images. In contrast to previous works, it was designed to process three-dimensional whole-brain images in a stable manner while optimizing computation resources. Using six different MRI datasets for healthy adults (n=1525 in total) with different acquisition parameters, we tested the model in (i) three pairwise harmonizations with site effects of various sizes, (ii) an overall harmonization of the six datasets with different age distributions, and (iii) a traveling-subject dataset. Our results for intensity distributions, brain volumes, image quality metrics and radiomic features indicated that the MRI characteristics at the various sites had been effectively homogenized. Next, brain age prediction experiments and the observed correlation between the gray-matter volume and age showed that thanks to an appropriate training strategy and despite biological differences between the dataset populations, the model reinforced biological patterns. Furthermore, radiologic analyses of the harmonized images attested to the conservation of the radiologic information in the original images. The robustness of the harmonization model (as judged with various datasets and metrics) demonstrates its potential for application in retrospective multicenter studies.
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Affiliation(s)
- Vincent Roca
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France
| | - Grégory Kuchcinski
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neurosciences & Cognition, F-59000 Lille, France
- CHU Lille, Department of Neuroradiology, F-59000 Lille, France
| | - Jean-Pierre Pruvo
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neurosciences & Cognition, F-59000 Lille, France
- CHU Lille, Department of Neuroradiology, F-59000 Lille, France
| | - Dorian Manouvriez
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France
| | - Xavier Leclerc
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neurosciences & Cognition, F-59000 Lille, France
- CHU Lille, Department of Neuroradiology, F-59000 Lille, France
| | - Renaud Lopes
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neurosciences & Cognition, F-59000 Lille, France
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22
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Kraft JD, Hampstead BM. A Systematic Review of tACS Effects on Cognitive Functioning in Older Adults Across the Healthy to Dementia Spectrum. Neuropsychol Rev 2023:10.1007/s11065-023-09621-3. [PMID: 37882864 PMCID: PMC11045666 DOI: 10.1007/s11065-023-09621-3] [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: 11/09/2022] [Accepted: 09/26/2023] [Indexed: 10/27/2023]
Abstract
Transcranial alternating current stimulation (tACS) is a form of noninvasive brain stimulation that has experienced rapid growth within the aging population over the past decade due to its potential for modulating cognitive functioning across the "intact" to dementia spectrum. For this reason, we performed a systematic review of the literature to evaluate the efficacy of tACS on cognitive functioning in older adults, including those with cognitive impairment. Our review was completed in June 2023 using Psych INFO, Embase, PubMed, and Cochrane databases. Out of 479 screened articles, 21 met inclusion criteria and were organized according to clinical diagnoses. Seven out of nine studies targeted cognitively intact older adults and showed some type of cognitive improvement after stimulation, whereas nine out of twelve studies targeted clinical diagnoses and showed improved cognitive performance to varying degrees. Studies showed considerable heterogeneity in methodology, stimulation parameters, participant characteristics, choice of cognitive task, and analytic strategy, all of which reinforce the need for standardized reporting of tACS methods. Through this heterogeneity, multiple patterns are described, such as disease progression influencing tACS effects and the need for individualized tailoring. For clinical translation, it is imperative that the field (a) better understand the physiological effects of tACS in these populations, especially in respect to biomarkers, (b) document a causal relationship between tACS delivery and neurophysiological/cognitive effects, and (c) systematically establish dosing parameters (e.g., amplitude, stimulation frequency, number and duration of sessions, need for booster/maintenance sessions).
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Affiliation(s)
- Jacob D Kraft
- Research Program On Cognition and Neuromodulation Based Interventions, Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI, 48105, USA.
- Department of Psychiatry &, Behavioral Health, The Ohio State University, Columbus, OH, 43210, USA.
| | - Benjamin M Hampstead
- Research Program On Cognition and Neuromodulation Based Interventions, Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI, 48105, USA
- Mental Health Service, Neuropsychology Section, VA Ann Arbor Healthcare System, Ann Arbor, MI, 48105, USA
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23
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Lamontagne-Caron R, Desrosiers P, Potvin O, Doyon N, Duchesne S. Predicting cognitive decline in a low-dimensional representation of brain morphology. Sci Rep 2023; 13:16793. [PMID: 37798311 PMCID: PMC10556003 DOI: 10.1038/s41598-023-43063-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 09/19/2023] [Indexed: 10/07/2023] Open
Abstract
Identifying early signs of neurodegeneration due to Alzheimer's disease (AD) is a necessary first step towards preventing cognitive decline. Individual cortical thickness measures, available after processing anatomical magnetic resonance imaging (MRI), are sensitive markers of neurodegeneration. However, normal aging cortical decline and high inter-individual variability complicate the comparison and statistical determination of the impact of AD-related neurodegeneration on trajectories. In this paper, we computed trajectories in a 2D representation of a 62-dimensional manifold of individual cortical thickness measures. To compute this representation, we used a novel, nonlinear dimension reduction algorithm called Uniform Manifold Approximation and Projection (UMAP). We trained two embeddings, one on cortical thickness measurements of 6237 cognitively healthy participants aged 18-100 years old and the other on 233 mild cognitively impaired (MCI) and AD participants from the longitudinal database, the Alzheimer's Disease Neuroimaging Initiative database (ADNI). Each participant had multiple visits ([Formula: see text]), one year apart. The first embedding's principal axis was shown to be positively associated ([Formula: see text]) with participants' age. Data from ADNI is projected into these 2D spaces. After clustering the data, average trajectories between clusters were shown to be significantly different between MCI and AD subjects. Moreover, some clusters and trajectories between clusters were more prone to host AD subjects. This study was able to differentiate AD and MCI subjects based on their trajectory in a 2D space with an AUC of 0.80 with 10-fold cross-validation.
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Affiliation(s)
- Rémi Lamontagne-Caron
- Département de médecine, Université Laval, Quebec, QC, G1V 0A6, Canada.
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada.
| | - Patrick Desrosiers
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Quebec, QC, G1V 0A6, Canada
- Département de physique, de génie physique et d'optique, Université Laval, Quebec, QC, G1V 0A6, Canada
| | | | - Nicolas Doyon
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Quebec, QC, G1V 0A6, Canada
- Département de mathématiques et de statistique, Université Laval, Quebec, QC, G1V 0A6, Canada
| | - Simon Duchesne
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Département de radiologie et médecine nucléaire, Université Laval, Quebec, QC, G1V 0A6, Canada
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24
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Paz V, Dashti HS, Garfield V. Is there an association between daytime napping, cognitive function, and brain volume? A Mendelian randomization study in the UK Biobank. Sleep Health 2023; 9:786-793. [PMID: 37344293 DOI: 10.1016/j.sleh.2023.05.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 04/14/2023] [Accepted: 05/03/2023] [Indexed: 06/23/2023]
Abstract
OBJECTIVES Daytime napping has been associated with cognitive function and brain health in observational studies. However, it remains elusive whether these associations are causal. Using Mendelian randomization, we studied the relationship between habitual daytime napping and cognition and brain structure. METHODS Data were from UK Biobank (maximum n = 378,932 and mean age = 57 years). Our exposure (daytime napping) was instrumented using 92 previously identified genome-wide, independent genetic variants (single-nucleotide polymorphisms, SNPs). Our outcomes were total brain volume, hippocampal volume, reaction time, and visual memory. Inverse-variance weighted was implemented, with sensitivity analyses (Mendelian randomization-Egger and Weighted Median Estimator) for horizontal pleiotropy. We tested different daytime napping instruments to ensure the robustness of our results. RESULTS Using Mendelian randomization, we found an association between habitual daytime napping and larger total brain volume (unstandardized ß = 15.80 cm3 and 95% CI = 0.25; 31.34) but not hippocampal volume (ß = -0.03 cm3 and 95% CI = -0.13;0.06), reaction time (expß = 1.01 and 95% CI = 1.00;1.03), or visual memory (expß = 0.99 and 95% CI = 0.94;1.05). Additional analyses with 47 SNPs (adjusted for excessive daytime sleepiness), 86 SNPs (excluding sleep apnea), and 17 SNPs (no sample overlap with UK Biobank) were largely consistent with our main findings. No evidence of horizontal pleiotropy was found. CONCLUSIONS Our findings suggest a modest causal association between habitual daytime napping and larger total brain volume. Future studies could focus on the associations between napping and other cognitive or brain outcomes and replication of these findings using other datasets and methods.
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Affiliation(s)
- Valentina Paz
- Instituto de Psicología Clínica, Facultad de Psicología, Universidad de la República, Montevideo, Uruguay; MRC Unit for Lifelong Health & Ageing, Institute of Cardiovascular Science, University College London, London, UK.
| | - Hassan S Dashti
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Broad Institute, Merkin Building, Cambridge, MA, USA; Department of Anesthesia, Critical Care & Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Victoria Garfield
- MRC Unit for Lifelong Health & Ageing, Institute of Cardiovascular Science, University College London, London, UK
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Kucikova L, Zeng J, Muñoz-Neira C, Muniz-Terrera G, Huang W, Gregory S, Ritchie C, O'Brien J, Su L. Genetic risk factors of Alzheimer's Disease disrupt resting-state functional connectivity in cognitively intact young individuals. J Neurol 2023; 270:4949-4958. [PMID: 37358635 PMCID: PMC10511575 DOI: 10.1007/s00415-023-11809-9] [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: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 06/27/2023]
Abstract
BACKGROUND Past evidence shows that changes in functional brain connectivity in multiple resting-state networks occur in cognitively healthy individuals who have non-modifiable risk factors for Alzheimer's Disease. Here, we aimed to investigate how those changes differ in early adulthood and how they might relate to cognition. METHODS We investigated the effects of genetic risk factors of AD, namely APOEe4 and MAPTA alleles, on resting-state functional connectivity in a cohort of 129 cognitively intact young adults (aged 17-22 years). We used Independent Component Analysis to identify networks of interest, and Gaussian Random Field Theory to compare connectivity between groups. Seed-based analysis was used to quantify inter-regional connectivity strength from the clusters that exhibited significant between-group differences. To investigate the relationship with cognition, we correlated the connectivity and the performance on the Stroop task. RESULTS The analysis revealed a decrease in functional connectivity in the Default Mode Network (DMN) in both APOEe4 carriers and MAPTA carriers in comparison with non-carriers. APOEe4 carriers showed decreased connectivity in the right angular gyrus (size = 246, p-FDR = 0.0079), which was correlated with poorer performance on the Stroop task. MAPTA carriers showed decreased connectivity in the left middle temporal gyrus (size = 546, p-FDR = 0.0001). In addition, we found that only MAPTA carriers had a decreased connectivity between the DMN and multiple other brain regions. CONCLUSIONS Our findings indicate that APOEe4 and MAPTA alleles modulate brain functional connectivity in the brain regions within the DMN in cognitively intact young adults. APOEe4 carriers also showed a link between connectivity and cognition.
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Affiliation(s)
- Ludmila Kucikova
- Department of Neuroscience, Faculty of Medicine, Dentistry and Heath, Sheffield Institute for Translational Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, SY, UK
- Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Jianmin Zeng
- Sino-Britain Centre for Cognition and Ageing Research, Faculty of Psychology, Southwest University, Chongqing, China.
| | - Carlos Muñoz-Neira
- Department of Neuroscience, Faculty of Medicine, Dentistry and Heath, Sheffield Institute for Translational Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, SY, UK
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Ohio University Heritage College of Osteopathic Medicine, Ohio University, Athens, OH, USA
| | - Weijie Huang
- Department of Neuroscience, Faculty of Medicine, Dentistry and Heath, Sheffield Institute for Translational Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, SY, UK
- Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Sarah Gregory
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Craig Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Scottish Brain Sciences, Edinburgh, UK
| | - John O'Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Li Su
- Department of Neuroscience, Faculty of Medicine, Dentistry and Heath, Sheffield Institute for Translational Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, SY, UK.
- Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK.
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
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Huang H, Cheng S, Yang X, Liu L, Cheng B, Meng P, Pan C, Wen Y, Jia Y, Liu H, Zhang F. Dissecting the Association between Gut Microbiota and Brain Structure Change Rate: A Two-Sample Bidirectional Mendelian Randomization Study. Nutrients 2023; 15:4227. [PMID: 37836511 PMCID: PMC10574136 DOI: 10.3390/nu15194227] [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: 08/30/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023] Open
Abstract
The connection between the gut microbiota and brain structure changes is still unclear. We conducted a Mendelian randomization (MR) study to examine the bidirectional causality between the gut microbiota (211 taxa, including 131 genera, 35 families, 20 orders, 16 classes and 9 phyla; N = 18,340 individuals) and age-independent/dependent longitudinal changes in brain structure across the lifespan (N = 15,640 individuals aged 4~99 years). We identified causal associations between the gut microbiota and age-independent/dependent longitudinal changes in brain structure, such as family Peptostreptococcaceae with age-independent longitudinal changes of cortical gray matter (GM) volume and genus Faecalibacterium with age-independent average cortical thickness and cortical GM volume. Taking age-independent longitudinal changes in brain structure across the lifespan as exposures, there were causal relationships between the surface area and genus Lachnospiraceae. Our findings may serve as fundamentals for further research on the genetic mechanisms and biological treatment of complex traits and diseases associated with the gut microbiota and the brain structure change rate.
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Affiliation(s)
- Huimei Huang
- Department of Nephrology, Xi’an Children’s Hospital, The Affiliated Children’s Hospital of Xi’an Jiaotong University, Xi’an 710003, China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China; (S.C.); (X.Y.); (L.L.); (B.C.); (P.M.); (C.P.); (Y.W.); (Y.J.); (H.L.)
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China; (S.C.); (X.Y.); (L.L.); (B.C.); (P.M.); (C.P.); (Y.W.); (Y.J.); (H.L.)
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China; (S.C.); (X.Y.); (L.L.); (B.C.); (P.M.); (C.P.); (Y.W.); (Y.J.); (H.L.)
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China; (S.C.); (X.Y.); (L.L.); (B.C.); (P.M.); (C.P.); (Y.W.); (Y.J.); (H.L.)
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China; (S.C.); (X.Y.); (L.L.); (B.C.); (P.M.); (C.P.); (Y.W.); (Y.J.); (H.L.)
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China; (S.C.); (X.Y.); (L.L.); (B.C.); (P.M.); (C.P.); (Y.W.); (Y.J.); (H.L.)
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China; (S.C.); (X.Y.); (L.L.); (B.C.); (P.M.); (C.P.); (Y.W.); (Y.J.); (H.L.)
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China; (S.C.); (X.Y.); (L.L.); (B.C.); (P.M.); (C.P.); (Y.W.); (Y.J.); (H.L.)
| | - Huan Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China; (S.C.); (X.Y.); (L.L.); (B.C.); (P.M.); (C.P.); (Y.W.); (Y.J.); (H.L.)
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China; (S.C.); (X.Y.); (L.L.); (B.C.); (P.M.); (C.P.); (Y.W.); (Y.J.); (H.L.)
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Cen S, Gebregziabher M, Moazami S, Azevedo CJ, Pelletier D. Toward precision medicine using a "digital twin" approach: modeling the onset of disease-specific brain atrophy in individuals with multiple sclerosis. Sci Rep 2023; 13:16279. [PMID: 37770560 PMCID: PMC10539386 DOI: 10.1038/s41598-023-43618-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 09/26/2023] [Indexed: 09/30/2023] Open
Abstract
Digital Twin (DT) is a novel concept that may bring a paradigm shift for precision medicine. In this study we demonstrate a DT application for estimating the age of onset of disease-specific brain atrophy in individuals with multiple sclerosis (MS) using brain MRI. We first augmented longitudinal data from a well-fitted spline model derived from a large cross-sectional normal aging data. Then we compared different mixed spline models through both simulated and real-life data and identified the mixed spline model with the best fit. Using the appropriate covariate structure selected from 52 different candidate structures, we augmented the thalamic atrophy trajectory over the lifespan for each individual MS patient and a corresponding hypothetical twin with normal aging. Theoretically, the age at which the brain atrophy trajectory of an MS patient deviates from the trajectory of their hypothetical healthy twin can be considered as the onset of progressive brain tissue loss. With a tenfold cross validation procedure through 1000 bootstrapping samples, we found the onset age of progressive brain tissue loss was, on average, 5-6 years prior to clinical symptom onset. Our novel approach also discovered two clear patterns of patient clusters: earlier onset versus simultaneous onset of brain atrophy.
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Affiliation(s)
- Steven Cen
- Department of Radiology/Neurology, University of Southern California, Los Angeles, USA.
| | - Mulugeta Gebregziabher
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, USA
| | - Saeed Moazami
- Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, USA
| | - Christina J Azevedo
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, USA
| | - Daniel Pelletier
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, USA
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Schilling KG, Chad JA, Chamberland M, Nozais V, Rheault F, Archer D, Li M, Gao Y, Cai L, Del'Acqua F, Newton A, Moyer D, Gore JC, Lebel C, Landman BA. White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.25.559330. [PMID: 37808645 PMCID: PMC10557619 DOI: 10.1101/2023.09.25.559330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Characterizing how, when and where the human brain changes across the lifespan is fundamental to our understanding of developmental processes of childhood and adolescence, degenerative processes of aging, and divergence from normal patterns in disease and disorders. We aimed to provide detailed descriptions of white matter pathways across the lifespan by thoroughly characterizing white matter microstructure, white matter macrostructure, and morphology of the cortex associated with white matter pathways. We analyzed 4 large, high-quality, publicly-available datasets comprising 2789 total imaging sessions, and participants ranging from 0 to 100 years old, using advanced tractography and diffusion modeling. We first find that all microstructural, macrostructural, and cortical features of white matter bundles show unique lifespan trajectories, with rates and timing of development and degradation that vary across pathways - describing differences between types of pathways and locations in the brain, and developmental milestones of maturation of each feature. Second, we show cross-sectional relationships between different features that may help elucidate biological changes occurring during different stages of the lifespan. Third, we show unique trajectories of age-associations across features. Finally, we find that age associations during development are strongly related to those during aging. Overall, this study reports normative data for several features of white matter pathways of the human brain that will be useful for studying normal and abnormal white matter development and degeneration.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan A Chad
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, ON, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Maxime Chamberland
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Francois Rheault
- Medical Imaging and Neuroinformatic (MINi) Lab, Department of Computer Science, University of Sherbrooke, Canada
| | - Derek Archer
- Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Muwei Li
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Leon Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Flavio Del'Acqua
- NatbrainLab, Department of Forensics and Neurodevelopmental Sciences, King's College London, London UK
| | - Allen Newton
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel Moyer
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - John C Gore
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Catherine Lebel
- Alberta Children's Hospital Research Institute (ACHRI), Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Bennett A Landman
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
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Eugenín J, Eugenín-von Bernhardi L, von Bernhardi R. Age-dependent changes on fractalkine forms and their contribution to neurodegenerative diseases. Front Mol Neurosci 2023; 16:1249320. [PMID: 37818457 PMCID: PMC10561274 DOI: 10.3389/fnmol.2023.1249320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 09/06/2023] [Indexed: 10/12/2023] Open
Abstract
The chemokine fractalkine (FKN, CX3CL1), a member of the CX3C subfamily, contributes to neuron-glia interaction and the regulation of microglial cell activation. Fractalkine is expressed by neurons as a membrane-bound protein (mCX3CL1) that can be cleaved by extracellular proteases generating several sCX3CL1 forms. sCX3CL1, containing the chemokine domain, and mCX3CL1 have high affinity by their unique receptor (CX3CR1) which, physiologically, is only found in microglia, a resident immune cell of the CNS. The activation of CX3CR1contributes to survival and maturation of the neural network during development, glutamatergic synaptic transmission, synaptic plasticity, cognition, neuropathic pain, and inflammatory regulation in the adult brain. Indeed, the various CX3CL1 forms appear in some cases to serve an anti-inflammatory role of microglia, whereas in others, they have a pro-inflammatory role, aggravating neurological disorders. In the last decade, evidence points to the fact that sCX3CL1 and mCX3CL1 exhibit selective and differential effects on their targets. Thus, the balance in their level and activity will impact on neuron-microglia interaction. This review is focused on the description of factors determining the emergence of distinct fractalkine forms, their age-dependent changes, and how they contribute to neuroinflammation and neurodegenerative diseases. Changes in the balance among various fractalkine forms may be one of the mechanisms on which converge aging, chronic CNS inflammation, and neurodegeneration.
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Affiliation(s)
- Jaime Eugenín
- Facultad de Química y Biología, Departamento de Biología, Universidad de Santiago de Chile, USACH, Santiago, Chile
| | | | - Rommy von Bernhardi
- Facultad de Ciencias para el Cuidado de la Salud, Universidad San Sebastián, Santiago, Chile
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Shi R, Xiang S, Jia T, Robbins TW, Kang J, Banaschewski T, Barker GJ, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Paus T, Poustka L, Hohmann S, Millenet S, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Lin X, Sahakian BJ, Feng J. Structural neurodevelopment at the individual level - a life-course investigation using ABCD, IMAGEN and UK Biobank data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.20.23295841. [PMID: 37790416 PMCID: PMC10543061 DOI: 10.1101/2023.09.20.23295841] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages and the neurobiological basis underlying individual heterogeneity remains poorly understood. Using structural magnetic resonance imaging from the IMAGEN cohort (n=1,543), we show that adolescents can be clustered into three groups defined by distinct developmental patterns of whole-brain gray matter volume (GMV). Genetic and epigenetic determinants of group clustering and long-term impacts of neurodevelopment in mid-to-late adulthood were investigated using data from the ABCD, IMAGEN and UK Biobank cohorts. Group 1, characterized by continuously decreasing GMV, showed generally the best neurocognitive performances during adolescence. Compared to Group 1, Group 2 exhibited a slower rate of GMV decrease and worsened neurocognitive development, which was associated with epigenetic changes and greater environmental burden. Further, Group 3 showed increasing GMV and delayed neurocognitive development during adolescence due to a genetic variation, while these disadvantages were attenuated in mid-to-late adulthood. In summary, our study revealed novel clusters of adolescent structural neurodevelopment and suggested that genetically-predicted delayed neurodevelopment has limited long-term effects on mental well-being and socio-economic outcomes later in life. Our results could inform future research on policy interventions aimed at reducing the financial and emotional burden of mental illness.
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Richter N, Brand S, Nellessen N, Dronse J, Gramespacher H, Schmieschek MHT, Fink GR, Kukolja J, Onur OA. Fine-grained age-matching improves atrophy-based detection of mild cognitive impairment more than amyloid-negative reference subjects. Neuroimage Clin 2023; 40:103508. [PMID: 37717383 PMCID: PMC10514218 DOI: 10.1016/j.nicl.2023.103508] [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: 04/24/2023] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 09/19/2023]
Abstract
INTRODUCTION In clinical practice, differentiating between age-related gray matter (GM) atrophy and neurodegeneration-related atrophy at early disease stages, such as mild cognitive impairment (MCI), remains challenging. We hypothesized that fined-grained adjustment for age effects and using amyloid-negative reference subjects could increase classification accuracy. METHODS T1-weighted magnetic resonance imaging (MRI) data of 131 cognitively normal (CN) individuals and 91 patients with MCI from the Alzheimer's disease neuroimaging initiative (ADNI) characterized concerning amyloid status, as well as 19 CN individuals and 19 MCI patients from an independent validation sample were segmented, spatially normalized and analyzed in the framework of voxel-based morphometry (VBM). For each participant, statistical maps of GM atrophy were computed as the deviation from the GM of CN reference groups at the voxel level. CN reference groups composed with different degrees of age-matching, and mixed and strictly amyloid-negative CN reference groups were examined regarding their effect on the accuracy in distinguishing between CN and MCI. Furthermore, the effects of spatial smoothing and atrophy threshold were assessed. RESULTS Approaches with a specific reference group for each age significantly outperformed all other age-adjustment strategies with a maximum area under the curve of 1.0 in the ADNI sample and 0.985 in the validation sample. Accounting for age in a regression-based approach improved classification accuracy over that of a single CN reference group in the age range of the patient sample. Using strictly amyloid-negative reference groups improved classification accuracy only when age was not considered. CONCLUSION Our results demonstrate that VBM can differentiate between age-related and MCI-associated atrophy with high accuracy. Crucially, age-specific reference groups significantly increased accuracy, more so than regression-based approaches and using amyloid-negative reference groups.
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Affiliation(s)
- Nils Richter
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, 52425 Jülich, Germany; Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany.
| | - Stefanie Brand
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
| | - Nils Nellessen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, 52425 Jülich, Germany; Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany; Department of Neurology and Clinical Neurophysiology, Helios University Hospital Wuppertal, 42283 Wuppertal, Germany; Faculty of Health, Witten/Herdecke University, 58448 Witten, Germany
| | - Julian Dronse
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, 52425 Jülich, Germany; Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
| | - Hannes Gramespacher
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
| | - Maximilian H T Schmieschek
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
| | - Gereon R Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, 52425 Jülich, Germany; Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
| | - Juraj Kukolja
- Department of Neurology and Clinical Neurophysiology, Helios University Hospital Wuppertal, 42283 Wuppertal, Germany; Faculty of Health, Witten/Herdecke University, 58448 Witten, Germany
| | - Oezguer A Onur
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, 52425 Jülich, Germany; Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
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Kimizoğlu O, Kirca ND, Kandis S, Micili SC, Harzadin NU, Kocturk S. Daily Consumption of High-Polyphenol Olive Oil Enhances Hippocampal Neurogenesis in Old Female Rats. JOURNAL OF THE AMERICAN NUTRITION ASSOCIATION 2023; 42:668-677. [PMID: 36416641 DOI: 10.1080/27697061.2022.2144540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/03/2022] [Accepted: 10/31/2022] [Indexed: 06/16/2023]
Abstract
OBJECTIVE The aim of this study is to evaluate the effect of daily consumption of high-polyphenol (HP) olive oil on neurogenesis by investigating neuronal cell proliferation and maturation in the hippocampus of old rats, and to evaluate the relationship between neurogenesis, spatial memory, and anxiety-like behavior. METHODS A total of 34 female, 20-22-month-old Sprague Dawley rats were divided into three groups: control group, low-polyphenol (LP) group, and high-polyphenol (HP) group. The animals were fed distilled water, LP olive oil and HP-extra virgin olive oil, respectively for 6 weeks using an oral gavage. At 43 days, animals were tested using the Morris Water Maze to evaluate spatial memory, and the Open-field test to evaluate anxiety-like behavior. Neural cell proliferation in the dentate gyrus (DG) was determined by BrdU labeling and Nestin protein expression. Neuronal maturation was determined by NeuN labeling. Synaptic density in the hippocampus and prefrontal cortex was examined by measuring Synaptophysin (SYN) levels. Hippocampal Calbindin levels were measured to assess cellular calcium metabolism. RESULTS Daily consumption of HP olive oil significantly improved cell proliferation and neuronal maturation in the DG of old rats. HP-olive oil significantly increased SYN levels in the prefrontal cortex, and nestin and calbindin levels in the hippocampus (p < 0.05). LP olive oil diet has shown no effect on any parameter (p > 0.05). We also did not find any statistically significant difference between the groups in terms of spatial memory and anxiety-like behavior (p > 0.05). CONCLUSION Our study is first to show that daily consumption of HP-olive oil enhances hippocampal neurogenesis in old rats, which has been confirmed by proliferation and maturation biomarkers. In addition, increased SYN and calbindin levels showed that the generated cells were also functionally developed in the HP group. We suggest that daily consumption of HP olive oil may have beneficial effects on brain aging by triggering neurogenesis.
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Affiliation(s)
- Ozgun Kimizoğlu
- Institute of Health Sciences, Department of Neurosciences, Dokuz Eylul University, Izmir, Turkey
| | - N Deniz Kirca
- Institute of Health Sciences, Department of Neurosciences, Dokuz Eylul University, Izmir, Turkey
| | - Sevim Kandis
- Faculty of Medicine, Department of Physiology, Dokuz Eylul University, Izmir, Turkey
| | - Serap Cilaker Micili
- Faculty of Medicine, Department of Histology and Embryology, Dokuz Eylul University, Izmir, Turkey
| | - Nazan Uysal Harzadin
- Faculty of Medicine, Department of Physiology, Dokuz Eylul University, Izmir, Turkey
| | - Semra Kocturk
- Institute of Health Sciences, Department of Neurosciences, Dokuz Eylul University, Izmir, Turkey
- Faculty of Medicine, Department of Medical Biochemistry, Dokuz Eylul University, Izmir, Turkey
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Warrier V, Stauffer EM, Huang QQ, Wigdor EM, Slob EAW, Seidlitz J, Ronan L, Valk SL, Mallard TT, Grotzinger AD, Romero-Garcia R, Baron-Cohen S, Geschwind DH, Lancaster MA, Murray GK, Gandal MJ, Alexander-Bloch A, Won H, Martin HC, Bullmore ET, Bethlehem RAI. Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes. Nat Genet 2023; 55:1483-1493. [PMID: 37592024 PMCID: PMC10600728 DOI: 10.1038/s41588-023-01475-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 07/13/2023] [Indexed: 08/19/2023]
Abstract
Our understanding of the genetics of the human cerebral cortex is limited both in terms of the diversity and the anatomical granularity of brain structural phenotypes. Here we conducted a genome-wide association meta-analysis of 13 structural and diffusion magnetic resonance imaging-derived cortical phenotypes, measured globally and at 180 bilaterally averaged regions in 36,663 individuals and identified 4,349 experiment-wide significant loci. These phenotypes include cortical thickness, surface area, gray matter volume, measures of folding, neurite density and water diffusion. We identified four genetic latent structures and causal relationships between surface area and some measures of cortical folding. These latent structures partly relate to different underlying gene expression trajectories during development and are enriched for different cell types. We also identified differential enrichment for neurodevelopmental and constrained genes and demonstrate that common genetic variants associated with cortical expansion are associated with cephalic disorders. Finally, we identified complex interphenotype and inter-regional genetic relationships among the 13 phenotypes, reflecting the developmental differences among them. Together, these analyses identify distinct genetic organizational principles of the cortex and their correlates with neurodevelopment.
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Affiliation(s)
- Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Department of Psychology, University of Cambridge, Cambridge, UK.
| | | | | | | | - Eric A W Slob
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Lisa Ronan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Sofie L Valk
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Andrew D Grotzinger
- Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Rafael Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Dpto. de Fisiología Médica y Biofísica, Seville, Spain
| | - Simon Baron-Cohen
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Daniel H Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Program in Neurogenetics, Department of Neurology, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Jane and TerrySemel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Institute of Precision Health, University of California, Los Angeles, CA, USA
| | - Madeline A Lancaster
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge, UK
| | - Graham K Murray
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Trust, Cambridge, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Michael J Gandal
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Aaron Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Hyejung Won
- Department of Genetics and the Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Trust, Cambridge, UK
| | - Richard A I Bethlehem
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Department of Psychology, University of Cambridge, Cambridge, UK.
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Xie Y, Sun J, Man W, Zhang Z, Zhang N. Personalized estimates of brain cortical structural variability in individuals with Autism spectrum disorder: the predictor of brain age and neurobiology relevance. Mol Autism 2023; 14:27. [PMID: 37507798 PMCID: PMC10375633 DOI: 10.1186/s13229-023-00558-1] [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: 03/24/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a heritable condition related to brain development that affects a person's perception and socialization with others. Here, we examined variability in the brain morphology in ASD children and adolescent individuals at the level of brain cortical structural profiles and the level of each brain regional measure. METHODS We selected brain structural MRI data in 600 ASDs and 729 normal controls (NCs) from Autism Brain Imaging Data Exchange (ABIDE). The personalized estimate of similarity between gray matter volume (GMV) profiles of an individual to that of others in the same group was assessed by using the person-based similarity index (PBSI). Regional contributions to PBSI score were utilized for brain age gap estimation (BrainAGE) prediction model establishment, including support vector regression (SVR), relevance vector regression (RVR), and Gaussian process regression (GPR). The association between BrainAGE prediction in ASD and clinical performance was investigated. We further explored the related inter-regional profiles of gene expression from the Allen Human Brain Atlas with variability differences in the brain morphology between groups. RESULTS The PBSI score of GMV was negatively related to age regardless of the sample group, and the PBSI score was significantly lower in ASDs than in NCs. The regional contributions to the PBSI score of 126 brain regions in ASDs showed significant differences compared to NCs. RVR model achieved the best performance for predicting brain age. Higher inter-individual brain morphology variability was related to increased brain age, specific to communication symptoms. A total of 430 genes belonging to various pathways were identified as associated with brain cortical morphometric variation. The pathways, including short-term memory, regulation of system process, and regulation of nervous system process, were dominated mainly by gene sets for manno midbrain neurotypes. LIMITATIONS There is a sample mismatch between the gene expression data and brain imaging data from ABIDE. A larger sample size can contribute to the model training of BrainAGE and the validation of the results. CONCLUSIONS ASD has personalized heterogeneity brain morphology. The brain age gap estimation and transcription-neuroimaging associations derived from this trait are replenished in an additional direction to boost the understanding of the ASD brain.
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Affiliation(s)
- Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Jie Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Weiqi Man
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
- Department of Radiology, Tianjin First Central Hospital, Tianjin, 300192, China
| | - Zhang Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China.
| | - Ningnannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China.
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Wang W, Shi L, Ma H, Zhu S, Ge Y, Xu K. Comparison of the clinical value of MRI and plasma markers for cognitive impairment in patients aged ≥75 years: a retrospective study. PeerJ 2023; 11:e15581. [PMID: 37366421 PMCID: PMC10290829 DOI: 10.7717/peerj.15581] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/26/2023] [Indexed: 06/28/2023] Open
Abstract
Background Dementia has become the main cause of disability in older adults aged ≥75 years. Cerebral small vessel disease (CSVD) is involved in cognitive impairment (CI) and dementia and is a cause of vascular CI (VCI), which is manageable and its onset and progression can be delayed. Simple and effective markers will be beneficial to the early detection and intervention of CI. The aim of this study is to investigate the clinical application value of plasma amyloid β1-42 (Aβ42), phosphorylated tau 181 (p-tau181) and conventional structural magnetic resonance imaging (MRI) parameters for cognitive impairment (CI) in patients aged ≥75 years. Methods We retrospectively selected patients who visited the Affiliated Hospital of Xuzhou Medical University and were clinically diagnosed with or without cognitive dysfunction between May 2018 and November 2021. Plasma indicators (Aβ42 and p-tau181) and conventional structural MRI parameters were collected and analyzed. Multivariate logistic regression and receiver operator characteristic (ROC) curve were used to evaluate the diagnostic value. Results One hundred and eighty-four subjects were included, including 54 cases in CI group and 130 cases in noncognitive impairment (NCI) groups, respectively. Univariate logistic regression analysis revealed that the percentages of Aβ42+, P-tau 181+, and Aβ42+/P-tau181+ showed no significant difference between the groups of CI and NCI (all P > 0.05). Multivariate logistic regression analysis showed that moderate/severe periventricular WMH (PVWMH) (OR 2.857, (1.365-5.983), P = 0.005), lateral ventricle body index (LVBI) (OR 0.413, (0.243-0.700), P = 0.001), and cortical atrophy (OR 1.304, (1.079-1.575), P = 0.006) were factors associated with CI. The combined model including PVWMH, LVBI, and cortical atrophy to detect CI and NCI showed an area under the ROC curve (AUROC) is 0.782, with the sensitivity and specificity 68.5% and 78.5%, respectively. Conclusion For individuals ≥75 years, plasma Aβ42 and P-tau181 might not be associated with cognitive impairment, and MRI parameters, including PVWMH, LVBI and cortical atrophy, are related to CI. The cognitive statuses of people over 75 years old were used as the endpoint event in this study. Therefore, it can be considered that these MRI markers might have more important clinical significance for early assessment and dynamic observation, but more studies are still needed to verify this hypothesis.
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Affiliation(s)
- Wei Wang
- Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Institute of Medical Imaging and Digital Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Lin Shi
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shangdong, China
| | - Hong Ma
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Institute of Medical Imaging and Digital Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Shiguang Zhu
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yaqiong Ge
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Kai Xu
- Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Institute of Medical Imaging and Digital Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China
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Liu C, Downey RJ, Mu Y, Richer N, Hwang J, Shah VA, Sato SD, Clark DJ, Hass CJ, Manini TM, Seidler RD, Ferris DP. Comparison of EEG Source Localization Using Simplified and Anatomically Accurate Head Models in Younger and Older Adults. IEEE Trans Neural Syst Rehabil Eng 2023; 31:2591-2602. [PMID: 37252873 PMCID: PMC10336858 DOI: 10.1109/tnsre.2023.3281356] [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] [Indexed: 06/01/2023]
Abstract
Accuracy of electroencephalography (EEG) source localization relies on the volume conduction head model. A previous analysis of young adults has shown that simplified head models have larger source localization errors when compared with head models based on magnetic resonance images (MRIs). As obtaining individual MRIs may not always be feasible, researchers often use generic head models based on template MRIs. It is unclear how much error would be introduced using template MRI head models in older adults that likely have differences in brain structure compared to young adults. The primary goal of this study was to determine the error caused by using simplified head models without individual-specific MRIs in both younger and older adults. We collected high-density EEG during uneven terrain walking and motor imagery for 15 younger (22±3 years) and 21 older adults (74±5 years) and obtained [Formula: see text]-weighted MRI for each individual. We performed equivalent dipole fitting after independent component analysis to obtain brain source locations using four forward modeling pipelines with increasing complexity. These pipelines included: 1) a generic head model with template electrode positions or 2) digitized electrode positions, 3) individual-specific head models with digitized electrode positions using simplified tissue segmentation, or 4) anatomically accurate segmentation. We found that when compared to the anatomically accurate individual-specific head models, performing dipole fitting with generic head models led to similar source localization discrepancies (up to 2 cm) for younger and older adults. Co-registering digitized electrode locations to the generic head models reduced source localization discrepancies by ∼ 6 mm. Additionally, we found that source depths generally increased with skull conductivity for the representative young adult but not as much for the older adult. Our results can help inform a more accurate interpretation of brain areas in EEG studies when individual MRIs are unavailable.
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37
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Stadler J, Brechmann A, Angenstein N. Effect of age on lateralized auditory processing. Hear Res 2023; 434:108791. [PMID: 37209509 DOI: 10.1016/j.heares.2023.108791] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/01/2023] [Accepted: 05/10/2023] [Indexed: 05/22/2023]
Abstract
The lateralization of processing in the auditory cortex for different acoustic parameters differs depending on stimuli and tasks. Thus, processing complex auditory stimuli requires an efficient hemispheric interaction. Anatomical connectivity decreases with aging and consequently affects the functional interaction between the left and right auditory cortex and lateralization of auditory processing. Here we studied with magnetic resonance imaging the effect of aging on the lateralization of processing and hemispheric interaction during two tasks utilizing the contralateral noise procedure. Categorization of tones according to their direction of frequency modulations (FM) is known to be processed mainly in the right auditory cortex. Sequential comparison of the same tones according to their FM direction strongly involves additionally the left auditory cortex and therefore a stronger hemispheric interaction than the categorization task. The results showed that older adults more strongly recruit the auditory cortex especially during the comparison task that requires stronger hemispheric interaction. This was the case although the task difficulty was adapted to achieve similar performance as the younger adults. Additionally, functional connectivity from auditory cortex to other brain areas was stronger in older than younger adults especially during the comparison task. Diffusion tensor imaging data showed a reduction in fractional anisotropy and an increase in mean diffusivity in the corpus callosum of older adults compared to younger adults. These changes indicate a reduction of anatomical interhemispheric connections in older adults that makes larger processing capacity necessary when tasks require functional hemispheric interaction.
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Affiliation(s)
- Jörg Stadler
- Leibniz Institute for Neurobiology, Combinatorial NeuroImaging Core Facility, Brenneckestr. 6, 39118 Magdeburg, Germany
| | - André Brechmann
- Leibniz Institute for Neurobiology, Combinatorial NeuroImaging Core Facility, Brenneckestr. 6, 39118 Magdeburg, Germany
| | - Nicole Angenstein
- Leibniz Institute for Neurobiology, Combinatorial NeuroImaging Core Facility, Brenneckestr. 6, 39118 Magdeburg, Germany.
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García-Marín LM, Reyes-Pérez P, Diaz-Torres S, Medina-Rivera A, Martin NG, Mitchell BL, Rentería ME. Shared molecular genetic factors influence subcortical brain morphometry and Parkinson's disease risk. NPJ Parkinsons Dis 2023; 9:73. [PMID: 37164954 PMCID: PMC10172359 DOI: 10.1038/s41531-023-00515-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 04/28/2023] [Indexed: 05/12/2023] Open
Abstract
Parkinson's disease (PD) is a late-onset and genetically complex neurodegenerative disorder. Here we sought to identify genes and molecular pathways underlying the associations between PD and the volume of ten brain structures measured through magnetic resonance imaging (MRI) scans. We leveraged genome-wide genetic data from several cohorts, including the International Parkinson's Disease Genomics Consortium (IPDG), the UK Biobank, the Adolescent Brain Cognitive Development (ABCD) study, the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), the Enhancing Neuroimaging Genetics through Meta-Analyses (ENIGMA), and 23andMe. We observed significant positive genetic correlations between PD and intracranial and subcortical brain volumes. Genome-wide association studies (GWAS) - pairwise analyses identified 210 genomic segments with shared aetiology between PD and at least one of these brain structures. Pathway enrichment results highlight potential links with chronic inflammation, the hypothalamic-pituitary-adrenal pathway, mitophagy, disrupted vesicle-trafficking, calcium-dependent, and autophagic pathways. Investigations for putative causal genetic effects suggest that a larger putamen volume could influence PD risk, independently of the potential causal genetic effects of intracranial volume (ICV) on PD. Our findings suggest that genetic variants influencing larger intracranial and subcortical brain volumes, possibly during earlier stages of life, influence the risk of developing PD later in life.
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Affiliation(s)
- Luis M García-Marín
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
- Laboratorio Internacional de Investigación del Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México.
| | - Paula Reyes-Pérez
- Laboratorio Internacional de Investigación del Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Santiago Diaz-Torres
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Alejandra Medina-Rivera
- Laboratorio Internacional de Investigación del Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Nicholas G Martin
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Brittany L Mitchell
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Miguel E Rentería
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
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Cen S, Gebregziabher M, Moazami S, Azevedo C, Pelletier D. Toward Precision Medicine Using a "Digital Twin" Approach: Modeling the Onset of Disease-Specific Brain Atrophy in Individuals with Multiple Sclerosis. RESEARCH SQUARE 2023:rs.3.rs-2833532. [PMID: 37205476 PMCID: PMC10187410 DOI: 10.21203/rs.3.rs-2833532/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Digital Twin (DT) is a novel concept that may bring a paradigm shift for precision medicine. In this study we demonstrate a DT application for estimating the age of onset of disease-specific brain atrophy in individuals with multiple sclerosis (MS) using brain MRI. We first augmented longitudinal data from a well-fitted spline model derived from a large cross-sectional normal aging data. Then we compared different mixed spline models through both simulated and real-life data and identified the mixed spline model with the best fit. Using the appropriate covariate structure selected from 52 different candidate structures, we augmented the thalamic atrophy trajectory over the lifespan for each individual MS patient and a corresponding hypothetical twin with normal aging. Theoretically, the age at which the brain atrophy trajectory of an MS patient deviates from the trajectory of their hypothetical healthy twin can be considered as the onset of progressive brain tissue loss. With a 10-fold cross validation procedure through 1000 bootstrapping samples, we found the onset age of progressive brain tissue loss was, on average, 5-6 years prior to clinical symptom onset. Our novel approach also discovered two clear patterns of patient clusters: earlier onset vs. simultaneous onset of brain atrophy.
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Schilling KG, Archer D, Rheault F, Lyu I, Huo Y, Cai LY, Bunge SA, Weiner KS, Gore JC, Anderson AW, Landman BA. Superficial white matter across development, young adulthood, and aging: volume, thickness, and relationship with cortical features. Brain Struct Funct 2023; 228:1019-1031. [PMID: 37074446 PMCID: PMC10320929 DOI: 10.1007/s00429-023-02642-x] [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: 07/20/2022] [Accepted: 04/08/2023] [Indexed: 04/20/2023]
Abstract
Superficial white matter (SWM) represents a significantly understudied part of the human brain, despite comprising a large portion of brain volume and making up a majority of cortico-cortical white matter connections. Using multiple, high-quality datasets with large sample sizes (N = 2421, age range 5-100) in combination with methodological advances in tractography, we quantified features of SWM volume and thickness across the brain and across development, young adulthood, and aging. We had four primary aims: (1) characterize SWM thickness across brain regions (2) describe associations between SWM volume and age (3) describe associations between SWM thickness and age, and (4) quantify relationships between SWM thickness and cortical features. Our main findings are that (1) SWM thickness varies across the brain, with patterns robust across individuals and across the population at the region-level and vertex-level; (2) SWM volume shows unique volumetric trajectories with age that are distinct from gray matter and other white matter trajectories; (3) SWM thickness shows nonlinear cross-sectional changes across the lifespan that vary across regions; and (4) SWM thickness is associated with features of cortical thickness and curvature. For the first time, we show that SWM volume follows a similar trend as overall white matter volume, peaking at a similar time in adolescence, leveling off throughout adulthood, and decreasing with age thereafter. Notably, the relative fraction of total brain volume of SWM continuously increases with age, and consequently takes up a larger proportion of total white matter volume, unlike the other tissue types that decrease with respect to total brain volume. This study represents the first characterization of SWM features across the large portion of the lifespan and provides the background for characterizing normal aging and insight into the mechanisms associated with SWM development and decline.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
| | - Derek Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Francois Rheault
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Ilwoo Lyu
- Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Yuankai Huo
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Silvia A Bunge
- Department of Psychology, University of California at Berkeley, Berkeley, USA
| | - Kevin S Weiner
- Department of Psychology, University of California at Berkeley, Berkeley, USA
- Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, USA
| | - John C Gore
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
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Bao H, Cao J, Chen M, Chen M, Chen W, Chen X, Chen Y, Chen Y, Chen Y, Chen Z, Chhetri JK, Ding Y, Feng J, Guo J, Guo M, He C, Jia Y, Jiang H, Jing Y, Li D, Li J, Li J, Liang Q, Liang R, Liu F, Liu X, Liu Z, Luo OJ, Lv J, Ma J, Mao K, Nie J, Qiao X, Sun X, Tang X, Wang J, Wang Q, Wang S, Wang X, Wang Y, Wang Y, Wu R, Xia K, Xiao FH, Xu L, Xu Y, Yan H, Yang L, Yang R, Yang Y, Ying Y, Zhang L, Zhang W, Zhang W, Zhang X, Zhang Z, Zhou M, Zhou R, Zhu Q, Zhu Z, Cao F, Cao Z, Chan P, Chen C, Chen G, Chen HZ, Chen J, Ci W, Ding BS, Ding Q, Gao F, Han JDJ, Huang K, Ju Z, Kong QP, Li J, Li J, Li X, Liu B, Liu F, Liu L, Liu Q, Liu Q, Liu X, Liu Y, Luo X, Ma S, Ma X, Mao Z, Nie J, Peng Y, Qu J, Ren J, Ren R, Song M, Songyang Z, Sun YE, Sun Y, Tian M, Wang S, Wang S, Wang X, Wang X, Wang YJ, Wang Y, Wong CCL, Xiang AP, Xiao Y, Xie Z, Xu D, Ye J, Yue R, Zhang C, Zhang H, Zhang L, Zhang W, Zhang Y, Zhang YW, Zhang Z, Zhao T, Zhao Y, Zhu D, Zou W, Pei G, Liu GH. Biomarkers of aging. SCIENCE CHINA. LIFE SCIENCES 2023; 66:893-1066. [PMID: 37076725 PMCID: PMC10115486 DOI: 10.1007/s11427-023-2305-0] [Citation(s) in RCA: 90] [Impact Index Per Article: 90.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 04/21/2023]
Abstract
Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.
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Affiliation(s)
- Hainan Bao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Jiani Cao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Mengting Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Min Chen
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Wei Chen
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Third Military Medical University, Chongqing, 400042, China
| | - Yanhao Chen
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Chen
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Yutian Chen
- The Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Zhiyang Chen
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China
| | - Jagadish K Chhetri
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Yingjie Ding
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junlin Feng
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jun Guo
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China
| | - Mengmeng Guo
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Chuting He
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Yujuan Jia
- Department of Neurology, First Affiliated Hospital, Shanxi Medical University, Taiyuan, 030001, China
| | - Haiping Jiang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Ying Jing
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Dingfeng Li
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China
| | - Jiaming Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyi Li
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Qinhao Liang
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China
| | - Rui Liang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China
| | - Feng Liu
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xiaoqian Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Zuojun Liu
- School of Life Sciences, Hainan University, Haikou, 570228, China
| | - Oscar Junhong Luo
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Jianwei Lv
- School of Life Sciences, Xiamen University, Xiamen, 361102, China
| | - Jingyi Ma
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Jiawei Nie
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinhua Qiao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xinpei Sun
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China
| | - Xiaoqiang Tang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianfang Wang
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qiaoran Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Siyuan Wang
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China
| | - Xuan Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China
| | - Yaning Wang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuhan Wang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Rimo Wu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China
| | - Kai Xia
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Fu-Hui Xiao
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Lingyan Xu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yingying Xu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Haoteng Yan
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Liang Yang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China
| | - Ruici Yang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yuanxin Yang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China
| | - Yilin Ying
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China
| | - Le Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Weiwei Zhang
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China
| | - Wenwan Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xing Zhang
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Zhuo Zhang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Min Zhou
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Qingchen Zhu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Zhengmao Zhu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Feng Cao
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China.
| | - Zhongwei Cao
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Piu Chan
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
| | - Chang Chen
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Guobing Chen
- Department of Microbiology and Immunology, School of Medicine, Jinan University, Guangzhou, 510632, China.
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, Guangzhou, 510000, China.
| | - Hou-Zao Chen
- Department of Biochemistryand Molecular Biology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
| | - Jun Chen
- Peking University Research Center on Aging, Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Department of Biochemistry and Molecular Biology, Department of Integration of Chinese and Western Medicine, School of Basic Medical Science, Peking University, Beijing, 100191, China.
| | - Weimin Ci
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Bi-Sen Ding
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Qiurong Ding
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Feng Gao
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Kai Huang
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Zhenyu Ju
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China.
| | - Qing-Peng Kong
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
| | - Ji Li
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jian Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China.
| | - Xin Li
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Baohua Liu
- School of Basic Medical Sciences, Shenzhen University Medical School, Shenzhen, 518060, China.
| | - Feng Liu
- Metabolic Syndrome Research Center, The Second Xiangya Hospital, Central South Unversity, Changsha, 410011, China.
| | - Lin Liu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China.
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
- Institute of Translational Medicine, Tianjin Union Medical Center, Nankai University, Tianjin, 300000, China.
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300350, China.
| | - Qiang Liu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China.
| | - Qiang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China.
- Tianjin Institute of Immunology, Tianjin Medical University, Tianjin, 300070, China.
| | - Xingguo Liu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China.
| | - Yong Liu
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China.
| | - Xianghang Luo
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China.
| | - Shuai Ma
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Xinran Ma
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
| | - Zhiyong Mao
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Jing Nie
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Yaojin Peng
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jie Ren
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ruibao Ren
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Center for Aging and Cancer, Hainan Medical University, Haikou, 571199, China.
| | - Moshi Song
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Zhou Songyang
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China.
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
| | - Yi Eve Sun
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China.
| | - Yu Sun
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Department of Medicine and VAPSHCS, University of Washington, Seattle, WA, 98195, USA.
| | - Mei Tian
- Human Phenome Institute, Fudan University, Shanghai, 201203, China.
| | - Shusen Wang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China.
| | - Si Wang
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
| | - Xia Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
| | - Xiaoning Wang
- Institute of Geriatrics, The second Medical Center, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
| | - Yan-Jiang Wang
- Department of Neurology and Center for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
| | - Yunfang Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China.
| | - Catherine C L Wong
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China.
| | - Andy Peng Xiang
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China.
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Yichuan Xiao
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Zhengwei Xie
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China.
- Beijing & Qingdao Langu Pharmaceutical R&D Platform, Beijing Gigaceuticals Tech. Co. Ltd., Beijing, 100101, China.
| | - Daichao Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China.
| | - Jing Ye
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China.
| | - Rui Yue
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Cuntai Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China.
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Hongbo Zhang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Liang Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yong Zhang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Yun-Wu Zhang
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, 361102, China.
| | - Zhuohua Zhang
- Key Laboratory of Molecular Precision Medicine of Hunan Province and Center for Medical Genetics, Institute of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, 410078, China.
- Department of Neurosciences, Hengyang Medical School, University of South China, Hengyang, 421001, China.
| | - Tongbiao Zhao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Yuzheng Zhao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Dahai Zhu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Weiguo Zou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Gang Pei
- Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Biomedicine, The Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200070, China.
| | - Guang-Hui Liu
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
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Großmann W. Listening with an Ageing Brain - a Cognitive Challenge. Laryngorhinootologie 2023; 102:S12-S34. [PMID: 37130528 PMCID: PMC10184676 DOI: 10.1055/a-1973-3038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Hearing impairment has been recently identified as a major modifiable risk factor for cognitive decline in later life and has been becoming of increasing scientific interest. Sensory and cognitive decline are connected by complex bottom-up and top-down processes, a sharp distinction between sensation, perception, and cognition is impossible. This review provides a comprehensive overview on the effects of healthy and pathological aging on auditory as well as cognitive functioning on speech perception and comprehension, as well as specific auditory deficits in the 2 most common neurodegenerative diseases in old age: Alzheimer disease and Parkinson syndrome. Hypotheses linking hearing loss to cognitive decline are discussed, and current knowledge on the effect of hearing rehabilitation on cognitive functioning is presented. This article provides an overview of the complex relationship between hearing and cognition in old age.
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Affiliation(s)
- Wilma Großmann
- Universitätsmedizin Rostock, Klinik und Poliklinik für Hals-Nasen-Ohrenheilkunde,Kopf- und Halschirurgie "Otto Körner"
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Chaikla R, Sremakaew M, Kothan S, Saekho S, Wantanajittikul K, Uthaikhup S. Effects of manual therapy combined with therapeutic exercise versus routine physical therapy on brain biomarkers in patients with chronic non-specific neck pain in Thailand: a study protocol for a single-blinded randomised controlled trial. BMJ Open 2023; 13:e072624. [PMID: 37094892 PMCID: PMC10151953 DOI: 10.1136/bmjopen-2023-072624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
INTRODUCTION Structural brain alterations in pain-related areas have been demonstrated in patients with non-specific neck pain. While manual therapy combined with therapeutic exercise is an effective management for neck pain, its underlying mechanisms are poorly understood. The primary objective of this trial is to investigate the effects of manual therapy combined with therapeutic exercise on grey matter volume and thickness in patients with chronic non-specific neck pain. The secondary objectives are to assess changes in white matter integrity, neurochemical biomarkers, clinical features of neck pain, cervical range of motion and cervical muscle strength. METHODS AND ANALYSIS This study is a single-blinded, randomised controlled trial. Fifty-two participants with chronic non-specific neck pain will be recruited into the study. Participants will be randomly allocated to either an intervention or control group (1:1 ratio). Participants in the intervention group will receive manual therapy combined with therapeutic exercise for 10 weeks (two visits per week). The control group will receive routine physical therapy. Primary outcomes are whole-brain and regional grey matter volume and thickness. Secondary outcomes are white matter integrity (fractional anisotropy and mean diffusivity), neurochemical biomarkers (N-acetylaspartate, creatine, glutamate/glutamine, myoinositol and choline), clinical features (neck pain intensity, duration, neck disability and psychological symptoms), cervical range of motion and cervical muscle strength. All outcome measures will be taken at baseline and postintervention. ETHICS AND DISSEMINATION Ethical approval of this study has been granted by Faculty of Associated Medical Science, Chiang Mai University. The results of this trial will be disseminated through a peer-reviewed publication. TRIAL REGISTRATION NUMBER NCT05568394.
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Affiliation(s)
- Rungtawan Chaikla
- Department of Physical Therapy, Chiang Mai University, Chiang Mai, Thailand
| | - Munlika Sremakaew
- Department of Physical Therapy, Chiang Mai University, Chiang Mai, Thailand
| | - Suchart Kothan
- Department of Radiologic Technology, Chiang Mai University, Chiang Mai, Thailand
| | - Suwit Saekho
- Department of Radiologic Technology, Chiang Mai University, Chiang Mai, Thailand
| | | | - S Uthaikhup
- Department of Physical Therapy, Chiang Mai University, Chiang Mai, Thailand
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Baranyi G, Buchanan CR, Conole EL, Backhouse EV, Maniega SM, Hernandez MV, Bastin ME, Wardlaw J, Deary IJ, Cox SR, Pearce J. Life-course neighbourhood deprivation and brain structure in older adults: The Lothian Birth Cohort 1936. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.13.23288523. [PMID: 37131666 PMCID: PMC10153312 DOI: 10.1101/2023.04.13.23288523] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Neighbourhood disadvantage may be associated with brain health but the importance at different stages of the life course is poorly understood. Utilizing the Lothian Birth Cohort 1936, we explored the relationship between residential neighbourhood deprivation from birth to late adulthood, and global and regional neuroimaging measures at age 73. We found that residing in disadvantaged neighbourhoods in mid- to late adulthood was associated with smaller total brain (β=-0.06; SE=0.02; n=390) and grey matter volume (β=-0.11; SE=0.03; n=390), thinner cortex (β=-0.15; SE=0.06; n=379), and lower general white matter fractional anisotropy (β=-0.19; SE=0.06; n=388). Regional analysis identified affected focal cortical areas and specific white matter tracts. Among individuals belonging to lower occupational social classes, the brain-neighbourhood associations were stronger, with the impact of neighbourhood deprivation accumulating across the life course. Our findings suggest that living in deprived neighbourhoods is associated with adverse brain morphologies, with occupational social class adding to the vulnerability.
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Affiliation(s)
- Gergő Baranyi
- Centre for Research on Environment, Society and Health, School of GeoSciences, The University of Edinburgh, Edinburgh, UK
| | - Colin R. Buchanan
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Eleanor L.S. Conole
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Ellen V. Backhouse
- Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh UK
| | - Susana Muñoz Maniega
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
- Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh UK
| | - Maria Valdes Hernandez
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
- Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh UK
| | - Mark E. Bastin
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
- Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, The University of Edinburgh, Edinburgh, UK
| | - Joanna Wardlaw
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
- Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh UK
| | - Ian J. Deary
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Simon R. Cox
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Jamie Pearce
- Centre for Research on Environment, Society and Health, School of GeoSciences, The University of Edinburgh, Edinburgh, UK
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Urdaneta ME, Kunigk NG, Peñaloza-Aponte JD, Currlin S, Malone IG, Fried SI, Otto KJ. Layer-dependent stability of intracortical recordings and neuronal cell loss. Front Neurosci 2023; 17:1096097. [PMID: 37090803 PMCID: PMC10113640 DOI: 10.3389/fnins.2023.1096097] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 03/13/2023] [Indexed: 04/08/2023] Open
Abstract
Intracortical recordings can be used to voluntarily control external devices via brain-machine interfaces (BMI). Multiple factors, including the foreign body response (FBR), limit the stability of these neural signals over time. Current clinically approved devices consist of multi-electrode arrays with a single electrode site at the tip of each shank, confining the recording interface to a single layer of the cortex. Advancements in manufacturing technology have led to the development of high-density electrodes that can record from multiple layers. However, the long-term stability of neural recordings and the extent of neuronal cell loss around the electrode across different cortical depths have yet to be explored. To answer these questions, we recorded neural signals from rats chronically implanted with a silicon-substrate microelectrode array spanning the layers of the cortex. Our results show the long-term stability of intracortical recordings varies across cortical depth, with electrode sites around L4-L5 having the highest stability. Using machine learning guided segmentation, our novel histological technique, DeepHisto, revealed that the extent of neuronal cell loss varies across cortical layers, with L2/3 and L4 electrodes having the largest area of neuronal cell loss. These findings suggest that interfacing depth plays a major role in the FBR and long-term performance of intracortical neuroprostheses.
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Affiliation(s)
- Morgan E. Urdaneta
- Department of Neuroscience, University of Florida, Gainesville, FL, United States
| | - Nicolas G. Kunigk
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Jesus D. Peñaloza-Aponte
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Seth Currlin
- Department of Neuroscience, University of Florida, Gainesville, FL, United States
| | - Ian G. Malone
- Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL, United States
| | - Shelley I. Fried
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Boston Veterans Affairs Healthcare System, Boston, MA, United States
| | - Kevin J. Otto
- Department of Neuroscience, University of Florida, Gainesville, FL, United States
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
- Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL, United States
- Department of Materials Science and Engineering, University of Florida, Gainesville, FL, United States
- Department of Neurology, University of Florida, Gainesville, FL, United States
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46
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Nava F, Margoni F, Herath N, Nava E. Age-dependent changes in intuitive and deliberative cooperation. Sci Rep 2023; 13:4457. [PMID: 36932178 PMCID: PMC10023788 DOI: 10.1038/s41598-023-31691-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/15/2023] [Indexed: 03/19/2023] Open
Abstract
Cooperation is one of the most advantageous strategies to have evolved in small- and large-scale human societies, often considered essential to their success or survival. We investigated how cooperation and the mechanisms influencing it change across the lifespan, by assessing cooperative choices from adolescence to old age (12-79 years, N = 382) forcing participants to decide either intuitively or deliberatively through the use of randomised time constraints. As determinants of these choices, we considered participants' level of altruism, their reciprocity expectations, their optimism, their desire to be socially accepted, and their attitude toward risk. We found that intuitive decision-making favours cooperation, but only from age 20 when a shift occurs: whereas in young adults, intuition favours cooperation, in adolescents it is reflection that favours cooperation. Participants' decisions were shown to be rooted in their expectations about other people's cooperative behaviour and influenced by individuals' level of optimism about their own future, revealing that the journey to the cooperative humans we become is shaped by reciprocity expectations and individual predispositions.
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Affiliation(s)
- Francesco Nava
- Department of Economics, London School of Economics, London, UK
| | - Francesco Margoni
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Social Studies, University of Stavanger, Stavanger, Norway
| | - Nilmini Herath
- Department of Economics, London School of Economics, London, UK
| | - Elena Nava
- Department of Psychology, University of Milano-Bicocca, Piazza Dell'Ateneo Nuovo 1, 20126, Milan, Italy.
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Kesidou E, Theotokis P, Damianidou O, Boziki M, Konstantinidou N, Taloumtzis C, Sintila SA, Grigoriadis P, Evangelopoulos ME, Bakirtzis C, Simeonidou C. CNS Ageing in Health and Neurodegenerative Disorders. J Clin Med 2023; 12:2255. [PMID: 36983254 PMCID: PMC10054919 DOI: 10.3390/jcm12062255] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/02/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
The process of ageing is characteristic of multicellular organisms associated with late stages of the lifecycle and is manifested through a plethora of phenotypes. Its underlying mechanisms are correlated with age-dependent diseases, especially neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD) and multiple sclerosis (MS) that are accompanied by social and financial difficulties for patients. Over time, people not only become more prone to neurodegeneration but they also lose the ability to trigger pivotal restorative mechanisms. In this review, we attempt to present the already known molecular and cellular hallmarks that characterize ageing in association with their impact on the central nervous system (CNS)'s structure and function intensifying possible preexisting pathogenetic conditions. A thorough and elucidative study of the underlying mechanisms of ageing will be able to contribute further to the development of new therapeutic interventions to effectively treat age-dependent manifestations of neurodegenerative diseases.
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Affiliation(s)
- Evangelia Kesidou
- Laboratory of Experimental Neurology and Neuroimmunology, 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 546 36 Thessaloniki, Greece (P.T.)
- Laboratory of Physiology, Faculty of Medicine, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
| | - Paschalis Theotokis
- Laboratory of Experimental Neurology and Neuroimmunology, 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 546 36 Thessaloniki, Greece (P.T.)
| | - Olympia Damianidou
- Laboratory of Experimental Neurology and Neuroimmunology, 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 546 36 Thessaloniki, Greece (P.T.)
| | - Marina Boziki
- Laboratory of Experimental Neurology and Neuroimmunology, 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 546 36 Thessaloniki, Greece (P.T.)
| | - Natalia Konstantinidou
- Laboratory of Experimental Neurology and Neuroimmunology, 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 546 36 Thessaloniki, Greece (P.T.)
| | - Charilaos Taloumtzis
- Laboratory of Experimental Neurology and Neuroimmunology, 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 546 36 Thessaloniki, Greece (P.T.)
| | - Styliani-Aggeliki Sintila
- Laboratory of Experimental Neurology and Neuroimmunology, 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 546 36 Thessaloniki, Greece (P.T.)
| | - Panagiotis Grigoriadis
- Laboratory of Experimental Neurology and Neuroimmunology, 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 546 36 Thessaloniki, Greece (P.T.)
| | | | - Christos Bakirtzis
- Laboratory of Experimental Neurology and Neuroimmunology, 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 546 36 Thessaloniki, Greece (P.T.)
| | - Constantina Simeonidou
- Laboratory of Physiology, Faculty of Medicine, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
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48
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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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49
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Scheib JPP, Stoll SEM, Randerath J. Does aging amplify the rule-based efficiency effect in action selection? Front Psychol 2023; 14:1012586. [PMID: 36936001 PMCID: PMC10014753 DOI: 10.3389/fpsyg.2023.1012586] [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: 08/05/2022] [Accepted: 02/06/2023] [Indexed: 03/05/2023] Open
Abstract
When it comes to the selection of adequate movements, people may apply varying strategies. Explicit if-then rules, compared to implicit prospective action planning, can facilitate action selection in young healthy adults. But aging alters cognitive processes. It is unknown whether older adults may similarly, profit from a rule-based approach to action selection. To investigate the potential effects of aging, the Rule/Plan Motor Cognition (RPMC) paradigm was applied to three different age groups between 31 and 90 years of age. Participants selected grips either instructed by a rule or by prospective planning. As a function of age, we found a general increase in a strategy-specific advantage as quantified by the difference in reaction time between plan- and rule-based action selection. However, in older age groups, these differences went in both directions: some participants initiated rule-based action selection faster, while for others, plan-based action selection seemed more efficient. The decomposition of reaction times into speed of the decision process, action encoding, and response caution components suggests that rule-based action selection may reduce action encoding demands in all age groups. There appears a tendency for the younger and middle age groups to have a speed advantage in the rule task when it comes to information accumulation for action selection. Thus, one influential factor determining the robustness of the rule-based efficiency effect across the lifespan may be presented by the reduced speed of information uptake. Future studies need to further specify the role of these parameters for efficient action selection.
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Affiliation(s)
| | - Sarah E. M. Stoll
- Department of Psychology, University of Konstanz, Konstanz, Germany
- Lurija Institute for Rehabilitation Science and Health Research, Kliniken Schmieder, Allensbach, Germany
| | - Jennifer Randerath
- Lurija Institute for Rehabilitation Science and Health Research, Kliniken Schmieder, Allensbach, Germany
- Outpatient Unit for Research, Teaching and Practice, Faculty of Psychology, University of Vienna, Vienna, Austria
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Watanabe K, Kokubun K, Yamakawa Y. Altered Grey Matter-Brain Healthcare Quotient: Interventions of Olfactory Training and Learning of Neuroplasticity. Life (Basel) 2023; 13:life13030667. [PMID: 36983823 PMCID: PMC10052964 DOI: 10.3390/life13030667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/09/2023] [Accepted: 02/24/2023] [Indexed: 03/05/2023] Open
Abstract
Recent studies revealed that grey matter (GM) changes due to various training and learning experiences, using magnetic resonance imaging. In this study, we investigate the effect of psychological characteristics and attitudes toward training and learning on GM changes. Ninety participants were recruited and distributed into three groups: an olfactory training group that underwent 40 olfactory training sessions designed for odour classification tasks, a group classified for learning of neuroplasticity and brain healthcare using a TED Talk video and 28 daily brain healthcare messages, and a control group. Further, we assessed psychological characteristics, such as curiosity and personal growth initiatives. In the olfactory training group, we conducted a questionnaire survey on olfactory training regarding their interests and sense of accomplishment. In the olfactory training group, the GM change was significantly correlated with the sense of achievement and interest in training. The learning of neuroplasticity and brain healthcare group showed a significantly smaller 2-month GM decline than did the control group. The Curiosity and Exploration Inventory-II scores were significantly correlated with GM changes in both intervention groups only. In conclusion, our result suggested that training or learning with a sense of accomplishment, interest, and curiosity would lead to greater GM changes.
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Affiliation(s)
- Keita Watanabe
- Institution of Open Innovation, Kyoto University, Kyoto 606-8501, Japan
- Correspondence:
| | - Keisuke Kokubun
- Smart-Aging Research Center, Tohoku University, Sendai 980-8575, Japan
| | - Yoshinori Yamakawa
- Institution of Open Innovation, Kyoto University, Kyoto 606-8501, Japan
- Institute of Innovative Research, Tokyo Institute of Technology, Tokyo 152-8550, Japan
- Academic and Industrial Innovation, Kobe University, Kobe 657-8501, Japan
- ImPACT Program of Council for Science, Technology, and Innovation (Cabinet Office, Government of Japan), Tokyo 100-8914, Japan
- BRAIN IMPACT General Incorporated Association, Kyoto 606-8501, Japan
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