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Jiang B, Ozkara BB, Zhu G, Boothroyd D, Allen JW, Barboriak DP, Chang P, Chan C, Chaudhari R, Chen H, Chukus A, Ding V, Douglas D, Filippi CG, Flanders AE, Godwin R, Hashmi S, Hess C, Hsu K, Lui YW, Maldjian JA, Michel P, Nalawade SS, Patel V, Raghavan P, Sair HI, Tanabe J, Welker K, Whitlow CT, Zaharchuk G, Wintermark M. Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition. AJNR Am J Neuroradiol 2024:ajnr.A8317. [PMID: 38663992 DOI: 10.3174/ajnr.a8317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 04/22/2024] [Indexed: 05/05/2024]
Abstract
BACKGROUND AND PURPOSE Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multicenter artificial intelligence competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. MATERIALS AND METHODS In total, 1201 anonymized, full-head NCCT clinical scans from 5 institutions were pooled to form the data set. The data set encompassed studies with normal findings as well as those with pathologies, including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these, task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient's age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed the performance of each model. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each artificial intelligence model, along with the area under the ROC curve. RESULTS Four teams processed 1177 studies. The median age of patients was 62 years, with an interquartile range of 33 years. Nineteen teams from various academic institutions registered for the competition. Of these, 4 teams submitted their final results. No commercial entities participated in the competition. For task 1, areas under the ROC curve ranged from 0.49 to 0.59. For task 2, two teams completed the task with area under the ROC curve values of 0.57 and 0.52. For task 3, teams had little-to-no agreement with the ground truth. CONCLUSIONS To assess the performance of artificial intelligence models in real-world clinical scenarios, we analyzed their performance in the ASFNR Artificial Intelligence Competition. The first ASFNR Competition underscored the gap between expectation and reality; and the models largely fell short in their assessments. As the integration of artificial intelligence tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.
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Affiliation(s)
- Bin Jiang
- From the Department of Radiology (B.J., C.C., R.C., A.C., D.D., S.H., G.Zaharchuk), Neuroradiology Division, Stanford University, Stanford, California
| | - Burak B Ozkara
- Department of Neuroradiology (B.B.O., H.C., M.W.), MD Anderson Cancer Center, Houston, Texas
| | - Guangming Zhu
- Department of Neurology (G.Zhu), The University of Arizona, Tucson, Arizona
| | - Derek Boothroyd
- Department of Medicine (D.B., V.D.), Stanford University School of Medicine, Stanford, California
| | - Jason W Allen
- Department of Radiology and Imaging Sciences (J.W.A.), Indiana University School of Medicine, Indianapolis, Indiana
| | - Daniel P Barboriak
- Department of Radiology (D.P.B.), Duke University Medical Center, Durham, North Carolina
| | - Peter Chang
- Department of Radiological Sciences (P.C.), University of California, Irvine, Irvine, California
| | - Cynthia Chan
- From the Department of Radiology (B.J., C.C., R.C., A.C., D.D., S.H., G.Zaharchuk), Neuroradiology Division, Stanford University, Stanford, California
| | - Ruchir Chaudhari
- From the Department of Radiology (B.J., C.C., R.C., A.C., D.D., S.H., G.Zaharchuk), Neuroradiology Division, Stanford University, Stanford, California
- Sutter Imaging (R.C.), Sutter Health, Sacramento, California
| | - Hui Chen
- Department of Neuroradiology (B.B.O., H.C., M.W.), MD Anderson Cancer Center, Houston, Texas
| | - Anjeza Chukus
- From the Department of Radiology (B.J., C.C., R.C., A.C., D.D., S.H., G.Zaharchuk), Neuroradiology Division, Stanford University, Stanford, California
| | - Victoria Ding
- Department of Medicine (D.B., V.D.), Stanford University School of Medicine, Stanford, California
| | - David Douglas
- From the Department of Radiology (B.J., C.C., R.C., A.C., D.D., S.H., G.Zaharchuk), Neuroradiology Division, Stanford University, Stanford, California
| | | | - Adam E Flanders
- Department of Radiology (A.E.F.), Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Ryan Godwin
- Department of Radiology (R.G.), University of Alabama at Birmingham, Birmingham, Alabama
| | - Syed Hashmi
- From the Department of Radiology (B.J., C.C., R.C., A.C., D.D., S.H., G.Zaharchuk), Neuroradiology Division, Stanford University, Stanford, California
| | - Christopher Hess
- Department of Radiology and Biomedical Imaging (C.H.), University of California, San Francisco, San Francisco, California
| | - Kevin Hsu
- Department of Radiology (K.H., Y.W.L), New York University Grossman School of Medicine, New York, New York
| | - Yvonne W Lui
- Department of Radiology (K.H., Y.W.L), New York University Grossman School of Medicine, New York, New York
| | - Joseph A Maldjian
- Department of Radiology (J.A.M., S.S.N.), University of Texas Southwestern Medical Center, Dallas, Texas
| | - Patrik Michel
- Department of Clinical Neurosciences (P.M.), Lausanne University Hospital, Lausanne, Switzerland
| | - Sahil S Nalawade
- Department of Radiology (J.A.M., S.S.N.), University of Texas Southwestern Medical Center, Dallas, Texas
| | - Vishal Patel
- Department of Radiology (V.P.), Mayo Clinic, Jacksonville, Florida
| | - Prashant Raghavan
- Department of Diagnostic Radiology and Nuclear Medicine (P.R.), University of Maryland School of Medicine, Baltimore, Maryland
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science (H.I.S.), Johns Hopkins University, Baltimore, Maryland
- The Malone Center for Engineering in Healthcare (H.I.S.), Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jody Tanabe
- Department of Radiology (J.T.), University of Colorado, Aurora, Colorado
| | - Kirk Welker
- Department of Radiology (K.W.), Mayo Clinic, Rochester, Minnesota
| | - Christopher T Whitlow
- Department of Radiology (C.T.W), Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Greg Zaharchuk
- From the Department of Radiology (B.J., C.C., R.C., A.C., D.D., S.H., G.Zaharchuk), Neuroradiology Division, Stanford University, Stanford, California
| | - Max Wintermark
- Department of Neuroradiology (B.B.O., H.C., M.W.), MD Anderson Cancer Center, Houston, Texas
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Gao C, Kim ME, Lee HH, Yang Q, Khairi NM, Kanakaraj P, Newlin NR, Archer DB, Jefferson AL, Taylor WD, Boyd BD, Beason-Held LL, Resnick SM, Huo Y, Van Schaik KD, Schilling KG, Moyer D, Išgum I, Landman BA. Predicting Age from White Matter Diffusivity with Residual Learning. ARXIV 2024:arXiv:2311.03500v2. [PMID: 37986731 PMCID: PMC10659451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.
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Affiliation(s)
- Chenyu Gao
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
| | - Michael E Kim
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Ho Hin Lee
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Qi Yang
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Nazirah Mohd Khairi
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
| | | | - Nancy R Newlin
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Derek B Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Medicine, Vanderbilt University Medical Center, Nashville, USA
| | - Warren D Taylor
- Dept. of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Brian D Boyd
- Vanderbilt Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, USA
| | - Yuankai Huo
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Katherine D Van Schaik
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Kurt G Schilling
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Daniel Moyer
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Ivana Išgum
- Dept. of Biomedical Engineering and Physics, Dept. of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Bennett A Landman
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
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Soumya Kumari LK, Sundarrajan R. A review on brain age prediction models. Brain Res 2024; 1823:148668. [PMID: 37951563 DOI: 10.1016/j.brainres.2023.148668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/23/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023]
Abstract
Brain age in neuroimaging has emerged over the last decade and reflects the estimated age based on the brain MRI scan from a person. As a person ages, their brain structure will change, and these changes will be exclusive to males and females and will differ for each. White matter and grey matter density have a deeper relationship with brain aging. Hence, if the white matter and grey matter concentrations vary, the rate at which the brain ages will also vary. Neurodegenerative illnesses can be detected using the biomarker known as brain age. The development of deep learning has made it possible to analyze structural neuroimaging data in new ways, notably by predicting brain ages. We introduce the techniques and possible therapeutic uses of brain age prediction in this cutting-edge review. Creating a machine learning regression model to analyze age-related changes in brain structure among healthy individuals is a typical procedure in studies focused on brain aging. Subsequently, this model is employed to forecast the aging of brains in new individuals. The concept of the "brain-age gap" refers to the difference between an individual's predicted brain age and their actual chronological age. This score may serve as a gauge of the general state of the brain's health while also reflecting neuroanatomical disorders. It may help differential diagnosis, prognosis, and therapy decisions as well as early identification of brain-based illnesses. The following is a summary of the many forecasting techniques utilized over the past 11 years to estimate brain age. The study's conundrums and potential outcomes of the brain age predicted by current models will both be covered.
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Affiliation(s)
- L K Soumya Kumari
- Computer Science Engineering, Mohandas College of Engineering and Technology, Anad, India.
| | - R Sundarrajan
- Information Technology, School of Computing, Kalasalingam Academy of Research and Education, India.
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Tian C, Ye Z, McCoy RG, Pan Y, Bi C, Gao S, Ma Y, Chen M, Yu J, Lu T, Hong LE, Kochunov P, Ma T, Chen S, Liu S. The causal effect of HbA1c on white matter brain aging by two-sample Mendelian randomization analysis. Front Neurosci 2024; 17:1335500. [PMID: 38274506 PMCID: PMC10808780 DOI: 10.3389/fnins.2023.1335500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/28/2023] [Indexed: 01/27/2024] Open
Abstract
Background Poor glycemic control with elevated levels of hemoglobin A1c (HbA1c) is associated with increased risk of cognitive impairment, with potentially varying effects between sexes. However, the causal impact of poor glycemic control on white matter brain aging in men and women is uncertain. Methods We used two nonoverlapping data sets from UK Biobank cohort: gene-outcome group (with neuroimaging data, (N = 15,193; males/females: 7,101/8,092)) and gene-exposure group (without neuroimaging data, (N = 279,011; males/females: 122,638/156,373)). HbA1c was considered the exposure and adjusted "brain age gap" (BAG) was calculated on fractional anisotropy (FA) obtained from brain imaging as the outcome, thereby representing the difference between predicted and chronological age. The causal effects of HbA1c on adjusted BAG were studied using the generalized inverse variance weighted (gen-IVW) and other sensitivity analysis methods, including Mendelian randomization (MR)-weighted median, MR-pleiotropy residual sum and outlier, MR-using mixture models, and leave-one-out analysis. Results We found that for every 6.75 mmol/mol increase in HbA1c, there was an increase of 0.49 (95% CI = 0.24, 0.74; p-value = 1.30 × 10-4) years in adjusted BAG. Subgroup analyses by sex and age revealed significant causal effects of HbA1c on adjusted BAG, specifically among men aged 60-73 (p-value = 2.37 × 10-8). Conclusion Poor glycemic control has a significant causal effect on brain aging, and is most pronounced among older men aged 60-73 years, which provides insights between glycemic control and the susceptibility to age-related neurodegenerative diseases.
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Affiliation(s)
- Cheng Tian
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Rozalina G. McCoy
- Division of Endocrinology, Diabetes, & Nutrition, Department of Medicine, School of Medicine, University of Maryland, Baltimore, MD, United States
- University of Maryland Institute for Health Computing, Bethesda, MD, United States
| | - Yezhi Pan
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Chuan Bi
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Si Gao
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Yizhou Ma
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Mo Chen
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Jiaao Yu
- Department of Mathematics, University of Maryland, College Park, MD, United States
| | - Tong Lu
- Department of Mathematics, University of Maryland, College Park, MD, United States
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, United States
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Song Liu
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
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Yang Y, Sathe A, Schilling K, Shashikumar N, Moore E, Dumitrescu L, Pechman KR, Landman BA, Gifford KA, Hohman TJ, Jefferson AL, Archer DB. A deep neural network estimation of brain age is sensitive to cognitive impairment and decline. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024; 29:148-162. [PMID: 38160276 PMCID: PMC10764074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62x10-32; T1: r=0.61, p=1.45x10-26, FW+T1: r=0.77, p=6.48x10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: β=-1.094, p=6.32x10-7; T1: β=-1.331, p=6.52x10-7; FW+T1: β=-1.476, p=2.53x10-10; executive function, FW: β=-1.276, p=1.46x10-9; T1: β=-1.337, p=2.52x10-7; FW+T1: β=-1.850, p=3.85x10-17) and longitudinal cognition (memory, FW: β=-0.091, p=4.62x10-11; T1: β=-0.097, p=1.40x10-8; FW+T1: β=-0.101, p=1.35x10-11; executive function, FW: β=-0.125, p=1.20x10-10; T1: β=-0.163, p=4.25x10-12; FW+T1: β=-0.158, p=1.65x10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.
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Affiliation(s)
- Yisu Yang
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Aditi Sathe
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Kurt Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Elizabeth Moore
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Kimberly R. Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Bennett A. Landman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA, 37212
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA, 37212
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Katherine A. Gifford
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Derek B. Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
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Nguyen H, Clément M, Mansencal B, Coupé P. Brain structure ages-A new biomarker for multi-disease classification. Hum Brain Mapp 2024; 45:e26558. [PMID: 38224546 PMCID: PMC10785199 DOI: 10.1002/hbm.26558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 11/20/2023] [Accepted: 11/25/2023] [Indexed: 01/17/2024] Open
Abstract
Age is an important variable to describe the expected brain's anatomy status across the normal aging trajectory. The deviation from that normative aging trajectory may provide some insights into neurological diseases. In neuroimaging, predicted brain age is widely used to analyze different diseases. However, using only the brain age gap information (i.e., the difference between the chronological age and the estimated age) can be not enough informative for disease classification problems. In this paper, we propose to extend the notion of global brain age by estimating brain structure ages using structural magnetic resonance imaging. To this end, an ensemble of deep learning models is first used to estimate a 3D aging map (i.e., voxel-wise age estimation). Then, a 3D segmentation mask is used to obtain the final brain structure ages. This biomarker can be used in several situations. First, it enables to accurately estimate the brain age for the purpose of anomaly detection at the population level. In this situation, our approach outperforms several state-of-the-art methods. Second, brain structure ages can be used to compute the deviation from the normal aging process of each brain structure. This feature can be used in a multi-disease classification task for an accurate differential diagnosis at the subject level. Finally, the brain structure age deviations of individuals can be visualized, providing some insights about brain abnormality and helping clinicians in real medical contexts.
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Affiliation(s)
- Huy‐Dung Nguyen
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Michaël Clément
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Boris Mansencal
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Pierrick Coupé
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
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7
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Joo Y, Namgung E, Jeong H, Kang I, Kim J, Oh S, Lyoo IK, Yoon S, Hwang J. Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms. Sci Rep 2023; 13:22388. [PMID: 38104173 PMCID: PMC10725434 DOI: 10.1038/s41598-023-49514-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023] Open
Abstract
The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex information. Our model for brain age prediction, built on deep neural networks, employed a dataset of 3004 healthy subjects aged 18 and above. The T1-weighted images were minimally preprocessed and analyzed using the convolutional neural network (CNN) algorithm. The categorical sex information was then incorporated using the multi-layer perceptron (MLP) algorithm. We trained and validated both a CNN-only algorithm (utilizing only brain structural imaging data), and a combined CNN-MLP algorithm (using both structural brain imaging data and sex information) for age prediction. By integrating sex information with T1-weighted imaging data, our proposed CNN-MLP algorithm outperformed not only the CNN-only algorithm but also established algorithms, such as brainageR, in prediction accuracy. Notably, this hybrid CNN-MLP algorithm effectively distinguished between mild cognitive impairment and Alzheimer's disease groups by identifying variances in brain age gaps between them, highlighting the algorithm's potential for clinical application. Overall, these results underscore the enhanced precision of the CNN-MLP algorithm in brain age prediction, achieved through the integration of sex information.
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Affiliation(s)
- Yoonji Joo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Eun Namgung
- Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea
| | - Hyeonseok Jeong
- Department of Radiology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Ilhyang Kang
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Jinsol Kim
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Sohyun Oh
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
| | - In Kyoon Lyoo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Sujung Yoon
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea.
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea.
| | - Jaeuk Hwang
- Department of Psychiatry, Soonchunhyang University College of Medicine, Seoul, South Korea.
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Yoon HS, Oh J, Kim YC. Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information. Brain Sci 2023; 13:1512. [PMID: 38002472 PMCID: PMC10669197 DOI: 10.3390/brainsci13111512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/19/2023] [Accepted: 10/23/2023] [Indexed: 11/26/2023] Open
Abstract
This study aimed to develop and validate machine learning (ML) models that predict age using intracranial vessels' tortuosity and diameter features derived from magnetic resonance angiography (MRA) data. A total of 171 subjects' three-dimensional (3D) time-of-flight MRA image data were considered for analysis. After annotations of two endpoints in each arterial segment, tortuosity features such as the sum of the angle metrics, triangular index, relative length, and product of the angle distance, as well as the vessels' diameter features, were extracted and used to train and validate the ML models for age prediction. Features extracted from the right and left internal carotid arteries (ICA) and basilar arteries were considered as the inputs to train and validate six ML regression models with a four-fold cross validation. The random forest regression model resulted in the lowest root mean square error of 14.9 years and the highest average coefficient of determination of 0.186. The linear regression model showed the lowest average mean absolute percentage error (MAPE) and the highest average Pearson correlation coefficient (0.532). The mean diameter of the right ICA vessel segment was the most important feature contributing to prediction of age in two out of the four regression models considered. An ML of tortuosity descriptors and diameter features extracted from MRA data showed a modest correlation between real age and ML-predicted age. Further studies are warranted for the assessment of the model's age predictions in patients with intracranial vessel diseases.
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Affiliation(s)
| | | | - Yoon-Chul Kim
- Division of Digital Healthcare, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju 26493, Republic of Korea; (H.-S.Y.); (J.O.)
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Yang Y, Sathe A, Schilling K, Shashikumar N, Moore E, Dumitrescu L, Pechman KR, Landman BA, Gifford KA, Hohman TJ, Jefferson AL, Archer DB. A deep neural network estimation of brain age is sensitive to cognitive impairment and decline. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.10.552494. [PMID: 37645837 PMCID: PMC10461919 DOI: 10.1101/2023.08.10.552494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62×10-32; T1: r=0.61, p=1.45×10-26, FW+T1: r=0.77, p=6.48×10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: β=-1.094, p=6.32×10-7; T1: β=-1.331, p=6.52×10-7; FW+T1: β=-1.476, p=2.53×10-10; executive function, FW: β=-1.276, p=1.46×10-9; T1: β=-1.337, p=2.52×10-7; FW+T1: β=-1.850, p=3.85×10-17) and longitudinal cognition (memory, FW: β=-0.091, p=4.62×10-11; T1: β=-0.097, p=1.40×10-8; FW+T1: β=-0.101, p=1.35×10-11; executive function, FW: β=-0.125, p=1.20×10-10; T1: β=-0.163, p=4.25×10-12; FW+T1: β=-0.158, p=1.65×10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.
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Affiliation(s)
- Yisu Yang
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Aditi Sathe
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Kurt Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Elizabeth Moore
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Kimberly R. Pechman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Bennett A. Landman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA, 37212
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA, 37212
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Katherine A. Gifford
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
| | - Derek B. Archer
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37212
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA, 37212
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10
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Terzi R. An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI. Diagnostics (Basel) 2023; 13:diagnostics13081494. [PMID: 37189595 DOI: 10.3390/diagnostics13081494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
This paper proposes ensemble strategies for the deep learning object detection models carried out by combining the variants of a model and different models to enhance the anatomical and pathological object detection performance in brain MRI. In this study, with the help of the novel Gazi Brains 2020 dataset, five different anatomical parts and one pathological part that can be observed in brain MRI were identified, such as the region of interest, eye, optic nerves, lateral ventricles, third ventricle, and a whole tumor. Firstly, comprehensive benchmarking of the nine state-of-the-art object detection models was carried out to determine the capabilities of the models in detecting the anatomical and pathological parts. Then, four different ensemble strategies for nine object detectors were applied to boost the detection performance using the bounding box fusion technique. The ensemble of individual model variants increased the anatomical and pathological object detection performance by up to 10% in terms of the mean average precision (mAP). In addition, considering the class-based average precision (AP) value of the anatomical parts, an up to 18% AP improvement was achieved. Similarly, the ensemble strategy of the best different models outperformed the best individual model by 3.3% mAP. Additionally, while an up to 7% better FAUC, which is the area under the TPR vs. FPPI curve, was achieved on the Gazi Brains 2020 dataset, a 2% better FAUC score was obtained on the BraTS 2020 dataset. The proposed ensemble strategies were found to be much more efficient in finding the anatomical and pathological parts with a small number of anatomic objects, such as the optic nerve and third ventricle, and producing higher TPR values, especially at low FPPI values, compared to the best individual methods.
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Affiliation(s)
- Ramazan Terzi
- Department of Big Data and Artificial Intelligence, Digital Transformation Office of the Presidency of Republic of Türkiye, Ankara 06100, Turkey
- Department of Computer Engineering, Amasya University, Amasya 05100, Turkey
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11
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Chen Y, Mayerhofer E, Parodi L, Harloff A, Batra P, Rosand J, Anderson CD. Beyond the Limits of Visual Learning: Prediction of Time From Onset From Noncontrast CT of Acute Ischemic Stroke. Stroke 2023; 54:e7-e8. [PMID: 36475469 PMCID: PMC9782690 DOI: 10.1161/strokeaha.122.041370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Yutong Chen
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, USA
- The Broad Institute of Harvard and MIT, Cambridge, USA
| | - Ernst Mayerhofer
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, USA
- The Broad Institute of Harvard and MIT, Cambridge, USA
- Department of Neurology and Neurophysiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Livia Parodi
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, USA
- The Broad Institute of Harvard and MIT, Cambridge, USA
- Department of Neurology, Brigham and Women’s Hospital, Boston, USA
| | - Andreas Harloff
- Department of Neurology and Neurophysiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Puneet Batra
- The Broad Institute of Harvard and MIT, Cambridge, USA
| | - Jonathan Rosand
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, USA
- The Broad Institute of Harvard and MIT, Cambridge, USA
| | - Christopher D Anderson
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, USA
- The Broad Institute of Harvard and MIT, Cambridge, USA
- Department of Neurology, Brigham and Women’s Hospital, Boston, USA
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12
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Mouches P, Wilms M, Aulakh A, Langner S, Forkert ND. Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors. Front Neurol 2022; 13:979774. [PMID: 36588902 PMCID: PMC9794870 DOI: 10.3389/fneur.2022.979774] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 11/16/2022] [Indexed: 12/15/2022] Open
Abstract
Introduction The difference between the chronological and biological brain age, called the brain age gap (BAG), has been identified as a promising biomarker to detect deviation from normal brain aging and to indicate the presence of neurodegenerative diseases. Moreover, the BAG has been shown to encode biological information about general health, which can be measured through cardiovascular risk factors. Current approaches for biological brain age estimation, and therefore BAG estimation, either depend on hand-crafted, morphological measurements extracted from brain magnetic resonance imaging (MRI) or on direct analysis of brain MRI images. The former can be processed with traditional machine learning models while the latter is commonly processed with convolutional neural networks (CNNs). Using a multimodal setting, this study aims to compare both approaches in terms of biological brain age prediction accuracy and biological information captured in the BAG. Methods T1-weighted MRI, containing brain tissue information, and magnetic resonance angiography (MRA), providing information about brain arteries, from 1,658 predominantly healthy adults were used. The volumes, surface areas, and cortical thickness of brain structures were extracted from the T1-weighted MRI data, while artery density and thickness within the major blood flow territories and thickness of the major arteries were extracted from MRA data. Independent multilayer perceptron and CNN models were trained to estimate the brain age from the hand-crafted features and image data, respectively. Next, both approaches were fused to assess the benefits of combining image data and hand-crafted features for brain age prediction. Results The combined model achieved a mean absolute error of 4 years between the chronological and predicted biological brain age. Among the independent models, the lowest mean absolute error was observed for the CNN using T1-weighted MRI data (4.2 years). When evaluating the BAGs obtained using the different approaches and imaging modalities, diverging associations between cardiovascular risk factors were found. For example, BAGs obtained from the CNN models showed an association with systolic blood pressure, while BAGs obtained from hand-crafted measurements showed greater associations with obesity markers. Discussion In conclusion, the use of more diverse sources of data can improve brain age estimation modeling and capture more diverse biological deviations from normal aging.
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Affiliation(s)
- Pauline Mouches
- Biomedical Engineering Program, University of Calgary, Calgary, AB, Canada,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Radiology, University of Calgary, Calgary, AB, Canada,*Correspondence: Pauline Mouches
| | - Matthias Wilms
- Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Agampreet Aulakh
- Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Sönke Langner
- Institute for Diagnostic Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Nils D. Forkert
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Radiology, University of Calgary, Calgary, AB, Canada,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
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13
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Ahmed R, Ryan C, Christman S, Elson D, Bermudez C, Landman BA, Szymkowicz SM, Boyd BD, Kang H, Taylor WD. Structural MRI-Based Measures of Accelerated Brain Aging do not Moderate the Acute Antidepressant Response in Late-Life Depression. Am J Geriatr Psychiatry 2022; 30:1015-1025. [PMID: 34949526 PMCID: PMC9142760 DOI: 10.1016/j.jagp.2021.11.011] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/14/2021] [Accepted: 11/21/2021] [Indexed: 01/22/2023]
Abstract
OBJECTIVE Late-life depression (LLD) is characterized by accelerated biological aging. Accelerated brain aging, estimated from structural magnetic resonance imaging (sMRI) data by a machine learning algorithm, is associated with LLD diagnosis, poorer cognitive performance, and disability. We hypothesized that accelerated brain aging moderates the antidepressant response. DESIGN AND INTERVENTIONS Following MRI, participants entered an 8-week randomized, controlled trial of escitalopram. Nonremitting participants then entered an open-label 8-week trial of bupropion. PARTICIPANTS Ninety-five individuals with LLD. MEASUREMENTS A machine learning algorithm estimated each participant's brain age from sMRI data. This was used to calculate the brain-age gap (BAG), or how estimated age differed from chronological age. Secondary sMRI measures of aging pathology included white matter hyperintensity (WMH) volumes and hippocampal volumes. Mixed models examined the relationship between sMRI measures and change in depression severity. Initial analyses tested for a moderating effect of MRI measures on change in depression severity with escitalopram. Subsequent analyses tested for the effect of MRI measures on change in depression severity over time across trials. RESULTS In the blinded initial phase, BAG was not significantly associated with a differential response to escitalopram over time. BAG was also not associated with a change in depression severity over time across both arms in the blinded phase or in the subsequent open-label bupropion phase. We similarly did not observe effects of WMH volume or hippocampal volume on change in depression severity over time. CONCLUSION sMRI markers of accelerated brain aging were not associated with treatment response in this sequential antidepressant trial.
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Affiliation(s)
- Ryan Ahmed
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Claire Ryan
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Seth Christman
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Damian Elson
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Camilo Bermudez
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Bennett A Landman
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Sarah M Szymkowicz
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Brian D Boyd
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Hakmook Kang
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Warren D Taylor
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN.
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Chen JV, Chaudhari G, Hess CP, Glenn OA, Sugrue LP, Rauschecker AM, Li Y. Deep Learning to Predict Neonatal and Infant Brain Age from Myelination on Brain MRI Scans. Radiology 2022; 305:678-687. [DOI: 10.1148/radiol.211860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Joshua Vic Chen
- From the School of Medicine (J.V.C., G.C.) and Department of Radiology and Biomedical Imaging (C.P.H., O.A.G., L.P.S., A.M.R., Y.L.), University of California, San Francisco, 505 Parnassus Avenue, M-391, San Francisco, CA 94143-0628
| | - Gunvant Chaudhari
- From the School of Medicine (J.V.C., G.C.) and Department of Radiology and Biomedical Imaging (C.P.H., O.A.G., L.P.S., A.M.R., Y.L.), University of California, San Francisco, 505 Parnassus Avenue, M-391, San Francisco, CA 94143-0628
| | - Christopher P. Hess
- From the School of Medicine (J.V.C., G.C.) and Department of Radiology and Biomedical Imaging (C.P.H., O.A.G., L.P.S., A.M.R., Y.L.), University of California, San Francisco, 505 Parnassus Avenue, M-391, San Francisco, CA 94143-0628
| | - Orit A. Glenn
- From the School of Medicine (J.V.C., G.C.) and Department of Radiology and Biomedical Imaging (C.P.H., O.A.G., L.P.S., A.M.R., Y.L.), University of California, San Francisco, 505 Parnassus Avenue, M-391, San Francisco, CA 94143-0628
| | - Leo P. Sugrue
- From the School of Medicine (J.V.C., G.C.) and Department of Radiology and Biomedical Imaging (C.P.H., O.A.G., L.P.S., A.M.R., Y.L.), University of California, San Francisco, 505 Parnassus Avenue, M-391, San Francisco, CA 94143-0628
| | - Andreas M. Rauschecker
- From the School of Medicine (J.V.C., G.C.) and Department of Radiology and Biomedical Imaging (C.P.H., O.A.G., L.P.S., A.M.R., Y.L.), University of California, San Francisco, 505 Parnassus Avenue, M-391, San Francisco, CA 94143-0628
| | - Yi Li
- From the School of Medicine (J.V.C., G.C.) and Department of Radiology and Biomedical Imaging (C.P.H., O.A.G., L.P.S., A.M.R., Y.L.), University of California, San Francisco, 505 Parnassus Avenue, M-391, San Francisco, CA 94143-0628
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15
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Charting the potential of brain computed tomography deep learning systems. J Clin Neurosci 2022; 99:217-223. [DOI: 10.1016/j.jocn.2022.03.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 02/17/2022] [Accepted: 03/08/2022] [Indexed: 12/22/2022]
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16
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Attention-guided deep learning for gestational age prediction using fetal brain MRI. Sci Rep 2022; 12:1408. [PMID: 35082346 PMCID: PMC8791965 DOI: 10.1038/s41598-022-05468-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 01/05/2022] [Indexed: 12/18/2022] Open
Abstract
Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly changing appearance of the fetal brain, variable image quality, and frequent motion artifacts. Here we present an end-to-end, attention-guided deep learning model that predicts gestational age with R2 score of 0.945, mean absolute error of 6.7 days, and concordance correlation coefficient of 0.970. The convolutional neural network was trained on a heterogeneous dataset of 741 developmentally normal fetal brain images ranging from 19 to 39 weeks in gestational age. We also demonstrate model performance and generalizability using independent datasets from four academic institutions across the U.S. and Turkey with R2 scores of 0.81–0.90 after minimal fine-tuning. The proposed regression algorithm provides an automated machine-enabled tool with the potential to better characterize in utero neurodevelopment and guide real-time gestational age estimation after the first trimester.
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Ray B, Duan K, Chen J, Fu Z, Suresh P, Johnson S, Calhoun VD, Liu J. Multimodal Brain Age Prediction with Feature Selection and Comparison. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3858-3864. [PMID: 34892076 DOI: 10.1109/embc46164.2021.9631007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Brain age, an estimated biological age from anatomical and/or functional brain imaging data, and its deviation from the chronological age (brain age gap) have shown the potential to serve as biomarkers for characterizing typical brain development, the abnormal aging process, and early indicators of clinical neuropsychiatric problems. In this study, we leverage multimodal brain imaging data for brain age prediction. We studied and compared the performance of individual data modalities (gray matter density in components and regions of interest, cortical and subcortical anatomical features, resting-state functional connectivity) and different combinations of multiple data modalities using data collected from 1417 participants with age between 8 and 22 years. The result indicates that feature selection and multimodal imaging data can improve brain age prediction with linear support vector and partial least squares regression models. We have achieved a mean absolute error of 1.22 years on the test data with 188 features selected equally from all data sources, better than any individual source. After bias correction, the brain age gap was significantly associated with attention accuracy/speed and motor speed in addition to age. Our results conclude that traditional machine learning with proper feature selection can achieve similar if not better performance compared to complex deep learning neural network methods for the used sample size.
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18
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Hong J, Yun HJ, Park G, Kim S, Ou Y, Vasung L, Rollins CK, Ortinau CM, Takeoka E, Akiyama S, Tarui T, Estroff JA, Grant PE, Lee JM, Im K. Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging. Front Neurosci 2021; 15:714252. [PMID: 34707474 PMCID: PMC8542770 DOI: 10.3389/fnins.2021.714252] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 09/08/2021] [Indexed: 11/23/2022] Open
Abstract
The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses between 15.9 and 38.7 weeks of gestational age (GA). We built a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices in different orthogonal planes without correction for interslice motion. In each fetus, multiple age predictions from different slices were generated, and the brain age was obtained using the mode that determined the most frequent value among the multiple predictions from the 2D single-channel CNN. We obtained a mean absolute error (MAE) of 0.125 weeks (0.875 days) between the GA and brain age across the fetuses. The use of multiplanar slices achieved significantly lower prediction error and its variance than the use of a single slice and a single MRI stack. Our 2D single-channel CNN with multiplanar slices yielded a significantly lower stack-wise MAE (0.304 weeks) than the 2D multi-channel (MAE = 0.979, p < 0.001) and 3D (MAE = 1.114, p < 0.001) CNNs. The saliency maps from our method indicated that the anatomical information describing the cortex and ventricles was the primary contributor to brain age prediction. With the application of the proposed method to external MRIs from 21 healthy fetuses, we obtained an MAE of 0.508 weeks. Based on the external MRIs, we found that the stack-wise MAE of the 2D single-channel CNN (0.743 weeks) was significantly lower than those of the 2D multi-channel (1.466 weeks, p < 0.001) and 3D (1.241 weeks, p < 0.001) CNNs. These results demonstrate that our method with multiplanar slices accurately predicts fetal brain age without the need for increased dimensionality or complex MRI preprocessing steps.
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Affiliation(s)
- Jinwoo Hong
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Hyuk Jin Yun
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Gilsoon Park
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Seonggyu Kim
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Yangming Ou
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
- Computational Health Informatics Program, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Lana Vasung
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Caitlin K. Rollins
- Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Cynthia M. Ortinau
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, United States
| | - Emiko Takeoka
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA, United States
| | - Shizuko Akiyama
- Center for Perinatal and Neonatal Medicine, Tohoku University Hospital, Sendai, Japan
| | - Tomo Tarui
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA, United States
| | - Judy A. Estroff
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Patricia Ellen Grant
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Kiho Im
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
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19
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Sivaranjini S, Sujatha CM. Morphological analysis of subcortical structures for assessment of cognitive dysfunction in Parkinson's disease using multi-atlas based segmentation. Cogn Neurodyn 2021; 15:835-845. [PMID: 34603545 PMCID: PMC8448821 DOI: 10.1007/s11571-021-09671-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 01/27/2021] [Accepted: 02/25/2021] [Indexed: 12/16/2022] Open
Abstract
Cognitive impairment in Parkinson's Disease (PD) is the most prevalent non-motor symptom that requires analysis of anatomical associations to cognitive decline in PD. The objective of this study is to analyse the morphological variations of the subcortical structures to assess cognitive dysfunction in PD. In this study, T1 MR images of 58 Healthy Control (HC) and 135 PD subjects categorised as 91 Cognitively normal PD (NC-PD), 25 PD with Mild Cognitive Impairment (PD-MCI) and 19 PD with Dementia (PD-D) subjects, based on cognitive scores are utilised. The 132 anatomical regions are segmented using spatially localized multi-atlas model and volumetric analysis is carried out. The morphological alterations through textural features are captured to differentiate among the HC and PD subjects under different cognitive domains. The volumetric differences in the segmented subcortical structures of accumbens, amygdala, caudate, putamen and thalamus are able to predict cognitive impairment in PD. The volumetric distribution of the subcortical structures in PD-MCI subjects exhibit an overlap with the HC group due to lack of spatial specificity in their atrophy levels. The 3D GLCM features extracted from the significant subcortical structures could discriminate HC, NC-PD, PD-MCI and PD-D subjects with better classification accuracies. The disease related atrophy levels of the subcortical structures captured through morphological analysis provide sensitive evaluation of cognitive impairment in PD.
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Affiliation(s)
- S. Sivaranjini
- Department of Electronics and Communication Engineering, College of Engineering (CEG), Anna University, Chennai, India
| | - C. M. Sujatha
- Department of Electronics and Communication Engineering, College of Engineering (CEG), Anna University, Chennai, India
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20
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Kawaguchi M, Kidokoro H, Ito R, Shiraki A, Suzuki T, Maki Y, Tanaka M, Sakaguchi Y, Yamamoto H, Takahashi Y, Naganawa S, Natsume J. Age estimates from brain magnetic resonance images of children younger than two years of age using deep learning. Magn Reson Imaging 2021; 79:38-44. [PMID: 33716113 DOI: 10.1016/j.mri.2021.03.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 01/19/2021] [Accepted: 03/09/2021] [Indexed: 11/16/2022]
Abstract
The accuracy of brain age estimates from magnetic resonance (MR) images has improved with the advent of deep learning artificial intelligence (AI) models. However, most previous studies on predicting age emphasized aging from childhood to adulthood and old age, and few studies have focused on early brain development in children younger than 2 years of age. Here, we performed brain age estimates based on MR images in children younger than 2 years of age using deep learning. Our AI model, developed with one slice each of raw T1- and T2-weighted images from each subject, estimated brain age with a mean absolute error of 8.2 weeks (1.9 months). The estimates of our AI model were close to those of human specialists. The AI model also estimated the brain age of subjects with a myelination delay as significantly younger than the chronological age. These results indicate that the prediction accuracy of our AI model approached that of human specialists and that our simple method requiring less data and preprocessing facilitates a radiological assessment of brain development, such as monitoring maturational changes in myelination.
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Affiliation(s)
- Masahiro Kawaguchi
- Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Hiroyuki Kidokoro
- Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan.
| | - Rintaro Ito
- Department of Innovative BioMedical Visualization, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Anna Shiraki
- Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Takeshi Suzuki
- Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yuki Maki
- Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Masaharu Tanaka
- Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yoko Sakaguchi
- Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Hiroyuki Yamamoto
- Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yosiyuki Takahashi
- Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Jun Natsume
- Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan
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21
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Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression. Transl Psychiatry 2020; 10:317. [PMID: 32948749 PMCID: PMC7501280 DOI: 10.1038/s41398-020-01004-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 09/04/2020] [Indexed: 12/20/2022] Open
Abstract
Depression is associated with markers of accelerated aging, but it is unclear how this relationship changes across the lifespan. We examined whether a brain-based measure of accelerated aging differed between depressed and never-depressed subjects across the adult lifespan and whether it was related to cognitive performance and disability. We applied a machine-learning approach that estimated brain age from structural MRI data in two depressed cohorts, respectively 170 midlife adults and 154 older adults enrolled in studies with common entry criteria. Both cohorts completed broad cognitive batteries and the older subgroup completed a disability assessment. The machine-learning model estimated brain age from MRI data, which was compared to chronological age to determine the brain-age gap (BAG; estimated age-chronological age). BAG did not differ between midlife depressed and nondepressed adults. Older depressed adults exhibited significantly higher BAG than nondepressed elders (Wald χ2 = 8.84, p = 0.0029), indicating a higher estimated brain age than chronological age. BAG was not associated with midlife cognitive performance. In the older cohort, higher BAG was associated with poorer episodic memory performance (Wald χ2 = 4.10, p = 0.0430) and, in the older depressed group alone, slower processing speed (Wald χ2 = 4.43, p = 0.0354). We also observed a statistical interaction where greater depressive symptom severity in context of higher BAG was associated with poorer executive function (Wald χ2 = 5.89, p = 0.0152) and working memory performance (Wald χ2 = 4.47, p = 0.0346). Increased BAG was associated with greater disability (Wald χ2 = 6.00, p = 0.0143). Unlike midlife depression, geriatric depression exhibits accelerated brain aging, which in turn is associated with cognitive and functional deficits.
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22
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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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23
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Feng X, Lipton ZC, Yang J, Small SA, Provenzano FA. Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging. Neurobiol Aging 2020; 91:15-25. [PMID: 32305781 PMCID: PMC7890463 DOI: 10.1016/j.neurobiolaging.2020.02.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 01/13/2020] [Accepted: 02/12/2020] [Indexed: 02/06/2023]
Abstract
Numerous studies have established that estimated brain age constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases. In this work, we curate a large-scale brain MRI data set of healthy individuals, on which we train a uniform deep learning model for brain age estimation. We demonstrate an age estimation accuracy on a hold-out test set (mean absolute error = 4.06 years, r = 0.970) and an independent life span evaluation data set (mean absolute error = 4.21 years, r = 0.960). We further demonstrate the utility of the estimated age in a life span aging analysis of cognitive functions. In summary, we achieve age estimation performance comparable to previous studies, but with a more heterogenous data set confirming the efficacy of this deep learning framework. We also evaluated training with varying age distributions. The analysis of regional contributions to our brain age predictions through multiple analyses, and confirmation of the association of divergence between the estimated and chronological brain age with neuropsychological measures, may be useful in the development and evaluation of similar imaging biomarkers.
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Affiliation(s)
- Xinyang Feng
- Department of Biomedical Engineering, Columbia University
| | | | - Jie Yang
- Department of Biomedical Engineering, Columbia University
| | - Scott A. Small
- Department of Neurology, Columbia University
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University
| | - Frank A. Provenzano
- Department of Neurology, Columbia University
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University
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24
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Vakli P, Deák-Meszlényi RJ, Auer T, Vidnyánszky Z. Predicting Body Mass Index From Structural MRI Brain Images Using a Deep Convolutional Neural Network. Front Neuroinform 2020; 14:10. [PMID: 32265681 PMCID: PMC7104804 DOI: 10.3389/fninf.2020.00010] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 03/02/2020] [Indexed: 12/21/2022] Open
Abstract
In recent years, deep learning (DL) has become more widespread in the fields of cognitive and clinical neuroimaging. Using deep neural network models to process neuroimaging data is an efficient method to classify brain disorders and identify individuals who are at increased risk of age-related cognitive decline and neurodegenerative disease. Here we investigated, for the first time, whether structural brain imaging and DL can be used for predicting a physical trait that is of significant clinical relevance—the body mass index (BMI) of the individual. We show that individual BMI can be accurately predicted using a deep convolutional neural network (CNN) and a single structural magnetic resonance imaging (MRI) brain scan along with information about age and sex. Localization maps computed for the CNN highlighted several brain structures that strongly contributed to BMI prediction, including the caudate nucleus and the amygdala. Comparison to the results obtained via a standard automatic brain segmentation method revealed that the CNN-based visualization approach yielded complementary evidence regarding the relationship between brain structure and BMI. Taken together, our results imply that predicting BMI from structural brain scans using DL represents a promising approach to investigate the relationship between brain morphological variability and individual differences in body weight and provide a new scope for future investigations regarding the potential clinical utility of brain-predicted BMI.
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Affiliation(s)
- Pál Vakli
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary
| | | | - Tibor Auer
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary
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25
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Couvy-Duchesne B, Faouzi J, Martin B, Thibeau-Sutre E, Wild A, Ansart M, Durrleman S, Dormont D, Burgos N, Colliot O. Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge. Front Psychiatry 2020; 11:593336. [PMID: 33384629 PMCID: PMC7770104 DOI: 10.3389/fpsyt.2020.593336] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 11/20/2020] [Indexed: 12/14/2022] Open
Abstract
We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the number of observations, in particular, two versions of best linear unbiased predictor (BLUP), support vector machine (SVM), two shallow convolutional neural networks (CNNs), and the famous ResNet and Inception V1. Ensemble learning was derived from estimating weights via linear regression in a hold-out subset of the training sample. We further evaluated and identified factors that could influence prediction accuracy: choice of algorithm, ensemble learning, and features used as input/MRI image processing. Our prediction error was correlated with age, and absolute error was greater for older participants, suggesting to increase the training sample for this subgroup. Our results may be used to guide researchers to build age predictors on healthy individuals, which can be used in research and in the clinics as non-specific predictors of disease status.
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Affiliation(s)
- Baptiste Couvy-Duchesne
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France.,Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia
| | - Johann Faouzi
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Benoît Martin
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Elina Thibeau-Sutre
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Adam Wild
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Manon Ansart
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Stanley Durrleman
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Didier Dormont
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France.,AP-HP, Hôpital de la Pitié-Salpêtrière, Department of Neuroradiology, Paris, France
| | - Ninon Burgos
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
| | - Olivier Colliot
- Paris Brain Institute, ICM, Paris, France.,Inserm, U 1127, Paris, France.,CNRS, UMR 7225, Paris, France.,Sorbonne Université, Paris, France.,Inria Paris, Aramis project-team, Paris, France
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26
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Kumar A, Pathak P, Stynes P. A Transfer Learning Approach to Classify the Brain Age from MRI Images. BIG DATA ANALYTICS 2020. [DOI: 10.1007/978-3-030-66665-1_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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27
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Anatomical Context Protects Deep Learning from Adversarial Perturbations in Medical Imaging. Neurocomputing 2019; 379:370-378. [PMID: 32863583 DOI: 10.1016/j.neucom.2019.10.085] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Deep learning has achieved impressive performance across a variety of tasks, including medical image processing. However, recent research has shown that deep neural networks are susceptible to small adversarial perturbations in the image. We study the impact of such adversarial perturbations in medical image processing where the goal is to predict an individual's age based on a 3D MRI brain image. We consider two models: a conventional deep neural network, and a hybrid deep learning model which additionally uses features informed by anatomical context. We find that we can introduce significant errors in predicted age by adding imperceptible noise to an image, can accomplish this even for large batches of images using a single perturbation, and that the hybrid model is much more robust to adversarial perturbations than the conventional deep neural network. Our work highlights limitations of current deep learning techniques in clinical applications, and suggests a path forward.
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