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Gustavson DE, Elman JA, Reynolds CA, Eyler LT, Fennema-Notestine C, Puckett OK, Panizzon MS, Gillespie NA, Neale MC, Lyons MJ, Franz CE, Kremen WS. Brain reserve in midlife is associated with executive function changes across 12 years. Neurobiol Aging 2024; 141:113-120. [PMID: 38852544 PMCID: PMC11246793 DOI: 10.1016/j.neurobiolaging.2024.05.001] [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: 08/01/2023] [Revised: 04/17/2024] [Accepted: 05/08/2024] [Indexed: 06/11/2024]
Abstract
We examined how brain reserve in midlife, measured by brain-predicted age difference scores (Brain-PADs), predicted executive function concurrently and longitudinally into early old age, and whether these associations were moderated by young adult cognitive reserve or APOE genotype. 508 men in the Vietnam Era Twin Study of Aging (VETSA) completed neuroimaging assessments at mean age 56 and six executive function tasks at mean ages 56, 62, and 68 years. Results indicated that greater brain reserve at age 56 was associated with better concurrent executive function (r=.10, p=.040) and less decline in executive function over 12 years (r=.34, p=.001). These associations were not moderated by cognitive reserve or APOE genotype. Twin analysis suggested associations with executive function slopes were driven by genetic influences. Our findings suggest that greater brain reserve allowed for better cognitive maintenance from middle- to old age, driven by a genetic association. The results are consistent with differential preservation of executive function based on brain reserve that is independent of young adult cognitive reserve or APOE genotype.
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Affiliation(s)
- Daniel E Gustavson
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA.
| | - Jeremy A Elman
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - Chandra A Reynolds
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Olivia K Puckett
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
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2
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Zhang X, Pan Y, Wu T, Zhao W, Zhang H, Ding J, Ji Q, Jia X, Li X, Lee Z, Zhang J, Bai L. Brain age prediction using interpretable multi-feature-based convolutional neural network in mild traumatic brain injury. Neuroimage 2024; 297:120751. [PMID: 39048043 DOI: 10.1016/j.neuroimage.2024.120751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 07/15/2024] [Accepted: 07/22/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Convolutional neural network (CNN) can capture the structural features changes of brain aging based on MRI, thus predict brain age in healthy individuals accurately. However, most studies use single feature to predict brain age in healthy individuals, ignoring adding information from multiple sources and the changes in brain aging patterns after mild traumatic brain injury (mTBI) were still unclear. METHODS Here, we leveraged the structural data from a large, heterogeneous dataset (N = 1464) to implement an interpretable 3D combined CNN model for brain-age prediction. In addition, we also built an atlas-based occlusion analysis scheme with a fine-grained human Brainnetome Atlas to reveal the age-sstratified contributed brain regions for brain-age prediction in healthy controls (HCs) and mTBI patients. The correlations between brain predicted age gaps (brain-PAG) following mTBI and individual's cognitive impairment, as well as the level of plasma neurofilament light were also examined. RESULTS Our model utilized multiple 3D features derived from T1w data as inputs, and reduced the mean absolute error (MAE) of age prediction to 3.08 years and improved Pearson's r to 0.97 on 154 HCs. The strong generalizability of our model was also validated across different centers. Regions contributing the most significantly to brain age prediction were the caudate and thalamus for HCs and patients with mTBI, and the contributive regions were mostly located in the subcortical areas throughout the adult lifespan. The left hemisphere was confirmed to contribute more in brain age prediction throughout the adult lifespan. Our research showed that brain-PAG in mTBI patients was significantly higher than that in HCs in both acute and chronic phases. The increased brain-PAG in mTBI patients was also highly correlated with cognitive impairment and a higher level of plasma neurofilament light, a marker of neurodegeneration. The higher brain-PAG and its correlation with severe cognitive impairment showed a longitudinal and persistent nature in patients with follow-up examinations. CONCLUSION We proposed an interpretable deep learning framework on a relatively large dataset to accurately predict brain age in both healthy individuals and mTBI patients. The interpretable analysis revealed that the caudate and thalamus became the most contributive role across the adult lifespan in both HCs and patients with mTBI. The left hemisphere contributed significantly to brain age prediction may enlighten us to be concerned about the lateralization of brain abnormality in neurological diseases in the future. The proposed interpretable deep learning framework might also provide hope for testing the performance of related drugs and treatments in the future.
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Affiliation(s)
- Xiang Zhang
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yizhen Pan
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Tingting Wu
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Wenpu Zhao
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Haonan Zhang
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Jierui Ding
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Qiuyu Ji
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xiaoyan Jia
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xuan Li
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Zhiqi Lee
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Jie Zhang
- Department of Radiation Medicine, School of Preventive Medicine, Air Force Medical University, Xi'an 710032, China.
| | - Lijun Bai
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.
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3
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Casanova R, Walker KA, Justice JN, Anderson A, Duggan MR, Cordon J, Barnard RT, Lu L, Hsu FC, Sedaghat S, Prizment A, Kritchevsky SB, Wagenknecht LE, Hughes TM. Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort. GeroScience 2024; 46:3861-3873. [PMID: 38438772 PMCID: PMC11226584 DOI: 10.1007/s11357-024-01112-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/26/2024] [Indexed: 03/06/2024] Open
Abstract
Machine learning models are increasingly being used to estimate "brain age" from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.
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Affiliation(s)
- Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA.
| | | | - Jamie N Justice
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Andrea Anderson
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | | | | | - Ryan T Barnard
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | - Lingyi Lu
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | - Fang-Chi Hsu
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | - Sanaz Sedaghat
- School of Public Health, Oncology and Transplantation, University of Minnesota, Minneapolis, MN, USA
| | - Anna Prizment
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Stephen B Kritchevsky
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Lynne E Wagenknecht
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Timothy M Hughes
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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4
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Ng C, Huang P, Cho Y, Lee P, Liu Y, Chang T. Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth. Hum Brain Mapp 2024; 45:e26777. [PMID: 39046114 PMCID: PMC11267564 DOI: 10.1002/hbm.26777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 06/03/2024] [Accepted: 06/19/2024] [Indexed: 07/25/2024] Open
Abstract
The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task-based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7-30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U-shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN-SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross-validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth. PRACTITIONER POINTS: This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task-state frontoparietal and salience networks. Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks. Leveraging advanced machine learning methods, we accurately predicted individuals' ages based on these brain circuits, with a more mature estimated brain age correlating with better math skills.
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Affiliation(s)
- Chan‐Tat Ng
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
| | - Po‐Hsien Huang
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Yi‐Cheng Cho
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
| | - Pei‐Hong Lee
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Yi‐Chang Liu
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Ting‐Ting Chang
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
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5
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Zhao L, Zhu D, Wang X, Liu X, Li T, Wang B, Yao Z, Zheng W, Hu B. An Attention-Based Hemispheric Relation Inference Network for Perinatal Brain Age Prediction. IEEE J Biomed Health Inform 2024; 28:4483-4493. [PMID: 38857141 DOI: 10.1109/jbhi.2024.3411620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
Brain anatomical age is an effective feature to assess the status of the brain, such as atypical development and aging. Although some deep learning models have been developed for estimating infant brain age, the performance of these models was unsatisfactory because few of them considered the developmental characteristics of brain anatomy during the perinatal period-the most rapid and complex developmental stage across the lifespan. The present study proposed an attention-based hemispheric relation inference network (HRINet) that takes advantage of the nature of brain structural lateralization during early development. This model captures the inter-hemispheric relationship using a graph attention mechanism and transmits lateralization information as features to describe the interactive development between bilateral hemispheres. The HRINet was used to estimate the brain age of 531 preterm and full-term neonates from the Developing Human Connectome Project (dHCP) database based on two metrics (mean curvature and sulcal depth) characterizing the folding morphology of the cortex. Our results showed that the HRINet outperformed other benchmark models in fitting the perinatal brain age, with mean absolute error of 0.53 and determination coefficient of 0.89. We also verified the generalizability of the HRINet on an extra independent dataset collected from the Gansu Provincial Maternity and Child-care Hospital. Furthermore, by applying the best-performing model to an independent dataset consisting of 47 scans of preterm infants at term-equivalent age, we showed that the predicted age was significantly lower than the chronological age, suggesting a delayed development of premature brains. Our results demonstrate the effectiveness and generalizability of the HRINet in estimating infant brain age, providing promising clinical applications for assessing neonatal brain maturity.
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6
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Dufumier B, Gori P, Petiton S, Louiset R, Mangin JF, Grigis A, Duchesnay E. Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry. Neuroimage 2024; 296:120665. [PMID: 38848981 DOI: 10.1016/j.neuroimage.2024.120665] [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: 11/15/2023] [Revised: 05/15/2024] [Accepted: 05/31/2024] [Indexed: 06/09/2024] Open
Abstract
The perspective of personalized medicine for brain disorders requires efficient learning models for anatomical neuroimaging-based prediction of clinical conditions. There is now a consensus on the benefit of deep learning (DL) in addressing many medical imaging tasks, such as image segmentation. However, for single-subject prediction problems, recent studies yielded contradictory results when comparing DL with Standard Machine Learning (SML) on top of classical feature extraction. Most existing comparative studies were limited in predicting phenotypes of little clinical interest, such as sex and age, and using a single dataset. Moreover, they conducted a limited analysis of the employed image pre-processing and feature selection strategies. This paper extensively compares DL and SML prediction capacity on five multi-site problems, including three increasingly complex clinical applications in psychiatry namely schizophrenia, bipolar disorder, and Autism Spectrum Disorder (ASD) diagnosis. To compensate for the relative scarcity of neuroimaging data on these clinical datasets, we also evaluate three pre-training strategies for transfer learning from brain imaging of the general healthy population: self-supervised learning, generative modeling and supervised learning with age. Overall, we find similar performance between randomly initialized DL and SML for the three clinical tasks and a similar scaling trend for sex prediction. This was replicated on an external dataset. We also show highly correlated discriminative brain regions between DL and linear ML models in all problems. Nonetheless, we demonstrate that self-supervised pre-training on large-scale healthy population imaging datasets (N≈10k), along with Deep Ensemble, allows DL to learn robust and transferable representations to smaller-scale clinical datasets (N≤1k). It largely outperforms SML on 2 out of 3 clinical tasks both in internal and external test sets. These findings suggest that the improvement of DL over SML in anatomical neuroimaging mainly comes from its capacity to learn meaningful and useful abstract representations of the brain anatomy, and it sheds light on the potential of transfer learning for personalized medicine in psychiatry.
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Affiliation(s)
- Benoit Dufumier
- Université Paris-Saclay, CEA, CNRS, UMR9027 Baobab, NeuroSpin, Saclay, France; LTCI, Télécom Paris, IPParis, Palaiseau, France.
| | - Pietro Gori
- LTCI, Télécom Paris, IPParis, Palaiseau, France
| | - Sara Petiton
- Université Paris-Saclay, CEA, CNRS, UMR9027 Baobab, NeuroSpin, Saclay, France
| | - Robin Louiset
- Université Paris-Saclay, CEA, CNRS, UMR9027 Baobab, NeuroSpin, Saclay, France; LTCI, Télécom Paris, IPParis, Palaiseau, France
| | | | - Antoine Grigis
- Université Paris-Saclay, CEA, CNRS, UMR9027 Baobab, NeuroSpin, Saclay, France
| | - Edouard Duchesnay
- Université Paris-Saclay, CEA, CNRS, UMR9027 Baobab, NeuroSpin, Saclay, France
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7
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Chen YS, Kuo CY, Lu CH, Wang YW, Chou KH, Lin WC. Multiscale brain age prediction reveals region-specific accelerated brain aging in Parkinson's disease. Neurobiol Aging 2024; 140:122-129. [PMID: 38776615 DOI: 10.1016/j.neurobiolaging.2024.05.003] [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: 10/26/2023] [Revised: 04/20/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
Brain biological age, which measures the aging process in the brain using neuroimaging data, has been used to assess advanced brain aging in neurodegenerative diseases, including Parkinson disease (PD). However, assuming that whole brain degeneration is uniform may not be sufficient for assessing the complex neurodegenerative processes in PD. In this study we constructed a multiscale brain age prediction models based on structural MRI of 1240 healthy participants. To assess the brain aging patterns using the brain age prediction model, 93 PD patients and 91 healthy controls matching for sex and age were included. We found increased global and regional brain age in PD patients. The advanced aging regions were predominantly noted in the frontal and temporal cortices, limbic system, basal ganglia, thalamus, and cerebellum. Furthermore, region-level rather than global brain age in PD patients was associated with disease severity. Our multiscale brain age prediction model could aid in the development of objective image-based biomarkers to detect advanced brain aging in neurodegenerative diseases.
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Affiliation(s)
- Yueh-Sheng Chen
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chen-Yuan Kuo
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Cheng-Hsien Lu
- Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Yuan-Wei Wang
- The Science & Technology Policy Research and Information Center, National Applied Research Laboratories(NARLabs), Taipei, Taiwan
| | - Kun-Hsien Chou
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Wei-Che Lin
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung, Taiwan.
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8
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Beck D, Whitmore L, MacSweeney N, Brieant A, Karl V, de Lange AMG, Westlye LT, Mills KL, Tamnes CK. Dimensions of early life adversity are differentially associated with patterns of delayed and accelerated brain maturation. Biol Psychiatry 2024:S0006-3223(24)01486-0. [PMID: 39084501 DOI: 10.1016/j.biopsych.2024.07.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 07/16/2024] [Accepted: 07/19/2024] [Indexed: 08/02/2024]
Abstract
BACKGROUND Different types of early-life adversity have been associated with children's brain structure and function. However, understanding the disparate influence of distinct adversity exposures on the developing brain remains a major challenge. METHODS This study investigates the neural correlates of 10 robust dimensions of early-life adversity identified through exploratory factor analysis in a large community sample of youth from the Adolescent Brain Cognitive Development (ABCD) Study. Brain age models were trained, validated, and tested separately on T1-weighted (T1; N = 9524), diffusion tensor (DTI; N = 8834), and resting-state functional (rs-fMRI; N = 8233) magnetic resonance imaging (MRI) data from two time points (mean age = 10.7 years, SD = 1.2, range = 8.9-13.8 years). RESULTS Bayesian multilevel modelling supported distinct associations between different types of early-life adversity exposures and younger- and older-looking brains. Dimensions generally related to emotional neglect, such as lack of primary and secondary caregiver support, and lack of caregiver supervision, were associated with lower brain age gaps (BAGs), i.e., younger-looking brains. In contrast, dimensions generally related to caregiver psychopathology, trauma exposure, family aggression, substance use and separation from biological parent, and socio-economic disadvantage and neighbourhood safety were associated with higher BAGs, i.e., older-looking brains. CONCLUSIONS The findings suggest that dimensions of early-life adversity are differentially associated with distinct neurodevelopmental patterns, indicative of dimension-specific delayed and accelerated brain maturation.
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Affiliation(s)
- Dani Beck
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway.
| | - Lucy Whitmore
- Department of Psychology, University of Oregon, Eugene, OR, USA
| | - Niamh MacSweeney
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway
| | - Alexis Brieant
- Department of Psychological Science, University of Vermont, Vermont, USA
| | - Valerie Karl
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ann-Marie G de Lange
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Clinical Neurosciences, CHUV and University of Lausanne, Lausanne, Switzerland; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Kathryn L Mills
- Department of Psychology, University of Oregon, Eugene, OR, USA
| | - Christian K Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway
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9
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Gaser C, Kalc P, Cole JH. A perspective on brain-age estimation and its clinical promise. NATURE COMPUTATIONAL SCIENCE 2024:10.1038/s43588-024-00659-8. [PMID: 39048692 DOI: 10.1038/s43588-024-00659-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 06/12/2024] [Indexed: 07/27/2024]
Abstract
Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings.
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Affiliation(s)
- Christian Gaser
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany.
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.
- German Centre for Mental Health (DZPG), Jena-Halle-Magdeburg, Jena, Germany.
| | - Polona Kalc
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
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Huang H, Wang J, Dunk MM, Guo J, Dove A, Ma J, Bennett DA, Xu W. Association of Cardiovascular Health With Brain Age Estimated Using Machine Learning Methods in Middle-Aged and Older Adults. Neurology 2024; 103:e209530. [PMID: 38889383 PMCID: PMC11226327 DOI: 10.1212/wnl.0000000000209530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/05/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Cardiovascular health (CVH) has been associated with cognitive decline and dementia, but the extent to which CVH affects brain health remains unclear. We investigated the association of CVH, assessed using Life's Essential 8 (LE8), with neuroimaging-based brain age and brain-predicted age difference (brain-PAD). METHODS This longitudinal community-based study was based on UK Biobank participants aged 40-69 years who were free from dementia and other neurologic diseases at baseline. LE8 score at baseline was assessed with 8 measures and tertiled as low, moderate, and high CVH. Structural and functional brain MRI scans were performed approximately 9 years after baseline, and 1,079 measures from 6 neuroimaging modalities were used to model brain age. A Least Absolute Shrinkage and Selection Operator regression model was trained in 4,355 healthy participants and then used to calculate brain age and brain-PAD in the whole population. Data were analyzed using linear regression models. RESULTS The study included 32,646 participants (mean age at baseline 54.74 years; 53.44% female; mean LE8 score: 71.90). In multivariable-adjusted linear regression, higher LE8 score was associated with younger brain age (β [95% CI] -0.037 [-0.043 to -0.031]) and more negative brain-PAD (β [95% CI] -0.043 [-0.048 to -0.038]) (brain looks younger for chronological age). Compared with high CVH, low/moderate CVH was associated with older brain age (β [95% CI] 1.030 [0.852-1.208]/0.475 [0.303-0.647]) and increased brain-PAD (β [95% CI] 1.193 [1.029-1.357]/0.528 [0.370-0.686]). The associations between low CVH and older brain age/brain-PAD remained similar and significant in both middle-aged (β [95% CI] 1.199 [0.992-1.405]/1.351 [1.159-1.542]) and older adults (β [95% CI] 0.764 [0.417-1.110]/0.948 [0.632-1.263]). DISCUSSION Low CVH is associated with older brain age and greater brain-PAD, even among middle-aged adults. Our findings suggest that optimizing CVH could support brain health. The main limitation of our study is that the study sample was healthier than the general population, thus caution is required when generalizing our findings to other populations.
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Affiliation(s)
- Huijie Huang
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Jiao Wang
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Michelle M Dunk
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Jie Guo
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Abigail Dove
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Jun Ma
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - David A Bennett
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
| | - Weili Xu
- From the Department of Epidemiology and Biostatistics (H.H., J.M., W.X.), School of Public Health, Tianjin Medical University; Department of Epidemiology (J.W.), College of Preventive Medicine, Third Military Medical University, China; Aging Research Center (M.M.D., J.G., A.D., W.X.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition and Health (J.G.), China Agricultural University, Beijing, China; and Rush Alzheimer's Disease Center (D.A.B.), Rush University Medical Center, Chicago, IL
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11
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Lu H, Li J. MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters. J Cent Nerv Syst Dis 2024; 16:11795735241266556. [PMID: 39049837 PMCID: PMC11268046 DOI: 10.1177/11795735241266556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 06/02/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Brain age model, including estimated brain age and brain-predicted age difference (brain-PAD), has shown great potentials for serving as imaging markers for monitoring normal ageing, as well as for identifying the individuals in the pre-diagnostic phase of neurodegenerative diseases. PURPOSE This study aimed to investigate the brain age models in normal ageing and mild cognitive impairments (MCI) converters and their values in classifying MCI conversion. METHODS Pre-trained brain age model was constructed using the structural magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) project (N = 609). The tested brain age model was built using the baseline, 1-year and 3-year follow-up MRI data from normal ageing (NA) adults (n = 32) and MCI converters (n = 22) drew from the Open Access Series of Imaging Studies (OASIS-2). The quantitative measures of morphometry included total intracranial volume (TIV), gray matter volume (GMV) and cortical thickness. Brain age models were calculated based on the individual's morphometric features using the support vector machine (SVM) algorithm. RESULTS With comparable chronological age, MCI converters showed significant increased TIV-based (Baseline: P = 0.021; 1-year follow-up: P = 0.037; 3-year follow-up: P = 0.001) and left GMV-based brain age than NA adults at all time points. Higher brain-PAD scores were associated with worse global cognition. Acceptable classification performance of TIV-based (AUC = 0.698) and left GMV-based brain age (AUC = 0.703) was found, which could differentiate the MCI converters from NA adults at the baseline. CONCLUSIONS This is the first demonstration that MRI-informed brain age models exhibit feature-specific patterns. The greater GMV-based brain age observed in MCI converters may provide new evidence for identifying the individuals at the early stage of neurodegeneration. Our findings added value to existing quantitative imaging markers and might help to improve disease monitoring and accelerate personalized treatments in clinical practice.
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Affiliation(s)
- Hanna Lu
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jing Li
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China
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12
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Zhu R, Qu J, Xu G, Wu Y, Xin J, Wang D. Clinical and multimodal imaging features of adult-onset neuronal intranuclear inclusion disease. Neurol Sci 2024:10.1007/s10072-024-07699-y. [PMID: 39023713 DOI: 10.1007/s10072-024-07699-y] [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: 04/19/2024] [Accepted: 07/09/2024] [Indexed: 07/20/2024]
Abstract
OBJECTIVES This study aimed to analyze the clinical and multimodal imaging manifestations of adult-onset neuronal intranuclear inclusion disease (NIID) patients and to investigate NIID-specific neuroimaging biomarkers. METHODS Forty patients were retrospectively enrolled from the Qilu Hospital of Shandong University. We analyzed the clinical and imaging characteristics of 40 adult-onset NIID patients and investigated the correlation between these characteristics and genetic markers and neuropsychological scores. We further explored NIID-specific alterations using multimodal imaging indices, including diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS), and brain age estimation. In addition, we summarized the dynamic evolution pattern of NIID by examining the changes in diffusion weighted imaging (DWI) signals over time. RESULTS The NIID patients' ages ranged from 31 to 77 years. Cognitive impairment was the most common symptom (30/40, 75.0%), while some patients (18/40, 45.0%) initially presented with episodic symptoms such as headache (10/40, 25.0%). Patients with cognitive impairment symptoms had more cerebral white matter damage (χ2 = 11.475, P = 0.009). The most prevalent imaging manifestation was a high signal on DWI in the corticomedullary junction area, which was observed in 80.0% (32/40) of patients. In addition, the DWI dynamic evolution patterns could be classified into four main patterns. Diffusion tensor imaging (DTI) revealed extensive thinning of cerebral white matter fibers. The estimated brain age surpassed the patient's chronological age, signifying advanced brain aging in NIID patients. CONCLUSIONS The clinical manifestations of NIID exhibit significant variability, usually leading to misdiagnosis. Our results provided new imaging perspectives for accurately diagnosing and exploring this disease's neuropathological mechanisms.
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Affiliation(s)
- Rui Zhu
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Junyu Qu
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Guihua Xu
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Yongsheng Wu
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Jiaxiang Xin
- MR Research Collaboration, Siemens Healthineers Ltd, Shanghai, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, China.
- Qilu Medical Imaging Institute of Shandong University, Jinan, 250012, China.
- Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging (MF), Jinan, 250012, China.
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13
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How EH, Chin SM, Teo CH, Parhar IS, Soga T. Accelerated biological brain aging in major depressive disorder. Rev Neurosci 2024; 0:revneuro-2024-0025. [PMID: 39002110 DOI: 10.1515/revneuro-2024-0025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 06/26/2024] [Indexed: 07/15/2024]
Abstract
Major depressive disorder (MDD) patients commonly encounter multiple types of functional disabilities, such as social, physical, and role functioning. MDD is related to an accreted risk of brain atrophy, aging-associated brain diseases, and mortality. Based on recently available studies, there are correlations between notable biological brain aging and MDD in adulthood. Despite several clinical and epidemiological studies that associate MDD with aging phenotypes, the underlying mechanisms in the brain remain unknown. The key areas in the study of biological brain aging in MDD are structural brain aging, impairment in functional connectivity, and the impact on cognitive function and age-related disorders. Various measurements have been used to determine the severity of brain aging, such as the brain age gap estimate (BrainAGE) or brain-predicted age difference (BrainPAD). This review summarized the current results of brain imaging data on the similarities between the manifestation of brain structural changes and the age-associated processes in MDD. This review also provided recent evidence of BrainPAD or BrainAGE scores in MDD, brain structural abnormalities, and functional connectivity, which are commonly observed between MDD and age-associated processes. It serves as a basis of current reference for future research on the potential areas of investigation for diagnostic, preventive, and potentially therapeutic purposes for brain aging in MDD.
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Affiliation(s)
- Eng Han How
- 65210 Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia , Jalan Lagoon Selatan, Bandar Sunway, 47500, Selangor, Malaysia
| | - Shar-Maine Chin
- 65210 Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia , Jalan Lagoon Selatan, Bandar Sunway, 47500, Selangor, Malaysia
| | - Chuin Hau Teo
- 65210 Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia , Jalan Lagoon Selatan, Bandar Sunway, 47500, Selangor, Malaysia
| | - Ishwar S Parhar
- Center Initiatives for Training International Researchers (CiTIR), University of Toyama, Gofuku, 930-8555 Toyama, Japan
| | - Tomoko Soga
- 65210 Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia , Jalan Lagoon Selatan, Bandar Sunway, 47500, Selangor, Malaysia
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14
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Yu Y, Cui H, Haas SS, New F, Sanford N, Yu K, Zhan D, Yang G, Gao J, Wei D, Qiu J, Banaj N, Boomsma DI, Breier A, Brodaty H, Buckner RL, Buitelaar JK, Cannon DM, Caseras X, Clark VP, Conrod PJ, Crivello F, Crone EA, Dannlowski U, Davey CG, de Haan L, de Zubicaray GI, Di Giorgio A, Fisch L, Fisher SE, Franke B, Glahn DC, Grotegerd D, Gruber O, Gur RE, Gur RC, Hahn T, Harrison BJ, Hatton S, Hickie IB, Hulshoff Pol HE, Jamieson AJ, Jernigan TL, Jiang J, Kalnin AJ, Kang S, Kochan NA, Kraus A, Lagopoulos J, Lazaro L, McDonald BC, McDonald C, McMahon KL, Mwangi B, Piras F, Rodriguez‐Cruces R, Royer J, Sachdev PS, Satterthwaite TD, Saykin AJ, Schumann G, Sevaggi P, Smoller JW, Soares JC, Spalletta G, Tamnes CK, Trollor JN, Van't Ent D, Vecchio D, Walter H, Wang Y, Weber B, Wen W, Wierenga LM, Williams SCR, Wu M, Zunta‐Soares GB, Bernhardt B, Thompson P, Frangou S, Ge R. Brain-age prediction: Systematic evaluation of site effects, and sample age range and size. Hum Brain Mapp 2024; 45:e26768. [PMID: 38949537 PMCID: PMC11215839 DOI: 10.1002/hbm.26768] [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: 12/19/2023] [Revised: 05/15/2024] [Accepted: 06/10/2024] [Indexed: 07/02/2024] Open
Abstract
Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.
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Affiliation(s)
- Yuetong Yu
- Djavad Mowafaghian Centre for Brain Health, Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Hao‐Qi Cui
- Djavad Mowafaghian Centre for Brain Health, Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Shalaila S. Haas
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Faye New
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Nicole Sanford
- Djavad Mowafaghian Centre for Brain Health, Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Kevin Yu
- Djavad Mowafaghian Centre for Brain Health, Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Denghuang Zhan
- School of Population and Public HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Guoyuan Yang
- Advanced Research Institute of Multidisciplinary Sciences, School of Medical Technology, School of Life ScienceBeijing Institute of TechnologyBeijingChina
| | - Jia‐Hong Gao
- Center for MRI ResearchPeking UniversityBeijingChina
| | - Dongtao Wei
- School of PsychologySouthwest UniversityChongqingChina
| | - Jiang Qiu
- School of PsychologySouthwest UniversityChongqingChina
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | - Dorret I. Boomsma
- Department of Biological PsychologyVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Alan Breier
- Department of PsychiatryIndiana University School of MedicineIndianapolisIndianaUSA
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | - Randy L. Buckner
- Department of Psychology, Center for Brain ScienceHarvard UniversityBostonMassachusettsUSA
- Department of Psychiatry, Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jan K. Buitelaar
- Department of Cognitive NeuroscienceDonders Institute for Brain, Cognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
| | - Dara M. Cannon
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience CentreCollege of Medicine Nursing and Health Sciences, University of GalwayGalwayIreland
| | - Xavier Caseras
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical NeurosciencesCardiff UniversityCardiffUK
| | - Vincent P. Clark
- Psychology Clinical Neuroscience Center, Department of PsychologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Patricia J. Conrod
- Department of Psychiatry and AddictionUniversité de Montréal, CHU Ste JustineMontrealQuebecCanada
| | - Fabrice Crivello
- Institut des Maladies NeurodégénérativesUniversité de BordeauxBordeauxFrance
| | - Eveline A. Crone
- Department of Psychology, Faculty of Social SciencesLeiden UniversityLeidenThe Netherlands
- Erasmus School of Social and Behavioral SciencesErasmus University RotterdamRotterdamThe Netherlands
| | - Udo Dannlowski
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | | | - Lieuwe de Haan
- Department of PsychiatryAmsterdam UMCAmsterdamThe Netherlands
| | - Greig I. de Zubicaray
- Faculty of Health, School of Psychology & CounsellingQueensland University of TechnologyBrisbaneQueenslandAustralia
| | | | - Lukas Fisch
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Simon E. Fisher
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - Barbara Franke
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
- Department of Cognitive NeuroscienceRadboud University Medical CenterNijmegenThe Netherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenThe Netherlands
| | - David C. Glahn
- Department of Psychiatry, Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Dominik Grotegerd
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General PsychiatryHeidelberg UniversityHeidelbergGermany
| | - Raquel E. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Tim Hahn
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Ben J. Harrison
- Department of PsychiatryThe University of MelbourneMelbourneVictoriaAustralia
| | - Sean Hatton
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
| | - Ian B. Hickie
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
| | - Hilleke E. Hulshoff Pol
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of PsychologyUtrecht UniversityUtrechtThe Netherlands
- Department of PsychiatryUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Alec J. Jamieson
- Department of PsychiatryThe University of MelbourneMelbourneVictoriaAustralia
| | - Terry L. Jernigan
- Center for Human Development, Departments of Cognitive Science, Psychiatry, and RadiologyUniversity of CaliforniaSan DiegoCaliforniaUSA
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | - Andrew J. Kalnin
- Department of RadiologyThe Ohio State University College of MedicineColumbusOhioUSA
| | - Sim Kang
- West Region, Institute of Mental HealthSingaporeSingapore
| | - Nicole A. Kochan
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | - Anna Kraus
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Jim Lagopoulos
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
| | - Luisa Lazaro
- Department of Child and Adolescent Psychiatry and PsychologyHospital Clínic, IDIBAPS, CIBERSAM, University of BarcelonaBarcelonaSpain
| | - Brenna C. McDonald
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience CentreCollege of Medicine Nursing and Health Sciences, University of GalwayGalwayIreland
| | - Katie L. McMahon
- School of Clinical Sciences, Centre for Biomedical TechnologiesQueensland University of TechnologyBrisbaneQueenslandAustralia
| | - Benson Mwangi
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | | | - Jessica Royer
- McConnell Brain Imaging CentreMcGill UniversityMontrealQuebecCanada
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | | | - Andrew J. Saykin
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Gunter Schumann
- Department of PsychiatryCCM, Charite Universitaetsmedizin BerlinBerlinGermany
- Centre for Population Neuroscience and Stratified Medicine (PONS), ISTBIFudan UniversityShanghaiChina
| | - Pierluigi Sevaggi
- Department of Translational Biomedicine and NeuroscienceUniversity of Bari Aldo MoroBariItaly
| | - Jordan W. Smoller
- Department of Psychiatry, Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Center for Genomic MedicineMassachusetts General HospitalBostonMassachusettsUSA
- Center for Precision PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Jair C. Soares
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | - Christian K. Tamnes
- PROMENTA Research Center, Department of PsychologyUniversity of OsloOsloNorway
| | - Julian N. Trollor
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNew South WalesAustralia
- Department of Developmental Disability Neuropsychiatry, School of Clinical MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | - Dennis Van't Ent
- Department of Biological PsychologyVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin BerlinCorporate Member of FU Berlin and Humboldt Universität zu BerlinBerlinGermany
| | - Yang Wang
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Bernd Weber
- Institute for Experimental Epileptology and Cognition ResearchUniversity of Bonn and University Hospital BonnBonnGermany
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | - Lara M. Wierenga
- Department of Psychology, Faculty of Social SciencesLeiden UniversityLeidenThe Netherlands
| | - Steven C. R. Williams
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Mon‐Ju Wu
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Giovana B. Zunta‐Soares
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Boris Bernhardt
- McConnell Brain Imaging CentreMcGill UniversityMontrealQuebecCanada
| | - Paul Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Sophia Frangou
- Djavad Mowafaghian Centre for Brain Health, Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ruiyang Ge
- Djavad Mowafaghian Centre for Brain Health, Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
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Nakaya M, Sato N, Suzuki F, Maikusa N, Matsuda H, Kimura Y, Shigemoto Y, Chiba E, Ota M, Yamamura T, Sato W, Okamoto T, Abe O. Multimodal imaging analyses in neuromyelitis optica spectrum disorder with or without visual disturbance. J Neurol Sci 2024; 462:123090. [PMID: 38865876 DOI: 10.1016/j.jns.2024.123090] [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: 11/27/2023] [Revised: 05/10/2024] [Accepted: 06/05/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND AND PURPOSE Neuromyelitis optica spectrum disorder is a demyelinating and inflammatory affliction that often leads to visual disturbance. Various imaging techniques, including free-water imaging, have been used to determine neuroinflammation and degeneration. Therefore, this study aimed at determining multimodal imaging differences between patients with neuromyelitis optica spectrum disorder, especially those with visual disturbance, and healthy controls. MATERIALS AND METHODS Eighty-five neuromyelitis optica spectrum disorder patients and 89 age- and sex-matched healthy controls underwent 3-T magnetic resonance imaging (MRI). We analyzed adjusted brain-predicted age difference, voxel-based morphometry, and free-water-corrected diffusion tensor imaging (DTI) by tract-based spatial statistics in each patient group (MRI-positive/negative neuromyelitis optica spectrum disorder patients with or without a history of visual disturbance) compared with the healthy control group. RESULTS MRI-positive neuromyelitis optica spectrum disorder patients exhibited reduced volumes of the bilateral thalamus. Tract-based spatial statistics showed diffuse white matter abnormalities in all DTI metrics in MRI-positive neuromyelitis optica spectrum disorder patients with a history of visual disturbance. In MRI-negative neuromyelitis optica spectrum disorder patients with a history of visual disturbance, voxel-based morphometry showed volume reduction of bilateral thalami and optic radiations, and tract-based spatial statistics revealed significantly lower free-water-corrected fractional anisotropy and higher mean diffusivity in the posterior dominant distributions, including the optic nerve radiation. CONCLUSION Free-water-corrected DTI and voxel-based morphometry analyses may reflect symptoms of visual disturbance in neuromyelitis optica spectrum disorder.
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Affiliation(s)
- Moto Nakaya
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan; Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Noriko Sato
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan.
| | - Fumio Suzuki
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan; Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Norihide Maikusa
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Hiroshi Matsuda
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan; Department of Biofunctional Imaging, Fukushima Medical University, 1 Hikariga-Oka, Fukushima 960-1295, Japan
| | - Yukio Kimura
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Yoko Shigemoto
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Emiko Chiba
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Miho Ota
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan; Department of Neuropsychiatry, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki 305-8576, Japan
| | - Takashi Yamamura
- Department of Immunology, Institute of Neuroscience, National Center of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Wakiro Sato
- Department of Immunology, Institute of Neuroscience, National Center of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Tomoko Okamoto
- Department of Neurology, National Center of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
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16
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Zhang D, She Y, Sun J, Cui Y, Yang X, Zeng X, Qin W. Brain Age Estimation from Overnight Sleep Electroencephalography with Multi-Flow Sequence Learning. Nat Sci Sleep 2024; 16:879-896. [PMID: 38974693 PMCID: PMC11227046 DOI: 10.2147/nss.s463495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 06/19/2024] [Indexed: 07/09/2024] Open
Abstract
Purpose This study aims to improve brain age estimation by developing a novel deep learning model utilizing overnight electroencephalography (EEG) data. Methods We address limitations in current brain age prediction methods by proposing a model trained and evaluated on multiple cohort data, covering a broad age range. The model employs a one-dimensional Swin Transformer to efficiently extract complex patterns from sleep EEG signals and a convolutional neural network with attentional mechanisms to summarize sleep structural features. A multi-flow learning-based framework attentively merges these two features, employing sleep structural information to direct and augment the EEG features. A post-prediction model is designed to integrate the age-related features throughout the night. Furthermore, we propose a DecadeCE loss function to address the problem of an uneven age distribution. Results We utilized 18,767 polysomnograms (PSGs) from 13,616 subjects to develop and evaluate the proposed model. The model achieves a mean absolute error (MAE) of 4.19 and a correlation of 0.97 on the mixed-cohort test set, and an MAE of 6.18 years and a correlation of 0.78 on an independent test set. Our brain age estimation work reduced the error by more than 1 year compared to other studies that also used EEG, achieving the level of neuroimaging. The estimated brain age index demonstrated longitudinal sensitivity and exhibited a significant increase of 1.27 years in individuals with psychiatric or neurological disorders relative to healthy individuals. Conclusion The multi-flow deep learning model proposed in this study, based on overnight EEG, represents a more accurate approach for estimating brain age. The utilization of overnight sleep EEG for the prediction of brain age is both cost-effective and adept at capturing dynamic changes. These findings demonstrate the potential of EEG in predicting brain age, presenting a noninvasive and accessible method for assessing brain aging.
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Affiliation(s)
- Di Zhang
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, People’s Republic of China
- Intelligent Non-Invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi’an, People’s Republic of China
| | - Yichong She
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, People’s Republic of China
- Intelligent Non-Invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi’an, People’s Republic of China
| | - Jinbo Sun
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, People’s Republic of China
- Intelligent Non-Invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi’an, People’s Republic of China
| | - Yapeng Cui
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, People’s Republic of China
- Intelligent Non-Invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi’an, People’s Republic of China
| | - Xuejuan Yang
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, People’s Republic of China
- Intelligent Non-Invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi’an, People’s Republic of China
| | - Xiao Zeng
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, People’s Republic of China
- Intelligent Non-Invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi’an, People’s Republic of China
| | - Wei Qin
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, People’s Republic of China
- Intelligent Non-Invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi’an, People’s Republic of China
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17
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Calabrese M, Preziosa P, Scalfari A, Colato E, Marastoni D, Absinta M, Battaglini M, De Stefano N, Di Filippo M, Hametner S, Howell OW, Inglese M, Lassmann H, Martin R, Nicholas R, Reynolds R, Rocca MA, Tamanti A, Vercellino M, Villar LM, Filippi M, Magliozzi R. Determinants and Biomarkers of Progression Independent of Relapses in Multiple Sclerosis. Ann Neurol 2024; 96:1-20. [PMID: 38568026 DOI: 10.1002/ana.26913] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/04/2024] [Accepted: 02/15/2024] [Indexed: 06/20/2024]
Abstract
Clinical, pathological, and imaging evidence in multiple sclerosis (MS) suggests that a smoldering inflammatory activity is present from the earliest stages of the disease and underlies the progression of disability, which proceeds relentlessly and independently of clinical and radiological relapses (PIRA). The complex system of pathological events driving "chronic" worsening is likely linked with the early accumulation of compartmentalized inflammation within the central nervous system as well as insufficient repair phenomena and mitochondrial failure. These mechanisms are partially lesion-independent and differ from those causing clinical relapses and the formation of new focal demyelinating lesions; they lead to neuroaxonal dysfunction and death, myelin loss, glia alterations, and finally, a neuronal network dysfunction outweighing central nervous system (CNS) compensatory mechanisms. This review aims to provide an overview of the state of the art of neuropathological, immunological, and imaging knowledge about the mechanisms underlying the smoldering disease activity, focusing on possible early biomarkers and their translation into clinical practice. ANN NEUROL 2024;96:1-20.
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Affiliation(s)
- Massimiliano Calabrese
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Antonio Scalfari
- Centre of Neuroscience, Department of Medicine, Imperial College, London, UK
| | - Elisa Colato
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Damiano Marastoni
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Martina Absinta
- Translational Neuropathology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Battaglini
- Siena Imaging S.r.l., Siena, Italy
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Massimiliano Di Filippo
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Simon Hametner
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Owain W Howell
- Institute of Life Sciences, Swansea University Medical School, Swansea, UK
| | - Matilde Inglese
- Dipartimento di neuroscienze, riabilitazione, oftalmologia, genetica e scienze materno-infantili - DINOGMI, University of Genova, Genoa, Italy
| | - Hans Lassmann
- Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Roland Martin
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
- Therapeutic Design Unit, Center for Molecular Medicine, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
- Cellerys AG, Schlieren, Switzerland
| | - Richard Nicholas
- Department of Brain Sciences, Faculty of Medicine, Burlington Danes, Imperial College London, London, UK
| | - Richard Reynolds
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, London, UK
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Agnese Tamanti
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Marco Vercellino
- Multiple Sclerosis Center & Neurologia I U, Department of Neuroscience, University Hospital AOU Città della Salute e della Scienza di Torino, Turin, Italy
| | - Luisa Maria Villar
- Department of Immunology, Ramon y Cajal University Hospital. IRYCIS. REI, Madrid, Spain
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Roberta Magliozzi
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
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18
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Vakli P, Weiss B, Rozmann D, Erőss G, Nárai Á, Hermann P, Vidnyánszky Z. The effect of head motion on brain age prediction using deep convolutional neural networks. Neuroimage 2024; 294:120646. [PMID: 38750907 DOI: 10.1016/j.neuroimage.2024.120646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/10/2024] [Accepted: 05/12/2024] [Indexed: 05/23/2024] Open
Abstract
Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age-referred to as brain-predicted age difference (brain-PAD)-is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.
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Affiliation(s)
- Pál Vakli
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary.
| | - Béla Weiss
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary; Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Óbuda University, Budapest 1034, Hungary.
| | - Dorina Rozmann
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - György Erőss
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Ádám Nárai
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary; Doctoral School of Biology and Sportbiology, Institute of Biology, Faculty of Sciences, University of Pécs, Pécs 7624, Hungary
| | - Petra Hermann
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary.
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19
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Liu YS, Baxi M, Madan CR, Zhan K, Makris N, Rosene DL, Killiany RJ, Cetin-Karayumak S, Pasternak O, Kubicki M, Cao B. Brain age of rhesus macaques over the lifespan. Neurobiol Aging 2024; 139:73-81. [PMID: 38643691 DOI: 10.1016/j.neurobiolaging.2024.02.014] [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: 09/11/2023] [Revised: 02/27/2024] [Accepted: 02/28/2024] [Indexed: 04/23/2024]
Abstract
Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level biological age prediction and identify key brain regions responsible for the prediction. In this study, we present the methodology of constructing a rhesus macaque brain age model using a machine learning algorithm and discuss the key predictive brain regions in comparison to the human brain, to shed light on cross-species primate similarities and differences. Structural information of the brain (e.g., parcellated volumes) from brain magnetic resonance imaging of 43 rhesus macaques were used to develop brain atlas-based features to build a brain age model that predicts biological age. The best-performing model used 22 selected features and achieved an R2 of 0.72. We also identified interpretable predictive brain features including Right Fronto-orbital Cortex, Right Frontal Pole, Right Inferior Lateral Parietal Cortex, and Bilateral Posterior Central Operculum. Our findings provide converging evidence of the parallel and comparable brain regions responsible for both non-human primates and human biological age prediction.
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Affiliation(s)
- Yang S Liu
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Madhura Baxi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Kevin Zhan
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Nikolaos Makris
- Department of Psychiatry, Center for Morphometric Analysis, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Douglas L Rosene
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Ronald J Killiany
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Center for Morphometric Analysis, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Bo Cao
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada; Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
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20
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Dabbagh A, Horn U, Kaptan M, Mildner T, Müller R, Lepsien J, Weiskopf N, Brooks JCW, Finsterbusch J, Eippert F. Reliability of task-based fMRI in the dorsal horn of the human spinal cord. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.22.572825. [PMID: 38187724 PMCID: PMC10769329 DOI: 10.1101/2023.12.22.572825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The application of functional magnetic resonance imaging (fMRI) to the human spinal cord is still a relatively small field of research and faces many challenges. Here we aimed to probe the limitations of task-based spinal fMRI at 3T by investigating the reliability of spinal cord blood oxygen level dependent (BOLD) responses to repeated nociceptive stimulation across two consecutive days in 40 healthy volunteers. We assessed the test-retest reliability of subjective ratings, autonomic responses, and spinal cord BOLD responses to short heat pain stimuli (1s duration) using the intraclass correlation coefficient (ICC). At the group level, we observed robust autonomic responses as well as spatially specific spinal cord BOLD responses at the expected location, but no spatial overlap in BOLD response patterns across days. While autonomic indicators of pain processing showed good-to-excellent reliability, both β-estimates and z-scores of task-related BOLD responses showed poor reliability across days in the target region (gray matter of the ipsilateral dorsal horn). When taking into account the sensitivity of gradient-echo echo planar imaging (GE-EPI) to draining vein signals by including the venous plexus in the analysis, we observed BOLD responses with fair reliability across days. Taken together, these results demonstrate that heat pain stimuli as short as one second are able to evoke a robust and spatially specific BOLD response, which is however strongly variable within participants across time, resulting in low reliability in the dorsal horn gray matter. Further improvements in data acquisition and analysis techniques are thus necessary before event-related spinal cord fMRI as used here can be reliably employed in longitudinal designs or clinical settings.
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Affiliation(s)
- Alice Dabbagh
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ulrike Horn
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Merve Kaptan
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, CA, USA
| | - Toralf Mildner
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Roland Müller
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jöran Lepsien
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, University of Leipzig, Leipzig, Germany
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Jonathan C W Brooks
- School of Psychology, University of East Anglia Wellcome Wolfson Brain Imaging Centre (UWWBIC), Norwich, United Kingdom
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Falk Eippert
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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21
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Schuurmans IK, Mulder RH, Baltramonaityte V, Lahtinen A, Qiuyu F, Rothmann LM, Staginnus M, Tuulari J, Burt SA, Buss C, Craig JM, Donald KA, Felix JF, Freeman TP, Grassi-Oliveira R, Huels A, Hyde LW, Jones SA, Karlsson H, Karlsson L, Koen N, Lawn W, Mitchell C, Monk CS, Mooney MA, Muetzel R, Nigg JT, Belangero SIN, Notterman D, O'Connor T, O'Donnell KJ, Pan PM, Paunio T, Ryabinin P, Saffery R, Salum GA, Seal M, Silk TJ, Stein DJ, Zar H, Walton E, Cecil CAM. Consortium Profile: The Methylation, Imaging and NeuroDevelopment (MIND) Consortium. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.23.24309353. [PMID: 38978656 PMCID: PMC11230303 DOI: 10.1101/2024.06.23.24309353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Epigenetic processes, such as DNA methylation, show potential as biological markers and mechanisms underlying gene-environment interplay in the prediction of mental health and other brain-based phenotypes. However, little is known about how peripheral epigenetic patterns relate to individual differences in the brain itself. An increasingly popular approach to address this is by combining epigenetic and neuroimaging data; yet, research in this area is almost entirely comprised of cross-sectional studies in adults. To bridge this gap, we established the Methylation, Imaging and NeuroDevelopment (MIND) Consortium, which aims to bring a developmental focus to the emerging field of Neuroimaging Epigenetics by (i) promoting collaborative, adequately powered developmental research via multi-cohort analyses; (ii) increasing scientific rigor through the establishment of shared pipelines and open science practices; and (iii) advancing our understanding of DNA methylation-brain dynamics at different developmental periods (from birth to emerging adulthood), by leveraging data from prospective, longitudinal pediatric studies. MIND currently integrates 15 cohorts worldwide, comprising (repeated) measures of DNA methylation in peripheral tissues (blood, buccal cells, and saliva) and neuroimaging by magnetic resonance imaging across up to five time points over a period of up to 21 years (Npooled DNAm = 11,299; Npooled neuroimaging = 10,133; Npooled combined = 4,914). By triangulating associations across multiple developmental time points and study types, we hope to generate new insights into the dynamic relationships between peripheral DNA methylation and the brain, and how these ultimately relate to neurodevelopmental and psychiatric phenotypes.
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22
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Cho HH, Kim J, Na I, Song HN, Choi JU, Baek IY, Lee JE, Chung JW, Kim CK, Oh K, Bang OY, Kim GM, Seo WK, Park H. Predicting cerebrovascular age and its clinical relevance: Modeling using 3D morphological features of brain vessels. Heliyon 2024; 10:e32375. [PMID: 38947444 PMCID: PMC11214500 DOI: 10.1016/j.heliyon.2024.e32375] [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: 02/06/2024] [Revised: 05/31/2024] [Accepted: 06/03/2024] [Indexed: 07/02/2024] Open
Abstract
Aging manifests as many phenotypes, among which age-related changes in brain vessels are important, but underexplored. Thus, in the present study, we constructed a model to predict age using cerebrovascular morphological features, further assessing their clinical relevance using a novel pipeline. Age prediction models were first developed using data from a normal cohort (n = 1181), after which their relevance was tested in two stroke cohorts (n = 564 and n = 455). Our novel pipeline adapted an existing framework to compute generic vessel features for brain vessels, resulting in 126 morphological features. We further built various machine learning models to predict age using only clinical factors, only brain vessel features, and a combination of both. We further assessed deviation from healthy aging using the age gap and explored its clinical relevance by correlating the predicted age and age gap with various risk factors. The models constructed using only brain vessel features and those combining clinical factors with vessel features were better predictors of age than the clinical factor-only model (r = 0.37, 0.48, and 0.26, respectively). Predicted age was associated with many known clinical factors, and the associations were stronger for the age gap in the normal cohort. The age gap was also associated with important factors in the pooled cohort atherosclerotic cardiovascular disease risk score and white matter hyperintensity measurements. Cerebrovascular age, computed using the morphological features of brain vessels, could serve as a potential individualized marker for the early detection of various cerebrovascular diseases.
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Affiliation(s)
- Hwan-ho Cho
- Department of Electronics Engineering, Incheon National University, Incheon, South Korea
| | - Jonghoon Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Inye Na
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Ha-Na Song
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jong-Un Choi
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea
| | - In-Young Baek
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Ji-Eun Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jong-Won Chung
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Chi-Kyung Kim
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Kyungmi Oh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Oh-Young Bang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Gyeong-Moon Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Woo-Keun Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea
| | - Hyunjin Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
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23
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Yook S, Choi SJ, Lee H, Joo EY, Kim H. Long-term night-shift work is associated with accelerates brain aging and worsens N3 sleep in female nurses. Sleep Med 2024; 121:69-76. [PMID: 38936046 DOI: 10.1016/j.sleep.2024.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 06/12/2024] [Accepted: 06/13/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Shift work disrupts circadian rhythms and alters sleep patterns, resulting in various health problems. To quantitatively assess the impact of shift work on brain health, we evaluated the brain age index (BAI) derived from sleep electroencephalography (EEG) results in night-shift workers and compared it with that in daytime workers. METHODS We studied 45 female night shift nurses (mean age: 28.2 ± 3.3 years) and 44 female daytime workers (30.5 ± 4.7 years). Sleep EEG data were analyzed to calculate BAI. The BAI of night shift workers who were asleep during the daytime with those of daytime workers who were asleep at night were statistically compared to explore associations between BAI, duration of shift work, and sleep quality. RESULTS Night-shift workers exhibited significantly higher BAI (2.14 ± 6.04 vs. 0 ± 5.35), suggesting accelerated brain aging and altered sleep architecture, including reduced delta and sigma wave frequency activity during non-rapid eye movement sleep than daytime workers. Furthermore, poor deep sleep quality, indicated by a higher percentage of N1, lower percentage of N3, and higher arousal index, was associated with increased BAI among shift workers. Additionally, a longer duration of night-shift work was correlated with increased BAI, particularly in older shift workers. CONCLUSION Night-shift work, especially over extended periods, may be associated with accelerated brain aging, as indicated by higher BAI and alterations in sleep architecture. Interventions are necessary to mitigate the health impacts of shift work. Further research on the long-term effects and potential strategies for sleep improvement and mitigating brain aging in shift workers is warranted.
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Affiliation(s)
- Soonhyun Yook
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90033, USA
| | - Su Jung Choi
- Graduate School of Clinical Nursing Science, Sungkyunkwan University, Seoul, 03063, South Korea
| | - Hanul Lee
- Department of Neurology, Samsung Medical Center, Seoul, 06351, South Korea
| | - Eun Yeon Joo
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Samsung Biomedical Research Institute, School of Medicine, Sungkyunkwan University, Seoul, 06351, South Korea.
| | - Hosung Kim
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90033, USA
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24
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Wittens MMJ, Denissen S, Sima DM, Fransen E, Niemantsverdriet E, Bastin C, Benoit F, Bergmans B, Bier JC, de Deyn PP, Deryck O, Hanseeuw B, Ivanoiu A, Picard G, Ribbens A, Salmon E, Segers K, Sieben A, Struyfs H, Thiery E, Tournoy J, van Binst AM, Versijpt J, Smeets D, Bjerke M, Nagels G, Engelborghs S. Brain age as a biomarker for pathological versus healthy ageing - a REMEMBER study. Alzheimers Res Ther 2024; 16:128. [PMID: 38877568 PMCID: PMC11179390 DOI: 10.1186/s13195-024-01491-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] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 06/04/2024] [Indexed: 06/16/2024]
Abstract
OBJECTIVES This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions. METHODS The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict 'brain age' and 'brain predicted age difference' (BPAD = brain age-chronological age) for every subject. RESULTS MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p < .001) and BPAD (r = -0.26,p < .001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p = .014) and moderate (p = .040) drinkers. CONCLUSIONS Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health.
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Affiliation(s)
- Mandy M J Wittens
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
| | - Stijn Denissen
- icometrix, Leuven, Belgium
- AIMS lab, Center for Neurosciences (C4N), Vrije Universiteit Brussel, UZ Brussel, Brussels, Belgium
| | - Diana M Sima
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- icometrix, Leuven, Belgium
| | - Erik Fransen
- Centre of Medical Genetics, University of Antwerp, and Antwerp University Hospital - UZA, Edegem, Belgium
| | | | - Christine Bastin
- GIGA-CRC-IVI, Liège University, Allée du Six Août, 8, Liège, 4000, Belgium
| | - Florence Benoit
- Geriatrics Department, Brugmann University Hospital, Universite Libre de Bruxelles, Brussels, Belgium
| | - Bruno Bergmans
- Neurology Department, AZ St-Jan Brugge, Brugge, Belgium
- Ghent University Hospital, Ghent, Belgium
| | - Jean-Christophe Bier
- Neurological department H. U. B. - Erasme Hospital - Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Peter Paul de Deyn
- Laboratory of Neurochemistry and Behavior, Experimental Neurobiology Unit, University of Antwerp, Antwerp, 2610, Belgium
- Memory Clinic, Ziekenhuisnetwerk, Antwerp, Belgium
| | - Olivier Deryck
- Neurology Department, AZ St-Jan Brugge, Brugge, Belgium
- Ghent University Hospital, Ghent, Belgium
| | - Bernard Hanseeuw
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, 1200, Belgium
- Department of Neurology, Clinique Universitaires Saint-Luc, Brussels, 1200, Belgium
- WELBIO Department, WEL Research Institute, Wavre, 1300, Belgium
| | - Adrian Ivanoiu
- Department of Neurology, Cliniques Universitaires St Luc, and Institute of Neuroscience, Université Catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Gaëtane Picard
- Department of Neurology, Clinique Saint-Pierre, Ottignies, Belgium
| | | | - Eric Salmon
- GIGA-CRC-IVI, Liège University, Allée du Six Août, 8, Liège, 4000, Belgium
- Department of Neurology, Memory Clinic, Centre Hospitalier Universitaire (CHU) Liège, Liège, Belgium
| | - Kurt Segers
- Memory Clinic - Neurology and Geriatrics Department, CHU Brugmann, Van Gehuchtenplein 4, Brussels, 1020, Belgium
| | - Anne Sieben
- Neuropathology Lab, IBB-NeuroBiobank BB190113, Born Bunge Institute, Antwerp, Belgium
- Department of Pathology, Antwerp University Hospital - UZA, Antwerp, Belgium
- Laboratory of Neurology, Translational Neurosciences, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Hanne Struyfs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Johnson and Johnson Innovative Medicine, Beerse, Belgium
| | - Evert Thiery
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | - Jos Tournoy
- Department of Chronic Diseases, Metabolism and Ageing, Geriatric Medicine and Memory Clinic, University Hospitals Leuven and KU Leuven, Louvain, Belgium
| | - Anne-Marie van Binst
- Radiology Department, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Jan Versijpt
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
| | - Dirk Smeets
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- icometrix, Leuven, Belgium
| | - Maria Bjerke
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- Department of Clinical Chemistry, Laboratory of Neurochemistry, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Guy Nagels
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- St. Edmund Hall, University of Oxford, Oxford, UK
- AIMS lab, Center for Neurosciences (C4N), Vrije Universiteit Brussel, UZ Brussel, Brussels, Belgium
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium.
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium.
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Xue L, Fu Y, Gao X, Feng G, Qian S, Wei L, Li L, Zhuo C, Zhang H, Tian M. [ 18F]FDG PET integrated with structural MRI for accurate brain age prediction. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06784-w. [PMID: 38839623 DOI: 10.1007/s00259-024-06784-w] [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: 01/29/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024]
Abstract
PURPOSE Brain aging is a complex and heterogeneous process characterized by both structural and functional decline. This study aimed to establish a novel deep learning (DL) method for predicting brain age by utilizing structural and metabolic imaging data. METHODS The dataset comprised participants from both the Universal Medical Imaging Diagnostic Center (UMIDC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The former recruited 395 normal control (NC) subjects, while the latter included 438 NC subjects, 51 mild cognitive impairment (MCI) subjects, and 56 Alzheimer's disease (AD) subjects. We developed a novel dual-pathway, 3D simple fully convolutional network (Dual-SFCNeXt) to estimate brain age using [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET) and structural magnetic resonance imaging (sMRI) images of NC subjects as input. Several prevailing DL models were trained and tested using either MRI or PET data for comparison. Model accuracies were evaluated using mean absolute error (MAE) and Pearson's correlation coefficient (r). Brain age gap (BAG), deviations of brain age from chronologic age, was correlated with cognitive assessments in MCI and AD subjects. RESULTS Both PET- and MRI-based models achieved high prediction accuracy. The leading model was the SFCNeXt (the single-pathway version) for PET (MAE = 2.92, r = 0.96) and MRI (MAE = 3.23, r = 0.95) on all samples. By integrating both PET and MRI images, the Dual-SFCNeXt demonstrated significantly improved accuracy (MAE = 2.37, r = 0.97) compared to all single-modality models. Significantly higher BAG was observed in both the AD (P < 0.0001) and MCI (P < 0.0001) groups compared to the NC group. BAG correlated significantly with Mini-Mental State Examination (MMSE) scores (r=-0.390 for AD, r=-0.436 for MCI) and the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) scores (r = 0.333 for AD, r = 0.372 for MCI). CONCLUSION The integration of [18F]FDG PET with structural MRI enhances the accuracy of brain age prediction, potentially introducing a new avenue for related multimodal brain age prediction studies.
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Affiliation(s)
- Le Xue
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yu Fu
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Gang Feng
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Shufang Qian
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Ling Wei
- Huashan Hospital & Human Phenome Institute, Fudan University, Shanghai, China
| | - Lanlan Li
- Huashan Hospital & Human Phenome Institute, Fudan University, Shanghai, China
| | - Cheng Zhuo
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hong Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, Zhejiang, China.
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, Zhejiang, China.
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Mei Tian
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, Zhejiang, China.
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, Zhejiang, China.
- Huashan Hospital & Human Phenome Institute, Fudan University, Shanghai, China.
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26
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Wang M, Wei M, Wang L, Song J, Rominger A, Shi K, Jiang J. Tau Protein Accumulation Trajectory-Based Brain Age Prediction in the Alzheimer's Disease Continuum. Brain Sci 2024; 14:575. [PMID: 38928575 PMCID: PMC11201453 DOI: 10.3390/brainsci14060575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 05/22/2024] [Accepted: 05/25/2024] [Indexed: 06/28/2024] Open
Abstract
Clinical cognitive advancement within the Alzheimer's disease (AD) continuum is intimately connected with sustained accumulation of tau protein pathology. The biological brain age and its gap show great potential for pathological risk and disease severity. In the present study, we applied multivariable linear support vector regression to train a normative brain age prediction model using tau brain images. We further assessed the predicted biological brain age and its gap for patients within the AD continuum. In the AD continuum, evaluated pathologic tau binding was found in the inferior temporal, parietal-temporal junction, precuneus/posterior cingulate, dorsal frontal, occipital, and inferior-medial temporal cortices. The biological brain age gaps of patients within the AD continuum were notably higher than those of the normal controls (p < 0.0001). Significant positive correlations were observed between the brain age gap and global tau protein accumulation levels for mild cognitive impairment (r = 0.726, p < 0.001), AD (r = 0.845, p < 0.001), and AD continuum (r = 0.797, p < 0.001). The pathologic tau-based age gap was significantly linked to neuropsychological scores. The proposed pathologic tau-based biological brain age model could track the tau protein accumulation trajectory of cognitive impairment and further provide a comprehensive quantification index for the tau accumulation risk.
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Affiliation(s)
- Min Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Min Wei
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, China
| | - Luyao Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Jun Song
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
- Computer Aided Medical Procedures, School of Computation, Information and Technology, Technical University of Munich, 85748 Munich, Germany
| | - Jiehui Jiang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
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27
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Han M. Predicted brain age (PBA), promising surrogate indicator for neurodevelopment in preterm baby: how to predict accurately? Eur Radiol 2024; 34:3598-3600. [PMID: 38078998 DOI: 10.1007/s00330-023-10481-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 11/05/2023] [Accepted: 11/25/2023] [Indexed: 06/12/2024]
Affiliation(s)
- Miran Han
- Department of Radiology, Ajou University Hospital, Ajou University School of Medicine, 164, World Cup-Ro, Yeongtong-Gu, Suwon, 16499, Republic of Korea.
- Department of Radiology, Graduate School of Kangwon National University, Chuncheon, Republic of Korea.
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28
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An WW, Bhowmik AC, Nelson CA, Wilkinson CL. Prediction of chronological age from resting-state EEG power in the first three years of life. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.31.24308275. [PMID: 38853932 PMCID: PMC11160894 DOI: 10.1101/2024.05.31.24308275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
The infant brain undergoes rapid and significant developmental changes in the first three years of life. Understanding these changes through the prediction of chronological age using neuroimaging data can provide insights into typical and atypical brain development. We utilized longitudinal resting-state EEG data from 457 typically developing infants, comprising 938 recordings, to develop age prediction models. The multilayer perceptron model demonstrated the highest accuracy with an R2 of 0.82 and a mean absolute error of 92.4 days. Aperiodic offset and periodic theta, alpha, and beta power were identified as key predictors of age via Shapley values. Application of the model to EEG data from infants later diagnosed with autism spectrum disorder or Down syndrome revealed significant underestimations of chronological age. This study establishes the feasibility of using EEG to assess brain maturation in early childhood and supports its potential as a clinical tool for early identification of alterations in brain development.
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Affiliation(s)
- Winko W. An
- Developmental Medicine, Boston Children’s Hospital, 300 Longwood Avenue, Boston, 02115, MA, USA
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, 300 Longwood Avenue, Boston, 02115, MA, USA
- Harvard Medical School, 25 Shattuck St, Boston, 02115, MA, USA
| | - Aprotim C. Bhowmik
- Developmental Medicine, Boston Children’s Hospital, 300 Longwood Avenue, Boston, 02115, MA, USA
| | - Charles A. Nelson
- Developmental Medicine, Boston Children’s Hospital, 300 Longwood Avenue, Boston, 02115, MA, USA
- Harvard Medical School, 25 Shattuck St, Boston, 02115, MA, USA
- Harvard Graduate School of Education, 13 Appian Way, Cambridge, 02138, MA, USA
| | - Carol L. Wilkinson
- Developmental Medicine, Boston Children’s Hospital, 300 Longwood Avenue, Boston, 02115, MA, USA
- Harvard Medical School, 25 Shattuck St, Boston, 02115, MA, USA
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29
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Liu M, Lu M, Kim SY, Lee HJ, Duffy BA, Yuan S, Chai Y, Cole JH, Wu X, Toga AW, Jahanshad N, Gano D, Barkovich AJ, Xu D, Kim H. Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates. Eur Radiol 2024; 34:3601-3611. [PMID: 37957363 PMCID: PMC11166741 DOI: 10.1007/s00330-023-10414-8] [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: 03/31/2023] [Revised: 09/06/2023] [Accepted: 09/16/2023] [Indexed: 11/15/2023]
Abstract
OBJECTIVES Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates.
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Affiliation(s)
- Mengting Liu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518107, China
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - Minhua Lu
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, 518060, China
| | - Sharon Y Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - Hyun Ju Lee
- Division of Neonatology, Department of Pediatrics, Hanyang University, Seoul, Korea
| | - Ben A Duffy
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - Shiyu Yuan
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - Yaqiong Chai
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Xiaotong Wu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518107, China
| | - Arthur W Toga
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - Neda Jahanshad
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - Dawn Gano
- Departments of Neurology and Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - Anthony James Barkovich
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Duan Xu
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Hosung Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA.
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30
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Hua JPY, Abram SV, Loewy RL, Stuart B, Fryer SL, Vinogradov S, Mathalon DH. Brain Age Gap in Early Illness Schizophrenia and the Clinical High-Risk Syndrome: Associations With Experiential Negative Symptoms and Conversion to Psychosis. Schizophr Bull 2024:sbae074. [PMID: 38815987 DOI: 10.1093/schbul/sbae074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
BACKGROUND AND HYPOTHESIS Brain development/aging is not uniform across individuals,spawning efforts to characterize brain age from a biological perspective to model the effects of disease and maladaptive life processes on the brain. The brain age gap represents the discrepancy between estimated brain biological age and chronological age (in this case, based on structural magnetic resonance imaging, MRI). Structural MRI studies report an increased brain age gap (biological age > chronological age) in schizophrenia, with a greater brain age gap related to greater negative symptom severity. Less is known regarding the nature of this gap early in schizophrenia (ESZ), if this gap represents a psychosis conversion biomarker in clinical high-risk (CHR-P) individuals, and how altered brain development and/or agingmap onto specific symptom facets. STUDY DESIGN Using structural MRI, we compared the brain age gap among CHR-P (n = 51), ESZ (n = 78), and unaffected comparison participants (UCP; n = 90), and examined associations with CHR-P psychosis conversion (CHR-P converters n = 10; CHR-P non-converters; n = 23) and positive and negative symptoms. STUDY RESULTS ESZ showed a greater brain age gap relative to UCP and CHR-P (Ps < .010). CHR-P individuals who converted to psychosis showed a greater brain age gap (P = .043) relative to CHR-P non-converters. A larger brain age gap in ESZ was associated with increased experiential (P = .008), but not expressive negative symptom severity. CONCLUSIONS Consistent with schizophrenia pathophysiological models positing abnormal brain maturation, results suggest abnormal brain development is present early in psychosis. An increased brain age gap may be especially relevant to motivational and functional deficits in schizophrenia.
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Affiliation(s)
- Jessica P Y Hua
- Sierra Pacific Mental Illness Research Education and Clinical Centers, San Francisco VA Medical Center, University of California, San Francisco, CA, USA
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Samantha V Abram
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Rachel L Loewy
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Barbara Stuart
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Susanna L Fryer
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sophia Vinogradov
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Daniel H Mathalon
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
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Matte Bon G, Kraft D, Comasco E, Derntl B, Kaufmann T. Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders. Biol Sex Differ 2024; 15:42. [PMID: 38750598 PMCID: PMC11097569 DOI: 10.1186/s13293-024-00615-1] [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: 08/10/2023] [Accepted: 04/29/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics. METHODS We here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV). RESULTS Limbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male-female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction. CONCLUSIONS Overall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.
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Affiliation(s)
- Gloria Matte Bon
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076, Tübingen, Germany.
- Department of Women's and Children's Health, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
| | - Dominik Kraft
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076, Tübingen, Germany
| | - Erika Comasco
- Department of Women's and Children's Health, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076, Tübingen, Germany
- German Center for Mental Health (DZPG), Partner Site Tübingen, Tübingen, Germany
| | - Tobias Kaufmann
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076, Tübingen, Germany.
- German Center for Mental Health (DZPG), Partner Site Tübingen, Tübingen, Germany.
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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Feng L, Ye Z, Du Z, Pan Y, Canida T, Ke H, Liu S, Chen S, Hong LE, Kochunov P, Chen J, Lei DK, Shenassa E, Ma T. Association between allostatic load and accelerated white matter brain aging: findings from the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.26.24301793. [PMID: 38343822 PMCID: PMC10854327 DOI: 10.1101/2024.01.26.24301793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
White matter (WM) brain age, a neuroimaging-derived biomarker indicating WM microstructural changes, helps predict dementia and neurodegenerative disorder risks. The cumulative effect of chronic stress on WM brain aging remains unknown. In this study, we assessed cumulative stress using a multi-system composite allostatic load (AL) index based on inflammatory, anthropometric, respiratory, lipidemia, and glucose metabolism measures, and investigated its association with WM brain age gap (BAG), computed from diffusion tensor imaging data using a machine learning model, among 22 951 European ancestries aged 40 to 69 (51.40% women) from UK Biobank. Linear regression, Mendelian randomization, along with inverse probability weighting and doubly robust methods, were used to evaluate the impact of AL on WM BAG adjusting for age, sex, socioeconomic, and lifestyle behaviors. We found increasing one AL score unit significantly increased WM BAG by 0.29 years in association analysis and by 0.33 years in Mendelian analysis. The age- and sex-stratified analysis showed consistent results among participants 45-54 and 55-64 years old, with no significant sex difference. This study demonstrated that higher chronic stress was significantly associated with accelerated brain aging, highlighting the importance of stress management in reducing dementia and neurodegenerative disease risks.
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Affiliation(s)
- Li Feng
- Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, Maryland, United States of America
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Zewen Du
- Department of Biostatistics, School of Global Public Health, New York University, New York, New York, United States of America
| | - Yezhi Pan
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Travis Canida
- Department of Mathematics, The college of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, Maryland, United States of America
| | - Hongjie Ke
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - L. Elliot Hong
- Louis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Peter Kochunov
- Louis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Jie Chen
- Department of Health Policy and Management, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - David K.Y. Lei
- Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, Maryland, United States of America
| | - Edmond Shenassa
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
- Maternal & Child Health Program, School of Public Health, University of Maryland, College Park, Maryland, United States of America
- Department of Epidemiology, School of Public Health, Brown University, Rhode Island, United States of America
- Department of Epidemiology & Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
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Yook S, Park HR, Joo EY, Kim H. Predicting the impact of CPAP on brain health: A study using the sleep EEG-derived brain age index. Ann Clin Transl Neurol 2024; 11:1172-1183. [PMID: 38396240 DOI: 10.1002/acn3.52032] [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: 11/17/2023] [Revised: 01/17/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
OBJECTIVE This longitudinal study investigated potential positive impact of CPAP treatment on brain health in individuals with obstructive sleep Apnea (OSA). To allow this, we aimed to employ sleep electroencephalogram (EEG)-derived brain age index (BAI) to quantify CPAP's impact on brain health and identify individually varying CPAP effects on brain aging using machine learning approaches. METHODS We retrospectively analyzed CPAP-treated (n = 98) and untreated OSA patients (n = 88) with a minimum 12-month follow-up of polysomnography. BAI was calculated by subtracting chronological age from the predicted brain age. To investigate BAI changes before and after CPAP treatment, we compared annual ΔBAI between CPAP-treated and untreated OSA patients. To identify individually varying CPAP effectiveness and factors influencing CPAP effectiveness, machine learning approaches were employed to predict which patient displayed positive outcomes (negative annual ΔBAI) based on their baseline clinical features. RESULTS CPAP-treated group showed lower annual ΔBAI than untreated (-0.6 ± 2.7 vs. 0.3 ± 2.6 years, p < 0.05). This BAI reduction with CPAP was reproduced independently in the Apnea, Bariatric surgery, and CPAP study cohort. Patients with more severe OSA at baseline displayed more positive annual ΔBAI (=accelerated brain aging) when untreated and displayed more negative annual ΔBAI (=decelerated brain aging) when CPAP-treated. Machine learning models achieved high accuracy (up to 86%) in predicting CPAP outcomes. INTERPRETATION CPAP treatment can alleviate brain aging in OSA, especially in severe cases. Sleep EEG-derived BAI has potential to assess CPAP's impact on brain health. The study provides insights into CPAP's effects and underscores BAI-based predictive modeling's utility in OSA management.
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Affiliation(s)
- Soonhyun Yook
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, 90033, USA
| | - Hea Ree Park
- Department of Neurology, Inje University College of Medicine, Ilsan Paik Hospital, Goyang, 10380, Korea
| | - Eun Yeon Joo
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Samsung Biomedical Research Institute, School of Medicine, Sungkyunkwan University, Seoul, 06351, Korea
| | - Hosung Kim
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, 90033, USA
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Valdes-Hernandez PA, Johnson AJ, Montesino-Goicolea S, Nodarse CL, Bashyam V, Davatzikos C, Fillingim RB, Cruz-Almeida Y. Accelerated Brain Aging Mediates the Association Between Psychological Profiles and Clinical Pain in Knee Osteoarthritis. THE JOURNAL OF PAIN 2024; 25:104423. [PMID: 37952863 PMCID: PMC11144298 DOI: 10.1016/j.jpain.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 10/12/2023] [Accepted: 11/02/2023] [Indexed: 11/14/2023]
Abstract
Chronic pain is driven by factors across the biopsychosocial spectrum. Previously, we demonstrated that magnetic resonance images (MRI)-based brain-predicted age differences (brain-PAD: brain-predicted age minus chronological age) were significantly associated with pain severity in individuals with chronic knee pain. We also previously identified four distinct, replicable, multidimensional psychological profiles significantly associated with clinical pain. The brain aging-psychological characteristics interface in persons with chronic pain promises elucidating factors contributing to their poor health outcomes, yet this relationship is barely understood. That is why we examined the interplay between the psychological profiles in participants having chronic knee pain impacting function, brain-PAD, and clinical pain severity. Controlling for demographics and MRI scanner, we compared the brain-PAD among psychological profiles at baseline (n = 164) and over two years (n = 90). We also explored whether profile-related differences in pain severity were mediated by brain-PAD. Brain-PAD differed significantly between profiles (ANOVA's omnibus test, P = .039). Specifically, participants in the profile 3 group (high negative/low positive emotions) had an average brain-PAD ∼4 years higher than those in profile- (low somatic reactivity), with P = .047, Bonferroni-corrected, and than those in profile 2 (high coping), with P = .027, uncorrected. Repeated measures ANOVA revealed no significant change in profile-related brain-PAD differences over time, but there was a significant decrease in brain-PAD for profile 4 (high optimism/high positive affect), with P = .045. Moreover, profile-related differences in pain severity at baseline were partly explained by brain-PAD differences between profile 3 and 1, or 2; but brain-PAD did not significantly mediate the influence of variations in profiles on changes in pain severity over time. PERSPECTIVE: Accelerated brain aging could underlie the psychological-pain relationship, and psychological characteristics may predispose individuals with chronic knee pain to worse health outcomes via neuropsychological processes.
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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
| | - 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
| | - 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
| | - Vishnu Bashyam
- Center for Biomedical Image Computing & Analytics, Department of Radiology, 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
- Center for Biomedical Image Computing & Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Roger B. Fillingim
- Department of Community Dentistry and Behavioral Science, University of Florida, USA
- Pain Research and Intervention Center of Excellence, 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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Reuben A, Richmond‐Rakerd LS, Milne B, Shah D, Pearson A, Hogan S, Ireland D, Keenan R, Knodt AR, Melzer T, Poulton R, Ramrakha S, Whitman ET, Hariri AR, Moffitt TE, Caspi A. Dementia, dementia's risk factors and premorbid brain structure are concentrated in disadvantaged areas: National register and birth-cohort geographic analyses. Alzheimers Dement 2024; 20:3167-3178. [PMID: 38482967 PMCID: PMC11095428 DOI: 10.1002/alz.13727] [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: 10/12/2023] [Revised: 12/15/2023] [Accepted: 01/11/2024] [Indexed: 04/06/2024]
Abstract
INTRODUCTION Dementia risk may be elevated in socioeconomically disadvantaged neighborhoods. Reasons for this remain unclear, and this elevation has yet to be shown at a national population level. METHODS We tested whether dementia was more prevalent in disadvantaged neighborhoods across the New Zealand population (N = 1.41 million analytic sample) over a 20-year observation. We then tested whether premorbid dementia risk factors and MRI-measured brain-structure antecedents were more prevalent among midlife residents of disadvantaged neighborhoods in a population-representative NZ-birth-cohort (N = 938 analytic sample). RESULTS People residing in disadvantaged neighborhoods were at greater risk of dementia (HR per-quintile-disadvantage-increase = 1.09, 95% confidence interval [CI]:1.08-1.10) and, decades before clinical endpoints typically emerge, evidenced elevated dementia-risk scores (CAIDE, LIBRA, Lancet, ANU-ADRI, DunedinARB; β's 0.31-0.39) and displayed dementia-associated brain structural deficits and cognitive difficulties/decline. DISCUSSION Disadvantaged neighborhoods have more residents with dementia, and decades before dementia is diagnosed, residents have more dementia-risk factors and brain-structure antecedents. Whether or not neighborhoods causally influence risk, they may offer scalable opportunities for primary dementia prevention.
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Affiliation(s)
- Aaron Reuben
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
- Department of Psychiatry and Behavioral SciencesMedical University of South CarolinaCharlestonSouth CarolinaUSA
| | | | - Barry Milne
- Centre for Methods and Policy Application in Society SciencesUniversity of AucklandAucklandNew Zealand
| | - Devesh Shah
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Amber Pearson
- Department of Geography, Environment, and Spatial SciencesMichigan State UniversityEast LansingMichiganUSA
- Department of Public HealthUniversity of OtagoWellingtonNew Zealand
| | - Sean Hogan
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - David Ireland
- Brain Health Research Centre, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Ross Keenan
- Brain Health Research Centre, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Annchen R. Knodt
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Tracy Melzer
- Department of MedicineUniversity of OtagoChristchurchNew Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Ethan T. Whitman
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Ahmad R. Hariri
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Terrie E. Moffitt
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
- King's College London, Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, & NeuroscienceLondonUK
- PROMENTA, Department of PsychologyUniversity of OsloOsloNorway
| | - Avshalom Caspi
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
- King's College London, Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, & NeuroscienceLondonUK
- PROMENTA, Department of PsychologyUniversity of OsloOsloNorway
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Yan S, Lu J, Zhu H, Tian T, Qin Y, Li Y, Zhu W. The influence of accelerated brain aging on coactivation pattern dynamics in Parkinson's disease. J Neurosci Res 2024; 102:e25357. [PMID: 38803227 DOI: 10.1002/jnr.25357] [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: 11/06/2023] [Revised: 04/27/2024] [Accepted: 05/05/2024] [Indexed: 05/29/2024]
Abstract
Aging is widely acknowledged as the primary risk factor for brain degeneration, with Parkinson's disease (PD) tending to follow accelerated aging trajectories. We aim to investigate the impact of structural brain aging on the temporal dynamics of a large-scale functional network in PD. We enrolled 62 PD patients and 32 healthy controls (HCs). The level of brain aging was determined by calculating global and local brain age gap estimates (G-brainAGE and L-brainAGE) from structural images. The neural network activity of the whole brain was captured by identifying coactivation patterns (CAPs) from resting-state functional images. Intergroup differences were assessed using the general linear model. Subsequently, a spatial correlation analysis between the L-brainAGE difference map and CAPs was conducted to uncover the anatomical underpinnings of functional alterations. Compared to HCs (-3.73 years), G-brainAGE was significantly higher in PD patients (+1.93 years), who also exhibited widespread elevation in L-brainAGE. G-brainAGE was correlated with disease severity and duration. PD patients spent less time in CAPs involving activated default mode and the fronto-parietal network (DMN-FPN), as well as the sensorimotor and salience network (SMN-SN), and had a reduced transition frequency from other CAPs to the DMN-FPN and SMN-SN CAPs. Furthermore, the pattern of localized brain age acceleration showed spatial similarities with the SMN-SN CAP. Accelerated structural brain aging in PD adversely affects brain function, manifesting as dysregulated brain network dynamics. These findings provide insights into the neuropathological mechanisms underlying neurodegenerative diseases and imply the possibility of interventions for modifying PD progression by slowing the brain aging process.
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Affiliation(s)
- Su Yan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jun Lu
- Department of CT & MRI, The First Affiliated Hospital, College of Medicine, Shihezi University, Shihezi, China
| | - Hongquan Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tian Tian
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuanhao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Beck D, de Lange AG, Gurholt TP, Voldsbekk I, Maximov II, Subramaniapillai S, Schindler L, Hindley G, Leonardsen EH, Rahman Z, van der Meer D, Korbmacher M, Linge J, Leinhard OD, Kalleberg KT, Engvig A, Sønderby I, Andreassen OA, Westlye LT. Dissecting unique and common variance across body and brain health indicators using age prediction. Hum Brain Mapp 2024; 45:e26685. [PMID: 38647042 PMCID: PMC11034003 DOI: 10.1002/hbm.26685] [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: 12/29/2023] [Revised: 03/21/2024] [Accepted: 04/04/2024] [Indexed: 04/25/2024] Open
Abstract
Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.
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Affiliation(s)
- Dani Beck
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Mental Health and Substance AbuseDiakonhjemmet HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ann‐Marie G. de Lange
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Tiril P. Gurholt
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Irene Voldsbekk
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ivan I. Maximov
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Sivaniya Subramaniapillai
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Louise Schindler
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Guy Hindley
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Esten H. Leonardsen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Zillur Rahman
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Max Korbmacher
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Jennifer Linge
- AMRA Medical ABLinköpingSweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | - Olof D. Leinhard
- AMRA Medical ABLinköpingSweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | | | - Andreas Engvig
- Department of Endocrinology, Obesity and Preventive Medicine, Section of Preventive CardiologyOslo University HospitalOsloNorway
| | - Ida Sønderby
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Medical GeneticsOslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
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38
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Hanson JL, Adkins DJ, Bacas E, Zhou P. Examining the reliability of brain age algorithms under varying degrees of participant motion. Brain Inform 2024; 11:9. [PMID: 38573551 PMCID: PMC10994881 DOI: 10.1186/s40708-024-00223-0] [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: 09/06/2023] [Accepted: 03/18/2024] [Indexed: 04/05/2024] Open
Abstract
Brain age algorithms using data science and machine learning techniques show promise as biomarkers for neurodegenerative disorders and aging. However, head motion during MRI scanning may compromise image quality and influence brain age estimates. We examined the effects of motion on brain age predictions in adult participants with low, high, and no motion MRI scans (Original N = 148; Analytic N = 138). Five popular algorithms were tested: brainageR, DeepBrainNet, XGBoost, ENIGMA, and pyment. Evaluation metrics, intraclass correlations (ICCs), and Bland-Altman analyses assessed reliability across motion conditions. Linear mixed models quantified motion effects. Results demonstrated motion significantly impacted brain age estimates for some algorithms, with ICCs dropping as low as 0.609 and errors increasing up to 11.5 years for high motion scans. DeepBrainNet and pyment showed greatest robustness and reliability (ICCs = 0.956-0.965). XGBoost and brainageR had the largest errors (up to 13.5 RMSE) and bias with motion. Findings indicate motion artifacts influence brain age estimates in significant ways. Furthermore, our results suggest certain algorithms like DeepBrainNet and pyment may be preferable for deployment in populations where motion during MRI acquisition is likely. Further optimization and validation of brain age algorithms is critical to use brain age as a biomarker relevant for clinical outcomes.
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Affiliation(s)
- Jamie L Hanson
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA.
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Dorthea J Adkins
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
| | - Eva Bacas
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
| | - Peiran Zhou
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
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39
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Du J, Pan Y, Jiang J, Liu Y, Lam BCP, Schutte AE, Tsang IW, Sachdev PS, Wen W. Association of Blood Pressure With Brain Ages: A Cohort Study of Gray and White Matter Aging Discrepancy in Mid-to-Older Adults From UK Biobank. Hypertension 2024; 81:906-916. [PMID: 38465593 DOI: 10.1161/hypertensionaha.123.22176] [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: 10/05/2023] [Accepted: 12/11/2023] [Indexed: 03/12/2024]
Abstract
BACKGROUND Gray matter (GM) and white matter (WM) impairments are both associated with raised blood pressure (BP), although whether elevated BP is differentially associated with the GM and WM aging process remains inadequately examined. METHODS We included 37 327 participants with diffusion-weighted imaging (DWI) and 39 630 participants with T1-weighted scans from UK Biobank. BP was classified into 4 categories: normal BP, high-normal BP, grade 1, and grade 2 hypertension. Brain age gaps (BAGs) for GM (BAGGM) and WM (BAGWM) were derived from diffusion-weighted imaging and T1 scans separately using 3-dimensional-convolutional neural network deep learning techniques. RESULTS There was an increase in both BAGGM and BAGWM with raised BP (P<0.05). BAGWM was significantly larger than BAGGM at high-normal BP (0.195 years older; P=0.006), grade 1 hypertension (0.174 years older; P=0.004), and grade 2 hypertension (0.510 years older; P<0.001), but not for normal BP. Mediation analysis revealed that the association between hypertension and cognitive decline was primarily mediated by WM impairment. Mendelian randomization analysis suggested a causal relationship between hypertension and WM aging acceleration (unstandardized B, 1.780; P=0.016) but not for GM (P>0.05). Sliding-window analysis indicated the association between hypertension and brain aging acceleration was moderated by chronological age, showing stronger correlations in midlife but weaker associations in the older age. CONCLUSIONS Compared with GM, WM was more vulnerable to raised BP. Our study provided compelling evidence that concerted efforts should be directed towards WM damage in individuals with hypertension in clinical practice.
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Affiliation(s)
- Jing Du
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales (UNSW), Sydney, Australia (J.D., J.J., B.C.P.L., P.S.S., W.W.)
| | - Yuangang Pan
- Centre for Frontier AI Research, A*STAR, Singapore (Y.P., I.W.T.)
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales (UNSW), Sydney, Australia (J.D., J.J., B.C.P.L., P.S.S., W.W.)
| | - Yue Liu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (Y.L.)
| | - Ben C P Lam
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales (UNSW), Sydney, Australia (J.D., J.J., B.C.P.L., P.S.S., W.W.)
| | - Aletta E Schutte
- School of Population Health, UNSW Medicine & Health, UNSW Sydney, NSW, Australia (A.E.S.)
- The George Institute for Global Health, Sydney, NSW, Australia (A.E.S.)
| | - Ivor W Tsang
- Centre for Frontier AI Research, A*STAR, Singapore (Y.P., I.W.T.)
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales (UNSW), Sydney, Australia (J.D., J.J., B.C.P.L., P.S.S., W.W.)
- Neuropsychiatric Institute, Euroa Centre, Prince of Wales Hospital, Randwick, NSW, Australia (P.S.S., W.W.)
| | - Wei Wen
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales (UNSW), Sydney, Australia (J.D., J.J., B.C.P.L., P.S.S., W.W.)
- Neuropsychiatric Institute, Euroa Centre, Prince of Wales Hospital, Randwick, NSW, Australia (P.S.S., W.W.)
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40
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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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Montella A, Tranfa M, Scaravilli A, Barkhof F, Brunetti A, Cole J, Gravina M, Marrone S, Riccio D, Riccio E, Sansone C, Spinelli L, Petracca M, Pisani A, Cocozza S, Pontillo G. Assessing brain involvement in Fabry disease with deep learning and the brain-age paradigm. Hum Brain Mapp 2024; 45:e26599. [PMID: 38520360 PMCID: PMC10960551 DOI: 10.1002/hbm.26599] [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: 07/11/2023] [Revised: 12/23/2023] [Accepted: 01/07/2024] [Indexed: 03/25/2024] Open
Abstract
While neurological manifestations are core features of Fabry disease (FD), quantitative neuroimaging biomarkers allowing to measure brain involvement are lacking. We used deep learning and the brain-age paradigm to assess whether FD patients' brains appear older than normal and to validate brain-predicted age difference (brain-PAD) as a possible disease severity biomarker. MRI scans of FD patients and healthy controls (HCs) from a single Institution were, retrospectively, studied. The Fabry stabilization index (FASTEX) was recorded as a measure of disease severity. Using minimally preprocessed 3D T1-weighted brain scans of healthy subjects from eight publicly available sources (N = 2160; mean age = 33 years [range 4-86]), we trained a model predicting chronological age based on a DenseNet architecture and used it to generate brain-age predictions in the internal cohort. Within a linear modeling framework, brain-PAD was tested for age/sex-adjusted associations with diagnostic group (FD vs. HC), FASTEX score, and both global and voxel-level neuroimaging measures. We studied 52 FD patients (40.6 ± 12.6 years; 28F) and 58 HC (38.4 ± 13.4 years; 28F). The brain-age model achieved accurate out-of-sample performance (mean absolute error = 4.01 years, R2 = .90). FD patients had significantly higher brain-PAD than HC (estimated marginal means: 3.1 vs. -0.1, p = .01). Brain-PAD was associated with FASTEX score (B = 0.10, p = .02), brain parenchymal fraction (B = -153.50, p = .001), white matter hyperintensities load (B = 0.85, p = .01), and tissue volume reduction throughout the brain. We demonstrated that FD patients' brains appear older than normal. Brain-PAD correlates with FD-related multi-organ damage and is influenced by both global brain volume and white matter hyperintensities, offering a comprehensive biomarker of (neurological) disease severity.
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Affiliation(s)
- Alfredo Montella
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
| | - Mario Tranfa
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
| | | | - Frederik Barkhof
- NMR Research Unit, Queen Square MS Centre, Department of NeuroinflammationUCL Institute of NeurologyLondonUK
- Department of Radiology and Nuclear MedicineMS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
- Centre for Medical Image ComputingUniversity College LondonLondonUK
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Arturo Brunetti
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
| | - James Cole
- Centre for Medical Image ComputingUniversity College LondonLondonUK
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Michela Gravina
- Department of Electrical Engineering and Information Technology (DIETI)University “Federico II”NaplesItaly
| | - Stefano Marrone
- Department of Electrical Engineering and Information Technology (DIETI)University “Federico II”NaplesItaly
| | - Daniele Riccio
- Department of Electrical Engineering and Information Technology (DIETI)University “Federico II”NaplesItaly
| | - Eleonora Riccio
- Department of Public Health, Nephrology UnitUniversity “Federico II”NaplesItaly
| | - Carlo Sansone
- Department of Electrical Engineering and Information Technology (DIETI)University “Federico II”NaplesItaly
| | - Letizia Spinelli
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
| | - Maria Petracca
- Department of Neurosciences and Reproductive and Odontostomatological SciencesUniversity “Federico II”NaplesItaly
- Department of Human NeurosciencesSapienza University of RomeRomeItaly
| | - Antonio Pisani
- Department of Public Health, Nephrology UnitUniversity “Federico II”NaplesItaly
| | - Sirio Cocozza
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
| | - Giuseppe Pontillo
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
- NMR Research Unit, Queen Square MS Centre, Department of NeuroinflammationUCL Institute of NeurologyLondonUK
- Department of Radiology and Nuclear MedicineMS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
- Department of Electrical Engineering and Information Technology (DIETI)University “Federico II”NaplesItaly
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42
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Montagnese M, Rittman T. Bridging modifiable risk factors and cognitive decline: the mediating role of brain age. THE LANCET. HEALTHY LONGEVITY 2024; 5:e243-e244. [PMID: 38555918 DOI: 10.1016/s2666-7568(24)00042-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/02/2024] Open
Affiliation(s)
- Marcella Montagnese
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0SZ, UK.
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0SZ, UK
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43
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Chen CL, Cheng SY, Montaser-Kouhsari L, Wu WC, Hsu YC, Tai CH, Tseng WYI, Kuo MC, Wu RM. Advanced brain aging in Parkinson's disease with cognitive impairment. NPJ Parkinsons Dis 2024; 10:62. [PMID: 38493188 PMCID: PMC10944471 DOI: 10.1038/s41531-024-00673-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 02/29/2024] [Indexed: 03/18/2024] Open
Abstract
Patients with Parkinson's disease and cognitive impairment (PD-CI) deteriorate faster than those without cognitive impairment (PD-NCI), suggesting an underlying difference in the neurodegeneration process. We aimed to verify brain age differences in PD-CI and PD-NCI and their clinical significance. A total of 94 participants (PD-CI, n = 27; PD-NCI, n = 34; controls, n = 33) were recruited. Predicted age difference (PAD) based on gray matter (GM) and white matter (WM) features were estimated to represent the degree of brain aging. Patients with PD-CI showed greater GM-PAD (7.08 ± 6.64 years) and WM-PAD (8.82 ± 7.69 years) than those with PD-NCI (GM: 1.97 ± 7.13, Padjusted = 0.011; WM: 4.87 ± 7.88, Padjusted = 0.049) and controls (GM: -0.58 ± 7.04, Padjusted = 0.004; WM: 0.88 ± 7.45, Padjusted = 0.002) after adjusting demographic factors. In patients with PD, GM-PAD was negatively correlated with MMSE (Padjusted = 0.011) and MoCA (Padjusted = 0.013) and positively correlated with UPDRS Part II (Padjusted = 0.036). WM-PAD was negatively correlated with logical memory of immediate and delayed recalls (Padjusted = 0.003 and Padjusted < 0.001). Also, altered brain regions in PD-CI were identified and significantly correlated with brain age measures, implicating the neuroanatomical underpinning of neurodegeneration in PD-CI. Moreover, the brain age metrics can improve the classification between PD-CI and PD-NCI. The findings suggest that patients with PD-CI had advanced brain aging that was associated with poor cognitive functions. The identified neuroimaging features and brain age measures can serve as potential biomarkers of PD-CI.
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Affiliation(s)
- Chang-Le Chen
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shao-Ying Cheng
- Department of Neurology, National Taiwan University Hospital Bei-Hu Branch, Taipei, Taiwan
| | | | - Wen-Chao Wu
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
| | | | - Chun-Hwei Tai
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan
| | - Wen-Yih Isaac Tseng
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan.
- Acroviz Inc, Taipei, Taiwan.
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan.
| | - Ming-Che Kuo
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan.
- Department of Medicine, National Taiwan University Cancer Center, Taipei, Taiwan.
- Neurobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan.
| | - Ruey-Meei Wu
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan
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44
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Lim H, Joo Y, Ha E, Song Y, Yoon S, Shin T. Brain Age Prediction Using Multi-Hop Graph Attention Combined with Convolutional Neural Network. Bioengineering (Basel) 2024; 11:265. [PMID: 38534539 DOI: 10.3390/bioengineering11030265] [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: 01/29/2024] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 03/28/2024] Open
Abstract
Convolutional neural networks (CNNs) have been used widely to predict biological brain age based on brain magnetic resonance (MR) images. However, CNNs focus mainly on spatially local features and their aggregates and barely on the connective information between distant regions. To overcome this issue, we propose a novel multi-hop graph attention (MGA) module that exploits both the local and global connections of image features when combined with CNNs. After insertion between convolutional layers, MGA first converts the convolution-derived feature map into graph-structured data by using patch embedding and embedding-distance-based scoring. Multi-hop connections between the graph nodes are modeled by using the Markov chain process. After performing multi-hop graph attention, MGA re-converts the graph into an updated feature map and transfers it to the next convolutional layer. We combined the MGA module with sSE (spatial squeeze and excitation)-ResNet18 for our final prediction model (MGA-sSE-ResNet18) and performed various hyperparameter evaluations to identify the optimal parameter combinations. With 2788 three-dimensional T1-weighted MR images of healthy subjects, we verified the effectiveness of MGA-sSE-ResNet18 with comparisons to four established, general-purpose CNNs and two representative brain age prediction models. The proposed model yielded an optimal performance with a mean absolute error of 2.822 years and Pearson's correlation coefficient (PCC) of 0.968, demonstrating the potential of the MGA module to improve the accuracy of brain age prediction.
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Affiliation(s)
- Heejoo Lim
- Division of Mechanical and Biomedical Engineering, Ewha W. University, Seoul 03760, Republic of Korea
- Graduate Program in Smart Factory, Ewha W. University, Seoul 03760, Republic of Korea
| | - Yoonji Joo
- Ewha Brain Institute, Ewha W. University, Seoul 03760, Republic of Korea
| | - Eunji Ha
- Ewha Brain Institute, Ewha W. University, Seoul 03760, Republic of Korea
| | - Yumi Song
- Ewha Brain Institute, Ewha W. University, Seoul 03760, Republic of Korea
- Department of Brain and Cognitive Sciences, Ewha W. University, Seoul 03760, Republic of Korea
| | - Sujung Yoon
- Ewha Brain Institute, Ewha W. University, Seoul 03760, Republic of Korea
- Department of Brain and Cognitive Sciences, Ewha W. University, Seoul 03760, Republic of Korea
| | - Taehoon Shin
- Division of Mechanical and Biomedical Engineering, Ewha W. University, Seoul 03760, Republic of Korea
- Graduate Program in Smart Factory, Ewha W. University, Seoul 03760, Republic of Korea
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45
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Kamarajan C, Ardekani BA, Pandey AK, Meyers JL, Chorlian DB, Kinreich S, Pandey G, Richard C, de Viteri SS, Kuang W, Porjesz B. Prediction of brain age in individuals with and at risk for alcohol use disorder using brain morphological features. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.01.582844. [PMID: 38496639 PMCID: PMC10942318 DOI: 10.1101/2024.03.01.582844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Brain age measures predicted from structural and functional brain features are increasingly being used to understand brain integrity, disorders, and health. While there is a vast literature showing aberrations in both structural and functional brain measures in individuals with and at risk for alcohol use disorder (AUD), few studies have investigated brain age in these groups. The current study examines brain age measures predicted using brain morphological features, such as cortical thickness and brain volume, in individuals with a lifetime diagnosis of AUD as well as in those at higher risk to develop AUD from families with multiple members affected with AUD (i.e., higher family history density (FHD) scores). The AUD dataset included a group of 30 adult males (mean age = 41.25 years) with a lifetime diagnosis of AUD and currently abstinent and a group of 30 male controls (mean age = 27.24 years) without any history of AUD. A second dataset of young adults who were categorized based on their FHD scores comprised a group of 40 individuals (20 males) with high FHD of AUD (mean age = 25.33 years) and a group of 31 individuals (18 males) with low FHD (mean age = 25.47 years). Brain age was predicted using 187 brain morphological features of cortical thickness and brain volume in an XGBoost regression model; a bias-correction procedure was applied to the predicted brain age. Results showed that both AUD and high FHD individuals showed an increase of 1.70 and 0.09 years (1.08 months), respectively, in their brain age relative to their chronological age, suggesting accelerated brain aging in AUD and risk for AUD. Increased brain age was associated with poor performance on neurocognitive tests of executive functioning in both AUD and high FHD individuals, indicating that brain age can also serve as a proxy for cognitive functioning and brain health. These findings on brain aging in these groups may have important implications for the prevention and treatment of AUD and ensuing cognitive decline.
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Affiliation(s)
- Chella Kamarajan
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Babak A. Ardekani
- Center for Advanced Brain Imaging, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
- Department of Psychiatry, Grossman School of Medicine, New York University, New York, NY 10016, USA
| | - Ashwini K. Pandey
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Jacquelyn L. Meyers
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - David B. Chorlian
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Sivan Kinreich
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Gayathri Pandey
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Christian Richard
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Stacey Saenz de Viteri
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Weipeng Kuang
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Bernice Porjesz
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
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Wood DA, Townend M, Guilhem E, Kafiabadi S, Hammam A, Wei Y, Al Busaidi A, Mazumder A, Sasieni P, Barker GJ, Ourselin S, Cole JH, Booth TC. Optimising brain age estimation through transfer learning: A suite of pre-trained foundation models for improved performance and generalisability in a clinical setting. Hum Brain Mapp 2024; 45:e26625. [PMID: 38433665 PMCID: PMC10910262 DOI: 10.1002/hbm.26625] [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: 08/14/2023] [Revised: 12/27/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024] Open
Abstract
Estimated age from brain MRI data has emerged as a promising biomarker of neurological health. However, the absence of large, diverse, and clinically representative training datasets, along with the complexity of managing heterogeneous MRI data, presents significant barriers to the development of accurate and generalisable models appropriate for clinical use. Here, we present a deep learning framework trained on routine clinical data (N up to 18,890, age range 18-96 years). We trained five separate models for accurate brain age prediction (all with mean absolute error ≤4.0 years, R2 ≥ .86) across five different MRI sequences (T2 -weighted, T2 -FLAIR, T1 -weighted, diffusion-weighted, and gradient-recalled echo T2 *-weighted). Our trained models offer dual functionality. First, they have the potential to be directly employed on clinical data. Second, they can be used as foundation models for further refinement to accommodate a range of other MRI sequences (and therefore a range of clinical scenarios which employ such sequences). This adaptation process, enabled by transfer learning, proved effective in our study across a range of MRI sequences and scan orientations, including those which differed considerably from the original training datasets. Crucially, our findings suggest that this approach remains viable even with limited data availability (as low as N = 25 for fine-tuning), thus broadening the application of brain age estimation to more diverse clinical contexts and patient populations. By making these models publicly available, we aim to provide the scientific community with a versatile toolkit, promoting further research in brain age prediction and related areas.
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Affiliation(s)
- David A. Wood
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | - Matthew Townend
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | - Emily Guilhem
- King's College Hospital NHS Foundation TrustLondonUK
| | | | - Ahmed Hammam
- King's College Hospital NHS Foundation TrustLondonUK
| | - Yiran Wei
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | | | | | - Peter Sasieni
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology, and NeuroscienceKing's College LondonLondonUK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | - James H. Cole
- Dementia Research Centre, Institute of NeurologyUniversity College LondonLondonUK
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
| | - Thomas C. Booth
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
- King's College Hospital NHS Foundation TrustLondonUK
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47
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Constantinides C, Baltramonaityte V, Caramaschi D, Han LKM, Lancaster TM, Zammit S, Freeman TP, Walton E. Assessing the association between global structural brain age and polygenic risk for schizophrenia in early adulthood: A recall-by-genotype study. Cortex 2024; 172:1-13. [PMID: 38154374 DOI: 10.1016/j.cortex.2023.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/22/2023] [Accepted: 11/23/2023] [Indexed: 12/30/2023]
Abstract
Neuroimaging studies consistently show advanced brain age in schizophrenia, suggesting that brain structure is often 'older' than expected at a given chronological age. Whether advanced brain age is linked to genetic liability for schizophrenia remains unclear. In this pre-registered secondary data analysis, we utilised a recall-by-genotype approach applied to a population-based subsample from the Avon Longitudinal Study of Parents and Children to assess brain age differences between young adults aged 21-24 years with relatively high (n = 96) and low (n = 93) polygenic risk for schizophrenia (SCZ-PRS). A global index of brain age (or brain-predicted age) was estimated using a publicly available machine learning model previously trained on a combination of region-wise gray-matter measures, including cortical thickness, surface area and subcortical volumes derived from T1-weighted magnetic resonance imaging (MRI) scans. We found no difference in mean brain-PAD (the difference between brain-predicted age and chronological age) between the high- and low-SCZ-PRS groups, controlling for the effects of sex and age at time of scanning (b = -.21; 95% CI -2.00, 1.58; p = .82; Cohen's d = -.034; partial R2 = .00029). These findings do not support an association between SCZ-PRS and brain-PAD based on global age-related structural brain patterns, suggesting that brain age may not be a vulnerability marker of common genetic risk for SCZ. Future studies with larger samples and multimodal brain age measures could further investigate global or localised effects of SCZ-PRS.
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Affiliation(s)
| | | | - Doretta Caramaschi
- Department of Psychology, Faculty of Health and Life Sciences, University of Exeter, UK
| | - Laura K M Han
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia; Orygen, Parkville, Australia
| | | | - Stanley Zammit
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK; Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom P Freeman
- Addiction and Mental Health Group (AIM), Department of Psychology, University of Bath, UK
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48
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Du J, Pan Y, Jiang J, Lam BCP, Thalamuthu A, Chen R, Tsang IW, Sachdev PS, Wen W. White matter brain age as a biomarker of cerebrovascular burden in the ageing brain. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01758-3. [PMID: 38424358 DOI: 10.1007/s00406-024-01758-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 01/13/2024] [Indexed: 03/02/2024]
Abstract
As the brain ages, it almost invariably accumulates vascular pathology, which differentially affects the cerebral white matter. A rich body of research has investigated the link between vascular risk factors and the brain. One of the less studied questions is that among various modifiable vascular risk factors, which is the most debilitating one for white matter health? A white matter specific brain age was developed to evaluate the overall white matter health from diffusion weighted imaging, using a three-dimensional convolutional neural network deep learning model in both cross-sectional UK biobank participants (n = 37,327) and a longitudinal subset (n = 1409). White matter brain age gap (WMBAG) was the difference between the white matter age and the chronological age. Participants with one, two, and three or more vascular risk factors, compared to those without any, showed an elevated WMBAG of 0.54, 1.23, and 1.94 years, respectively. Diabetes was most strongly associated with an increased WMBAG (1.39 years, p < 0.001) among all risk factors followed by hypertension (0.87 years, p < 0.001) and smoking (0.69 years, p < 0.001). Baseline WMBAG was associated significantly with processing speed, executive and global cognition. Significant associations of diabetes and hypertension with poor processing speed and executive function were found to be mediated through the WMBAG. White matter specific brain age can be successfully targeted for the examination of the most relevant risk factors and cognition, and for tracking an individual's cerebrovascular ageing process. It also provides clinical basis for the better management of specific risk factors.
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Affiliation(s)
- Jing Du
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia.
| | - Yuangang Pan
- Centre for Frontier AI Research (CFAR), A*STAR, Singapore, 138623, Singapore
- Australian Artificial Intelligence Institute (AAII), UTS, Sydney, NSW, 2007, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia
| | - Ben C P Lam
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia
| | - Rory Chen
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia
| | - Ivor W Tsang
- Centre for Frontier AI Research (CFAR), A*STAR, Singapore, 138623, Singapore
- Australian Artificial Intelligence Institute (AAII), UTS, Sydney, NSW, 2007, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia
- Neuropsychiatric Institute (NPI), Euroa Centre, Prince of Wales Hospital, Randwick, NSW, 2031, Australia
| | - Wei Wen
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia.
- Neuropsychiatric Institute (NPI), Euroa Centre, Prince of Wales Hospital, Randwick, NSW, 2031, Australia.
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49
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Shi R, Ye J, Fan H, Hu X, Wu X, Wang D, Zhao B, Dai X, Liu X. Lactobacillus plantarum LLY-606 Supplementation Ameliorates the Cognitive Impairment of Natural Aging in Mice: The Potential Role of Gut Microbiota Homeostasis. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:4049-4062. [PMID: 38373323 DOI: 10.1021/acs.jafc.3c07041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
This work explored the effects of Lactobacillus plantarum LLY-606 (LLY-606) on cognitive function in aging mice. Our findings demonstrated that LLY-606 effectively prolonged the lifespan of mice and improved age-related cognitive impairments. Additionally, our study revealed that supplementation with LLY-606 resulted in the downregulation of inflammatory cytokine levels and the upregulation of antioxidant capacity. Furthermore, probiotic supplementation effectively mitigated the deterioration of the intestinal barrier function in aging mice. Amplicon analysis indicated the successful colonization of probiotics, facilitating the regulation of age-induced gut microbiota dysbiosis. Notably, the functional abundance prediction of microbiota indicated that tryptophan metabolism pathways, glutamatergic synapse pathways, propanoate metabolism pathways, and arginine and proline metabolism pathways were enriched after the LLY-606 intervention. In summary, LLY-606 emerged as a potential functional probiotic capable of influencing cognitive function in aging mice. This effect was achieved through the modulation of gut microbiota, the regulation of synaptic plasticity, and the enhancement of neurotrophic factor levels.
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Affiliation(s)
- Renjie Shi
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China
| | - Jin Ye
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China
| | - Hua Fan
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China
| | - Xinyun Hu
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China
| | - Xiaoning Wu
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China
| | - Danna Wang
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China
| | - Beita Zhao
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China
| | - Xiaoshuang Dai
- BGI Institute of Applied Agriculture, BGI-Shenzhen, Shenzhen 518120, China
| | - Xuebo Liu
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China
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50
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A. Shirsath M, O'Connor JD, Boyle R, Newman L, Knight SP, Hernandez B, Whelan R, Meaney JF, Kenny RA. Slower speed of blood pressure recovery after standing is associated with accelerated brain aging: Evidence from The Irish Longitudinal Study on Ageing (TILDA). CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2024; 6:100212. [PMID: 38445293 PMCID: PMC10912350 DOI: 10.1016/j.cccb.2024.100212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 01/17/2024] [Accepted: 02/02/2024] [Indexed: 03/07/2024]
Abstract
Background Impaired recovery of blood pressure (BP) in response to standing up is a prevalent condition in older individuals. We evaluated the relationship between the early recovery of hemodynamic responses to standing and brain health in adults over 50. Methods Participants from The Irish Longitudinal Study on Ageing (TILDA) (n=411; age 67.6 ± 7.3 years; 53.4 % women) performed an active stand challenge while blood pressure and heart rate were continuously monitored. The recovery of these parameters was determined as the slope of the BP and HR response, following the initial drop/rise after standing. We have previously reported a novel and validated measure of brain ageing using MRI data, which measures the difference between biological brain age and chronological age, providing a brain-predicted age difference (brainPAD) score. Results Slower recovery of systolic and diastolic BP was found to be significantly associated with higher brainPAD scores (i.e., biologically older brains), where a one-year increase in brainPAD was associated with a decrease of 0.02 mmHg/s and 0.01 mmHg/s in systolic and diastolic BP recovery, respectively, after standing. Heart rate (HR) recovery was not significantly associated with brainPAD score. Conclusion These results demonstrate that slower systolic and diastolic BP recovery in the early phase after standing is associated with accelerated brain aging in older individuals. This suggests that the BP response to standing, measured using beat-to-beat monitoring, has the potential to be used as a marker of accelerated brain aging, relying on a simple procedure and devices that are easily accessible.
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Affiliation(s)
- Morgana A. Shirsath
- The Irish Longitudinal Study on Ageing (TILDA), School of Medicine, Trinity College, University of, Ireland
| | - John D. O'Connor
- The Irish Longitudinal Study on Ageing (TILDA), School of Medicine, Trinity College, University of, Ireland
- School of Engineering, Ulster University, Northern Ireland, UK
| | - Rory Boyle
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Louise Newman
- The Irish Longitudinal Study on Ageing (TILDA), School of Medicine, Trinity College, University of, Ireland
| | - Silvin P. Knight
- The Irish Longitudinal Study on Ageing (TILDA), School of Medicine, Trinity College, University of, Ireland
| | - Belinda Hernandez
- The Irish Longitudinal Study on Ageing (TILDA), School of Medicine, Trinity College, University of, Ireland
| | - Robert Whelan
- Trinity College Institute of Neuroscience, Trinity College, University of Dublin, Ireland
- Global Brain Health Institute, Trinity College, Trinity College Dublin, Ireland
| | - James F. Meaney
- National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital, Dublin, Ireland
| | - Rose Anne Kenny
- The Irish Longitudinal Study on Ageing (TILDA), School of Medicine, Trinity College, University of, Ireland
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