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Li Y, Zhuo Z, Liu C, Duan Y, Shi Y, Wang T, Li R, Wang Y, Jiang J, Xu J, Tian D, Zhang X, Shi F, Zhang X, Carass A, Barkhof F, Prince JL, Ye C, Liu Y. Deep learning enables accurate brain tissue microstructure analysis based on clinically feasible diffusion magnetic resonance imaging. Neuroimage 2024:120858. [PMID: 39317273 DOI: 10.1016/j.neuroimage.2024.120858] [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: 08/05/2024] [Revised: 09/14/2024] [Accepted: 09/17/2024] [Indexed: 09/26/2024] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) allows non-invasive assessment of brain tissue microstructure. Current model-based tissue microstructure reconstruction techniques require a large number of diffusion gradients, which is not clinically feasible due to imaging time constraints, and this has limited the use of tissue microstructure information in clinical settings. Recently, approaches based on deep learning (DL) have achieved promising tissue microstructure reconstruction results using clinically feasible dMRI. However, it remains unclear whether the subtle tissue changes associated with disease or age are properly preserved with DL approaches and whether DL reconstruction results can benefit clinical applications. Here, we provide the first evidence that DL approaches to tissue microstructure reconstruction yield reliable brain tissue microstructure analysis based on clinically feasible dMRI scans. Specifically, we reconstructed tissue microstructure from four different brain dMRI datasets with only 12 diffusion gradients, a clinically feasible protocol, and the neurite orientation dispersion and density imaging (NODDI) and spherical mean technique (SMT) models were considered. With these results we show that disease-related and age-dependent alterations of brain tissue were accurately identified. These findings demonstrate that DL tissue microstructure reconstruction can accurately quantify microstructural alterations in the brain based on clinically feasible dMRI.
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Affiliation(s)
- Yuxing Li
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chenghao Liu
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yulu Shi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tingting Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Runzhi Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Yanli Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Jiwei Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Jun Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Decai Tian
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xinghu Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fudong Shi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China; Department of Neurology and Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaofeng Zhang
- School of Information and Electronics, Beijing Institute of Technology, Zhuhai, China
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, 1081 HV, The Netherlands
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | - Chuyang Ye
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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Korbmacher M, van der Meer D, Beck D, Askeland-Gjerde DE, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Distinct Longitudinal Brain White Matter Microstructure Changes and Associated Polygenic Risk of Common Psychiatric Disorders and Alzheimer's Disease in the UK Biobank. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100323. [PMID: 39132576 PMCID: PMC11313202 DOI: 10.1016/j.bpsgos.2024.100323] [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: 03/21/2024] [Revised: 03/24/2024] [Accepted: 04/16/2024] [Indexed: 08/13/2024] Open
Abstract
Background During the course of adulthood and aging, white matter (WM) structure and organization are characterized by slow degradation processes such as demyelination and shrinkage. An acceleration of such aging processes has been linked to the development of a range of diseases. Thus, an accurate description of healthy brain maturation, particularly in terms of WM features, is fundamental to the understanding of aging. Methods We used longitudinal diffusion magnetic resonance imaging to provide an overview of WM changes at different spatial and temporal scales in the UK Biobank (UKB) (n = 2678; agescan 1 = 62.38 ± 7.23 years; agescan 2 = 64.81 ± 7.1 years). To examine the genetic overlap between WM structure and common clinical conditions, we tested the associations between WM structure and polygenic risk scores for the most common neurodegenerative disorder, Alzheimer's disease, and common psychiatric disorders (unipolar and bipolar depression, anxiety, obsessive-compulsive disorder, autism, schizophrenia, attention-deficit/hyperactivity disorder) in longitudinal (n = 2329) and cross-sectional (n = 31,056) UKB validation data. Results Our findings indicate spatially distributed WM changes across the brain, as well as distributed associations of polygenic risk scores with WM. Importantly, brain longitudinal changes reflected genetic risk for disorder development better than the utilized cross-sectional measures, with regional differences giving more specific insights into gene-brain change associations than global averages. Conclusions We extend recent findings by providing a detailed overview of WM microstructure degeneration on different spatial levels, helping to understand fundamental brain aging processes. Further longitudinal research is warranted to examine aging-related gene-brain associations.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Dennis van der Meer
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Dani Beck
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Daniel E. Askeland-Gjerde
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I. Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
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Poirier SE, Suskin NG, Khaw AV, Thiessen JD, Shoemaker JK, Anazodo UC. Probing Evidence of Cerebral White Matter Microstructural Disruptions in Ischemic Heart Disease Before and Following Cardiac Rehabilitation: A Diffusion Tensor MR Imaging Study. J Magn Reson Imaging 2024; 59:2137-2149. [PMID: 37589418 DOI: 10.1002/jmri.28964] [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: 05/05/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND Ischemic heart disease (IHD) is linked to brain white matter (WM) breakdown but how age or disease effects WM integrity, and whether it is reversible using cardiac rehabilitation (CR), remains unclear. PURPOSE To assess the effects of brain aging, cardiovascular disease, and CR on WM microstructure in brains of IHD patients following a cardiac event. STUDY TYPE Retrospective. POPULATION Thirty-five IHD patients (9 females; mean age = 59 ± 8 years), 21 age-matched healthy controls (10 females; mean age = 59 ± 8 years), and 25 younger controls (14 females; mean age = 26 ± 4 years). FIELD STRENGTH/SEQUENCE 3 T diffusion-weighted imaging with single-shot echo planar imaging acquired at 3 months and 9 months post-cardiac event. ASSESSMENT Tract-based spatial statistics (TBSS) and tractometry were used to compare fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in cerebral WM between: 1) older and younger controls to distinguish age-related from disease-related WM changes; 2) IHD patients at baseline (pre-CR) and age-matched controls to investigate if cardiovascular disease exacerbates age-related WM changes; and 3) IHD patients pre-CR and post-CR to investigate the neuroplastic effect of CR on WM microstructure. STATISTICAL TESTS Two-sample unpaired t-test (age: older vs. younger controls; IHD: IHD pre-CR vs. age-matched controls). One-sample paired t-test (CR: IHD pre- vs. post-CR). Statistical threshold: P < 0.05 (FWE-corrected). RESULTS TBSS and tractometry revealed widespread WM changes in older controls compared to younger controls while WM clusters of decreased FA in the fornix and increased MD in body of corpus callosum were observed in IHD patients pre-CR compared to age-matched controls. Robust WM improvements (increased FA, increased AD) were observed in IHD patients post-CR. DATA CONCLUSION In IHD, both brain aging and cardiovascular disease may contribute to WM disruptions. IHD-related WM disruptions may be favorably modified by CR. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Stefan E Poirier
- Lawson Imaging, Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Neville G Suskin
- Division of Cardiology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Alexander V Khaw
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Jonathan D Thiessen
- Lawson Imaging, Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Joel K Shoemaker
- School of Kinesiology, Western University, London, Ontario, Canada
| | - Udunna C Anazodo
- Lawson Imaging, Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Research Centre for Studies in Aging, McGill University, Montréal, Québec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
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Moisseinen N, Ahveninen L, Martínez‐Molina N, Sairanen V, Melkas S, Kleber B, Sihvonen AJ, Särkämö T. Choir singing is associated with enhanced structural connectivity across the adult lifespan. Hum Brain Mapp 2024; 45:e26705. [PMID: 38716698 PMCID: PMC11077432 DOI: 10.1002/hbm.26705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/06/2024] [Accepted: 04/21/2024] [Indexed: 05/12/2024] Open
Abstract
The global ageing of populations calls for effective, ecologically valid methods to support brain health across adult life. Previous evidence suggests that music can promote white matter (WM) microstructure and grey matter (GM) volume while supporting auditory and cognitive functioning and emotional well-being as well as counteracting age-related cognitive decline. Adding a social component to music training, choir singing is a popular leisure activity among older adults, but a systematic account of its potential to support healthy brain structure, especially with regard to ageing, is currently missing. The present study used quantitative anisotropy (QA)-based diffusion MRI connectometry and voxel-based morphometry to explore the relationship of lifetime choir singing experience and brain structure at the whole-brain level. Cross-sectional multiple regression analyses were carried out in a large, balanced sample (N = 95; age range 21-88) of healthy adults with varying levels of choir singing experience across the whole age range and within subgroups defined by age (young, middle-aged, and older adults). Independent of age, choir singing experience was associated with extensive increases in WM QA in commissural, association, and projection tracts across the brain. Corroborating previous work, these overlapped with language and limbic networks. Enhanced corpus callosum microstructure was associated with choir singing experience across all subgroups. In addition, choir singing experience was selectively associated with enhanced QA in the fornix in older participants. No associations between GM volume and choir singing were found. The present study offers the first systematic account of amateur-level choir singing on brain structure. While no evidence for counteracting GM atrophy was found, the present evidence of enhanced structural connectivity coheres well with age-typical structural changes. Corroborating previous behavioural studies, the present results suggest that regular choir singing holds great promise for supporting brain health across the adult life span.
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Affiliation(s)
- Nella Moisseinen
- Cognitive Brain Research Unit, Centre of Excellence in Music, Mind, Body and the Brain, Department of Psychology and Logopedics, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
| | - Lotta Ahveninen
- Cognitive Brain Research Unit, Centre of Excellence in Music, Mind, Body and the Brain, Department of Psychology and Logopedics, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
| | - Noelia Martínez‐Molina
- Cognitive Brain Research Unit, Centre of Excellence in Music, Mind, Body and the Brain, Department of Psychology and Logopedics, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
- Center for Brain and Cognition, Department of Information and Communication TechnologiesUniversity Pompeu FabraBarcelonaSpain
| | - Viljami Sairanen
- Department of RadiologyKanta‐Häme Central HospitalHämeenlinnaFinland
- Baby Brain Activity Center, Children's HospitalHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Susanna Melkas
- Clinical Neurosciences, NeurologyUniversity of HelsinkiHelsinkiFinland
| | - Boris Kleber
- Center for Music in the Brain, Department of Clinical MedicineAarhus University and The Royal Academy of Music Aarhus/AalborgAarhusDenmark
| | - Aleksi J. Sihvonen
- Cognitive Brain Research Unit, Centre of Excellence in Music, Mind, Body and the Brain, Department of Psychology and Logopedics, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
- Centre for Clinical Research, School of Health and Rehabilitation SciencesUniversity of QueenslandBrisbaneAustralia
- Department of NeurologyHelsinki University HospitalHelsinkiFinland
| | - Teppo Särkämö
- Cognitive Brain Research Unit, Centre of Excellence in Music, Mind, Body and the Brain, Department of Psychology and Logopedics, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
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Korbmacher M, van der Meer D, Beck D, de Lange AMG, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Brain asymmetries from mid- to late life and hemispheric brain age. Nat Commun 2024; 15:956. [PMID: 38302499 PMCID: PMC10834516 DOI: 10.1038/s41467-024-45282-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/19/2024] [Indexed: 02/03/2024] Open
Abstract
The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N=48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway.
- Mohn Medical Imaging and Visualization Centre (MMIV), Bergen, Norway.
| | - Dennis van der Meer
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Dani Beck
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ann-Marie G de Lange
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre (MMIV), Bergen, Norway
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A Andreassen
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway.
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Wang R, Deng Y, Zhang W, Ning J, Li H, Feng J, Cheng W, Yu J. Associations between adiposity and white matter hyperintensities: Cross-sectional and longitudinal analyses of 34,653 participants. Hum Brain Mapp 2024; 45:e26560. [PMID: 38224536 PMCID: PMC10789203 DOI: 10.1002/hbm.26560] [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: 06/04/2023] [Revised: 11/15/2023] [Accepted: 11/28/2023] [Indexed: 01/17/2024] Open
Abstract
OBJECTIVES White matter hyperintensities (WMH) increase the risk of stroke and cognitive impairment. This study aims to determine the cross-sectional and longitudinal associations between adiposity and WMH. METHODS Participants were enrolled from the UK Biobank cohort. Associations of concurrent, past, and changes in overall and central adiposity with WMH were investigated by linear and nonlinear regression models. The association of longitudinal adiposity and WMH volume changes was determined by a linear mixed model. Mediation analysis investigated the potential mediating effect of blood pressure. RESULTS In 34,653 participants with available adiposity measures and imaging data, the concurrent obese group had a 25.3% (β [95% CI] = 0.253 [0.222-0.284]) higher WMH volume than the ideal weight group. Increment in all adiposity measures was associated with a higher WMH volume. Among them, waist circumference demonstrated the strongest effect (β [95% CI] = 0.113 [0.101-0.125]). Past adiposity also demonstrated similar effects. Among the subset of 2664 participants with available WMH follow-up data, adiposity measures were predictive of WMH change. Regarding changes of adiposity, compared with ideal weight stable group, those who turned from ideal weight to overweight/obese had a 8.1% higher WMH volume (β [95% CI] = 0.081 [0.039-0.123]), while participants who turned from overweight/obese to ideal weight demonstrated no significant WMH volume change. Blood pressure partly meditates the associations between adiposity and WMH. CONCLUSIONS Both concurrent and past adiposity were associated with a higher WMH volume. The detrimental effects of adiposity on WMH occurred throughout midlife and in the elderly and may still exist after changes in obesity status.
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Affiliation(s)
- Rong‐Ze Wang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical CollegeFudan UniversityShanghaiChina
| | - Yue‐Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical CollegeFudan UniversityShanghaiChina
| | - Wei Zhang
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain Inspired IntelligenceFudan University, Ministry of EducationShanghaiChina
| | - Jing Ning
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical CollegeFudan UniversityShanghaiChina
| | - Hong‐Qi Li
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical CollegeFudan UniversityShanghaiChina
| | - Jian‐Feng Feng
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain Inspired IntelligenceFudan University, Ministry of EducationShanghaiChina
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical CollegeFudan UniversityShanghaiChina
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain Inspired IntelligenceFudan University, Ministry of EducationShanghaiChina
| | - Jin‐Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical CollegeFudan UniversityShanghaiChina
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Carver CM, Gomez PT, Rodriguez SL, Kachergus JM, Liu Y, Shi J, Tran T, Wang L, Melov S, Thompson EA, Schafer MJ. Senescent and disease-associated microglia are modifiable features of aged brain white matter. RESEARCH SQUARE 2023:rs.3.rs-3467812. [PMID: 37961365 PMCID: PMC10635389 DOI: 10.21203/rs.3.rs-3467812/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: 11/15/2023]
Abstract
Brain white matter tracts undergo structural and functional changes linked to late-life cognitive decline, but the cellular and molecular contributions to their selective vulnerability are not well defined. In naturally aged mice, we demonstrate that senescent and disease-associated microglia (DAM) phenotypes converge in hippocampus-adjacent white matter. Through gold-standard gene expression and immunolabeling combined with high-dimensional spatial mapping, we identified microglial cell fates in aged white matter characterized by aberrant morphology, microenvironment reorganization, and expression of senescence and DAM markers, including galectin 3 (GAL3/Lgals3), B-cell lymphoma 2 (Bcl2), and cyclin dependent kinase inhibitors, including Cdkn2a/p16ink4a. Pharmacogenetic or pharmacological targeting of p16ink4a or BCL2 reduced white matter GAL3+ DAM abundance and rejuvenated microglial fimbria organization. Our results demonstrate dynamic changes in microglial identity in aged white matter that can be reverted by senotherapeutic intervention to promote homeostatic maintenance in the aged brain.
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Affiliation(s)
- Chase M. Carver
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Paul T. Gomez
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Sonia L. Rodriguez
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | | | - Yi Liu
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Ji Shi
- Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, USA
| | - Tommy Tran
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Liguo Wang
- Division of Computational Biology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Simon Melov
- Buck Institute for Research on Aging, Novato, CA, USA
| | | | - Marissa J. Schafer
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
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Korbmacher M, Wang M, Eikeland R, Buchert R, Andreassen OA, Espeseth T, Leonardsen E, Westlye LT, Maximov II, Specht K. Considerations on brain age predictions from repeatedly sampled data across time. Brain Behav 2023; 13:e3219. [PMID: 37587620 PMCID: PMC10570486 DOI: 10.1002/brb3.3219] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 07/05/2023] [Accepted: 08/02/2023] [Indexed: 08/18/2023] Open
Abstract
INTRODUCTION Brain age, the estimation of a person's age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for the same individual at various time points and validate our findings with age-matched healthy controls. METHODS We used densely sampled T1-weighted MRI data from four individuals (from two densely sampled datasets) to observe how brain age corresponds to age and is influenced by acquisition and quality parameters. For validation, we used two cross-sectional datasets. Brain age was predicted by a pretrained deep learning model. RESULTS We found small within-subject correlations between age and brain age. We also found evidence for the influence of field strength on brain age which replicated in the cross-sectional validation data and inconclusive effects of scan quality. CONCLUSION The absence of maturation effects for the age range in the presented sample, brain age model bias (including training age distribution and field strength), and model error are potential reasons for small relationships between age and brain age in densely sampled longitudinal data. Clinical applications of brain age models should consider of the possibility of apparent biases caused by variation in the data acquisition process.
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Affiliation(s)
- Max Korbmacher
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- Norwegian Centre for Mental Disorders Research (NORMENT)Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOsloNorway
- Mohn Medical Imaging and Visualisation Center (MMIV)BergenNorway
| | - Meng‐Yun Wang
- Mohn Medical Imaging and Visualisation Center (MMIV)BergenNorway
- Department of Biological and Medical PsychologyUniversity of BergenBergenNorway
| | - Rune Eikeland
- Mohn Medical Imaging and Visualisation Center (MMIV)BergenNorway
- Department of Biological and Medical PsychologyUniversity of BergenBergenNorway
| | - Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear MedicineUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT)Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | - Thomas Espeseth
- Department of PsychologyUniversity of OsloOsloNorway
- Department of PsychologyOslo New University CollegeOsloNorway
| | - Esten Leonardsen
- Norwegian Centre for Mental Disorders Research (NORMENT)Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT)Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ivan I. Maximov
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- Norwegian Centre for Mental Disorders Research (NORMENT)Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOsloNorway
| | - Karsten Specht
- Mohn Medical Imaging and Visualisation Center (MMIV)BergenNorway
- Department of Biological and Medical PsychologyUniversity of BergenBergenNorway
- Department of EducationUiT The Arctic University of NorwayTromsøNorway
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Korbmacher M, de Lange AM, van der Meer D, Beck D, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Brain-wide associations between white matter and age highlight the role of fornix microstructure in brain ageing. Hum Brain Mapp 2023; 44:4101-4119. [PMID: 37195079 PMCID: PMC10258541 DOI: 10.1002/hbm.26333] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/16/2023] [Accepted: 04/26/2023] [Indexed: 05/18/2023] Open
Abstract
Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6-82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.
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Affiliation(s)
- Max Korbmacher
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Mohn Medical Imaging and Visualisation Center (MMIV)BergenNorway
| | - Ann Marie de Lange
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Department of PsychiatryUniversity of OxfordOxfordUK
- LREN, Centre for Research in Neurosciences–Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Dennis van der Meer
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtNetherlands
| | - Dani Beck
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Department of Psychiatric Research, Diakonhjemmet HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Eli Eikefjord
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- Mohn Medical Imaging and Visualisation Center (MMIV)BergenNorway
| | - Arvid Lundervold
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- Mohn Medical Imaging and Visualisation Center (MMIV)BergenNorway
- Department of RadiologyHaukeland University HospitalBergenNorway
- Department of BiomedicineUniversity of BergenBergenNorway
| | - Ole A. Andreassen
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | - Lars T. Westlye
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | - Ivan I. Maximov
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
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