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Li J, Lam LCW, Lu H. Decoding MRI-informed brain age using mutual information. Insights Imaging 2024; 15:216. [PMID: 39186199 PMCID: PMC11347523 DOI: 10.1186/s13244-024-01791-9] [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: 04/12/2024] [Accepted: 07/31/2024] [Indexed: 08/27/2024] Open
Abstract
OBJECTIVE We aimed to develop a standardized method to investigate the relationship between estimated brain age and regional morphometric features, meeting the criteria for simplicity, generalization, and intuitive interpretability. METHODS We utilized T1-weighted magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience project (N = 609) and employed a support vector regression method to train a brain age model. The pre-trained brain age model was applied to the dataset of the brain development project (N = 547). Kraskov (KSG) estimator was used to compute the mutual information (MI) value between brain age and regional morphometric features, including gray matter volume (GMV), white matter volume (WMV), cerebrospinal fluid (CSF) volume, and cortical thickness (CT). RESULTS Among four types of brain features, GMV had the highest MI value (8.71), peaking in the pre-central gyrus (0.69). CSF volume was ranked second (7.76), with the highest MI value in the cingulate (0.87). CT was ranked third (6.22), with the highest MI value in superior temporal gyrus (0.53). WMV had the lowest MI value (4.59), with the insula showing the highest MI value (0.53). For brain parenchyma, the volume of the superior frontal gyrus exhibited the highest MI value (0.80). CONCLUSION This is the first demonstration that MI value between estimated brain age and morphometric features may serve as a benchmark for assessing the regional contributions to estimated brain age. Our findings highlighted that both GMV and CSF are the key features that determined the estimated brain age, which may add value to existing computational models of brain age. CRITICAL RELEVANCE STATEMENT Mutual information (MI) analysis reveals gray matter volume (GMV) and cerebrospinal fluid (CSF) volume as pivotal in computing individuals' brain age. KEY POINTS Mutual information (MI) interprets estimated brain age with morphometric features. Gray matter volume in the pre-central gyrus has the highest MI value for estimated brain age. Cerebrospinal fluid volume in the cingulate has the highest MI value. Regarding brain parenchymal volume, the superior frontal gyrus has the highest MI value. The value of mutual information underscores the key brain regions related to brain age.
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Affiliation(s)
- Jing Li
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Linda Chiu Wa Lam
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Hanna Lu
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China.
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China.
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2
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MacLean A, Horn M, Midkiff C, Van Zandt A, Saied A. Combination antiretroviral therapy prevents SIV- induced aging in the hippocampus and neurodegeneration throughout the brain. RESEARCH SQUARE 2024:rs.3.rs-4681317. [PMID: 39149452 PMCID: PMC11326353 DOI: 10.21203/rs.3.rs-4681317/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: 08/17/2024]
Abstract
Virus-induced accelerated aging has been proposed as a potential mechanism underlying the persistence of HIV-associated neurocognitive disorders (HAND) despite advances in access and adherence to combination antiretroviral therapies (cART). While some studies have demonstrated evidence of accelerated aging in PLWH, studies examining acute infection, and cART intervention are limited, with most studies being in vitro or utilizing small animal models. Here, we utilized FFPE tissues from Simian immunodeficiency virus (SIV) infected rhesus macaques to assess the levels of two proteins commonly associated with aging - the cellular senescence marker p16INK4a (p16) and the NAD-dependent deacetylase sirtuin 1 (SIRT1). Our central hypothesis was that SIV infection induces accelerated aging phenotypes in the brain characterized by increased expression of p16 and altered expression of SIRT1 that correlate with increased neurodegeneration, and that cART inhibits this process. We found that SIV infection induced increased GFAP, p16, SIRT1, and neurodegeneration in multiple brain regions, and treatment with cART reduced GFAP expression in SIV-infected animals and thus likely decreases inflammation in the brain. Importantly, cART reversed SIV-induced accelerated aging (p16 and SIRT1) and neurodegeneration in the frontal lobe and hippocampus. Combined, these data suggest that cART is both safe and effective in reducing neuroinflammation and age-associated alterations in astrocytes that contribute to neurodegeneration, providing possible therapeutic targets in the treatment of HAND.
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3
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Ndhlovu LC, Bendall ML, Dwaraka V, Pang APS, Dopkins N, Carreras N, Smith R, Nixon DF, Corley MJ. Retro-age: A unique epigenetic biomarker of aging captured by DNA methylation states of retroelements. Aging Cell 2024:e14288. [PMID: 39092674 DOI: 10.1111/acel.14288] [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: 02/15/2024] [Revised: 07/11/2024] [Accepted: 07/16/2024] [Indexed: 08/04/2024] Open
Abstract
Reactivation of retroelements in the human genome has been linked to aging. However, whether the epigenetic state of specific retroelements can predict chronological age remains unknown. We provide evidence that locus-specific retroelement DNA methylation can be used to create retroelement-based epigenetic clocks that accurately measure chronological age in the immune system, across human tissues, and pan-mammalian species. We also developed a highly accurate retroelement epigenetic clock compatible with EPICv.2.0 data that was constructed from CpGs that did not overlap with existing first- and second-generation epigenetic clocks, suggesting a unique signal for epigenetic clocks not previously captured. We found retroelement-based epigenetic clocks were reversed during transient epigenetic reprogramming, accelerated in people living with HIV-1, and responsive to antiretroviral therapy. Our findings highlight the utility of retroelement-based biomarkers of aging and support a renewed emphasis on the role of retroelements in geroscience.
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Affiliation(s)
- Lishomwa C Ndhlovu
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, New York City, USA
| | - Matthew L Bendall
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, New York City, USA
| | | | - Alina P S Pang
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, New York City, USA
| | - Nicholas Dopkins
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, New York City, USA
| | | | | | - Douglas F Nixon
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, New York City, USA
| | - Michael J Corley
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, New York City, USA
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4
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Zheng X, Su B, Zhao Y, Chen C, Vellas B, Michel JP, Shao R. Foundations and implications of Human Aging Omics: A framework for identifying cumulative health risks from embryo to senescence. Sci Bull (Beijing) 2024:S2095-9273(24)00537-1. [PMID: 39198091 DOI: 10.1016/j.scib.2024.04.073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 04/02/2024] [Accepted: 04/05/2024] [Indexed: 09/01/2024]
Affiliation(s)
- Xiaoying Zheng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; APEC Health Science Academy (HeSAY), Peking University, Beijing 100871, China.
| | - Binbin Su
- Department of Geriatric Health Sciences, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Yihao Zhao
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Chen Chen
- Department of Geriatric Health Sciences, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Bruno Vellas
- Gérontopôle & Department of Geriatric Internal Medicine, Toulouse University Hospital, Toulouse 31000, France
| | | | - Ruitai Shao
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
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5
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Brkić-Jovanović N, Karaman M, Andrić V, Marić D, Brkić S, Bugarski-Ignjatović V. Neurocognitive profile in HIV subjects on INSTI-regimen- one year follow up: Is there room for optimism? PLoS One 2024; 19:e0306278. [PMID: 38923982 PMCID: PMC11207154 DOI: 10.1371/journal.pone.0306278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
The introduction of antiretroviral therapy (ART) has successfully changed the clinical course of people with HIV, leading to a significant decline in the incidence of HIV-related neurocognitive disorders. Integrase strand transferase inhibitors (INSTI) are recommended and preferred first-line ART for the treatment of HIV-1 infection in ART-naïve subjects. This type of therapy regimen is expected to have higher CNS penetration, which may bring more cognitive stability or even make significant cognitive improvement in people with HIV. The study aimed to follow up on neurocognitive performance in HIV subjects on two types of INSTI therapy regimens at two-time points, one year apart. The study sample consisted of 61 ART naïve male participants, of which 32 were prescribed raltegravir (RAL) and 29 dolutegravir (DTG). There was no significant difference between subsamples according to the main sociodemographic (age, education level) and clinical characteristics (duration of therapy, nadir CD4 cells level, CD4 cells count, CD8 cells, CD4/CD8 ratio). For neurocognitive assessment, six measures were used: general cognitive ability (MoCA test), verbal fluency (total sum score for phonemic and category fluency), verbal working memory (digit span forward), cognitive capacity (digit span backwards), sustained attention (Color Trail Test 1), and divided attention (Color Trail Test 2). In both therapy groups (RAL and DTG), there was no significant decrease in neurocognitive achievement on all used measures over a one-year follow-up in both therapy groups. A statistically significant interactive effect of time and type of therapy was found on the measure of divided attention-DTG group showed slight improvement, whereas RAL group showed slight decrease in performance. During the one-year follow-up of persons on INSTI-based regimen, no significant changes in cognitive achievement were recorded, which suggests that the existing therapy can have a potentially positive effect on the maintenance of neurocognitive achievement.
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Affiliation(s)
- Nina Brkić-Jovanović
- Department of Psychology, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
| | - Mina Karaman
- Department of Psychology, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
| | - Vanja Andrić
- Department of Infectious Diseases, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
| | - Daniela Marić
- Department of Infectious Diseases, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
| | - Snežana Brkić
- Department of Infectious Diseases, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
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Dular L, Pernuš F, Špiclin Ž. Extensive T1-weighted MRI preprocessing improves generalizability of deep brain age prediction models. Comput Biol Med 2024; 173:108320. [PMID: 38531250 DOI: 10.1016/j.compbiomed.2024.108320] [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: 02/23/2023] [Revised: 01/09/2024] [Accepted: 03/12/2024] [Indexed: 03/28/2024]
Abstract
Brain age is an estimate of chronological age obtained from T1-weighted magnetic resonance images (T1w MRI), representing a straightforward diagnostic biomarker of brain aging and associated diseases. While the current best accuracy of brain age predictions on T1w MRIs of healthy subjects ranges from two to three years, comparing results across studies is challenging due to differences in the datasets, T1w preprocessing pipelines, and evaluation protocols used. This paper investigates the impact of T1w image preprocessing on the performance of four deep learning brain age models from recent literature. Four preprocessing pipelines, which differed in terms of registration transform, grayscale correction, and software implementation, were evaluated. The results showed that the choice of software or preprocessing steps could significantly affect the prediction error, with a maximum increase of 0.75 years in mean absolute error (MAE) for the same model and dataset. While grayscale correction had no significant impact on MAE, using affine rather than rigid registration to brain atlas statistically significantly improved MAE. Models trained on 3D images with isotropic 1mm3 resolution exhibited less sensitivity to the T1w preprocessing variations compared to 2D models or those trained on downsampled 3D images. Our findings indicate that extensive T1w preprocessing improves MAE, especially when predicting on a new dataset. This runs counter to prevailing research literature, which suggests that models trained on minimally preprocessed T1w scans are better suited for age predictions on MRIs from unseen scanners. We demonstrate that, irrespective of the model or T1w preprocessing used during training, applying some form of offset correction is essential to enable the model's performance to generalize effectively on datasets from unseen sites, regardless of whether they have undergone the same or different T1w preprocessing as the training set.
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Affiliation(s)
- Lara Dular
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
| | - Franjo Pernuš
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
| | - Žiga Špiclin
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia.
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Seitz-Holland J, Haas SS, Penzel N, Reichenberg A, Pasternak O. BrainAGE, brain health, and mental disorders: A systematic review. Neurosci Biobehav Rev 2024; 159:105581. [PMID: 38354871 PMCID: PMC11119273 DOI: 10.1016/j.neubiorev.2024.105581] [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: 11/09/2023] [Revised: 02/05/2024] [Accepted: 02/09/2024] [Indexed: 02/16/2024]
Abstract
The imaging-based method of brainAGE aims to characterize an individual's vulnerability to age-related brain changes. The present study systematically reviewed brainAGE findings in neuropsychiatric conditions and discussed the potential of brainAGE as a marker for biological age. A systematic PubMed search (from inception to March 6th, 2023) identified 273 articles. The 30 included studies compared brainAGE between neuropsychiatric and healthy groups (n≥50). We presented results qualitatively and adapted a bias risk assessment questionnaire. The imaging modalities, design, and input features varied considerably between studies. While the studies found higher brainAGE in neuropsychiatric conditions (11 mild cognitive impairment/ dementia, 11 schizophrenia spectrum/ other psychotic and bipolar disorder, six depression/ anxiety, two multiple groups), the associations with clinical characteristics were mixed. While brainAGE is sensitive to group differences, limitations include the lack of diverse training samples, multi-modal studies, and external validation. Only a few studies obtained longitudinal data, and all have used algorithms built solely to predict chronological age. These limitations impede the validity of brainAGE as a biological age marker.
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Affiliation(s)
- Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nora Penzel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Abraham Reichenberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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8
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Speidell A, Agbey C, Mocchetti I. Accelerated neurodegeneration of basal forebrain cholinergic neurons in HIV-1 gp120 transgenic mice: Critical role of the p75 neurotrophin receptor. Brain Behav Immun 2024; 117:347-355. [PMID: 38266662 PMCID: PMC10935610 DOI: 10.1016/j.bbi.2024.01.215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 01/19/2024] [Accepted: 01/20/2024] [Indexed: 01/26/2024] Open
Abstract
Human Immunodeficiency Virus-1 (HIV) infection of the brain induces HIV-associated neurocognitive disorders (HAND). The set of molecular events employed by HIV to drive cognitive impairments in people living with HIV are diverse and remain not completely understood. We have shown that the HIV envelope protein gp120 promotes loss of synapses and decreases performance on cognitive tasks through the p75 neurotrophin receptor (p75NTR). This receptor is abundant on cholinergic neurons of the basal forebrain and contributes to cognitive impairment in various neurological disorders. In this study, we examined cholinergic neurons of gp120 transgenic (gp120tg) mice for signs of degeneration. We observed that the number of choline acetyltransferase-expressing cells is decreased in old (12-14-month-old) gp120tg mice when compared to age matched wild type. In the same animals, we observed an increase in the levels of pro-nerve growth factor, a ligand of p75NTR, as well as a disruption of consolidation of extinction of conditioned fear, a behavior regulated by cholinergic neurons of the basal forebrain. Both biochemical and behavioral outcomes of gp120tg mice were rescued by the deletion of the p75NTR gene, strongly supporting the role that this receptor plays in the neurotoxic effects of gp120. These data indicate that future p75NTR-directed pharmacotherapies could provide an adjunct therapy against synaptic simplification caused by HIV.
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Affiliation(s)
- Andrew Speidell
- Interdisciplinary Program in Neuroscience, and Department of Neuroscience, NRB WP13, Georgetown University Medical Center, Washington, DC 20057, USA
| | - Christy Agbey
- Interdisciplinary Program in Neuroscience, and Department of Neuroscience, NRB WP13, Georgetown University Medical Center, Washington, DC 20057, USA
| | - Italo Mocchetti
- Interdisciplinary Program in Neuroscience, and Department of Neuroscience, NRB WP13, Georgetown University Medical Center, Washington, DC 20057, USA.
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9
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Zhang Y, Xie R, Beheshti I, Liu X, Zheng G, Wang Y, Zhang Z, Zheng W, Yao Z, Hu B. Improving brain age prediction with anatomical feature attention-enhanced 3D-CNN. Comput Biol Med 2024; 169:107873. [PMID: 38181606 DOI: 10.1016/j.compbiomed.2023.107873] [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: 03/31/2023] [Revised: 11/17/2023] [Accepted: 12/17/2023] [Indexed: 01/07/2024]
Abstract
Currently, significant progress has been made in predicting brain age from structural Magnetic Resonance Imaging (sMRI) data using deep learning techniques. However, despite the valuable structural information they contain, the traditional engineering features known as anatomical features have been largely overlooked in this context. To address this issue, we propose an attention-based network design that integrates anatomical and deep convolutional features, leveraging an anatomical feature attention (AFA) module to effectively capture salient anatomical features. In addition, we introduce a fully convolutional network, which simplifies the extraction of deep convolutional features and overcomes the high computational memory requirements associated with deep learning. Our approach outperforms several widely-used models on eight publicly available datasets (n = 2501), with a mean absolute error (MAE) of 2.20 years in predicting brain age. Comparisons with deep learning models lacking the AFA module demonstrate that our fusion model effectively improves overall performance. These findings provide a promising approach for combining anatomical and deep convolutional features from sMRI data to predict brain age, with potential applications in clinical diagnosis and treatment, particularly for populations with age-related cognitive decline or neurological disorders.
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Affiliation(s)
- Yu Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Rui Xie
- Department of Psychiatric, Tianshui Third People's Hospital, Tianshui, 741000, China
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Canada
| | - Xia Liu
- School of Computer Science, Qinghai Normal University, Xining, Qinghai Province, China
| | - Guowei Zheng
- School of Computer Science and Technology, Harbin Institute of Technology, Weihai, China
| | - Yin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Zhenwen Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China; School of Medical Technology, Beijing Institute of Technology, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, China.
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10
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Liu L, Lin L, Sun S, Wu S. Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset. Bioengineering (Basel) 2024; 11:124. [PMID: 38391610 PMCID: PMC10886122 DOI: 10.3390/bioengineering11020124] [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: 12/15/2023] [Revised: 01/17/2024] [Accepted: 01/24/2024] [Indexed: 02/24/2024] Open
Abstract
Accelerated brain aging (ABA) intricately links with age-associated neurodegenerative and neuropsychiatric diseases, emphasizing the critical need for a nuanced exploration of heterogeneous ABA patterns. This investigation leveraged data from the UK Biobank (UKB) for a comprehensive analysis, utilizing structural magnetic resonance imaging (sMRI), diffusion magnetic resonance imaging (dMRI), and resting-state functional magnetic resonance imaging (rsfMRI) from 31,621 participants. Pre-processing employed tools from the FMRIB Software Library (FSL, version 5.0.10), FreeSurfer, DTIFIT, and MELODIC, seamlessly integrated into the UKB imaging processing pipeline. The Lasso algorithm was employed for brain-age prediction, utilizing derived phenotypes obtained from brain imaging data. Subpopulations of accelerated brain aging (ABA) and resilient brain aging (RBA) were delineated based on the error between actual age and predicted brain age. The ABA subgroup comprised 1949 subjects (experimental group), while the RBA subgroup comprised 3203 subjects (control group). Semi-supervised heterogeneity through discriminant analysis (HYDRA) refined and characterized the ABA subgroups based on distinctive neuroimaging features. HYDRA systematically stratified ABA subjects into three subtypes: SubGroup 2 exhibited extensive gray-matter atrophy, distinctive white-matter patterns, and unique connectivity features, displaying lower cognitive performance; SubGroup 3 demonstrated minimal atrophy, superior cognitive performance, and higher physical activity; and SubGroup 1 occupied an intermediate position. This investigation underscores pronounced structural and functional heterogeneity in ABA, revealing three subtypes and paving the way for personalized neuroprotective treatments for age-related neurological, neuropsychiatric, and neurodegenerative diseases.
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Affiliation(s)
- Lingyu Liu
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Lan Lin
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Shen Sun
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Shuicai Wu
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
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11
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Dular L, Špiclin Ž. BASE: Brain Age Standardized Evaluation. Neuroimage 2024; 285:120469. [PMID: 38065279 DOI: 10.1016/j.neuroimage.2023.120469] [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: 07/27/2023] [Revised: 10/31/2023] [Accepted: 11/20/2023] [Indexed: 01/13/2024] Open
Abstract
Brain age, most commonly inferred from T1-weighted magnetic resonance images (T1w MRI), is a robust biomarker of brain health and related diseases. Superior accuracy in brain age prediction, often falling within a 2-3 year range, is achieved predominantly through deep neural networks. However, comparing study results is difficult due to differences in datasets, evaluation methodologies and metrics. Addressing this, we introduce Brain Age Standardized Evaluation (BASE), which includes (i) a standardized T1w MRI dataset including multi-site, new unseen site, test-retest and longitudinal data, and an associated (ii) evaluation protocol, including repeated model training and upon based comprehensive set of performance metrics measuring accuracy, robustness, reproducibility and consistency aspects of brain age predictions, and (iii) statistical evaluation framework based on linear mixed-effects models for rigorous performance assessment and cross-comparison. To showcase BASE, we comprehensively evaluate four deep learning based brain age models, appraising their performance in scenarios that utilize multi-site, test-retest, unseen site, and longitudinal T1w brain MRI datasets. Ensuring full reproducibility and application in future studies, we have made all associated data information and code publicly accessible at https://github.com/AralRalud/BASE.git.
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Affiliation(s)
- Lara Dular
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana, 1000, Slovenia
| | - Žiga Špiclin
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana, 1000, Slovenia.
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Corley MJ, Pang APS, Shikuma CM, Ndhlovu LC. Cell-type specific impact of metformin on monocyte epigenetic age reversal in virally suppressed older people living with HIV. Aging Cell 2024; 23:e13926. [PMID: 37675817 PMCID: PMC10776116 DOI: 10.1111/acel.13926] [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: 05/05/2023] [Revised: 06/22/2023] [Accepted: 06/28/2023] [Indexed: 09/08/2023] Open
Abstract
The anti-diabetic drug metformin may promote healthy aging. However, few clinical trials of metformin assessing biomarkers of aging have been completed. In this communication, we retrospectively examined the effect of metformin on epigenetic age using principal component (PC)-based epigenetic clocks, mitotic clocks, and pace of aging in peripheral monocytes and CD8+ T cells from participants in two clinical trials of virologically-suppressed people living with HIV (PLWH) with normal glucose receiving metformin. In a small 24-week clinical trial that randomized participants to receive either adjunctive metformin or observation, we observed significantly decreased PCPhenoAge and PCGrimAge estimates of monocytes from only participants in the metformin arm by a mean decrease of 3.53 and 1.84 years from baseline to Week 24. In contrast, we observed no significant differences in all PC clocks for participants in the observation arm over 24 weeks. Notably, our analysis of epigenetic mitotic clocks revealed significant increases for monocytes in the metformin arm when comparing baseline to Week 24, suggesting an impact of metformin on myeloid cell kinetics. Analysis of a single-arm clinical trial of adjunctive metformin in eight PLWH revealed no significant differences across all epigenetic clocks assessed in CD8+ T cells at 4- and 8-week time points. Our results suggest cell-type-specific myeloid effects of metformin captured by PC-based epigenetic clock biomarkers. Larger clinical studies of metformin are needed to validate these observations and this report highlights the need for further inclusion of PLWH in geroscience trials evaluating the effect of metformin on increasing healthspan and lifespan.
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Affiliation(s)
- Michael J. Corley
- Department of Medicine, Division of Infectious DiseasesWeill Cornell MedicineNew York CityNew YorkUSA
| | - Alina P. S. Pang
- Department of Medicine, Division of Infectious DiseasesWeill Cornell MedicineNew York CityNew YorkUSA
| | - Cecilia M. Shikuma
- Hawaii Center for AIDS, John A. Burns School of MedicineUniversity of HawaiiHonoluluHawaiiUSA
| | - Lishomwa C. Ndhlovu
- Department of Medicine, Division of Infectious DiseasesWeill Cornell MedicineNew York CityNew YorkUSA
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Ndhlovu LC, Bendall ML, Dwaraka V, Pang APS, Dopkins N, Carreras N, Smith R, Nixon DF, Corley MJ. Retroelement-Age Clocks: Epigenetic Age Captured by Human Endogenous Retrovirus and LINE-1 DNA methylation states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.06.570422. [PMID: 38106164 PMCID: PMC10723416 DOI: 10.1101/2023.12.06.570422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Human endogenous retroviruses (HERVs), the remnants of ancient viral infections embedded within the human genome, and long interspersed nuclear elements 1 (LINE-1), a class of autonomous retrotransposons, are silenced by host epigenetic mechanisms including DNA methylation. The resurrection of particular retroelements has been linked to biological aging. Whether the DNA methylation states of locus specific HERVs and LINEs can be used as a biomarker of chronological age in humans remains unclear. We show that highly predictive epigenetic clocks of chronological age can be constructed from retroelement DNA methylation states in the immune system, across human tissues, and pan-mammalian species. We found retroelement epigenetic clocks were reversed during transient epigenetic reprogramming, accelerated in people living with HIV-1, responsive to antiretroviral therapy, and accurate in estimating long-term culture ages of human brain organoids. Our findings support the hypothesis of epigenetic dysregulation of retroelements as a potential contributor to the biological hallmarks of aging.
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Affiliation(s)
- Lishomwa C. Ndhlovu
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
| | - Matthew L. Bendall
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
| | | | - Alina PS Pang
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
| | - Nicholas Dopkins
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
| | | | | | - Douglas F. Nixon
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
| | - Michael J. Corley
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
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14
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Valdes-Hernandez PA, Nodarse CL, Johnson AJ, Montesino-Goicolea S, Bashyam V, Davatzikos C, Peraza JA, Cole JH, Huo Z, Fillingim RB, Cruz-Almeida Y. Brain-predicted age difference estimated using DeepBrainNet is significantly associated with pain and function-a multi-institutional and multiscanner study. Pain 2023; 164:2822-2838. [PMID: 37490099 PMCID: PMC10805955 DOI: 10.1097/j.pain.0000000000002984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 05/31/2023] [Indexed: 07/26/2023]
Abstract
ABSTRACT Brain age predicted differences (brain-PAD: predicted brain age minus chronological age) have been reported to be significantly larger for individuals with chronic pain compared with those without. However, a debate remains after one article showed no significant differences. Using Gaussian Process Regression, an article provides evidence that these negative results might owe to the use of mixed samples by reporting a differential effect of chronic pain on brain-PAD across pain types. However, some remaining methodological issues regarding training sample size and sex-specific effects should be tackled before settling this controversy. Here, we explored differences in brain-PAD between musculoskeletal pain types and controls using a novel convolutional neural network for predicting brain-PADs, ie, DeepBrainNet. Based on a very large, multi-institutional, and heterogeneous training sample and requiring less magnetic resonance imaging preprocessing than other methods for brain age prediction, DeepBrainNet offers robust and reproducible brain-PADs, possibly highly sensitive to neuropathology. Controlling for scanner-related variability, we used a large sample (n = 660) with different scanners, ages (19-83 years), and musculoskeletal pain types (chronic low back [CBP] and osteoarthritis [OA] pain). Irrespective of sex, brain-PAD of OA pain participants was ∼3 to 4.7 years higher than that of CBP and controls, whereas brain-PAD did not significantly differ among controls and CBP. Moreover, brain-PAD was significantly related to multiple variables underlying the multidimensional pain experience. This comprehensive work adds evidence of pain type-specific effects of chronic pain on brain age. This could help in the clarification of the debate around possible relationships between brain aging mechanisms and pain.
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Affiliation(s)
- Pedro A. Valdes-Hernandez
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - Chavier Laffitte Nodarse
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - Alisa J. Johnson
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
| | - Soamy Montesino-Goicolea
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - Vishnu Bashyam
- AI2D Center for AI and Data Science for Integrated Diagnostics; and Center for Biomedical Image Computing & Analytics, Perelman School of Medicine, University of Pennsylvania, USA
- Artificial Intelligence in Biomedical Imaging Lab (AIBIL), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Christos Davatzikos
- AI2D Center for AI and Data Science for Integrated Diagnostics; and Center for Biomedical Image Computing & Analytics, Perelman School of Medicine, University of Pennsylvania, USA
| | - Julio A. Peraza
- Department of Physics, Florida International University, USA
| | - James H. Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, UK
| | - Zhiguang Huo
- Department of Biostatistics, College of Public Health and Health Professions and College of Medicine, University of Florida, USA
| | - Roger B. Fillingim
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
| | - Yenisel Cruz-Almeida
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, University of Florida, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
- Department of Neuroscience, College of Medicine, University of Florida, USA
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15
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Hong GS, Jang M, Kyung S, Cho K, Jeong J, Lee GY, Shin K, Kim KD, Ryu SM, Seo JB, Lee SM, Kim N. Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning. Korean J Radiol 2023; 24:1061-1080. [PMID: 37724586 PMCID: PMC10613849 DOI: 10.3348/kjr.2023.0393] [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: 04/27/2023] [Revised: 07/01/2023] [Accepted: 07/30/2023] [Indexed: 09/21/2023] Open
Abstract
Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.
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Affiliation(s)
- Gil-Sun Hong
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Miso Jang
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sunggu Kyung
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Kyungjin Cho
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jiheon Jeong
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Grace Yoojin Lee
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Keewon Shin
- Laboratory for Biosignal Analysis and Perioperative Outcome Research, Biomedical Engineering Center, Asan Institute of Lifesciences, Asan Medical Center, Seoul, Republic of Korea
| | - Ki Duk Kim
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seung Min Ryu
- Department of Orthopedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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16
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Dular L, Pernuš F, Špiclin Ž. Extensive T1-weighted MRI Preprocessing Improves Generalizability of Deep Brain Age Prediction Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.10.540134. [PMID: 37214863 PMCID: PMC10197652 DOI: 10.1101/2023.05.10.540134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Brain age is an estimate of chronological age obtained from T1-weighted magnetic resonance images (T1w MRI) and represents a simple diagnostic biomarker of brain ageing and associated diseases. While the current best accuracy of brain age predictions on T1w MRIs of healthy subjects ranges from two to three years, comparing results from different studies is challenging due to differences in the datasets, T1w preprocessing pipelines, and performance metrics used. This paper investigates the impact of T1w image preprocessing on the performance of four deep learning brain age models presented in recent literature. Four preprocessing pipelines were evaluated, differing in terms of registration, grayscale correction, and software implementation. The results showed that the choice of software or preprocessing steps can significantly affect the prediction error, with a maximum increase of 0.7 years in mean absolute error (MAE) for the same model and dataset. While grayscale correction had no significant impact on MAE, the affine registration, compared to the rigid registration of T1w images to brain atlas was shown to statistically significantly improve MAE. Models trained on 3D images with isotropic 1 mm3 resolution exhibited less sensitivity to the T1w preprocessing variations compared to 2D models or those trained on downsampled 3D images. Some proved invariant to the preprocessing pipeline, however only after offset correction. Our findings generally indicate that extensive T1w preprocessing enhances the MAE, especially when applied to a new dataset. This runs counter to prevailing research literature which suggests that models trained on minimally preprocessed T1w scans are better poised for age predictions on MRIs from unseen scanners. Regardless of model or T1w preprocessing used, we show that to enable generalization of model's performance on a new dataset with either the same or different T1w preprocessing than the one applied in model training, some form of offset correction should be applied.
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Affiliation(s)
- Lara Dular
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
| | - Franjo Pernuš
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
| | - Žiga Špiclin
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
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17
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Vines L, Sotelo D, Giddens N, Manza P, Volkow ND, Wang GJ. Neurological, Behavioral, and Pathophysiological Characterization of the Co-Occurrence of Substance Use and HIV: A Narrative Review. Brain Sci 2023; 13:1480. [PMID: 37891847 PMCID: PMC10605099 DOI: 10.3390/brainsci13101480] [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: 09/25/2023] [Revised: 10/10/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
Combined antiretroviral therapy (cART) has greatly reduced the severity of HIV-associated neurocognitive disorders in people living with HIV (PLWH); however, PLWH are more likely than the general population to use drugs and suffer from substance use disorders (SUDs) and to exhibit risky behaviors that promote HIV transmission and other infections. Dopamine-boosting psychostimulants such as cocaine and methamphetamine are some of the most widely used substances among PLWH. Chronic use of these substances disrupts brain function, structure, and cognition. PLWH with SUD have poor health outcomes driven by complex interactions between biological, neurocognitive, and social factors. Here we review the effects of comorbid HIV and psychostimulant use disorders by discussing the distinct and common effects of HIV and chronic cocaine and methamphetamine use on behavioral and neurological impairments using evidence from rodent models of HIV-associated neurocognitive impairments (Tat or gp120 protein expression) and clinical studies. We also provide a biopsychosocial perspective by discussing behavioral impairment in differentially impacted social groups and proposing interventions at both patient and population levels.
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Affiliation(s)
- Leah Vines
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892, USA; (L.V.); (D.S.); (P.M.); (N.D.V.)
| | - Diana Sotelo
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892, USA; (L.V.); (D.S.); (P.M.); (N.D.V.)
| | - Natasha Giddens
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI 53719, USA;
| | - Peter Manza
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892, USA; (L.V.); (D.S.); (P.M.); (N.D.V.)
| | - Nora D. Volkow
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892, USA; (L.V.); (D.S.); (P.M.); (N.D.V.)
| | - Gene-Jack Wang
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892, USA; (L.V.); (D.S.); (P.M.); (N.D.V.)
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18
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de Ruiter MB, Deardorff RL, Blommaert J, Chen BT, Dumas JA, Schagen SB, Sunaert S, Wang L, Cimprich B, Peltier S, Dittus K, Newhouse PA, Silverman DH, Schroyen G, Deprez S, Saykin AJ, McDonald BC. Brain gray matter reduction and premature brain aging after breast cancer chemotherapy: a longitudinal multicenter data pooling analysis. Brain Imaging Behav 2023; 17:507-518. [PMID: 37256494 PMCID: PMC10652222 DOI: 10.1007/s11682-023-00781-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/29/2023] [Indexed: 06/01/2023]
Abstract
Brain gray matter (GM) reductions have been reported after breast cancer chemotherapy, typically in small and/or cross-sectional cohorts, most commonly using voxel-based morphometry (VBM). There has been little examination of approaches such as deformation-based morphometry (DBM), machine-learning-based brain aging metrics, or the relationship of clinical and demographic risk factors to GM reduction. This international data pooling study begins to address these questions. Participants included breast cancer patients treated with (CT+, n = 183) and without (CT-, n = 155) chemotherapy and noncancer controls (NC, n = 145), scanned pre- and post-chemotherapy or comparable intervals. VBM and DBM examined GM volume. Estimated brain aging was compared to chronological aging. Correlation analyses examined associations between VBM, DBM, and brain age, and between neuroimaging outcomes, baseline age, and time since chemotherapy completion. CT+ showed longitudinal GM volume reductions, primarily in frontal regions, with a broader spatial extent on DBM than VBM. CT- showed smaller clusters of GM reduction using both methods. Predicted brain aging was significantly greater in CT+ than NC, and older baseline age correlated with greater brain aging. Time since chemotherapy negatively correlated with brain aging and annual GM loss. This large-scale data pooling analysis confirmed findings of frontal lobe GM reduction after breast cancer chemotherapy. Milder changes were evident in patients not receiving chemotherapy. CT+ also demonstrated premature brain aging relative to NC, particularly at older age, but showed evidence for at least partial GM recovery over time. When validated in future studies, such knowledge could assist in weighing the risks and benefits of treatment strategies.
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Affiliation(s)
- Michiel B de Ruiter
- Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Rachael L Deardorff
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, and Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jeroen Blommaert
- Department of Oncology, KU Leuven, Leuven, Belgium and Research Foundation Flanders (FWO), Brussels, Belgium
| | - Bihong T Chen
- City of Hope National Medical Center, Duarte, CA, USA
| | | | - Sanne B Schagen
- Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Stefan Sunaert
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Lei Wang
- Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | | | | | - Kim Dittus
- University of Vermont Cancer Center, University of Vermont, Burlington, VT, USA
| | - Paul A Newhouse
- Center for Cognitive Medicine, Vanderbilt University Medical Center and Geriatric Research Educational and Clinical Center, Tennessee Valley VA Health System, Nashville, TN, USA
| | | | - Gwen Schroyen
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Leuven Cancer Institute, KU Leuven, Leuven, Belgium
| | - Sabine Deprez
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Leuven Cancer Institute, KU Leuven, Leuven, Belgium
| | - Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, and Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Brenna C McDonald
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, and Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA.
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19
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Lew BJ, McCusker MC, O'Neill J, Bares SH, Wilson TW, Doucet GE. Resting state network connectivity alterations in HIV: Parallels with aging. Hum Brain Mapp 2023; 44:4679-4691. [PMID: 37417797 PMCID: PMC10400792 DOI: 10.1002/hbm.26409] [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: 09/29/2022] [Revised: 05/10/2023] [Accepted: 06/12/2023] [Indexed: 07/08/2023] Open
Abstract
The increasing incidence of age-related comorbidities in people with HIV (PWH) has led to accelerated aging theories. Functional neuroimaging research, including functional connectivity (FC) using resting-state functional magnetic resonance imaging (rs-fMRI), has identified neural aberrations related to HIV infection. Yet little is known about the relationship between aging and resting-state FC in PWH. This study included 86 virally suppressed PWH and 99 demographically matched controls spanning 22-72 years old who underwent rs-fMRI. The independent and interactive effects of HIV and aging on FC were investigated both within- and between-network using a 7-network atlas. The relationship between HIV-related cognitive deficits and FC was also examined. We also conducted network-based statistical analyses using a brain anatomical atlas (n = 512 regions) to ensure similar results across independent approaches. We found independent effects of age and HIV in between-network FC. The age-related increases in FC were widespread, while PWH displayed further increases above and beyond aging, particularly between-network FC of the default-mode and executive control networks. The results were overall similar using the regional approach. Since both HIV infection and aging are associated with independent increases in between-network FC, HIV infection may be associated with a reorganization of the major brain networks and their functional interactions in a manner similar to aging.
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Affiliation(s)
- Brandon J. Lew
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
- College of MedicineUniversity of Nebraska Medical Center (UNMC)OmahaNebraskaUSA
| | - Marie C. McCusker
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
- Interdepartmental Neuroscience ProgramYale University School of MedicineNew HavenConnecticutUSA
| | - Jennifer O'Neill
- Department of Internal Medicine, Division of Infectious DiseasesUNMCOmahaNebraskaUSA
| | - Sara H. Bares
- Department of Internal Medicine, Division of Infectious DiseasesUNMCOmahaNebraskaUSA
| | - Tony W. Wilson
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
- College of MedicineUniversity of Nebraska Medical Center (UNMC)OmahaNebraskaUSA
- Department of Pharmacology & NeuroscienceCreighton UniversityOmahaNebraskaUSA
| | - Gaelle E. Doucet
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
- Department of Pharmacology & NeuroscienceCreighton UniversityOmahaNebraskaUSA
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20
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Wilmskoetter J, Busby N, He X, Caciagli L, Roth R, Kristinsson S, Davis KA, Rorden C, Bassett DS, Fridriksson J, Bonilha L. Dynamic network properties of the superior temporal gyrus mediate the impact of brain age gap on chronic aphasia severity. Commun Biol 2023; 6:727. [PMID: 37452209 PMCID: PMC10349039 DOI: 10.1038/s42003-023-05119-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 07/07/2023] [Indexed: 07/18/2023] Open
Abstract
Brain structure deteriorates with aging and predisposes an individual to more severe language impairments (aphasia) after a stroke. However, the underlying mechanisms of this relation are not well understood. Here we use an approach to model brain network properties outside the stroke lesion, network controllability, to investigate relations among individualized structural brain connections, brain age, and aphasia severity in 93 participants with chronic post-stroke aphasia. Controlling for the stroke lesion size, we observe that lower average controllability of the posterior superior temporal gyrus (STG) mediates the relation between advanced brain aging and aphasia severity. Lower controllability of the left posterior STG signifies that activity in the left posterior STG is less likely to yield a response in other brain regions due to the topological properties of the structural brain networks. These results indicate that advanced brain aging among individuals with post-stroke aphasia is associated with disruption of dynamic properties of a critical language-related area, the STG, which contributes to worse aphasic symptoms. Because brain aging is variable among individuals with aphasia, our results provide further insight into the mechanisms underlying the variance in clinical trajectories in post-stroke aphasia.
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Affiliation(s)
- Janina Wilmskoetter
- Department of Health and Rehabilitation Sciences, College of Health Professions, Medical University of South Carolina, Charleston, SC, USA.
| | - Natalie Busby
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Xiaosong He
- Department of Psychology, University of Science and Technology of China, Beijing, China
| | - Lorenzo Caciagli
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Rebecca Roth
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Sigfus Kristinsson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chris Rorden
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, New Mexico, NM, USA
| | - Julius Fridriksson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
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21
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Leonardsen EH, Vidal-Piñeiro D, Roe JM, Frei O, Shadrin AA, Iakunchykova O, de Lange AMG, Kaufmann T, Taschler B, Smith SM, Andreassen OA, Wolfers T, Westlye LT, Wang Y. Genetic architecture of brain age and its causal relations with brain and mental disorders. Mol Psychiatry 2023; 28:3111-3120. [PMID: 37165155 PMCID: PMC10615751 DOI: 10.1038/s41380-023-02087-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/18/2023] [Accepted: 04/19/2023] [Indexed: 05/12/2023]
Abstract
The difference between chronological age and the apparent age of the brain estimated from brain imaging data-the brain age gap (BAG)-is widely considered a general indicator of brain health. Converging evidence supports that BAG is sensitive to an array of genetic and nongenetic traits and diseases, yet few studies have examined the genetic architecture and its corresponding causal relationships with common brain disorders. Here, we estimate BAG using state-of-the-art neural networks trained on brain scans from 53,542 individuals (age range 3-95 years). A genome-wide association analysis across 28,104 individuals (40-84 years) from the UK Biobank revealed eight independent genomic regions significantly associated with BAG (p < 5 × 10-8) implicating neurological, metabolic, and immunological pathways - among which seven are novel. No significant genetic correlations or causal relationships with BAG were found for Parkinson's disease, major depressive disorder, or schizophrenia, but two-sample Mendelian randomization indicated a causal influence of AD (p = 7.9 × 10-4) and bipolar disorder (p = 1.35 × 10-2) on BAG. These results emphasize the polygenic architecture of brain age and provide insights into the causal relationship between selected neurological and neuropsychiatric disorders and BAG.
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Affiliation(s)
- Esten H Leonardsen
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
| | - Didac Vidal-Piñeiro
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - James M Roe
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
| | - Alexey A Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Olena Iakunchykova
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Ann-Marie G de Lange
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, 1015, Lausanne, Switzerland
- Department of Psychiatry, University of Oxford, OX1 2JD, Oxford, UK
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72074, Tübingen, Germany
| | - Bernd Taschler
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, OX3 9DU, Oxford, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, OX3 9DU, Oxford, United Kingdom
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72074, Tübingen, Germany
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Yunpeng Wang
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway.
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22
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Brier MR, Li Z, Ly M, Karim HT, Liang L, Du W, McCarthy JE, Cross AH, Benzinger TLS, Naismith RT, Chahin S. "Brain age" predicts disability accumulation in multiple sclerosis. Ann Clin Transl Neurol 2023; 10:990-1001. [PMID: 37119507 PMCID: PMC10270248 DOI: 10.1002/acn3.51782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/17/2023] [Accepted: 04/10/2023] [Indexed: 05/01/2023] Open
Abstract
OBJECTIVE Neurodegenerative conditions often manifest radiologically with the appearance of premature aging. Multiple sclerosis (MS) biomarkers related to lesion burden are well developed, but measures of neurodegeneration are less well-developed. The appearance of premature aging quantified by machine learning applied to structural MRI assesses neurodegenerative pathology. We assess the explanatory and predictive power of "brain age" analysis on disability in MS using a large, real-world dataset. METHODS Brain age analysis is predicated on the over-estimation of predicted brain age in patients with more advanced pathology. We compared the performance of three brain age algorithms in a large, longitudinal dataset (>13,000 imaging sessions from >6,000 individual MS patients). Effects of MS, MS disease course, disability, lesion burden, and DMT efficacy were assessed using linear mixed effects models. RESULTS MS was associated with advanced predicted brain age cross-sectionally and accelerated brain aging longitudinally in all techniques. While MS disease course (relapsing vs. progressive) did contribute to advanced brain age, disability was the primary correlate of advanced brain age. We found that advanced brain age at study enrollment predicted more disability accumulation longitudinally. Lastly, a more youthful appearing brain (predicted brain age less than actual age) was associated with decreased disability. INTERPRETATION Brain age is a technically tractable and clinically relevant biomarker of disease pathology that correlates with and predicts increasing disability in MS. Advanced brain age predicts future disability accumulation.
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Affiliation(s)
- Matthew R. Brier
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
| | - Zhuocheng Li
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
| | - Maria Ly
- Mallinckrodt Institute of RadiologyWashington University in St. LouisSt LouisMissouriUSA
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Helmet T. Karim
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
- Department of BioengineeringUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Leda Liang
- Department of Mathematics and StatisticsWashington University in St. LouisSt LouisMissouriUSA
| | - Weixin Du
- Department of Mathematics and StatisticsWashington University in St. LouisSt LouisMissouriUSA
| | - John E. McCarthy
- Department of Mathematics and StatisticsWashington University in St. LouisSt LouisMissouriUSA
| | - Anne H. Cross
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
| | - Tammie L. S. Benzinger
- Mallinckrodt Institute of RadiologyWashington University in St. LouisSt LouisMissouriUSA
| | - Robert T. Naismith
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
| | - Salim Chahin
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
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23
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Heany SJ, Levine AJ, Lesosky M, Phillips N, Fouche JP, Myer L, Zar HJ, Stein DJ, Horvath S, Hoare J. Persistent accelerated epigenetic ageing in a longitudinal cohort of vertically infected HIV-positive adolescents. J Neurovirol 2023; 29:272-282. [PMID: 37179258 PMCID: PMC10404174 DOI: 10.1007/s13365-023-01130-6] [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: 07/15/2022] [Revised: 10/24/2022] [Accepted: 03/28/2023] [Indexed: 05/15/2023]
Abstract
We have previously shown accelerated ageing in adolescents perinatally infected with HIV (PHIV +), based on discrepancies between epigenetic and chronological age. The current study examines follow-up longitudinal patterns of epigenetic ageing and the association of epigenetic ageing with cognition as well as whole brain structure changes in PHIV + and healthy controls enrolled in the Cape Town Adolescent Antiretroviral Cohort Study (CTAAC). The Illumina EPIC array was used to generate blood DNA methylation data from 60 PHIV + adolescents and 36 age-matched controls aged 9-12 years old at baseline and again at a 36-month follow-up. Epigenetic clock software estimated two measures of epigenetic age acceleration: extrinsic epigenetic accelerated ageing (EEAA) and age acceleration difference (AAD) at both time points. At follow-up, each participant completed neuropsychological testing, structural magnetic resonance imaging, and diffusion tensor imaging. At follow-up, PHIV infection remains associated with increased EEAA and AAD. Accelerated epigenetic ageing remained positively associated with viral load and negatively associated with CD4 ratio. EEAA was positively associated with whole brain grey matter volume and alterations in whole brain white matter integrity. AAD and EEAA were not associated with cognitive function within the PHIV + group. Measures of epigenetic ageing, as detected in DNA methylation patterns, remain increased in PHIV + adolescents across a 36-month period. Associations between epigenetic ageing measures, viral biomarkers, and alterations in brain micro- and macrostructure also persist at 36-month follow-up. Further study should determine if epigenetic age acceleration is associated with cognitive functional changes due to brain alterations in later life.
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Affiliation(s)
- Sarah J Heany
- SA MRC Unit On Risk & Resilience in Mental Disorders, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa.
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
| | - Andrew J Levine
- Department of Neurology, David Geffen School of Medicineat the , University of California, Los Angeles, Los Angeles, CA, USA
| | - Maia Lesosky
- Division of Epidemiology and Biostatistics, School of Public Health & Family Medicine, University of Cape Town, Cape Town, South Africa
| | - Nicole Phillips
- SA MRC Unit On Risk & Resilience in Mental Disorders, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Jean-Paul Fouche
- SA MRC Unit On Risk & Resilience in Mental Disorders, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Landon Myer
- Centre for Infectious Disease Epidemiology and Research, Division of Epidemiology and Biostatistics, School of Public Health & Family Medicine, University of Cape Town, Cape Town, South Africa
| | - Heather J Zar
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, Cape Town, South Africa
- Medical Research Council Unit On Child and Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - Dan J Stein
- SA MRC Unit On Risk & Resilience in Mental Disorders, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biostatistics, School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jacqueline Hoare
- SA MRC Unit On Risk & Resilience in Mental Disorders, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
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24
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Cooley SA, Nelson B, Boerwinkle A, Yarasheski KE, Kirmess KM, Meyer MR, Schindler SE, Morris JC, Fagan A, Ances BM, O’Halloran JA. Plasma Aβ42/Aβ40 Ratios in Older People With Human Immunodeficiency Virus. Clin Infect Dis 2023; 76:1776-1783. [PMID: 36610788 PMCID: PMC10209437 DOI: 10.1093/cid/ciad001] [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: 10/24/2022] [Revised: 12/19/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND As people with human immunodeficiency virus (HIV) (PWH) age, it remains unclear whether they are at higher risk for age-related neurodegenerative disorders-for example, Alzheimer disease (AD)-and, if so, how to differentiate HIV-associated neurocognitive impairment from AD. We examined a clinically available blood biomarker test for AD (plasma amyloid-β [Aβ] 42/Aβ40 ratio) in PWH who were cognitively normal (PWH_CN) or cognitively impaired (PWH_CI) and people without HIV (PWoH) who were cognitively normal (PWoH_CN) or had symptomatic AD (PWoH_AD). METHODS A total of 66 PWH (age >40 years) (HIV RNA <50 copies/mL) and 195 PWoH provided blood samples, underwent magnetic resonance imaging, and completed a neuropsychological battery or clinical dementia rating scale. Participants were categorized by impairment (PWH_CN, n = 43; PWH_CI, n = 23; PWoH_CN, n = 138; PWoH_AD, n = 57). Plasma Aβ42 and Aβ40 concentrations were obtained using a liquid chromatography-tandem mass spectrometry method to calculate the PrecivityAD amyloid probability score (APS). The APS incorporates age and apolipoprotein E proteotype into a risk score for brain amyloidosis. Plasma Aβ42/Aβ40 ratios and APSs were compared between groups and assessed for relationships with hippocampal volumes or cognition and HIV clinical characteristics (PWH only). RESULTS The plasma Aβ42/Aβ40 ratio was significantly lower, and the APS higher, in PWoH_AD than in other groups. A lower Aβ42/Aβ40 ratio and higher APS was associated with smaller hippocampal volumes for PWoH_AD. The Aβ42/Aβ40 ratio and APS were not associated with cognition or HIV clinical measures for PWH. CONCLUSIONS The plasma Aβ42/Aβ40 ratio can serve as a screening tool for AD and may help differentiate effects of HIV from AD within PWH, but larger studies with older PWH are needed.
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Affiliation(s)
- Sarah A Cooley
- Department of Neurology, Washington University in St Louis, St Louis, Missouri, USA
| | - Brittany Nelson
- Department of Neurology, Washington University in St Louis, St Louis, Missouri, USA
| | - Anna Boerwinkle
- Department of Neurology, Washington University in St Louis, St Louis, Missouri, USA
| | | | | | | | - Suzanne E Schindler
- Department of Neurology, Washington University in St Louis, St Louis, Missouri, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, Missouri, USA
| | - John C Morris
- Department of Neurology, Washington University in St Louis, St Louis, Missouri, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, Missouri, USA
- Department of Radiology, Washington University in St Louis, St Louis, Missouri, USA
| | - Anne Fagan
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, Missouri, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St Louis, St Louis, Missouri, USA
| | - Jane A O’Halloran
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St Louis, Missouri, USA
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25
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More S, Antonopoulos G, Hoffstaedter F, Caspers J, Eickhoff SB, Patil KR. Brain-age prediction: A systematic comparison of machine learning workflows. Neuroimage 2023; 270:119947. [PMID: 36801372 DOI: 10.1016/j.neuroimage.2023.119947] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 02/18/2023] Open
Abstract
The difference between age predicted using anatomical brain scans and chronological age, i.e., the brain-age delta, provides a proxy for atypical aging. Various data representations and machine learning (ML) algorithms have been used for brain-age estimation. However, how these choices compare on performance criteria important for real-world applications, such as; (1) within-dataset accuracy, (2) cross-dataset generalization, (3) test-retest reliability, and (4) longitudinal consistency, remains uncharacterized. We evaluated 128 workflows consisting of 16 feature representations derived from gray matter (GM) images and eight ML algorithms with diverse inductive biases. Using four large neuroimaging databases covering the adult lifespan (total N = 2953, 18-88 years), we followed a systematic model selection procedure by sequentially applying stringent criteria. The 128 workflows showed a within-dataset mean absolute error (MAE) between 4.73-8.38 years, from which 32 broadly sampled workflows showed a cross-dataset MAE between 5.23-8.98 years. The test-retest reliability and longitudinal consistency of the top 10 workflows were comparable. The choice of feature representation and the ML algorithm both affected the performance. Specifically, voxel-wise feature spaces (smoothed and resampled), with and without principal components analysis, with non-linear and kernel-based ML algorithms performed well. Strikingly, the correlation of brain-age delta with behavioral measures disagreed between within-dataset and cross-dataset predictions. Application of the best-performing workflow on the ADNI sample showed a significantly higher brain-age delta in Alzheimer's and mild cognitive impairment patients compared to healthy controls. However, in the presence of age bias, the delta estimates in the patients varied depending on the sample used for bias correction. Taken together, brain-age shows promise, but further evaluation and improvements are needed for its real-world application.
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Affiliation(s)
- Shammi More
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Georgios Antonopoulos
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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26
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Petersen KJ, Lu T, Wisch J, Roman J, Metcalf N, Cooley SA, Babulal GM, Paul R, Sotiras A, Vaida F, Ances BM. Effects of clinical, comorbid, and social determinants of health on brain ageing in people with and without HIV: a retrospective case-control study. Lancet HIV 2023; 10:e244-e253. [PMID: 36764319 PMCID: PMC10065928 DOI: 10.1016/s2352-3018(22)00373-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/22/2022] [Accepted: 12/06/2022] [Indexed: 02/10/2023]
Abstract
BACKGROUND Neuroimaging reveals structural brain changes linked with HIV infection and related neurocognitive disorders; however, group-level comparisons between people with HIV and people without HIV do not account for within-group heterogeneity. The aim of this study was to quantify the effects of comorbidities such as cardiovascular disease and adverse social determinants of health on brain ageing in people with HIV and people without HIV. METHODS In this retrospective case-control study, people with HIV from Washington University in St Louis, MO, USA, and people without HIV identified through community organisations or the Research Participant Registry were clinically characterised and underwent 3-Tesla T1-weighted MRI between Dec 3, 2008, and Oct 4, 2022. Exclusion criteria were established by a combination of self-reports and medical records. DeepBrainNet, a publicly available machine learning algorithm, was applied to estimate brain-predicted age from MRI for people with HIV and people without HIV. The brain-age gap, defined as the difference between brain-predicted age and true chronological age, was modelled as a function of clinical, comorbid, and social factors by use of linear regression. Variables were first examined singly for associations with brain-age gap, then combined into multivariate models with best-subsets variable selection. FINDINGS In people with HIV (mean age 44·8 years [SD 15·5]; 78% [296 of 379] male; 69% [260] Black; 78% [295] undetectable viral load), brain-age gap was associated with Framingham cardiovascular risk score (p=0·0034), detectable viral load (>50 copies per mL; p=0·0023), and hepatitis C co-infection (p=0·0065). After variable selection, the final model for people with HIV retained Framingham score, hepatitis C, and added unemployment (p=0·0015). Educational achievement assayed by reading proficiency was linked with reduced brain-age gap (p=0·016) for people without HIV but not for people with HIV, indicating a potential resilience factor. When people with HIV and people without HIV were modelled jointly, selection resulted in a model containing cardiovascular risk (p=0·0039), hepatitis C (p=0·037), Area Deprivation Index (p=0·033), and unemployment (p=0·00010). Male sex (p=0·078) and alcohol use history (p=0·090) were also included in the model but were not individually significant. INTERPRETATION Our findings indicate that comorbid and social determinants of health are associated with brain ageing in people with HIV, alongside traditional HIV metrics such as viral load and CD4 cell count, suggesting the need for a broadened clinical perspective on healthy ageing with HIV, with additional focus on comorbidities, lifestyle changes, and social factors. FUNDING National Institute of Mental Health, National Institute of Nursing Research, and National Institute of Drug Abuse.
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Affiliation(s)
- Kalen J. Petersen
- Department of Neurology, Washington University School of Medicine, St. Louis MO, USA
| | - Tina Lu
- Department of Neurology, Washington University School of Medicine, St. Louis MO, USA
| | - Julie Wisch
- Department of Neurology, Washington University School of Medicine, St. Louis MO, USA
| | - June Roman
- Department of Neurology, Washington University School of Medicine, St. Louis MO, USA
| | - Nicholas Metcalf
- Department of Neurology, Washington University School of Medicine, St. Louis MO, USA
| | - Sarah A. Cooley
- Department of Neurology, Washington University School of Medicine, St. Louis MO, USA
| | - Ganesh M. Babulal
- Department of Neurology, Washington University School of Medicine, St. Louis MO, USA
| | - Rob Paul
- Missouri Institute of Mental Health, University of Missouri – St. Louis MO, USA
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis MO, USA
| | - Florin Vaida
- Department of Family Medicine, The University of California – San Diego, USA
| | - Beau M. Ances
- Department of Neurology, Washington University School of Medicine, St. Louis MO, USA
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27
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O’Connor EE, Sullivan EV, Chang L, Hammoud DA, Wilson TW, Ragin AB, Meade CS, Coughlin J, Ances BM. Imaging of Brain Structural and Functional Effects in People With Human Immunodeficiency Virus. J Infect Dis 2023; 227:S16-S29. [PMID: 36930637 PMCID: PMC10022717 DOI: 10.1093/infdis/jiac387] [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] [Indexed: 03/18/2023] Open
Abstract
Before the introduction of antiretroviral therapy, human immunodeficiency virus (HIV) infection was often accompanied by central nervous system (CNS) opportunistic infections and HIV encephalopathy marked by profound structural and functional alterations detectable with neuroimaging. Treatment with antiretroviral therapy nearly eliminated CNS opportunistic infections, while neuropsychiatric impairment and peripheral nerve and organ damage have persisted among virally suppressed people with HIV (PWH), suggesting ongoing brain injury. Neuroimaging research must use methods sensitive for detecting subtle HIV-associated brain structural and functional abnormalities, while allowing for adjustments for potential confounders, such as age, sex, substance use, hepatitis C coinfection, cardiovascular risk, and others. Here, we review existing and emerging neuroimaging tools that demonstrated promise in detecting markers of HIV-associated brain pathology and explore strategies to study the impact of potential confounding factors on these brain measures. We emphasize neuroimaging approaches that may be used in parallel to gather complementary information, allowing efficient detection and interpretation of altered brain structure and function associated with suboptimal clinical outcomes among virally suppressed PWH. We examine the advantages of each imaging modality and systematic approaches in study design and analysis. We also consider advantages of combining experimental and statistical control techniques to improve sensitivity and specificity of biotype identification and explore the costs and benefits of aggregating data from multiple studies to achieve larger sample sizes, enabling use of emerging methods for combining and analyzing large, multifaceted data sets. Many of the topics addressed in this article were discussed at the National Institute of Mental Health meeting "Biotypes of CNS Complications in People Living with HIV," held in October 2021, and are part of ongoing research initiatives to define the role of neuroimaging in emerging alternative approaches to identifying biotypes of CNS complications in PWH. An outcome of these considerations may be the development of a common neuroimaging protocol available for researchers to use in future studies examining neurological changes in the brains of PWH.
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Affiliation(s)
- Erin E O’Connor
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Edith V Sullivan
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA
- Center for Health Sciences, SRI International, Menlo Park, California, USA
| | - Linda Chang
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Dima A Hammoud
- Center for Infectious Disease Imaging, Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, Maryland, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, Nebraska, USA
| | - Ann B Ragin
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Christina S Meade
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jennifer Coughlin
- Department of Neurology, Washington University School of Medicine, St Louis, Missouri, USA
| | - Beau M Ances
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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28
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Heaton RK, Ellis RJ, Tang B, Marra CM, Rubin LH, Clifford DB, McCutchan JA, Gelman BB, Morgello S, Franklin DR, Letendre SL. Twelve-year neurocognitive decline in HIV is associated with comorbidities, not age: a CHARTER study. Brain 2023; 146:1121-1131. [PMID: 36477867 PMCID: PMC10169412 DOI: 10.1093/brain/awac465] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/14/2022] [Accepted: 11/07/2022] [Indexed: 12/12/2022] Open
Abstract
Modern antiretroviral therapy (ART) has increased longevity of people with HIV and shifted the age distribution of the HIV pandemic upward toward that of the general population. This positive development has also led to concerns about premature and/or accelerated neurocognitive and physical ageing due to the combined effects of chronic HIV, accumulating comorbidities, adverse effects or possible toxicities of ART and biological ageing. Here we present results of comprehensive assessments over 12 years of 402 people with HIV in the CNS HIV ART Effects Research (CHARTER) programme, who at follow-up were composed of younger (<60 years) and older (≥60 years) subgroups. Over the 12 years, ART use and viral suppression increased in both subgroups as did systemic and psychiatric comorbidities; participants in both subgroups also evidenced neurocognitive decline beyond what is expected in typical ageing. Contrary to expectations, all these adverse effects were comparable in the younger and older CHARTER subgroups, and unrelated to chronological age. Neurocognitive decline was unrelated to HIV disease or treatment characteristics but was significantly predicted by the presence of comorbid conditions, specifically diabetes, hypertension, chronic pulmonary disease, frailty, neuropathic pain, depression and lifetime history of cannabis use disorder. These results are not consistent with premature or accelerated neurocognitive ageing due to HIV itself but suggest important indirect effects of multiple, potentially treatable comorbidities that are more common among people with HIV than in the general population. Good medical management of HIV disease did not prevent these adverse outcomes, and increased attention to a range of comorbid conditions in people with HIV may be warranted in their care.
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Affiliation(s)
- Robert K Heaton
- Department of Psychiatry, University of California San Diego, San Diego, CA 92093, USA
| | - Ronald J Ellis
- Department of Psychiatry, University of California San Diego, San Diego, CA 92093, USA
- Department of Neurosciences, University of California, San Diego, CA 92093, USA
| | - Bin Tang
- Department of Psychiatry, University of California San Diego, San Diego, CA 92093, USA
| | - Christina M Marra
- Department of Neurology, University of Washington, Seattle, WA 98104, USA
| | - Leah H Rubin
- Department of Neurology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - David B Clifford
- Department of Neurology, Washington University at St. Louis, St. Louis, MO 63110, USA
| | - J Allen McCutchan
- Department of Medicine, University of California San Diego, San Diego, CA 92093, USA
| | - Benjamin B Gelman
- Department of Pathology, University of Texas Medical Branch, Galveston, TX 77555, USA
- Department of Neuroscience and Cell Biology, University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Susan Morgello
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Donald R Franklin
- Department of Psychiatry, University of California San Diego, San Diego, CA 92093, USA
| | - Scott L Letendre
- Department of Psychiatry, University of California San Diego, San Diego, CA 92093, USA
- Department of Medicine, University of California San Diego, San Diego, CA 92093, USA
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29
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Peterson JA, Johnson A, Nordarse CL, Huo Z, Cole J, Fillingim RB, Cruz-Almeida Y. Brain predicted age difference mediates pain impact on physical performance in community dwelling middle to older aged adults. Geriatr Nurs 2023; 50:181-187. [PMID: 36787663 PMCID: PMC10360023 DOI: 10.1016/j.gerinurse.2023.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 02/16/2023]
Abstract
The purpose of the study was to examine associations between physical performance and brain aging in individuals with knee pain and whether the association between pain and physical performance is mediated by brain aging. Participants (n=202) with low impact knee pain (n=111), high impact knee pain (n=60) and pain-free controls (n=31) completed self-reported pain, magnetic resonance imaging (MRI), and a Short Physical Performance Battery (SPPB) that included balance, walking, and sit to stand tasks. Brain predicted age difference, calculated using machine learning from MRI images, significantly mediated the relationships between walking and knee pain impact (CI: -0.124; -0.013), walking and pain-severity (CI: -0.008; -0.001), total SPPB score and knee pain impact (CI: -0.232; -0.025), and total SPPB scores and pain-severity (CI: -0.019; -0.001). Brain-aging begins to explain the association between pain and physical performance, especially walking. This study supports the idea that a brain aging prediction can be calculated from shorter duration MRI sequences and possibly implemented in a clinical setting to be used to identify individuals with pain who are at risk for accelerated brain atrophy and increased likelihood of disability.
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Affiliation(s)
- Jessica A Peterson
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA; Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL 32610, USA
| | - Alisa Johnson
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA; Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL 32610, USA
| | - Chavier Laffitte Nordarse
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA; Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL 32610, USA
| | - Zhiguang Huo
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - James Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK; Psychology and Neuroscience, King's College London, Institute of Psychiatry, London, UK; Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Roger B Fillingim
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA; Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL 32610, USA
| | - Yenisel Cruz-Almeida
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA; Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL 32610, USA.
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Yin C, Imms P, Cheng M, Amgalan A, Chowdhury NF, Massett RJ, Chaudhari NN, Chen X, Thompson PM, Bogdan P, Irimia A. Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment. Proc Natl Acad Sci U S A 2023; 120:e2214634120. [PMID: 36595679 PMCID: PMC9926270 DOI: 10.1073/pnas.2214634120] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 11/10/2022] [Indexed: 01/05/2023] Open
Abstract
The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer's disease (AD, N = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.
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Affiliation(s)
- Chenzhong Yin
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Phoebe Imms
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
| | - Mingxi Cheng
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Anar Amgalan
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
| | - Nahian F. Chowdhury
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
| | - Roy J. Massett
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
| | - Nikhil N. Chaudhari
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Xinghe Chen
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Paul M. Thompson
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA90033
- Department of Quantitative & Computational Biology, Dana & David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA90089
- Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- Department of Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
| | - Paul Bogdan
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
- Department of Quantitative & Computational Biology, Dana & David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA90089
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Millar PR, Gordon BA, Luckett PH, Benzinger TLS, Cruchaga C, Fagan AM, Hassenstab JJ, Perrin RJ, Schindler SE, Allegri RF, Day GS, Farlow MR, Mori H, Nübling G, Bateman RJ, Morris JC, Ances BM. Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study. eLife 2023; 12:e81869. [PMID: 36607335 PMCID: PMC9988262 DOI: 10.7554/elife.81869] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 12/30/2022] [Indexed: 01/07/2023] Open
Abstract
Background Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. Funding This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN).
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Affiliation(s)
- Peter R Millar
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Brian A Gordon
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
| | - Patrick H Luckett
- Department of Neurosurgery, Washington University in St. LouisSt LouisUnited States
| | - Tammie LS Benzinger
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
- Department of Neurosurgery, Washington University in St. LouisSt LouisUnited States
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. LouisSt LouisUnited States
| | - Anne M Fagan
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Jason J Hassenstab
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Richard J Perrin
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
- Department of Pathology and Immunology, Washington University in St. LouisSt LouisUnited States
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Ricardo F Allegri
- Department of Cognitive Neurology, Institute for Neurological Research (FLENI)Buenos AiresArgentina
| | - Gregory S Day
- Department of Neurology, Mayo Clinic FloridaJacksonvilleUnited States
| | - Martin R Farlow
- Department of Neurology, Indiana University School of MedicineIndianapolisUnited States
| | - Hiroshi Mori
- Department of Clinical Neuroscience, Osaka Metropolitan University Medical School, Nagaoka Sutoku UniversityOsakaJapan
| | - Georg Nübling
- Department of Neurology, Ludwig-Maximilians UniversityMunichGermany
- German Center for Neurodegenerative DiseasesMunichGermany
| | - Randall J Bateman
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - John C Morris
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Beau M Ances
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
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Peterson JA, Strath LJ, Nodarse CL, Rani A, Huo Z, Meng L, Yoder S, Cole JH, Foster TC, Fillingim RB, Cruz-Almeida Y. Epigenetic Aging Mediates the Association between Pain Impact and Brain Aging in Middle to Older Age Individuals with Knee Pain. Epigenetics 2022; 17:2178-2187. [PMID: 35950599 PMCID: PMC9665126 DOI: 10.1080/15592294.2022.2111752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 08/05/2022] [Indexed: 02/01/2023] Open
Abstract
Chronic musculoskeletal pain is a health burden that may accelerate the aging process. Accelerated brain aging and epigenetic aging have separately been observed in those with chronic pain. However, it is unknown whether these biological markers of aging are associated with each other in those with chronic pain. We aimed to explore the association of epigenetic aging and brain aging in middle-to-older age individuals with varying degrees of knee pain. Participants (57.91 ± 8.04 y) with low impact knee pain (n = 95), high impact knee pain (n = 53), and pain-free controls (n = 26) completed self-reported pain, a blood draw, and an MRI scan. We used an epigenetic clock previously associated with knee pain (DNAmGrimAge), the subsequent difference of predicted epigenetic and brain age from chronological age (DNAmGrimAge-Difference and Brain-PAD, respectively). There was a significant main effect for pain impact group (F (2,167) = 3.847, P = 0.023, r o t a t i o n a l e n e r g y = 1 / 2 I ω 2 = 0.038, ANCOVA) on Brain-PAD and DNAmGrimAge-difference (F (2,167) = 6.800, P = 0.001, I = m k 2 = 0.075, ANCOVA) after controlling for covariates. DNAmGrimAge-Difference and Brain-PAD were modestly correlated (r =0.198; P =0.010). Exploratory analysis revealed that DNAmGrimAge-difference mediated GCPS pain impact, GCPS pain severity, and pain-related disability scores on Brain-PAD. Based upon the current study findings, we suggest that pain could be a driver for accelerated brain aging via epigenome interactions.
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Affiliation(s)
- Jessica A. Peterson
- Pain Research & Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL, USA
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, USA
| | - Larissa J. Strath
- Pain Research & Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL, USA
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, USA
| | - Chavier Laffitte Nodarse
- Pain Research & Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL, USA
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, USA
| | - Asha Rani
- Department of Neuroscience, McKnight Brain Institute, Gainesville, Florida, USA
| | - Zhiguang Huo
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Lingsong Meng
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Sean Yoder
- Molecular Genomics Core Facility, Moffit Cancer Center, Tampa, FL, USA
| | - James H. Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, England
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, England
| | - Thomas C. Foster
- Genetics and Genomics Program, University of Florida, Gainesville, FL, USA
| | - Roger B. Fillingim
- Pain Research & Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL, USA
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, USA
| | - Yenisel Cruz-Almeida
- Pain Research & Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL, USA
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, USA
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Johnson AJ, Buchanan T, Laffitte Nodarse C, Valdes Hernandez PA, Huo Z, Cole JH, Buford TW, Fillingim RB, Cruz-Almeida Y. Cross-Sectional Brain-Predicted Age Differences in Community-Dwelling Middle-Aged and Older Adults with High Impact Knee Pain. J Pain Res 2022; 15:3575-3587. [PMID: 36415658 PMCID: PMC9676000 DOI: 10.2147/jpr.s384229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 10/25/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose Knee OA-related pain varies in impact across individuals and may relate to central nervous system alterations like accelerated brain aging processes. We previously reported that older adults with chronic musculoskeletal pain had a significantly greater brain-predicted age, compared to pain-free controls, indicating an "older" appearing brain. Yet this association is not well understood. This cross-sectional study examines brain-predicted age differences associated with chronic knee osteoarthritis pain, in a larger, more demographically diverse sample with consideration for pain's impact. Patients and Methods Participants (mean age = 57.8 ± 8.0 years) with/without knee OA-related pain were classified according to pain's impact on daily function (ie, impact): low-impact (n=111), and high-impact (n=60) pain, and pain-free controls (n=31). Participants completed demographic, pain, and psychosocial assessments, and T1-weighted magnetic resonance imaging. Brain-predicted age difference (brain-PAD) was compared across groups using analysis of covariance. Partial correlations examined associations of brain-PAD with pain and psychosocial variables. Results Individuals with high-impact chronic knee pain had significantly "older" brains for their age compared to individuals with low-impact knee pain (p < 0.05). Brain-PAD was also significantly associated with clinical pain, negative affect, passive coping, and pain catastrophizing (p's<0.05). Conclusion Our findings suggest that high impact chronic knee pain is associated with an older appearing brain on MRI. Future studies are needed to determine the impact of pain-related interference and pain management on somatosensory processing and brain aging biomarkers for high-risk populations and effective intervention strategies.
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Affiliation(s)
- Alisa J Johnson
- Pain Research & Intervention Center of Excellence, University of Florida, Gainesville, FL, USA,Department of Community Dentistry & Behavioral Science, College of Dentistry, University of Florida, Gainesville, FL, USA
| | - Taylor Buchanan
- Department of Medicine, University of Alabama, Birmingham, AL, USA
| | - Chavier Laffitte Nodarse
- Pain Research & Intervention Center of Excellence, University of Florida, Gainesville, FL, USA,Department of Community Dentistry & Behavioral Science, College of Dentistry, University of Florida, Gainesville, FL, USA
| | - Pedro A Valdes Hernandez
- Pain Research & Intervention Center of Excellence, University of Florida, Gainesville, FL, USA,Department of Community Dentistry & Behavioral Science, College of Dentistry, University of Florida, Gainesville, FL, USA
| | - Zhiguang Huo
- Department of Biostatistics, College of Public Health & Health Professions College of Medicine, University of Florida, Gainesville, FL, USA
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK,Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Thomas W Buford
- Department of Medicine, University of Alabama, Birmingham, AL, USA
| | - Roger B Fillingim
- Pain Research & Intervention Center of Excellence, University of Florida, Gainesville, FL, USA,Department of Community Dentistry & Behavioral Science, College of Dentistry, University of Florida, Gainesville, FL, USA,Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Yenisel Cruz-Almeida
- Pain Research & Intervention Center of Excellence, University of Florida, Gainesville, FL, USA,Department of Community Dentistry & Behavioral Science, College of Dentistry, University of Florida, Gainesville, FL, USA,Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, USA,Correspondence: Yenisel Cruz-Almeida, University of Florida, PO Box 103628, 1329 SW 16th Street, Ste 5180, Gainesville, FL, 32608, USA, Tel +1 352-294-8584, Fax +1 352-273-5985, Email
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Sone D, Beheshti I. Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry. J Pers Med 2022; 12:jpm12111850. [PMID: 36579560 PMCID: PMC9695293 DOI: 10.3390/jpm12111850] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/01/2022] [Accepted: 11/01/2022] [Indexed: 11/10/2022] Open
Abstract
It is now possible to estimate an individual's brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, Jikei University School of Medicine, Tokyo 105-8461, Japan
- Correspondence: ; Tel.: +81-03-3433
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB R3E 3P5, Canada
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Kim Y, Varosanec M, Kosa P, Bielekova B. Confounder-adjusted MRI-based predictors of multiple sclerosis disability. FRONTIERS IN RADIOLOGY 2022; 2:971157. [PMID: 37492673 PMCID: PMC10365278 DOI: 10.3389/fradi.2022.971157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/02/2022] [Indexed: 07/27/2023]
Abstract
Introduction Both aging and multiple sclerosis (MS) cause central nervous system (CNS) atrophy. Excess brain atrophy in MS has been interpreted as "accelerated aging." Current paper tests an alternative hypothesis: MS causes CNS atrophy by mechanism(s) different from physiological aging. Thus, subtracting effects of physiological confounders on CNS structures would isolate MS-specific effects. Methods Standardized brain MRI and neurological examination were acquired prospectively in 646 participants enrolled in ClinicalTrials.gov Identifier: NCT00794352 protocol. CNS volumes were measured retrospectively, by automated Lesion-TOADS algorithm and by Spinal Cord Toolbox, in a blinded fashion. Physiological confounders identified in 80 healthy volunteers were regressed out by stepwise multiple linear regression. MS specificity of confounder-adjusted MRI features was assessed in non-MS cohort (n = 158). MS patients were randomly split into training (n = 277) and validation (n = 131) cohorts. Gradient boosting machine (GBM) models were generated in MS training cohort from unadjusted and confounder-adjusted CNS volumes against four disability scales. Results Confounder adjustment highlighted MS-specific progressive loss of CNS white matter. GBM model performance decreased substantially from training to cross-validation, to independent validation cohorts, but all models predicted cognitive and physical disability with low p-values and effect sizes that outperform published literature based on recent meta-analysis. Models built from confounder-adjusted MRI predictors outperformed models from unadjusted predictors in the validation cohort. Conclusion GBM models from confounder-adjusted volumetric MRI features reflect MS-specific CNS injury, and due to stronger correlation with clinical outcomes compared to brain atrophy these models should be explored in future MS clinical trials.
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The related factors of new HIV infection among older men in Sichuan, China: A case-control study. Epidemiol Infect 2022; 150:e156. [PMID: 35968710 PMCID: PMC9472032 DOI: 10.1017/s0950268822001352] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Human immunodeficiency virus (HIV) has been widely prevalent among older men (aged ≥50 years old) in Sichuan Province. The study aimed to discover associated factors with the new HIV infection in older men, and provide a scientific basis for the prevention and control of acquired immunodeficiency syndrome (AIDS) in this group. A cross-sectional survey study of newly reported HIV/AIDS and general male residents aged 50 years and older was conducted between April and June 2019, with a resample of respondents to identify cases and controls, followed by a case–control study. Logistic regression was applied to analyse the association between the selected factors and new HIV infection among older men. At last, 242 cases and 968 controls were included. The results of multiple logistic regression suggested that many factors including living alone/concentrated (OR 1.56, 95% CI 1.20–2.04, P = 0.001), have a history of migrant worker (OR 2.10, 95% CI 1.61–2.73, P < 0.001), have commercial sexual behaviour (OR 1.71, 95% CI 1.32–2.22, P < 0.001), married (OR 0.48, 95% CI 0.37–0.64, P < 0.001), have a history of HIV antibody testing (OR 0.73, 95% CI 0.56–0.96, P = 0.026), HIV-related knowledge (OR 0.55, 95% CI 0.42–0.72, P < 0.001) were associated with new HIV infection among older men. The present study revealed some potential risky/protective factors altogether. The results highlighted the direction of HIV/AIDS prevention and control among older men, and it is a social issue that requires the joint participation of the whole society.
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Wion RK, Fazeli PL, Vance DE. The Association Between Leisure Activity Engagement and Health-Related Quality of Life in Middle-Aged and Older People With HIV. THE GERONTOLOGIST 2022; 62:1018-1028. [PMID: 34792135 PMCID: PMC9372889 DOI: 10.1093/geront/gnab172] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Middle-aged and older adults with human immunodeficiency virus (HIV) are at risk for decreased health-related quality of life (HRQoL), which may be improved by engaging in leisure activities. We examined associations between HRQoL and participation in cognitive, physical, social, and passive leisure activities, and whether depressive symptoms mediated these relationships. Wilson and Cleary's conceptual model of HRQoL guided this study. RESEARCH DESIGN AND METHODS In this cross-sectional observational study, we enrolled 174 adults living with HIV aged 40 and older (M = 51.3, SD = 7.03). Participants completed assessments of leisure activities, depressive symptoms, and HRQoL. Data were analyzed using Spearman's rho correlations, hierarchal multiple regression, and mediation analyses. RESULTS Greater engagement in physical activities was associated with higher physical HRQoL (b = 2.02, p < .05). Greater engagement in social activities was associated with both higher physical (b = 1.44, p < .05) and mental HRQoL (b = 1.95, p < .01). However, all associations between leisure activities and HRQoL were fully attenuated by depressive symptoms. Cognitive and passive leisure activities were not significantly correlated with HRQoL. Mediation analyses confirmed that depressive symptoms were the mediator mechanism by which social activities affected mental and physical HRQoL. DISCUSSION AND IMPLICATIONS More frequent engagement in physical and social leisure activities is associated with better HRQoL, and social leisure activities improve HRQoL via their impact on mood. Interventions to increase leisure activities, especially among people living with HIV who have poorer affective functioning, may be the most effective approach to improving HRQoL.
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Affiliation(s)
- Rachel K Wion
- School of Nursing, Indiana University, Indianapolis, Indiana, USA
| | - Pariya L Fazeli
- School of Nursing, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - David E Vance
- School of Nursing, University of Alabama at Birmingham, Birmingham, Alabama, USA
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38
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Hung PSP, Zhang JY, Noorani A, Walker MR, Huang M, Zhang JW, Laperriere N, Rudzicz F, Hodaie M. Differential expression of a brain aging biomarker across discrete chronic pain disorders. Pain 2022; 163:1468-1478. [PMID: 35202044 DOI: 10.1097/j.pain.0000000000002613] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 02/07/2022] [Indexed: 11/26/2022]
Abstract
ABSTRACT Chronic pain has widespread, detrimental effects on the human nervous system and its prevalence and burden increase with age. Machine learning techniques have been applied on brain images to produce statistical models of brain aging. Specifically, the Gaussian process regression is particularly effective at predicting chronological age from neuroimaging data which permits the calculation of a brain age gap estimate (brain-AGE). Pathological biological processes such as chronic pain can influence brain-AGE. Because chronic pain disorders can differ in etiology, severity, pain frequency, and sex-linked prevalence, we hypothesize that the expression of brain-AGE may be pain specific and differ between discrete chronic pain disorders. We built a machine learning model using T1-weighted anatomical MRI from 812 healthy controls to extract brain-AGE for 45 trigeminal neuralgia (TN), 52 osteoarthritis (OA), and 50 chronic low back pain (BP) subjects. False discovery rate corrected Welch t tests were conducted to detect significant differences in brain-AGE between each discrete pain cohort and age-matched and sex-matched controls. Trigeminal neuralgia and OA, but not BP subjects, have significantly larger brain-AGE. Across all 3 pain groups, we observed female-driven elevation in brain-AGE. Furthermore, in TN, a significantly larger brain-AGE is associated with response to Gamma Knife radiosurgery for TN pain and is inversely correlated with the age at diagnosis. As brain-AGE expression differs across distinct pain disorders with a pronounced sex effect for female subjects. Younger women with TN may therefore represent a vulnerable subpopulation requiring expedited chronic pain intervention. To this end, brain-AGE holds promise as an effective biomarker of pain treatment response.
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Affiliation(s)
- Peter Shih-Ping Hung
- Division of Brain, Imaging & Behaviour Systems Neuroscience, Krembil Brain Institute, University Health Network, Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Jia Y Zhang
- Schulich School of Medicine & Dentistry, Western University, London, Canada
| | - Alborz Noorani
- Institute of Medical Science, University of Toronto, Toronto, Canada
- MD Program, Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Matthew R Walker
- Division of Brain, Imaging & Behaviour Systems Neuroscience, Krembil Brain Institute, University Health Network, Toronto, Canada
| | - Megan Huang
- Department of Pharmacology & Therapeutics, McGill University, Montreal, Canada
| | - Jason W Zhang
- Human Biology Program, University of Toronto, Toronto, Canada
| | - Normand Laperriere
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Frank Rudzicz
- Department of Computer Science, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Canada
- Li Ka Shing Knowledge Institute, St Michaels Hospital, Toronto, Canada
| | - Mojgan Hodaie
- Division of Brain, Imaging & Behaviour Systems Neuroscience, Krembil Brain Institute, University Health Network, Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
- Department of Surgery, Faculty of Medicine, University of Toronto, Toronto, Canada
- Division of Neurosurgery, Krembil Neuroscience Centre, Toronto Western Hospital, University Health Network, Toronto, Canada
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Yuan NY, Maung R, Xu Z, Han X, Kaul M. Arachidonic Acid Cascade and Eicosanoid Production Are Elevated While LTC4 Synthase Modulates the Lipidomics Profile in the Brain of the HIVgp120-Transgenic Mouse Model of NeuroHIV. Cells 2022; 11:2123. [PMID: 35805207 PMCID: PMC9265961 DOI: 10.3390/cells11132123] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 06/24/2022] [Accepted: 06/26/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Combination antiretroviral therapy (cART) has transformed HIV infection from a terminal disease to a manageable chronic health condition, extending patients' life expectancy to that of the general population. However, the incidence of HIV-associated neurocognitive disorders (HANDs) has persisted despite virological suppression. Patients with HIV display persistent signs of immune activation and inflammation despite cART. The arachidonic acid (AA) cascade is an important immune response system responsible for both pro- and anti-inflammatory processes. METHODS Lipidomics, mRNA and Western blotting analysis provide valuable insights into the molecular mechanisms surrounding arachidonic acid metabolism and the resulting inflammation caused by perturbations thereof. RESULTS Here, we report the presence of inflammatory eicosanoids in the brains of a transgenic mouse model of NeuroHIV that expresses soluble HIV-1 envelope glycoprotein in glial cells (HIVgp120tg mice). Additionally, we report that the effect of LTC4S knockout in HIVgp120tg mice resulted in the sexually dimorphic transcription of COX- and 5-LOX-related genes. Furthermore, the absence of LTC4S suppressed ERK1/2 and p38 MAPK signaling activity in female mice only. The mass spectrometry-based lipidomic profiling of these mice reveals beneficial alterations to lipids in the brain. CONCLUSION Targeting the AA cascade may hold potential in the treatment of neuroinflammation observed in NeuroHIV and HANDs.
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Affiliation(s)
- Nina Y. Yuan
- Division of Biomedical Sciences, School of Medicine, University of California Riverside, 900 University Ave, Riverside, CA 92521, USA; (N.Y.Y.); (R.M.)
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Ricky Maung
- Division of Biomedical Sciences, School of Medicine, University of California Riverside, 900 University Ave, Riverside, CA 92521, USA; (N.Y.Y.); (R.M.)
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Ziying Xu
- Barshop Institute for Longevity and Aging Studies, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (Z.X.); (X.H.)
| | - Xianlin Han
- Barshop Institute for Longevity and Aging Studies, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (Z.X.); (X.H.)
- Department of Medicine-Diabetes, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Marcus Kaul
- Division of Biomedical Sciences, School of Medicine, University of California Riverside, 900 University Ave, Riverside, CA 92521, USA; (N.Y.Y.); (R.M.)
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA
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40
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Denissen S, Engemann DA, De Cock A, Costers L, Baijot J, Laton J, Penner IK, Grothe M, Kirsch M, D'hooghe MB, D'Haeseleer M, Dive D, De Mey J, Van Schependom J, Sima DM, Nagels G. Brain age as a surrogate marker for cognitive performance in multiple sclerosis. Eur J Neurol 2022; 29:3039-3049. [PMID: 35737867 PMCID: PMC9541923 DOI: 10.1111/ene.15473] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/04/2022] [Accepted: 06/15/2022] [Indexed: 11/28/2022]
Abstract
Background and purpose Data from neuro‐imaging techniques allow us to estimate a brain's age. Brain age is easily interpretable as ‘how old the brain looks’ and could therefore be an attractive communication tool for brain health in clinical practice. This study aimed to investigate its clinical utility by investigating the relationship between brain age and cognitive performance in multiple sclerosis (MS). Methods A linear regression model was trained to predict age from brain magnetic resonance imaging volumetric features and sex in a healthy control dataset (HC_train, n = 1673). This model was used to predict brain age in two test sets: HC_test (n = 50) and MS_test (n = 201). Brain‐predicted age difference (BPAD) was calculated as BPAD = brain age minus chronological age. Cognitive performance was assessed by the Symbol Digit Modalities Test (SDMT). Results Brain age was significantly related to SDMT scores in the MS_test dataset (r = −0.46, p < 0.001) and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r = −0.24, p < 0.001) and a significant weight (−0.25, p = 0.002) in a multivariate regression equation with age. Conclusions Brain age is a candidate biomarker for cognitive dysfunction in MS and an easy to grasp metric for brain health.
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Affiliation(s)
- S Denissen
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.,Kolonel Begaultlaan 1b, 3012, Belgium
| | - D A Engemann
- Université Paris-Saclay, CEA, 1 Rue Honoré d'Estienne d'Orves, 91120, Palaiseau, France.,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1A, D-04103, Leipzig, Germany
| | - A De Cock
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - L Costers
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.,Kolonel Begaultlaan 1b, 3012, Belgium
| | - J Baijot
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - J Laton
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.,Nuffield Department of Clinical Neurosciences, University of Oxford, Headley Way, Headington, Oxford, OX3 9DU, United Kingdom
| | - I K Penner
- Cogito Center for Applied Neurocognition and Neuropsychological Research, Merowingerplatz 1, 40225, Düsseldorf, Germany.,Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Universitätsstr. 1, 40225, Düsseldorf, Germany
| | - M Grothe
- Department of Neurology, University Medicine Greifswald, Ferdinand-Sauerbruchstraße, 17475, Greifswald, Germany
| | - M Kirsch
- Institute for Diagnostic Radiology and Neuroradiology, University Medicine of Greifswald, Ferdinand-Sauerbruch-Straße, 17489, Greifswald, Germany
| | - M B D'hooghe
- National Multiple Sclerosis Center Melsbroek, Vereeckenstraat 44, 1820, Melsbroek, Belgium.,Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium
| | - M D'Haeseleer
- National Multiple Sclerosis Center Melsbroek, Vereeckenstraat 44, 1820, Melsbroek, Belgium
| | - D Dive
- Department of Neurology, University Hospital of Liege, Rue Grandfosse 31/33, 4130, Esneux, Belgium
| | - J De Mey
- Department of Radiology, UZ Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium
| | - J Van Schependom
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.,Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - D M Sima
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.,Kolonel Begaultlaan 1b, 3012, Belgium
| | - G Nagels
- AIMS lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.,Kolonel Begaultlaan 1b, 3012, Belgium.,St Edmund Hall, University of Oxford, Queen's Lane, Oxford, OX1 4AR, UK
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de Ruiter MB, Groot PFC, Deprez S, Pullens P, Sunaert S, de Ruysscher D, Schagen SB, Belderbos J. Hippocampal avoidance prophylactic cranial irradiation (HA-PCI) for small cell lung cancer reduces hippocampal atrophy compared to conventional PCI. Neuro Oncol 2022; 25:167-176. [PMID: 35640975 PMCID: PMC9825336 DOI: 10.1093/neuonc/noac148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Reducing radiation dose to the hippocampus with hippocampal avoidance prophylactic cranial irradiation (HA-PCI) is proposed to prevent cognitive decline. It has, however, not been investigated whether hippocampal atrophy is actually mitigated by this approach. Here, we determined whether HA-PCI reduces hippocampal atrophy. Additionally, we evaluated neurotoxicity of (HA-)PCI to other brain regions. Finally, we evaluated associations of hippocampal atrophy and brain neurotoxicity with memory decline. METHODS High-quality research MRI scans were acquired in the multicenter, randomized phase 3 trial NCT01780675. Hippocampal atrophy was evaluated for 4 months (57 HA-PCI patients and 46 PCI patients) and 12 months (28 HA-PCI patients and 27 PCI patients) after (HA-)PCI. We additionally studied multimodal indices of brain injury. Memory was assessed with the Hopkins Verbal Learning Test-Revised (HVLT-R). RESULTS HA-PCI reduced hippocampal atrophy at 4 months (1.8% for HA-PCI and 3.0% for PCI) and at 12 months (3.0% for HA-PCI and 5.8% for PCI). Both HA-PCI and PCI were associated with considerable reductions in gray matter and normal-appearing white matter, increases in white matter hyperintensities, and brain aging. There were no significant associations between hippocampal atrophy and memory. CONCLUSIONS HA-PCI reduces hippocampal atrophy at 4 and 12 months compared to regular PCI. Both types of radiotherapy are associated with considerable brain injury. We did not find evidence for excessive brain injury after HA-PCI relative to PCI. Hippocampal atrophy was not associated with memory decline in this population as measured with HVLT-R. The usefulness of HA-PCI is still subject to debate.
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Affiliation(s)
- Michiel B de Ruiter
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Paul F C Groot
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location AMC, University of Amsterdam, The Netherlands
| | - Sabine Deprez
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium,Leuven Cancer Institute, KU Leuven, Leuven, Belgium
| | - Pim Pullens
- Department of Radiology, Ghent University, Ghent, Belgium
| | - Stefan Sunaert
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Dirk de Ruysscher
- Radiation Oncology (MAASTRO), School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Sanne B Schagen
- Corresponding Author: Sanne B. Schagen, PhD, Brain and Cognition, Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129 B, 1018 WS, Amsterdam, the Netherlands ()
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Petersen KJ, Strain J, Cooley S, Vaida F, Ances BM. Machine Learning Quantifies Accelerated White-Matter Aging in Persons With HIV. J Infect Dis 2022; 226:49-58. [PMID: 35481983 PMCID: PMC9890925 DOI: 10.1093/infdis/jiac156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/22/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Persons with HIV (PWH) undergo white matter changes, which can be quantified using the brain-age gap (BAG), the difference between chronological age and neuroimaging-based brain-predicted age. Accumulation of microstructural damage may be accelerated in PWH, especially with detectable viral load (VL). METHODS In total, 290 PWH (85% with undetectable VL) and 165 HIV-negative controls participated in neuroimaging and cognitive testing. BAG was measured using a Gaussian process regression model trained to predict age from diffusion magnetic resonance imaging in publicly available normative controls. To test for accelerated aging, BAG was modeled as an age × VL interaction. The relationship between BAG and global neuropsychological performance was examined. Other potential predictors of pathological aging were investigated in an exploratory analysis. RESULTS Age and detectable VL had a significant interactive effect: PWH with detectable VL accumulated +1.5 years BAG/decade versus HIV-negative controls (P = .018). PWH with undetectable VL accumulated +0.86 years BAG/decade, although this did not reach statistical significance (P = .052). BAG was associated with poorer global cognition only in PWH with detectable VL (P < .001). Exploratory analysis identified Framingham cardiovascular risk as an additional predictor of pathological aging (P = .027). CONCLUSIONS Aging with detectable HIV and cardiovascular disease may lead to white matter pathology and contribute to cognitive impairment.
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Affiliation(s)
- Kalen J Petersen
- Correspondence: Kalen J. Petersen, PhD, Washington University in St Louis, 600 South Euclid Avenue, Box 8111, St Louis, MO 63130 ()
| | - Jeremy Strain
- Department of Neurology, Washington University School of Medicine, St Louis, Missouri, USA
| | - Sarah Cooley
- Department of Neurology, Washington University School of Medicine, St Louis, Missouri, USA
| | - Florin Vaida
- Department of Family and Preventive Medicine, University of California, San Diego, California, USA
| | - Beau M Ances
- Department of Neurology, Washington University School of Medicine, St Louis, Missouri, USA
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Ipser JC, Joska J, Sevenoaks T, Gouse H, Freeman C, Kaufmann T, Andreassen OA, Shoptaw S, Stein DJ. Limited evidence for a moderating effect of HIV status on brain age in heavy episodic drinkers. J Neurovirol 2022; 28:383-391. [PMID: 35355213 DOI: 10.1007/s13365-022-01072-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 03/04/2022] [Accepted: 03/07/2022] [Indexed: 12/01/2022]
Abstract
We set out to test the hypothesis that greater brain ageing will be observed in people with HIV (PWH) and those who engage in heavy episodic drinking (HED), with their combined effects being especially detrimental in cognitive control brain networks. We correlated measures of "brain age gap" (BAG) and neurocognitive impairment in participants with and without HIV and HED. Sixty-nine participants were recruited from a community health centre in Cape Town: HIV - /HED - (N = 17), HIV + /HED - (N = 14), HIV - /HED + (N = 21), and HIV + /HED + (N = 17). Brain age was modelled using structural MRI features from the whole brain or one of six brain regions. Linear regression models were employed to identify differences in BAG between patient groups and controls. Associations between BAG and clinical data were tested using bivariate statistical methods. Compared to controls, greater global BAG was observed in heavy drinkers, both with (Cohen's d = 1.52) and without (d = 1.61) HIV. Differences in BAG between HED participants and controls were observed for the cingulate and parietal cortex, as well as subcortically. A larger BAG was associated with higher total drinking scores but not nadir CD4 count or current HIV viral load. The association between heavy episodic drinking and BAG, independent of HIV status, points to the importance of screening for alcohol use disorders in primary care. The relatively large contribution of cognitive control brain regions to BAG highlights the utility of assessing the contribution of different brain regions to brain age.
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Affiliation(s)
- Jonathan C Ipser
- Department of Psychiatry and Mental Health, HIV Mental Health Research Unit, University of Cape Town, Cape Town, South Africa. .,Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
| | - John Joska
- Department of Psychiatry and Mental Health, HIV Mental Health Research Unit, University of Cape Town, Cape Town, South Africa.,Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Tatum Sevenoaks
- Department of Psychiatry and Mental Health, HIV Mental Health Research Unit, University of Cape Town, Cape Town, South Africa
| | - Hetta Gouse
- Department of Psychiatry and Mental Health, HIV Mental Health Research Unit, University of Cape Town, Cape Town, South Africa
| | - Carla Freeman
- Department of Psychiatry and Mental Health, HIV Mental Health Research Unit, University of Cape Town, Cape Town, South Africa
| | - Tobias Kaufmann
- Division of Mental Health and Addiction, Institute of Clinical Medicine, NORMENT Oslo University Hospital & University of Oslo, Tübingen, Germany.,Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - Ole A Andreassen
- Division of Mental Health and Addiction, Institute of Clinical Medicine, NORMENT Oslo University Hospital & University of Oslo, Tübingen, Germany
| | - Steve Shoptaw
- Department of Psychiatry and Mental Health, HIV Mental Health Research Unit, University of Cape Town, Cape Town, South Africa.,Department of Family Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Dan J Stein
- Department of Psychiatry and Mental Health, HIV Mental Health Research Unit, University of Cape Town, Cape Town, South Africa.,MRC Unit On Risk & Resilience in Mental Disorders, University of Cape Town, Cape Town, South Africa.,Neuroscience Institute, University of Cape Town, Cape Town, South Africa
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44
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Montano M, Oursler KK, Xu K, Sun YV, Marconi VC. Biological ageing with HIV infection: evaluating the geroscience hypothesis. THE LANCET. HEALTHY LONGEVITY 2022; 3:e194-e205. [PMID: 36092375 PMCID: PMC9454292 DOI: 10.1016/s2666-7568(21)00278-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Although people with HIV are living longer, as they age they remain disproportionately burdened with multimorbidity that is exacerbated in resource-poor settings. The geroscience hypothesis postulates that a discrete set of between five and ten hallmarks of biological ageing drive multimorbidity, but these processes have not been systematically examined in the context of people with HIV. We examine four major hallmarks of ageing (macromolecular damage, senescence, inflammation, and stem-cell dysfunction) as gerodrivers in the context of people with HIV. As a counterbalance, we introduce healthy ageing, physiological reserve, intrinsic capacity, and resilience as promoters of geroprotection that counteract gerodrivers. We discuss emerging geroscience-based diagnostic biomarkers and therapeutic strategies, and provide examples based on recent advances in cellular senescence, and other, non-pharmacological approaches. Finally, we present a conceptual model of biological ageing in the general population and in people with HIV that integrates gerodrivers and geroprotectors as modulators of homoeostatic reserves and organ function over the lifecourse.
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Rodés B, Cadiñanos J, Esteban-Cantos A, Rodríguez-Centeno J, Arribas JR. Ageing with HIV: Challenges and biomarkers. EBioMedicine 2022; 77:103896. [PMID: 35228014 PMCID: PMC8889090 DOI: 10.1016/j.ebiom.2022.103896] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 02/08/2022] [Accepted: 02/09/2022] [Indexed: 12/13/2022] Open
Abstract
The antiretroviral treatment (ART) developed to control HIV infection led to a revolution in the prognosis of people living with HIV (PLWH). PLWH underwent from suffering severe disease and often fatal complications at young ages to having a chronic condition and a life expectancy close to the general population. Nevertheless, chronic age-related diseases increase as PLWH age. The harmful effect of HIV infection on the individual's immune system adds to its deterioration during ageing, exacerbating comorbidities. In addition, PLWH are more exposed to risk factors affecting ageing, such as coinfections or harmful lifestyles. The ART initiation reverses the biological ageing process but only partially, and additionally can have some toxicities that influence ageing. Observational studies suggest premature ageing in PLWH. Therefore, there is considerable interest in the early prediction of unhealthy ageing through validated biomarkers, easy to implement in HIV-clinical settings. The most promising biomarkers are second-generation epigenetic clocks and integrative algorithms.
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Affiliation(s)
- Berta Rodés
- HIV/AIDS and Infectious Diseases Research Group, Hospital Universitario La Paz Institute for Health Research-IdiPAZ, Paseo de la Castellana 261, Madrid 28046, Spain; CIBER of Infectious Diseases (CIBER-INFECT), 28029 Madrid, Spain.
| | - Julen Cadiñanos
- HIV/AIDS and Infectious Diseases Research Group, Hospital Universitario La Paz Institute for Health Research-IdiPAZ, Paseo de la Castellana 261, Madrid 28046, Spain; Infectious Diseases Unit, Department of Internal Medicine, Hospital Universitario La Paz, Paseo de la Castellana 261, Madrid 28046, Spain; CIBER of Infectious Diseases (CIBER-INFECT), 28029 Madrid, Spain
| | - Andrés Esteban-Cantos
- HIV/AIDS and Infectious Diseases Research Group, Hospital Universitario La Paz Institute for Health Research-IdiPAZ, Paseo de la Castellana 261, Madrid 28046, Spain; CIBER of Infectious Diseases (CIBER-INFECT), 28029 Madrid, Spain
| | - Javier Rodríguez-Centeno
- HIV/AIDS and Infectious Diseases Research Group, Hospital Universitario La Paz Institute for Health Research-IdiPAZ, Paseo de la Castellana 261, Madrid 28046, Spain; CIBER of Infectious Diseases (CIBER-INFECT), 28029 Madrid, Spain
| | - José Ramón Arribas
- HIV/AIDS and Infectious Diseases Research Group, Hospital Universitario La Paz Institute for Health Research-IdiPAZ, Paseo de la Castellana 261, Madrid 28046, Spain; Infectious Diseases Unit, Department of Internal Medicine, Hospital Universitario La Paz, Paseo de la Castellana 261, Madrid 28046, Spain; CIBER of Infectious Diseases (CIBER-INFECT), 28029 Madrid, Spain.
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Beheshti I, Maikusa N, Matsuda H. The accuracy of T1-weighted voxel-wise and region-wise metrics for brain age estimation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106585. [PMID: 34933227 DOI: 10.1016/j.cmpb.2021.106585] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 11/28/2021] [Accepted: 12/09/2021] [Indexed: 06/14/2023]
Abstract
INTRODUCTION The brain age score has recently been introduced for robust monitoring of brain morphological alterations throughout the lifespan, prediction of mortality risk, and early detection of neurological disorders. METHODS We assessed the brain age prediction accuracy of the widely used T1-weighted voxel-wise and region-wise metrics (i.e., T1-weighted magnetic resonance imaging [MRI]-wise metrics)) separately and their integration. We assessed 788 healthy individuals (age, 18-94 years) in a training set to build a brain age estimation framework based on different T1-weighted MRI-wise metrics (15 different metrics in total) and then validated each T1-weighted MRI-wise metric in an independent test set comprising 88 healthy individuals. We also assessed the accuracy of each T1-weighted MRI-wise metric in a clinical set of 70 patients with mild cognitive impairment and another of 30 patients with Alzheimer's disease. RESULTS Integration of gray matter voxel-wise maps and all region-wise metrics achieved the highest brain age prediction accuracy (mean absolute error, 4.63 years). These metrics on their own achieved lower accuracy (mean absolute error, 4.97 years and 5.75 years, respectively). DISCUSSION For tracing brain atrophy levels in neurological disorders at the clinical level, integration of voxel-wise and region-wise metrics may contribute to a more sensitive brain age framework than when these metrics are used on their own.
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Affiliation(s)
- Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada; Cyclotron and Drug Discovery Research Center, Southern TOHOKU Research Institute for Neuroscience 7- 61-2, Yatsuyamada Koriyama, 963-8052, Japan.
| | - Norihide Maikusa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo 187-8551, Japan
| | - Hiroshi Matsuda
- Cyclotron and Drug Discovery Research Center, Southern TOHOKU Research Institute for Neuroscience 7- 61-2, Yatsuyamada Koriyama, 963-8052, Japan; Department of Biofunctional Imaging, Fukushima Medical University, 1Hikariga-oka, Fukushima City, Fukushima 960-1295, Japan; Department of Radiology, National Center of Neurology and Psychiatry 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo 187-8551, Japan
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Neuroimaging-derived brain age is associated with life satisfaction in cognitively unimpaired elderly: A community-based study. Transl Psychiatry 2022; 12:25. [PMID: 35058431 PMCID: PMC8776862 DOI: 10.1038/s41398-022-01793-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 12/21/2021] [Accepted: 01/10/2022] [Indexed: 12/01/2022] Open
Abstract
With the widespread increase in elderly populations, the quality of life and mental health in old age are issues of great interest. The human brain changes with age, and the brain aging process is biologically complex and varies widely among individuals. In this cross-sectional study, to clarify the effects of mental health, as well as common metabolic factors (e.g., diabetes) on healthy brain aging in late life, we analyzed structural brain MRI findings to examine the relationship between predicted brain age and life satisfaction, depressive symptoms, resilience, and lifestyle-related factors in elderly community-living individuals with unimpaired cognitive function. We extracted data from a community-based cohort study in Arakawa Ward, Tokyo. T1-weighted images of 773 elderly participants aged ≥65 years were analyzed, and the predicted brain age of each subject was calculated by machine learning from anatomically standardized gray-matter images. Specifically, we examined the relationships between the brain-predicted age difference (Brain-PAD: real age subtracted from predicted age) and life satisfaction, depressive symptoms, resilience, alcohol consumption, smoking, diabetes, hypertension, and dyslipidemia. Brain-PAD showed significant negative correlations with life satisfaction (Spearman's rs= -0.102, p = 0.005) and resilience (rs= -0.105, p = 0.004). In a multiple regression analysis, life satisfaction (p = 0.038), alcohol use (p = 0.040), and diabetes (p = 0.002) were independently correlated with Brain-PAD. Thus, in the cognitively unimpaired elderly, higher life satisfaction was associated with a 'younger' brain, whereas diabetes and alcohol use had negative impacts on life satisfaction. Subjective life satisfaction, as well as the prevention of diabetes and alcohol use, may protect the brain from accelerated aging.
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49
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Jacobs SAH, Muraro PA, Cencioni MT, Knowles S, Cole JH, Nicholas R. Worse Physical Disability Is Associated With the Expression of PD-1 on Inflammatory T-Cells in Multiple Sclerosis Patients With Older Appearing Brains. Front Neurol 2022; 12:801097. [PMID: 35069428 PMCID: PMC8770747 DOI: 10.3389/fneur.2021.801097] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 12/15/2021] [Indexed: 12/30/2022] Open
Abstract
Background: Magnetic Resonance Imaging (MRI) analysis method "brain-age" paradigm could offer an intuitive prognostic metric (brain-predicted age difference: brain-PAD) for disability in Multiple Sclerosis (MS), reflecting structural brain health adjusted for aging. Equally, cellular senescence has been reported in MS using T-cell biomarker CD8+CD57+. Objective: Here we explored links between MRI-derived brain-age and blood-derived cellular senescence. We examined the value of combining brain-PAD with CD8+CD57+(ILT2+PD-1+) T-cells when predicting disability score in MS and considered whether age-related biological mechanisms drive disability. Methods: Brain-age analysis was applied to T1-weighted MRI images. Disability was assessed and peripheral blood was examined for CD8+CD57+ T-cell phenotypes. Linear regression models were used, adjusted for sex, age and normalized brain volume. Results: We included 179 mainly relapsing-remitting MS patients. A high brain-PAD was associated with high physical disability (mean brain-PAD = +6.54 [5.12-7.95]). CD8+CD57+(ILT2+PD-1+) T-cell frequency was neither associated with disability nor with brain-PAD. Physical disability was predicted by the interaction between brain-PAD and CD8+CD57+ILT2+PD-1+ T-cell frequency (AR 2 = 0.196), yet without improvement compared to brain-PAD alone (AR 2 = 0.206; AICc = 1.8). Conclusion: Higher frequency of CD8+CD57+ILT2+PD-1+ T-cells in the peripheral blood in patients with an older appearing brain was associated with worse disability scores, suggesting a role of these cells in the development of disability in MS patients with poorer brain health.
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Affiliation(s)
- Sophie A. H. Jacobs
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Neurology, Imperial College Healthcare, London, United Kingdom
| | - Paolo A. Muraro
- Department of Neurology, Imperial College Healthcare, London, United Kingdom
- Division of Clinical Neurology, Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Maria T. Cencioni
- Division of Clinical Neurology, Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Sarah Knowles
- Department of Neurology, Imperial College Healthcare, London, United Kingdom
| | - James H. Cole
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Richard Nicholas
- Department of Neurology, Imperial College Healthcare, London, United Kingdom
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Wrigglesworth J, Yaacob N, Ward P, Woods RL, McNeil J, Storey E, Egan G, Murray A, Shah RC, Jamadar SD, Trevaks R, Ward S, Harding IH, Ryan J. Brain-predicted age difference is associated with cognitive processing in later-life. Neurobiol Aging 2022; 109:195-203. [PMID: 34775210 PMCID: PMC8832483 DOI: 10.1016/j.neurobiolaging.2021.10.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 10/07/2021] [Accepted: 10/12/2021] [Indexed: 01/08/2023]
Abstract
Brain age is a neuroimaging-based biomarker of aging. This study examined whether the difference between brain age and chronological age (brain-PAD) is associated with cognitive function at baseline and longitudinally. Participants were relatively healthy, predominantly white community-dwelling older adults (n = 531, aged ≥70 years), with high educational attainment (61% ≥12 years) and socioeconomic status (59% ≥75th percentile). Brain age was estimated from T1-weighted magnetic resonance images using an algorithm by Cole et al., 2018. After controlling for age, gender, education, depression and body mass index, brain-PAD was negatively associated with psychomotor speed (Symbol Digit Modalities Test) at baseline (Bonferroni p < 0.006), but was not associated with baseline verbal fluency (Controlled Oral Word Association Test), delayed recall (Hopkins Learning Test Revised), or general cognitive status (Mini-Mental State Examination). Baseline brain-PAD was not associated with 3-year change in cognition (Bonferroni p > 0.006). These findings indicate that even in relatively healthy older people, accelerated brain aging is associated with worse psychomotor speed, but future longitudinal research into changes in brain-PAD is needed.
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Affiliation(s)
- Jo Wrigglesworth
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Nurathifah Yaacob
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Phillip Ward
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia; Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Victoria, Australia
| | - Robyn L Woods
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - John McNeil
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Elsdon Storey
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Gary Egan
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Victoria, Australia
| | - Anne Murray
- Berman Center for Outcomes & Clinical Research, Hennepin Healthcare Research Institute, Minneapolis, MN, USA; Department of Medicine, Division of Geriatrics, Hennepin Healthcare, University of Minnesota, Minneapolis, MN, USA
| | - Raj C Shah
- Department of Family Medicine and the Rush Alzheimer's Disease Centre, Rush University Medical Centre, Chicago, IL, USA
| | - Sharna D Jamadar
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia; Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Victoria, Australia
| | - Ruth Trevaks
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Stephanie Ward
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, New South Wales, Australia; Department of Geriatric Medicine, Prince of Wales Hospital, Randwick, New South Wales, Australia
| | - Ian H Harding
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Joanne Ryan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
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