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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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Moon HS, Mahzarnia A, Stout J, Anderson RJ, Badea CT, Badea A. Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.13.571574. [PMID: 38168445 PMCID: PMC10760088 DOI: 10.1101/2023.12.13.571574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Alzheimer's disease (AD) remains one of the most extensively researched neurodegenerative disorders due to its widespread prevalence and complex risk factors. Age is a crucial risk factor for AD, which can be estimated by the disparity between physiological age and estimated brain age. To model AD risk more effectively, integrating biological, genetic, and cognitive markers is essential. Here, we utilized mouse models expressing the major APOE human alleles and human nitric oxide synthase 2 to replicate genetic risk for AD and a humanized innate immune response. We estimated brain age employing a multivariate dataset that includes brain connectomes, APOE genotype, subject traits such as age and sex, and behavioral data. Our methodology used Feature Attention Graph Neural Networks (FAGNN) for integrating different data types. Behavioral data were processed with a 2D Convolutional Neural Network (CNN), subject traits with a 1D CNN, brain connectomes through a Graph Neural Network using quadrant attention module. The model yielded a mean absolute error for age prediction of 31.85 days, with a root mean squared error of 41.84 days, outperforming other, reduced models. In addition, FAGNN identified key brain connections involved in the aging process. The highest weights were assigned to the connections between cingulum and corpus callosum, striatum, hippocampus, thalamus, hypothalamus, cerebellum, and piriform cortex. Our study demonstrates the feasibility of predicting brain age in models of aging and genetic risk for AD. To verify the validity of our findings, we compared Fractional Anisotropy (FA) along the tracts of regions with the highest connectivity, the Return-to-Origin Probability (RTOP), Return-to-Plane Probability (RTPP), and Return-to-Axis Probability (RTAP), which showed significant differences between young, middle-aged, and old age groups. Younger mice exhibited higher FA, RTOP, RTAP, and RTPP compared to older groups in the selected connections, suggesting that degradation of white matter tracts plays a critical role in aging and for FAGNN's selections. Our analysis suggests a potential neuroprotective role of APOE2, relative to APOE3 and APOE4, where APOE2 appears to mitigate age-related changes. Our findings highlighted a complex interplay of genetics and brain aging in the context of AD risk modeling.
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Affiliation(s)
- Hae Sol Moon
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Ali Mahzarnia
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Jacques Stout
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Robert J Anderson
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Cristian T. Badea
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Alexandra Badea
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
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3
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Hu L, Wan Q, Huang L, Tang J, Huang S, Chen X, Bai X, Kong L, Deng J, Liang H, Liu G, Liu H, Lu L. MRI-based brain age prediction model for children under 3 years old using deep residual network. Brain Struct Funct 2023; 228:1771-1784. [PMID: 37603065 DOI: 10.1007/s00429-023-02686-z] [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: 03/08/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023]
Abstract
Early identification and intervention of abnormal brain development individual subjects are of great significance, especially during the earliest and most active stage of brain development in children aged under 3. Neuroimage-based brain's biological age has been associated with health, ability, and remaining life. However, the existing brain age prediction models based on neuroimage are predominantly adult-oriented. Here, we collected 658 T1-weighted MRI scans from 0 to 3 years old healthy controls and developed an accurate brain age prediction model for young children using deep learning techniques with high accuracy in capturing age-related changes. The performance of the deep learning-based model is comparable to that of the SVR-based model, showcasing remarkable precision and yielding a noteworthy correlation of 91% between the predicted brain age and the chronological age. Our results demonstrate the accuracy of convolutional neural network (CNN) brain-predicted age using raw T1-weighted MRI data with minimum preprocessing necessary. We also applied our model to children with low birth weight, premature delivery history, autism, and ADHD, and discovered that the brain age was delayed in children with extremely low birth weight (less than 1000 g) while ADHD may cause accelerated aging of the brain. Our child-specific brain age prediction model can be a valuable quantitative tool to detect abnormal brain development and can be helpful in the early identification and intervention of age-related brain disorders.
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Affiliation(s)
- Lianting Hu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Qirong Wan
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, 4330060, Hubei, China
| | - Li Huang
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Jiajie Tang
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Shuai Huang
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Xuanhui Chen
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Xiaohe Bai
- School of Physical Sciences, University of California San Diego, La Jolla, San Diego, CA, 92093, USA
| | - Lingcong Kong
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Jingyi Deng
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Huiying Liang
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Guangjian Liu
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Hongsheng Liu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China.
| | - Long Lu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China.
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China.
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4
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Shida AF, Massett RJ, Imms P, Vegesna RV, Amgalan A, Irimia A. Significant Acceleration of Regional Brain Aging and Atrophy After Mild Traumatic Brain Injury. J Gerontol A Biol Sci Med Sci 2023; 78:1328-1338. [PMID: 36879433 PMCID: PMC10395568 DOI: 10.1093/gerona/glad079] [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: 08/01/2022] [Indexed: 03/08/2023] Open
Abstract
Brain regions' rates of age-related volumetric change after traumatic brain injury (TBI) are unknown. Here, we quantify these rates cross-sectionally in 113 persons with recent mild TBI (mTBI), whom we compare against 3 418 healthy controls (HCs). Regional gray matter (GM) volumes were extracted from magnetic resonance images. Linear regression yielded regional brain ages and the annualized average rates of regional GM volume loss. These results were compared across groups after accounting for sex and intracranial volume. In HCs, the steepest rates of volume loss were recorded in the nucleus accumbens, amygdala, and lateral orbital sulcus. In mTBI, approximately 80% of GM structures had significantly steeper rates of annual volume loss than in HCs. The largest group differences involved the short gyri of the insula and both the long gyrus and central sulcus of the insula. No significant sex differences were found in the mTBI group, regional brain ages being the oldest in prefrontal and temporal structures. Thus, mTBI involves significantly steeper regional GM loss rates than in HCs, reflecting older-than-expected regional brain ages.
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Affiliation(s)
- Alexander F Shida
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Roy J Massett
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Phoebe Imms
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Ramanand V Vegesna
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Anar Amgalan
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
- Department of Quantitative & Computational Biology, Dana and David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, California, USA
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5
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Massett R, Maher A, Imms P, Amgalan A, Chaudhari N, Chowdhury N, Irimia A. Regional Neuroanatomic Effects on Brain Age Inferred Using Magnetic Resonance Imaging and Ridge Regression. J Gerontol A Biol Sci Med Sci 2023; 78:872-881. [PMID: 36183259 PMCID: PMC10235198 DOI: 10.1093/gerona/glac209] [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: 03/11/2022] [Indexed: 11/14/2022] Open
Abstract
The biological age of the brain differs from its chronological age (CA) and can be used as biomarker of neural/cognitive disease processes and as predictor of mortality. Brain age (BA) is often estimated from magnetic resonance images (MRIs) using machine learning (ML) that rarely indicates how regional brain features contribute to BA. Leveraging an aggregate training sample of 3 418 healthy controls (HCs), we describe a ridge regression model that quantifies each region's contribution to BA. After model testing on an independent sample of 651 HCs, we compute the coefficient of partial determination R¯p2 for each regional brain volume to quantify its contribution to BA. Model performance is also evaluated using the correlation r between chronological and biological ages, the mean absolute error (MAE ) and mean squared error (MSE) of BA estimates. On training data, r=0.92, MSE=70.94 years, MAE=6.57 years, and R¯2=0.81; on test data, r=0.90, MSE=81.96 years, MAE=7.00 years, and R¯2=0.79. The regions whose volumes contribute most to BA are the nucleus accumbens (R¯p2=7.27%), inferior temporal gyrus (R¯p2=4.03%), thalamus (R¯p2=3.61%), brainstem (R¯p2=3.29%), posterior lateral sulcus (R¯p2=3.22%), caudate nucleus (R¯p2=3.05%), orbital gyrus (R¯p2=2.96%), and precentral gyrus (R¯p2=2.80%). Our ridge regression, although outperformed by the most sophisticated ML approaches, identifies the importance and relative contribution of each brain structure to overall BA. Aside from its interpretability and quasi-mechanistic insights, our model can be used to validate future ML approaches for BA estimation.
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Affiliation(s)
- Roy J Massett
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Alexander S Maher
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Phoebe E Imms
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Anar Amgalan
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Nikhil N Chaudhari
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Nahian F Chowdhury
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
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6
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Cai H, Gao Y, Liu M. Graph Transformer Geometric Learning of Brain Networks Using Multimodal MR Images for Brain Age Estimation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:456-466. [PMID: 36374874 DOI: 10.1109/tmi.2022.3222093] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Brain age is considered as an important biomarker for detecting aging-related diseases such as Alzheimer's Disease (AD). Magnetic resonance imaging (MRI) have been widely investigated with deep neural networks for brain age estimation. However, most existing methods cannot make full use of multimodal MRIs due to the difference in data structure. In this paper, we propose a graph transformer geometric learning framework to model the multimodal brain network constructed by structural MRI (sMRI) and diffusion tensor imaging (DTI) for brain age estimation. First, we build a two-stream convolutional autoencoder to learn the latent representations for each imaging modality. The brain template with prior knowledge is utilized to calculate the features from the regions of interest (ROIs). Then, a multi-level construction of the brain network is proposed to establish the hybrid ROI connections in space, feature and modality. Next, a graph transformer network is proposed to model the cross-modal interaction and fusion by geometric learning for brain age estimation. Finally, the difference between the estimated age and the chronological age is used as an important biomarker for AD diagnosis. Our method is evaluated with the sMRI and DTI data from UK Biobank and Alzheimer's Disease Neuroimaging Initiative database. Experimental results demonstrate that our method has achieved promising performances for brain age estimation and AD diagnosis.
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7
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Yin C, Imms P, Cheng M, Amgalan A, Chowdhury NF, Massett RJ, Chaudhari NN, Chen X, Thompson PM, Bogdan P, Irimia A. Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment. Proc Natl Acad Sci U S A 2023; 120:e2214634120. [PMID: 36595679 PMCID: PMC9926270 DOI: 10.1073/pnas.2214634120] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 11/10/2022] [Indexed: 01/05/2023] Open
Abstract
The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer's disease (AD, N = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.
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Affiliation(s)
- Chenzhong Yin
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Phoebe Imms
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
| | - Mingxi Cheng
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Anar Amgalan
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
| | - Nahian F. Chowdhury
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
| | - Roy J. Massett
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
| | - Nikhil N. Chaudhari
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Xinghe Chen
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Paul M. Thompson
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA90033
- Department of Quantitative & Computational Biology, Dana & David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA90089
- Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- Department of Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
| | - Paul Bogdan
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA90089
- Department of Quantitative & Computational Biology, Dana & David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA90089
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8
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Irimia A, Ngo V, Chaudhari NN, Zhang F, Joshi SH, Penkova AN, O'Donnell LJ, Sheikh-Bahaei N, Zheng X, Chui HC. White matter degradation near cerebral microbleeds is associated with cognitive change after mild traumatic brain injury. Neurobiol Aging 2022; 120:68-80. [PMID: 36116396 PMCID: PMC9759713 DOI: 10.1016/j.neurobiolaging.2022.08.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 08/17/2022] [Accepted: 08/20/2022] [Indexed: 11/28/2022]
Abstract
To explore how cerebral microbleeds (CMBs) accompanying mild traumatic brain injury (mTBI) reflect white matter (WM) degradation and cognitive decline, magnetic resonance images were acquired from 62 mTBI adults (imaged ∼7 days and ∼6 months post-injury) and 203 matched healthy controls. On average, mTBI participants had a count of 2.7 ± 2.6 traumatic CMBs in WM, located 6.1 ± 4.4 mm from cortex. At ∼6-month follow-up, 97% of CMBs were associated with significant reductions (34% ± 11%, q < 0.05) in the fractional anisotropy of WM streamlines within ∼1 cm of CMB locations. Male sex and older age were significant risk factors for larger reductions (q < 0.05). For CMBs in the corpus callosum, cingulum bundle, inferior and middle longitudinal fasciculi, fractional anisotropy changes were significantly and positively associated with changes in cognitive functions mediated by these structures (q < 0.05). Our findings distinguish traumatic from non-traumatic CMBs by virtue of surrounding WM alterations and challenge the assumption that traumatic CMBs are neurocognitively silent. Thus, mTBI with CMB findings can be described as a clinical endophenotype warranting longitudinal cognitive assessment.
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Affiliation(s)
- Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA; Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
| | - Van Ngo
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Nikhil N Chaudhari
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Fan Zhang
- Laboratory of Mathematics in Imaging, Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shantanu H Joshi
- Ahmanson Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Anita N Penkova
- Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Lauren J O'Donnell
- Laboratory of Mathematics in Imaging, Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nasim Sheikh-Bahaei
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xiaoyu Zheng
- Department of Materials Science & Engineering, University of California, Berkeley, CA, USA
| | - Helena C Chui
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Amgalan A, Maher AS, Ghosh S, Chui HC, Bogdan P, Irimia A. Brain age estimation reveals older adults' accelerated senescence after traumatic brain injury. GeroScience 2022; 44:2509-2525. [PMID: 35792961 PMCID: PMC9768106 DOI: 10.1007/s11357-022-00597-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 05/23/2022] [Indexed: 01/06/2023] Open
Abstract
Adults aged 60 and over are most vulnerable to mild traumatic brain injury (mTBI). Nevertheless, the extent to which chronological age (CA) at injury affects TBI-related brain aging is unknown. This study applies Gaussian process regression to T1-weighted magnetic resonance images (MRIs) acquired within [Formula: see text]7 days and again [Formula: see text]6 months after a single mTBI sustained by 133 participants aged 20-83 (CA [Formula: see text] = 42.6 ± 17 years; 51 females). Brain BAs are estimated, modeled, and compared as a function of sex and CA at injury using a statistical model selection procedure. On average, the brains of older adults age by 15.3 ± 6.9 years after mTBI, whereas those of younger adults age only by 1.8 ± 5.6 years, a significant difference (Welch's t32 = - 9.17, p ≃ 9.47 × 10-11). For an adult aged [Formula: see text]30 to [Formula: see text]60, the expected amount of TBI-related brain aging is [Formula: see text]3 years greater than in an individual younger by a decade. For an individual over [Formula: see text]60, the respective amount is [Formula: see text]7 years. Despite no significant sex differences in brain aging (Welch's t108 = 0.78, p > 0.78), the statistical test is underpowered. BAs estimated at acute baseline versus chronic follow-up do not differ significantly (t264 = 0.41, p > 0.66, power = 80%), suggesting negligible TBI-related brain aging during the chronic stage of TBI despite accelerated aging during the acute stage. Our results indicate that a single mTBI sustained after age [Formula: see text]60 involves approximately [Formula: see text]10 years of premature and lasting brain aging, which is MRI detectable as early as [Formula: see text]7 days post-injury.
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Affiliation(s)
- Anar Amgalan
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Alexander S Maher
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Satyaki Ghosh
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
- Department of Electronics and Electrical Engineering, Indian Institute of Technology, Guwahati, Assam, India
| | - Helena C Chui
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Paul Bogdan
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
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10
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Imms P, Chui HC, Irimia A. Alzheimer's disease after mild traumatic brain injury. Aging (Albany NY) 2022; 14:5292-5293. [PMID: 35830464 PMCID: PMC9320556 DOI: 10.18632/aging.204179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 07/11/2022] [Indexed: 12/02/2022]
Affiliation(s)
- Phoebe Imms
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
| | - Helena C. Chui
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033 USA
- Alzheimer’s Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Andrei Irimia
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
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11
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Amgalan A, Maher AS, Imms P, Ha MY, Fanelle TA, Irimia A. Functional Connectome Dynamics After Mild Traumatic Brain Injury According to Age and Sex. Front Aging Neurosci 2022; 14:852990. [PMID: 35663576 PMCID: PMC9158471 DOI: 10.3389/fnagi.2022.852990] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 04/05/2022] [Indexed: 11/17/2022] Open
Abstract
Neural and cognitive deficits after mild traumatic brain injury (mTBI) are paralleled by changes in resting state functional correlation (FC) networks that mirror post-traumatic pathophysiology effects on functional outcomes. Using functional magnetic resonance images acquired both acutely and chronically after injury (∼1 week and ∼6 months post-injury, respectively), we map post-traumatic FC changes across 136 participants aged 19-79 (52 females), both within and between the brain's seven canonical FC networks: default mode, dorsal attention, frontoparietal, limbic, somatomotor, ventral attention, and visual. Significant sex-dependent FC changes are identified between (A) visual and limbic, and between (B) default mode and somatomotor networks. These changes are significantly associated with specific functional recovery patterns across all cognitive domains (p < 0.05, corrected). Changes in FC between default mode, somatomotor, and ventral attention networks, on the one hand, and both temporal and occipital regions, on the other hand, differ significantly by age group (p < 0.05, corrected), and are paralleled by significant sex differences in cognitive recovery independently of age at injury (p < 0.05, corrected). Whereas females' networks typically feature both significant (p < 0.036, corrected) and insignificant FC changes, males more often exhibit significant FC decreases between networks (e.g., between dorsal attention and limbic, visual and limbic, default-mode and somatomotor networks, p < 0.0001, corrected), all such changes being accompanied by significantly weaker recovery of cognitive function in males, particularly older ones (p < 0.05, corrected). No significant FC changes were found across 35 healthy controls aged 66-92 (20 females). Thus, male sex and older age at injury are risk factors for significant FC alterations whose patterns underlie post-traumatic cognitive deficits. This is the first study to map, systematically, how mTBI impacts FC between major human functional networks.
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Affiliation(s)
- Anar Amgalan
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Alexander S. Maher
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Phoebe Imms
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Michelle Y. Ha
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Timothy A. Fanelle
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
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12
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Robles DJ, Dharani A, Rostowsky KA, Chaudhari NN, Ngo V, Zhang F, O'Donnell LJ, Green L, Sheikh-Bahaei N, Chui HC, Irimia A. Older age, male sex, and cerebral microbleeds predict white matter loss after traumatic brain injury. GeroScience 2022; 44:83-102. [PMID: 34704219 PMCID: PMC8811069 DOI: 10.1007/s11357-021-00459-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 09/11/2021] [Indexed: 10/20/2022] Open
Abstract
Little is known on how mild traumatic brain injury affects white matter based on age at injury, sex, cerebral microbleeds, and time since injury. Here, we study the fractional anisotropy of white matter to study these effects in 109 participants aged 18-77 (46 females, age μ ± σ = 40 ± 17 years) imaged within [Formula: see text] 1 week and [Formula: see text] 6 months post-injury. Age is found to be linearly associated with white matter degradation, likely due not only to injury but also to cumulative effects of other pathologies and to their interactions with injury. Age is associated with mean anisotropy decreases in the corpus callosum, middle longitudinal fasciculi, inferior longitudinal and occipitofrontal fasciculi, and superficial frontal and temporal fasciculi. Over [Formula: see text] 6 months, the mean anisotropies of the corpus callosum, left superficial frontal fasciculi, and left corticospinal tract decrease significantly. Independently of other predictors, age and cerebral microbleeds contribute to anisotropy decrease in the callosal genu. Chronically, the white matter of commissural tracts, left superficial frontal fasciculi, and left corticospinal tract degrade appreciably, independently of other predictors. Our findings suggest that large commissural and intra-hemispheric structures are at high risk for post-traumatic degradation. This study identifies detailed neuroanatomic substrates consistent with brain injury patients' age-dependent deficits in information processing speed, interhemispheric communication, motor coordination, visual acuity, sensory integration, reading speed/comprehension, executive function, personality, and memory. We also identify neuroanatomic features underlying white matter degradation whose severity is associated with the male sex. Future studies should compare our findings to functional measures and other neurodegenerative processes.
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Affiliation(s)
- David J Robles
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Ammar Dharani
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Kenneth A Rostowsky
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Nikhil N Chaudhari
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Van Ngo
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Lauren Green
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Nasim Sheikh-Bahaei
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Radiology, Keck School of Medicine, University of Southern California, 1520 San Pablo Street, Los Angeles, CA, 90033, USA
| | - Helena C Chui
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA.
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
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13
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Yeh FC, Irimia A, Bastos DCDA, Golby AJ. Tractography methods and findings in brain tumors and traumatic brain injury. Neuroimage 2021; 245:118651. [PMID: 34673247 PMCID: PMC8859988 DOI: 10.1016/j.neuroimage.2021.118651] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 10/05/2021] [Accepted: 10/11/2021] [Indexed: 12/31/2022] Open
Abstract
White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments.
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Affiliation(s)
- Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA; Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | | | - Alexandra J Golby
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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14
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MacDonald ME, Pike GB. MRI of healthy brain aging: A review. NMR IN BIOMEDICINE 2021; 34:e4564. [PMID: 34096114 DOI: 10.1002/nbm.4564] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 05/08/2021] [Accepted: 05/11/2021] [Indexed: 06/12/2023]
Abstract
We present a review of the characterization of healthy brain aging using MRI with an emphasis on morphology, lesions, and quantitative MR parameters. A scope review found 6612 articles encompassing the keywords "Brain Aging" and "Magnetic Resonance"; papers involving functional MRI or not involving imaging of healthy human brain aging were discarded, leaving 2246 articles. We first consider some of the biogerontological mechanisms of aging, and the consequences of aging in terms of cognition and onset of disease. Morphological changes with aging are reviewed for the whole brain, cerebral cortex, white matter, subcortical gray matter, and other individual structures. In general, volume and cortical thickness decline with age, beginning in mid-life. Prevalent silent lesions such as white matter hyperintensities, microbleeds, and lacunar infarcts are also observed with increasing frequency. The literature regarding quantitative MR parameter changes includes T1 , T2 , T2 *, magnetic susceptibility, spectroscopy, magnetization transfer, diffusion, and blood flow. We summarize the findings on how each of these parameters varies with aging. Finally, we examine how the aforementioned techniques have been used for age prediction. While relatively large in scope, we present a comprehensive review that should provide the reader with sound understanding of what MRI has been able to tell us about how the healthy brain ages.
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Affiliation(s)
- M Ethan MacDonald
- Department of Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
- Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
- Healthy Brain Aging Laboratory, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - G Bruce Pike
- Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
- Healthy Brain Aging Laboratory, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
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15
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Butler ER, Chen A, Ramadan R, Le TT, Ruparel K, Moore TM, Satterthwaite TD, Zhang F, Shou H, Gur RC, Nichols TE, Shinohara RT. Pitfalls in brain age analyses. Hum Brain Mapp 2021; 42:4092-4101. [PMID: 34190372 PMCID: PMC8357007 DOI: 10.1002/hbm.25533] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/08/2021] [Accepted: 04/29/2021] [Indexed: 01/02/2023] Open
Abstract
Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap.” Researchers have identified that the brain age gap, as a linear transformation of an out‐of‐sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R2 will be artificially inflated to the extent that it is highly improbable that an R2 value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.
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Affiliation(s)
- Ellyn R. Butler
- Brain Behavior Laboratory, Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Andrew Chen
- Penn Statistics in Imaging and Visualization Endeavor, Center for Clinical Epidemiology and BiostatisticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and AnalyticsDepartment of Radiology, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Rabie Ramadan
- Mathematics DepartmentTemple UniversityPhiladelphiaPennsylvaniaUSA
| | - Trang T. Le
- Department of Biostatistics, Epidemiology and InformaticsInstitute for Biomedical Informatics, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kosha Ruparel
- Brain Behavior Laboratory, Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Tyler M. Moore
- Brain Behavior Laboratory, Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Theodore D. Satterthwaite
- Penn Lifespan Informatics & Neuroimaging Center, Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Fengqing Zhang
- Department of PsychologyDrexel UniversityPhiladelphiaPennsylvaniaUSA
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor, Center for Clinical Epidemiology and BiostatisticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and AnalyticsDepartment of Radiology, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Brain Behavior Laboratory, Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Thomas E. Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and DiscoveryUniversity of OxfordOxfordUK
- FMRIB, Wellcome Centre for Integrative NeuroimagingOxfordUK
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Endeavor, Center for Clinical Epidemiology and BiostatisticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and AnalyticsDepartment of Radiology, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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16
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Irimia A. Cross-Sectional Volumes and Trajectories of the Human Brain, Gray Matter, White Matter and Cerebrospinal Fluid in 9473 Typically Aging Adults. Neuroinformatics 2021; 19:347-366. [PMID: 32856237 DOI: 10.1007/s12021-020-09480-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Accurate knowledge of adult human brain volume (BV) is critical for studies of aging- and disease-related brain alterations, and for monitoring the trajectories of neural and cognitive functions in conditions like Alzheimer's disease and traumatic brain injury. This scoping meta-analysis aggregates normative reference values for BV and three related volumetrics-gray matter volume (GMV), white matter volume (WMV) and cerebrospinal fluid volume (CSFV)-from typically-aging adults studied cross-sectionally using magnetic resonance imaging (MRI). Drawing from an aggregate sample of 9473 adults, this study provides (A) regression coefficients β describing the age-dependent trajectories of volumetric measures by sex within the range from 20 to 70 years based on both linear and quadratic models, and (B) average values for BV, GMV, WMV and CSFV at the representative ages of 20 (young age), 45 (middle age) and 70 (old age). The results provided synthesize ~20 years of brain volumetrics research and allow one to estimate BV at any age between 20 and 70. Importantly, however, such estimates should be used and interpreted with caution because they depend on MRI hardware specifications (e.g. scanner manufacturer, magnetic field strength), data acquisition parameters (e.g. spatial resolution, weighting), and brain segmentation algorithms. Guidelines are proposed to facilitate future meta- and mega-analyses of brain volumetrics.
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Affiliation(s)
- Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA.
- Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, 1042 Downey Way, Los Angeles, CA, 90089, USA.
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17
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Boyle R, Jollans L, Rueda-Delgado LM, Rizzo R, Yener GG, McMorrow JP, Knight SP, Carey D, Robertson IH, Emek-Savaş DD, Stern Y, Kenny RA, Whelan R. Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis. Brain Imaging Behav 2021; 15:327-345. [PMID: 32141032 DOI: 10.1007/s11682-020-00260-3] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Brain-predicted age difference scores are calculated by subtracting chronological age from 'brain' age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylül University (n = 175), the Cognitive Reserve/Reference Ability Neural Network study (n = 380), and The Irish Longitudinal Study on Ageing (n = 487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.
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Affiliation(s)
- Rory Boyle
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Lee Jollans
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, München, Germany
| | - Laura M Rueda-Delgado
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Rossella Rizzo
- Physics Department, University of Calabria, Rende, CS, Italy
| | - Görsev G Yener
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, İzmir, Turkey
- Department of Neurology, Dokuz Eylul University Medical School, İzmir, Turkey
- Brain Dynamics Multidisciplinary Research Center, Dokuz Eylul University, İzmir, Turkey
| | - Jason P McMorrow
- Centre for Advanced Medical Imaging, St. James's Hospital, Dublin 8, Ireland
- School of Medicine, Trinity College Dublin, Dublin 2, Ireland
| | - Silvin P Knight
- School of Medicine, Trinity College Dublin, Dublin 2, Ireland
- The Irish Longitudinal Study on Ageing, Trinity College Dublin, Dublin 2, Ireland
| | - Daniel Carey
- The Irish Longitudinal Study on Ageing, Trinity College Dublin, Dublin 2, Ireland
- Department of Medical Gerontology, Trinity College Dublin, Dublin 2, Ireland
| | - Ian H Robertson
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
- Global Brain Health Institute, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Derya D Emek-Savaş
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, İzmir, Turkey
- Global Brain Health Institute, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
- Department of Psychology, Faculty of Letters, Dokuz Eylul University, İzmir, Turkey
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY, USA
| | - Rose Anne Kenny
- School of Medicine, Trinity College Dublin, Dublin 2, Ireland
- The Irish Longitudinal Study on Ageing, Trinity College Dublin, Dublin 2, Ireland
- Mercer's Institute for Successful Ageing, St. James's Hospital, Dublin 8, Ireland
| | - Robert Whelan
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland.
- Global Brain Health Institute, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland.
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18
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Acute cognitive impairment after traumatic brain injury predicts the occurrence of brain atrophy patterns similar to those observed in Alzheimer's disease. GeroScience 2021; 43:2015-2039. [PMID: 33900530 DOI: 10.1007/s11357-021-00355-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 03/10/2021] [Indexed: 10/21/2022] Open
Abstract
Traumatic brain injuries (TBIs) are often followed by persistent structural brain alterations and by cognitive sequalae, including memory deficits, reduced neural processing speed, impaired social function, and decision-making difficulties. Although mild TBI (mTBI) is a risk factor for Alzheimer's disease (AD), the extent to which these conditions share patterns of macroscale neurodegeneration has not been quantified. Comparing such patterns can not only reveal how the neurodegenerative trajectories of TBI and AD are similar, but may also identify brain atrophy features which can be leveraged to prognosticate AD risk after TBI. The primary aim of this study is to systematically map how TBI affects white matter (WM) and gray matter (GM) properties in AD-analogous patterns. Our findings identify substantial similarities in the regional macroscale neurodegeneration patterns associated with mTBI and AD. In cerebral GM, such similarities are most extensive in brain areas involved in memory and executive function, such as the temporal poles and orbitofrontal cortices, respectively. Our results indicate that the spatial pattern of cerebral WM degradation observed in AD is broadly similar to the pattern of diffuse axonal injury observed in TBI, which frequently affects WM structures like the fornix, corpus callosum, and corona radiata. Using machine learning, we find that the severity of AD-like brain changes observed during the chronic stage of mTBI can be accurately prognosticated based on acute assessments of post-traumatic mild cognitive impairment. These findings suggest that acute post-traumatic cognitive impairment predicts the magnitude of AD-like brain atrophy, which is itself associated with AD risk.
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19
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Kottlarz I, Berg S, Toscano-Tejeida D, Steinmann I, Bähr M, Luther S, Wilke M, Parlitz U, Schlemmer A. Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities. Front Physiol 2021; 11:614565. [PMID: 33597891 PMCID: PMC7882607 DOI: 10.3389/fphys.2020.614565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 12/16/2020] [Indexed: 11/30/2022] Open
Abstract
In this study, ordinal pattern analysis and classical frequency-based EEG analysis methods are used to differentiate between EEGs of different age groups as well as individuals. As characteristic features, functional connectivity as well as single-channel measures in both the time and frequency domain are considered. We compare the separation power of each feature set after nonlinear dimensionality reduction using t-distributed stochastic neighbor embedding and demonstrate that ordinal pattern-based measures yield results comparable to frequency-based measures applied to preprocessed data, and outperform them if applied to raw data. Our analysis yields no significant differences in performance between single-channel features and functional connectivity features regarding the question of age group separation.
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Affiliation(s)
- Inga Kottlarz
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.,Institute for the Dynamics of Complex Systems, Georg-August-Universität Göttingen, Göttingen, Germany
| | - Sebastian Berg
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Diana Toscano-Tejeida
- Department of Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Iris Steinmann
- Department of Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Mathias Bähr
- Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Stefan Luther
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.,Institute of Pharmacology and Toxicology, University Medical Center Göttingen, Göttingen, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Melanie Wilke
- Department of Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany.,German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Ulrich Parlitz
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.,Institute for the Dynamics of Complex Systems, Georg-August-Universität Göttingen, Göttingen, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Alexander Schlemmer
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany
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20
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Levakov G, Rosenthal G, Shelef I, Raviv TR, Avidan G. From a deep learning model back to the brain-Identifying regional predictors and their relation to aging. Hum Brain Mapp 2020; 41:3235-3252. [PMID: 32320123 PMCID: PMC7426775 DOI: 10.1002/hbm.25011] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 02/27/2020] [Accepted: 04/07/2020] [Indexed: 12/16/2022] Open
Abstract
We present a Deep Learning framework for the prediction of chronological age from structural magnetic resonance imaging scans. Previous findings associate increased brain age with neurodegenerative diseases and higher mortality rates. However, the importance of brain age prediction goes beyond serving as biomarkers for neurological disorders. Specifically, utilizing convolutional neural network (CNN) analysis to identify brain regions contributing to the prediction can shed light on the complex multivariate process of brain aging. Previous work examined methods to attribute pixel/voxel-wise contributions to the prediction in a single image, resulting in "explanation maps" that were found noisy and unreliable. To address this problem, we developed an inference scheme for combining these maps across subjects, thus creating a population-based, rather than a subject-specific map. We applied this method to a CNN ensemble trained on predicting subjects' age from raw T1 brain images in a lifespan sample of 10,176 subjects. Evaluating the model on an untouched test set resulted in mean absolute error of 3.07 years and a correlation between chronological and predicted age of r = 0.98. Using the inference method, we revealed that cavities containing cerebrospinal fluid, previously found as general atrophy markers, had the highest contribution for age prediction. Comparing maps derived from different models within the ensemble allowed to assess differences and similarities in brain regions utilized by the model. We showed that this method substantially increased the replicability of explanation maps, converged with results from voxel-based morphometry age studies and highlighted brain regions whose volumetric variability correlated the most with the prediction error.
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Affiliation(s)
- Gidon Levakov
- Department of Cognitive and Brain SciencesBen‐Gurion University of the NegevBeer‐ShevaIsrael
- Zlotowski Center for NeuroscienceBen‐Gurion University of the NegevBeer‐ShevaIsrael
| | - Gideon Rosenthal
- Department of Cognitive and Brain SciencesBen‐Gurion University of the NegevBeer‐ShevaIsrael
- Zlotowski Center for NeuroscienceBen‐Gurion University of the NegevBeer‐ShevaIsrael
| | - Ilan Shelef
- Zlotowski Center for NeuroscienceBen‐Gurion University of the NegevBeer‐ShevaIsrael
- Department of Diagnostic ImagingBen‐Gurion University of the NegevBeer‐ShevaIsrael
| | - Tammy Riklin Raviv
- Zlotowski Center for NeuroscienceBen‐Gurion University of the NegevBeer‐ShevaIsrael
- The School of Electrical and Computer EngineeringBen Gurion University of the NegevBeer‐ShevaIsrael
| | - Galia Avidan
- Department of Cognitive and Brain SciencesBen‐Gurion University of the NegevBeer‐ShevaIsrael
- Zlotowski Center for NeuroscienceBen‐Gurion University of the NegevBeer‐ShevaIsrael
- Department of PsychologyBen‐Gurion University of the NegevBeer‐ShevaIsrael
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21
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Irimia A, Maher AS, Chaudhari NN, Chowdhury NF, Jacobs EB. Acute cognitive deficits after traumatic brain injury predict Alzheimer's disease-like degradation of the human default mode network. GeroScience 2020; 42:1411-1429. [PMID: 32743786 DOI: 10.1007/s11357-020-00245-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 07/29/2020] [Indexed: 02/06/2023] Open
Abstract
Traumatic brain injury (TBI) and Alzheimer's disease (AD) are prominent neurological conditions whose neural and cognitive commonalities are poorly understood. The extent of TBI-related neurophysiological abnormalities has been hypothesized to reflect AD-like neurodegeneration because TBI can increase vulnerability to AD. However, it remains challenging to prognosticate AD risk partly because the functional relationship between acute posttraumatic sequelae and chronic AD-like degradation remains elusive. Here, functional magnetic resonance imaging (fMRI), network theory, and machine learning (ML) are leveraged to study the extent to which geriatric mild TBI (mTBI) can lead to AD-like alteration of resting-state activity in the default mode network (DMN). This network is found to contain modules whose extent of AD-like, posttraumatic degradation can be accurately prognosticated based on the acute cognitive deficits of geriatric mTBI patients with cerebral microbleeds. Aside from establishing a predictive physiological association between geriatric mTBI, cognitive impairment, and AD-like functional degradation, these findings advance the goal of acutely forecasting mTBI patients' chronic deviations from normality along AD-like functional trajectories. The association of geriatric mTBI with AD-like changes in functional brain connectivity as early as ~6 months post-injury carries substantial implications for public health because TBI has relatively high prevalence in the elderly.
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Affiliation(s)
- Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA. .,Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
| | - Alexander S Maher
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Nikhil N Chaudhari
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Nahian F Chowdhury
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Elliot B Jacobs
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
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22
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Quantification of the Biological Age of the Brain Using Neuroimaging. HEALTHY AGEING AND LONGEVITY 2019. [DOI: 10.1007/978-3-030-24970-0_19] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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23
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Beheshti I, Maikusa N, Matsuda H. The association between "Brain-Age Score" (BAS) and traditional neuropsychological screening tools in Alzheimer's disease. Brain Behav 2018; 8:e01020. [PMID: 29931756 PMCID: PMC6085898 DOI: 10.1002/brb3.1020] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 04/05/2018] [Accepted: 05/09/2018] [Indexed: 12/29/2022] Open
Abstract
INTRODUCTION We present the Brain-Age Score (BAS) as a magnetic resonance imaging (MRI)-based index for Alzheimer's disease (AD). We developed a fully automated framework for estimating the BAS in healthy controls (HCs) and individuals with mild cognitive impairment (MCI) or AD, using MRI scans. METHODS We trained the proposed framework using 385 HCs from the IXI and OASIS datasets and evaluated 146 HCs, 102 stable-MCI (sMCI), 112 progressive-MCI (pMCI), and 147 AD patients from the J-ADNI dataset. We used a correlation test to determine the association between the BAS and four traditional screening tools of AD: the Mini-Mental State Examination (MMSE), Clinical Dementia Ratio (CDR), Alzheimer's Disease Assessment Score (ADAS), and Functional Assessment Questionnaire (FAQ). Furthermore, we assessed the association between BAS and anatomical MRI measurements: the normalized gray matter (nGM), normalized white matter (nWM), normalized cerebrospinal fluid (nCSF), mean cortical thickness as well as hippocampus volume. RESULTS The correlation results demonstrated that the BAS is in line with traditional screening tools of AD (i.e., the MMSE, CDR, ADAS, and FAQ scores) as well as anatomical MRI measurements (i.e., nGM, nCSF, mean cortical thickness, and hippocampus volume). DISCUSSION The BAS may be useful for diagnosing the brain atrophy level and can be a reliable automated index for clinical applications and neuropsychological screening tools.
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Affiliation(s)
- Iman Beheshti
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Norihide Maikusa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
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24
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Madan CR, Kensinger EA. Predicting age from cortical structure across the lifespan. Eur J Neurosci 2018; 47:399-416. [PMID: 29359873 PMCID: PMC5835209 DOI: 10.1111/ejn.13835] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 01/12/2018] [Accepted: 01/15/2018] [Indexed: 01/22/2023]
Abstract
Despite interindividual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. This study assessed how accurately an individual's age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from one region to 1000 regions. The age prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated nonlinear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology.
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Affiliation(s)
- Christopher R. Madan
- School of Psychology, University of Nottingham, Nottingham, UK
- Department of Psychology, Boston College, Chestnut Hill, MA, USA
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25
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Lancaster J, Lorenz R, Leech R, Cole JH. Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction. Front Aging Neurosci 2018; 10:28. [PMID: 29483870 PMCID: PMC5816033 DOI: 10.3389/fnagi.2018.00028] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Accepted: 01/23/2018] [Indexed: 02/02/2023] Open
Abstract
Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16-90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18-88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimization framework to the new dataset, out-performing the parameters optimized for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimization is conducted in specific contexts. This potentially motivates use of optimization techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias.
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Affiliation(s)
- Jenessa Lancaster
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, London, United Kingdom
| | - Romy Lorenz
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, London, United Kingdom
| | - Rob Leech
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, London, United Kingdom
| | - James H. Cole
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, London, United Kingdom,Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom,*Correspondence: James H. Cole, ;
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26
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Cole JH, Poudel RPK, Tsagkrasoulis D, Caan MWA, Steves C, Spector TD, Montana G. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage 2017; 163:115-124. [PMID: 28765056 DOI: 10.1016/j.neuroimage.2017.07.059] [Citation(s) in RCA: 410] [Impact Index Per Article: 58.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2017] [Revised: 07/20/2017] [Accepted: 07/28/2017] [Indexed: 01/02/2023] Open
Abstract
Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of 'brain-predicted age' as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h2 ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90-0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83-0.96) and poor-moderate levels for WM and raw data (0.51-0.77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings.
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Affiliation(s)
- James H Cole
- Computational, Cognitive & Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, UK
| | - Rudra P K Poudel
- Department of Biomedical Engineering, King's College London, London, UK
| | | | - Matthan W A Caan
- Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands
| | - Claire Steves
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Giovanni Montana
- Department of Biomedical Engineering, King's College London, London, UK; Department of Mathematics, Imperial College London, London, UK.
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27
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Li Y, Liu Y, Wang P, Wang J, Xu S, Qiu M. Dependency criterion based brain pathological age estimation of Alzheimer's disease patients with MR scans. Biomed Eng Online 2017; 16:50. [PMID: 28438167 PMCID: PMC5404315 DOI: 10.1186/s12938-017-0342-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Accepted: 04/19/2017] [Indexed: 12/20/2022] Open
Abstract
Objectives Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to the accelerated brain aging. Methods This paper considers this deviation and obtains it by maximizing the correlation between the estimated brain age and the class label rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to the prior knowledge. Secondly, use the support vector regression as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the correlation criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. Results The experimental results showed that the separability of the samples was apparently improved. For normal control- Alzheimer’s disease (NC-AD), normal control- mild cognition impairment (NC-MCI), and mild cognition impairment—Alzheimer’s disease (MCI-AD), the average improvements were 0.164 (31.66%), 0.1284 (34.29%), and 0.0206 (7.1%), respectively. For NC-MCI-AD, the average improvement was 0.2002 (50.39%). The estimated brain pathological age could be not only more helpful for the classification of AD but also more precisely reflect the accelerated brain aging. Conclusion In conclusion, this paper proposes a new kind of brain age—brain pathological age and offers an estimation method for it that can distinguish different states of AD, thereby better reflecting accelerated brain aging. Besides, the brain pathological age is most helpful for feature reduction, thereby simplifying the relevant classification algorithm.
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Affiliation(s)
- Yongming Li
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China. .,Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038, China. .,Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, 400044, China.
| | - Yuchuan Liu
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China
| | - Pin Wang
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China
| | - Jie Wang
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China
| | - Sha Xu
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China
| | - Mingguo Qiu
- Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038, China
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28
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Li Y, Li F, Wang P, Zhu X, Liu S, Qiu M, Zhang J, Zeng X. Estimating the brain pathological age of Alzheimer's disease patients from MR image data based on the separability distance criterion. Phys Med Biol 2016; 61:7162-7186. [PMID: 27649031 DOI: 10.1088/0031-9155/61/19/7162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Traditional age estimation methods are based on the same idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to accelerated brain aging. This paper considers this deviation and searches for it by maximizing the separability distance value rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to prior knowledge. Secondly, use the support vector regression (SVR) as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the separability distance criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. The experimental results showed that the separability was apparently improved. For normal control-Alzheimer's disease (NC-AD), normal control-mild cognition impairment (NC-MCI), and MCI-AD, the average improvements were 0.178 (35.11%), 0.033 (14.47%), and 0.017 (39.53%), respectively. For NC-MCI-AD, the average improvement was 0.2287 (64.22%). The estimated brain pathological age could be not only more helpful to the classification of AD but also more precisely reflect accelerated brain aging. In conclusion, this paper offers a new method for brain age estimation that can distinguish different states of AD and can better reflect the extent of accelerated aging.
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Affiliation(s)
- Yongming Li
- College of Communication Engineering, Chongqing University, Chongqing 400044, People's Republic of China. Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing 400038, People's Republic of China
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Differences between chronological and brain age are related to education and self-reported physical activity. Neurobiol Aging 2016; 40:138-144. [PMID: 26973113 DOI: 10.1016/j.neurobiolaging.2016.01.014] [Citation(s) in RCA: 136] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 01/13/2016] [Accepted: 01/25/2016] [Indexed: 02/03/2023]
Abstract
This study investigated the relationship between education and physical activity and the difference between a physiological prediction of age and chronological age (CA). Cortical and subcortical gray matter regional volumes were calculated from 331 healthy adults (range: 19-79 years). Multivariate analyses identified a covariance pattern of brain volumes best predicting CA (R(2) = 47%). Individual expression of this brain pattern served as a physiologic measure of brain age (BA). The difference between CA and BA was predicted by education and self-report measures of physical activity. Education and the daily number of flights of stairs climbed (FOSC) were the only 2 significant predictors of decreased BA. Effect sizes demonstrated that BA decreased by 0.95 years for each year of education and by 0.58 years for 1 additional FOSC daily. Effects of education and FOSC on regional brain volume were largely driven by temporal and subcortical volumes. These results demonstrate that higher levels of education and daily FOSC are related to larger brain volume than predicted by CA which supports the utility of regional gray matter volume as a biomarker of healthy brain aging.
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30
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Measures of Morphological Complexity of Gray Matter on Magnetic Resonance Imaging for Control Age Grouping. ENTROPY 2015. [DOI: 10.3390/e17127868] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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