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Xie Z, Zhang T, Kim S, Lu J, Zhang W, Lin CH, Wu MR, Davis A, Channa R, Giancardo L, Chen H, Wang S, Chen R, Zhi D. iGWAS: Image-based genome-wide association of self-supervised deep phenotyping of retina fundus images. PLoS Genet 2024; 20:e1011273. [PMID: 38728357 DOI: 10.1371/journal.pgen.1011273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 04/25/2024] [Indexed: 05/12/2024] Open
Abstract
Existing imaging genetics studies have been mostly limited in scope by using imaging-derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self-supervised deep representation learning, we propose a new approach, image-based genome-wide association study (iGWAS), for identifying genetic factors associated with phenotypes discovered from medical images using contrastive learning. Using retinal fundus photos, our model extracts a 128-dimensional vector representing features of the retina as phenotypes. After training the model on 40,000 images from the EyePACS dataset, we generated phenotypes from 130,329 images of 65,629 British White participants in the UK Biobank. We conducted GWAS on these phenotypes and identified 14 loci with genome-wide significance (p<5×10-8 and intersection of hits from left and right eyes). We also did GWAS on the retina color, the average color of the center region of the retinal fundus photos. The GWAS of retina colors identified 34 loci, 7 are overlapping with GWAS of raw image phenotype. Our results establish the feasibility of this new framework of genomic study based on self-supervised phenotyping of medical images.
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Affiliation(s)
- Ziqian Xie
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Tao Zhang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Sangbae Kim
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Jiaxiong Lu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Wanheng Zhang
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Cheng-Hui Lin
- Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Man-Ru Wu
- Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Alexander Davis
- Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Luca Giancardo
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Han Chen
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Sui Wang
- Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Rui Chen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Degui Zhi
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
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Yook S, Park HR, Joo EY, Kim H. Predicting the impact of CPAP on brain health: A study using the sleep EEG-derived brain age index. Ann Clin Transl Neurol 2024; 11:1172-1183. [PMID: 38396240 DOI: 10.1002/acn3.52032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/17/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
OBJECTIVE This longitudinal study investigated potential positive impact of CPAP treatment on brain health in individuals with obstructive sleep Apnea (OSA). To allow this, we aimed to employ sleep electroencephalogram (EEG)-derived brain age index (BAI) to quantify CPAP's impact on brain health and identify individually varying CPAP effects on brain aging using machine learning approaches. METHODS We retrospectively analyzed CPAP-treated (n = 98) and untreated OSA patients (n = 88) with a minimum 12-month follow-up of polysomnography. BAI was calculated by subtracting chronological age from the predicted brain age. To investigate BAI changes before and after CPAP treatment, we compared annual ΔBAI between CPAP-treated and untreated OSA patients. To identify individually varying CPAP effectiveness and factors influencing CPAP effectiveness, machine learning approaches were employed to predict which patient displayed positive outcomes (negative annual ΔBAI) based on their baseline clinical features. RESULTS CPAP-treated group showed lower annual ΔBAI than untreated (-0.6 ± 2.7 vs. 0.3 ± 2.6 years, p < 0.05). This BAI reduction with CPAP was reproduced independently in the Apnea, Bariatric surgery, and CPAP study cohort. Patients with more severe OSA at baseline displayed more positive annual ΔBAI (=accelerated brain aging) when untreated and displayed more negative annual ΔBAI (=decelerated brain aging) when CPAP-treated. Machine learning models achieved high accuracy (up to 86%) in predicting CPAP outcomes. INTERPRETATION CPAP treatment can alleviate brain aging in OSA, especially in severe cases. Sleep EEG-derived BAI has potential to assess CPAP's impact on brain health. The study provides insights into CPAP's effects and underscores BAI-based predictive modeling's utility in OSA management.
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Affiliation(s)
- Soonhyun Yook
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, 90033, USA
| | - Hea Ree Park
- Department of Neurology, Inje University College of Medicine, Ilsan Paik Hospital, Goyang, 10380, Korea
| | - Eun Yeon Joo
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Samsung Biomedical Research Institute, School of Medicine, Sungkyunkwan University, Seoul, 06351, Korea
| | - Hosung Kim
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, 90033, USA
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Ruffle JK, Gray RJ, Mohinta S, Pombo G, Kaul C, Hyare H, Rees G, Nachev P. Computational limits to the legibility of the imaged human brain. Neuroimage 2024; 291:120600. [PMID: 38569979 DOI: 10.1016/j.neuroimage.2024.120600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 03/08/2024] [Accepted: 03/31/2024] [Indexed: 04/05/2024] Open
Abstract
Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted an individual's psychology better than the coincidence of common chronic disease (p < 0.05). Serology predicted chronic disease (p < 0.05) and was best predicted by it (p < 0.001), followed by structural neuroimaging (p < 0.05). Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the human brain. We make our models and code openly available.
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Affiliation(s)
- James K Ruffle
- Queen Square Institute of Neurology, University College London, London, United Kingdom.
| | - Robert J Gray
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Samia Mohinta
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Guilherme Pombo
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Chaitanya Kaul
- School of Computing Science, University of Glasgow, Glasgow, United Kingdom
| | - Harpreet Hyare
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Geraint Rees
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Parashkev Nachev
- Queen Square Institute of Neurology, University College London, London, United Kingdom.
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Dular L, Pernuš F, Špiclin Ž. Extensive T1-weighted MRI preprocessing improves generalizability of deep brain age prediction models. Comput Biol Med 2024; 173:108320. [PMID: 38531250 DOI: 10.1016/j.compbiomed.2024.108320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 01/09/2024] [Accepted: 03/12/2024] [Indexed: 03/28/2024]
Abstract
Brain age is an estimate of chronological age obtained from T1-weighted magnetic resonance images (T1w MRI), representing a straightforward diagnostic biomarker of brain aging and associated diseases. While the current best accuracy of brain age predictions on T1w MRIs of healthy subjects ranges from two to three years, comparing results across studies is challenging due to differences in the datasets, T1w preprocessing pipelines, and evaluation protocols used. This paper investigates the impact of T1w image preprocessing on the performance of four deep learning brain age models from recent literature. Four preprocessing pipelines, which differed in terms of registration transform, grayscale correction, and software implementation, were evaluated. The results showed that the choice of software or preprocessing steps could significantly affect the prediction error, with a maximum increase of 0.75 years in mean absolute error (MAE) for the same model and dataset. While grayscale correction had no significant impact on MAE, using affine rather than rigid registration to brain atlas statistically significantly improved MAE. Models trained on 3D images with isotropic 1mm3 resolution exhibited less sensitivity to the T1w preprocessing variations compared to 2D models or those trained on downsampled 3D images. Our findings indicate that extensive T1w preprocessing improves MAE, especially when predicting on a new dataset. This runs counter to prevailing research literature, which suggests that models trained on minimally preprocessed T1w scans are better suited for age predictions on MRIs from unseen scanners. We demonstrate that, irrespective of the model or T1w preprocessing used during training, applying some form of offset correction is essential to enable the model's performance to generalize effectively on datasets from unseen sites, regardless of whether they have undergone the same or different T1w preprocessing as the training set.
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Affiliation(s)
- Lara Dular
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
| | - Franjo Pernuš
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
| | - Žiga Špiclin
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia.
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5
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Cheek CL, Lindner P, Grigorenko EL. Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods. Behav Genet 2024; 54:233-251. [PMID: 38336922 DOI: 10.1007/s10519-024-10177-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 01/24/2024] [Indexed: 02/12/2024]
Abstract
Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate to current deep learning approaches, highlighting each approach's strengths and weaknesses and elongating it with the field's development. We conclude by discussing selected remaining challenges and prospects for the field.
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Affiliation(s)
- Connor L Cheek
- Texas Institute for Evaluation, Measurement, and Statistics, University of Houston, Houston, TX, USA.
- Department of Physics, University of Houston, Houston, TX, USA.
| | - Peggy Lindner
- Texas Institute for Evaluation, Measurement, and Statistics, University of Houston, Houston, TX, USA
- Department of Information Science Technology, University of Houston, Houston, TX, USA
| | - Elena L Grigorenko
- Texas Institute for Evaluation, Measurement, and Statistics, University of Houston, Houston, TX, USA
- Department of Psychology, University of Houston, Houston, TX, USA
- Baylor College of Medicine, Houston, TX, USA
- Sirius University of Science and Technology, Sochi, Russia
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6
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Fan F, Liu H, Dai X, Liu G, Liu J, Deng X, Peng Z, Wang C, Zhang K, Chen H, Yin C, Zhan M, Deng Z. Automated bone age assessment from knee joint by integrating deep learning and MRI-based radiomics. Int J Legal Med 2024; 138:927-938. [PMID: 38129687 DOI: 10.1007/s00414-023-03148-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 12/09/2023] [Indexed: 12/23/2023]
Abstract
Bone age assessment (BAA) is a crucial task in clinical, forensic, and athletic fields. Since traditional age estimation methods are suffered from potential radiation damage, this study aimed to develop and evaluate a deep learning radiomics method based on multiparametric knee MRI for noninvasive and automatic BAA. This retrospective study enrolled 598 patients (age range,10.00-29.99 years) who underwent MR examinations of the knee joint (T1/T2*/PD-weighted imaging). Three-dimensional convolutional neural networks (3D CNNs) were trained to extract and fuse multimodal and multiscale MRI radiomic features for age estimation and compared to traditional machine learning models based on hand-crafted features. The age estimation error was greater in individuals aged 25-30 years; thus, this method may not be suitable for individuals over 25 years old. In the test set aged 10-25 years (n = 95), the 3D CNN (a fusion of T1WI, T2*WI, and PDWI) demonstrated the lowest mean absolute error of 1.32 ± 1.01 years, which is higher than that of other MRI modalities and the hand-crafted models. In the classification for 12-, 14-, 16-, and 18- year thresholds, accuracies and the areas under the ROC curves were all over 0.91 and 0.96, which is similar to the manual methods. Visualization of important features showed that 3D CNN estimated age by focusing on the epiphyseal plates. The deep learning radiomics method enables non-invasive and automated BAA from multimodal knee MR images. The use of 3D CNN and MRI-based radiomics has the potential to assist radiologists or medicolegists in age estimation.
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Affiliation(s)
- Fei Fan
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Han Liu
- College of Computer Science, Sichuan University, Chengdu, 610064, People's Republic of China
| | - Xinhua Dai
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Guangfeng Liu
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Junhong Liu
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Xiaodong Deng
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Zhao Peng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Chang Wang
- Department of Radiology, Anhui Provincial Children's Hospital, Hefei, 230054, People's Republic of China
| | - Kui Zhang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Hu Chen
- College of Computer Science, Sichuan University, Chengdu, 610064, People's Republic of China
| | - Chuangao Yin
- Department of Radiology, Anhui Provincial Children's Hospital, Hefei, 230054, People's Republic of China.
| | - Mengjun Zhan
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China.
| | - Zhenhua Deng
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China.
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Jeon YJ, Park SE, Baek HM. Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits. Brain Sci 2024; 14:401. [PMID: 38672050 PMCID: PMC11048383 DOI: 10.3390/brainsci14040401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/15/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
The morphology of the brain undergoes changes throughout the aging process, and accurately predicting a person's brain age and gender using brain morphology features can aid in detecting atypical brain patterns. Neuroimaging-based estimation of brain age is commonly used to assess an individual's brain health relative to a typical aging trajectory, while accurately classifying gender from neuroimaging data offers valuable insights into the inherent neurological differences between males and females. In this study, we aimed to compare the efficacy of classical machine learning models with that of a quantum machine learning method called a variational quantum circuit in estimating brain age and predicting gender based on structural magnetic resonance imaging data. We evaluated six classical machine learning models alongside a quantum machine learning model using both combined and sub-datasets, which included data from both in-house collections and public sources. The total number of participants was 1157, ranging from ages 14 to 89, with a gender distribution of 607 males and 550 females. Performance evaluation was conducted within each dataset using training and testing sets. The variational quantum circuit model generally demonstrated superior performance in estimating brain age and gender classification compared to classical machine learning algorithms when using the combined dataset. Additionally, in benchmark sub-datasets, our approach exhibited better performance compared to previous studies that utilized the same dataset for brain age prediction. Thus, our results suggest that variational quantum algorithms demonstrate comparable effectiveness to classical machine learning algorithms for both brain age and gender prediction, potentially offering reduced error and improved accuracy.
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Affiliation(s)
- Yeong-Jae Jeon
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon 21999, Republic of Korea;
- Department of BioMedical Science, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Republic of Korea;
| | - Shin-Eui Park
- Department of BioMedical Science, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Republic of Korea;
| | - Hyeon-Man Baek
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon 21999, Republic of Korea;
- Department of Molecular Medicine, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Republic of Korea
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Wen J, Tian YE, Skampardoni I, Yang Z, Cui Y, Anagnostakis F, Mamourian E, Zhao B, Toga AW, Zaleskey A, Davatzikos C. The Genetic Architecture of Biological Age in Nine Human Organ Systems. medRxiv 2024:2023.06.08.23291168. [PMID: 37398441 PMCID: PMC10312870 DOI: 10.1101/2023.06.08.23291168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Understanding the genetic basis of biological aging in multi-organ systems is vital for elucidating age-related disease mechanisms and identifying therapeutic interventions. This study characterized the genetic architecture of the biological age gap (BAG) across nine human organ systems in 377,028 individuals of European ancestry from the UK Biobank. We discovered 393 genomic loci-BAG pairs (P-value<5×10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary, and renal systems. We observed BAG-organ specificity and inter-organ connections. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system while exerting pleiotropic effects on traits linked to multiple organ systems. A gene-drug-disease network confirmed the involvement of the metabolic BAG-associated genes in drugs targeting various metabolic disorders. Genetic correlation analyses supported Cheverud's Conjecture1 - the genetic correlation between BAGs mirrors their phenotypic correlation. A causal network revealed potential causal effects linking chronic diseases (e.g., Alzheimer's disease), body weight, and sleep duration to the BAG of multiple organ systems. Our findings shed light on promising therapeutic interventions to enhance human organ health within a complex multi-organ network, including lifestyle modifications and potential drug repositioning strategies for treating chronic diseases. All results are publicly available at https://labs-laboratory.com/medicine.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Ye Ella Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Filippos Anagnostakis
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Andrew Zaleskey
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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Patel K, Xie Z, Yuan H, Islam SMS, Xie Y, He W, Zhang W, Gottlieb A, Chen H, Giancardo L, Knaack A, Fletcher E, Fornage M, Ji S, Zhi D. Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging. Commun Biol 2024; 7:414. [PMID: 38580839 PMCID: PMC10997628 DOI: 10.1038/s42003-024-06096-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 03/22/2024] [Indexed: 04/07/2024] Open
Abstract
Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.
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Affiliation(s)
- Khush Patel
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Ziqian Xie
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Hao Yuan
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | | | - Yaochen Xie
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Wei He
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Wanheng Zhang
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Assaf Gottlieb
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Han Chen
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Luca Giancardo
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Alexander Knaack
- Department of Neurology and Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, 95618, USA
| | - Evan Fletcher
- Department of Neurology and Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, 95618, USA
| | - Myriam Fornage
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
- McGovern Medical School, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Shuiwang Ji
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Degui Zhi
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.
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10
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Chang JR, Yao ZF, Hsieh S, Nordling TEM. Age Prediction Using Resting-State Functional MRI. Neuroinformatics 2024; 22:119-134. [PMID: 38341830 DOI: 10.1007/s12021-024-09653-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2023] [Indexed: 02/13/2024]
Abstract
The increasing lifespan and large individual differences in cognitive capability highlight the importance of comprehending the aging process of the brain. Contrary to visible signs of bodily ageing, like greying of hair and loss of muscle mass, the internal changes that occur within our brains remain less apparent until they impair function. Brain age, distinct from chronological age, reflects our brain's health status and may deviate from our actual chronological age. Notably, brain age has been associated with mortality and depression. The brain is plastic and can compensate even for severe structural damage by rewiring. Functional characterization offers insights that structural cannot provide. Contrary to the multitude of studies relying on structural magnetic resonance imaging (MRI), we utilize resting-state functional MRI (rsfMRI). We also address the issue of inclusion of subjects with abnormal brain ageing through outlier removal. In this study, we employ the Least Absolute Shrinkage and Selection Operator (LASSO) to identify the 39 most predictive correlations derived from the rsfMRI data. The data is from a cohort of 176 healthy right-handed volunteers, aged 18-78 years (95/81 male/female, mean age 48, SD 17) collected at the Mind Research Imaging Center at the National Cheng Kung University. We establish a normal reference model by excluding 68 outliers, which achieves a leave-one-out mean absolute error of 2.48 years. By asking which additional features that are needed to predict the chronological age of the outliers with a smaller error, we identify correlations predictive of abnormal aging. These are associated with the Default Mode Network (DMN). Our normal reference model has the lowest prediction error among published models evaluated on adult subjects of almost all ages and is thus a candidate for screening for abnormal brain aging that has not yet manifested in cognitive decline. This study advances our ability to predict brain aging and provides insights into potential biomarkers for assessing brain age, suggesting that the role of DMN in brain aging should be studied further.
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Affiliation(s)
- Jose Ramon Chang
- Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
| | - Zai-Fu Yao
- College of Education, National Tsing Hua University, Hsinchu, 30013, Taiwan
- Research Center for Education and Mind Sciences, National Tsing Hua University, Hsinchu, 30013, Taiwan
- Department of Kinesiology, National Tsing Hua University, Hsinchu, 30013, Taiwan
- Basic Psychology Group, Department of Educational Psychology and Counseling, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Shulan Hsieh
- Department of Psychology, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
- Institute of Allied Health Sciences, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
- Department of Public Health, College of Medicine, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
| | - Torbjörn E M Nordling
- Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan.
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11
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Loizillon S, Bottani S, Maire A, Ströer S, Dormont D, Colliot O, Burgos N. Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data. Med Image Anal 2024; 93:103073. [PMID: 38176355 DOI: 10.1016/j.media.2023.103073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 12/12/2023] [Accepted: 12/21/2023] [Indexed: 01/06/2024]
Abstract
Containing the medical data of millions of patients, clinical data warehouses (CDWs) represent a great opportunity to develop computational tools. Magnetic resonance images (MRIs) are particularly sensitive to patient movements during image acquisition, which will result in artefacts (blurring, ghosting and ringing) in the reconstructed image. As a result, a significant number of MRIs in CDWs are corrupted by these artefacts and may be unusable. Since their manual detection is impossible due to the large number of scans, it is necessary to develop tools to automatically exclude (or at least identify) images with motion in order to fully exploit CDWs. In this paper, we propose a novel transfer learning method from research to clinical data for the automatic detection of motion in 3D T1-weighted brain MRI. The method consists of two steps: a pre-training on research data using synthetic motion, followed by a fine-tuning step to generalise our pre-trained model to clinical data, relying on the labelling of 4045 images. The objectives were both (1) to be able to exclude images with severe motion, (2) to detect mild motion artefacts. Our approach achieved excellent accuracy for the first objective with a balanced accuracy nearly similar to that of the annotators (balanced accuracy>80 %). However, for the second objective, the performance was weaker and substantially lower than that of human raters. Overall, our framework will be useful to take advantage of CDWs in medical imaging and highlight the importance of a clinical validation of models trained on research data.
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Affiliation(s)
- Sophie Loizillon
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France
| | - Simona Bottani
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France
| | - Aurélien Maire
- AP-HP, Innovation & Données - Département des Services Numériques, Paris 75012, France
| | - Sebastian Ströer
- AP-HP, Hôpital de la Pitié Salpêtrière, Department of Neuroradiology, Paris 75013, France
| | - Didier Dormont
- AP-HP, Hôpital de la Pitié Salpêtrière, Department of Neuroradiology, Paris 75013, France; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, DMU DIAMENT, Paris 75013, France
| | - Olivier Colliot
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France
| | - Ninon Burgos
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France.
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12
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Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, Davatzikos C. The genetic architecture of multimodal human brain age. Nat Commun 2024; 15:2604. [PMID: 38521789 PMCID: PMC10960798 DOI: 10.1038/s41467-024-46796-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 03/06/2024] [Indexed: 03/25/2024] Open
Abstract
The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Zhen Zhou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yogasudha Veturi
- Department of Biobehavioral Health and Statistics, Penn State University, University Park, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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13
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Guo M, Higashita R, Lin C, Hu L, Chen W, Li F, Lai GWK, Nguyen A, Sakata R, Okamoto K, Tang B, Xu Y, Fu H, Gao F, Aihara M, Zhang X, Yuan J, Lin S, Leung CKS, Liu J. Crystalline lens nuclear age prediction as a new biomarker of nucleus degeneration. Br J Ophthalmol 2024; 108:513-521. [PMID: 37495263 DOI: 10.1136/bjo-2023-323176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 05/22/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND The crystalline lens is a transparent structure of the eye to focus light on the retina. It becomes muddy, hard and dense with increasing age, which makes the crystalline lens gradually lose its function. We aim to develop a nuclear age predictor to reflect the degeneration of the crystalline lens nucleus. METHODS First we trained and internally validated the nuclear age predictor with a deep-learning algorithm, using 12 904 anterior segment optical coherence tomography (AS-OCT) images from four diverse Asian and American cohorts: Zhongshan Ophthalmic Center with Machine0 (ZOM0), Tomey Corporation (TOMEY), University of California San Francisco and the Chinese University of Hong Kong. External testing was done on three independent datasets: Tokyo University (TU), ZOM1 and Shenzhen People's Hospital (SPH). We also demonstrate the possibility of detecting nuclear cataracts (NCs) from the nuclear age gap. FINDINGS In the internal validation dataset, the nuclear age could be predicted with a mean absolute error (MAE) of 2.570 years (95% CI 1.886 to 2.863). Across the three external testing datasets, the algorithm achieved MAEs of 4.261 years (95% CI 3.391 to 5.094) in TU, 3.920 years (95% CI 3.332 to 4.637) in ZOM1-NonCata and 4.380 years (95% CI 3.730 to 5.061) in SPH-NonCata. The MAEs for NC eyes were 8.490 years (95% CI 7.219 to 9.766) in ZOM1-NC and 9.998 years (95% CI 5.673 to 14.642) in SPH-NC. The nuclear age gap outperformed both ophthalmologists in detecting NCs, with areas under the receiver operating characteristic curves of 0.853 years (95% CI 0.787 to 0.917) in ZOM1 and 0.909 years (95% CI 0.828 to 0.978) in SPH. INTERPRETATION The nuclear age predictor shows good performance, validating the feasibility of using AS-OCT images as an effective screening tool for nucleus degeneration. Our work also demonstrates the potential use of the nuclear age gap to detect NCs.
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Affiliation(s)
- Mengjie Guo
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, Guangdong, China
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Risa Higashita
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Tomey Corporation, Nagoya, Aichi, Japan
| | - Chen Lin
- Shenzhen People's Hospital, Shenzhen, Guangdong, China
| | - Lingxi Hu
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Wan Chen
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Fei Li
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Gilda Wing Ki Lai
- Department of Ophthalmology, The University of Hong Kong, Hong Kong, Hong Kong
| | - Anwell Nguyen
- Department of Ophthalmology, University of California, San Francisco, California, USA
| | - Rei Sakata
- Department of Ophthalmology, The University of Tokyo, Tokyo, Japan
| | | | - Bo Tang
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yanwu Xu
- Intelligent Healthcare Unit, Baidu Inc, Beijing, China
| | - Huazhu Fu
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
| | - Fei Gao
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Makoto Aihara
- Department of Ophthalmology, The University of Tokyo, Tokyo, Japan
| | - Xiulan Zhang
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jin Yuan
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Shan Lin
- Department of Ophthalmology, University of California, San Francisco, California, USA
- Glaucoma Center of San Francisco, San Francisco, California, USA
| | - Christopher Kai-Shun Leung
- Department of Ophthalmology, The University of Hong Kong, Hong Kong, Hong Kong
- Department of Ophthalmology and Visual Sciences, The Chinese University, Hong Kong, Hong Kong
| | - Jiang Liu
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences, Cixi, Zhejiang, China
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14
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Zhou Y, Cosentino J, Yun T, Biradar MI, Shreibati J, Lai D, Schwantes-An TH, Luben R, McCaw Z, Engmann J, Providencia R, Schmidt AF, Munroe P, Yang H, Carroll A, Khawaja AP, McLean CY, Behsaz B, Hormozdiari F. Utilizing multimodal AI to improve genetic analyses of cardiovascular traits. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304547. [PMID: 38562791 PMCID: PMC10984061 DOI: 10.1101/2024.03.19.24304547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Electronic health records, biobanks, and wearable biosensors contain multiple high-dimensional clinical data (HDCD) modalities (e.g., ECG, Photoplethysmography (PPG), and MRI) for each individual. Access to multimodal HDCD provides a unique opportunity for genetic studies of complex traits because different modalities relevant to a single physiological system (e.g., circulatory system) encode complementary and overlapping information. We propose a novel multimodal deep learning method, M-REGLE, for discovering genetic associations from a joint representation of multiple complementary HDCD modalities. We showcase the effectiveness of this model by applying it to several cardiovascular modalities. M-REGLE jointly learns a lower representation (i.e., latent factors) of multimodal HDCD using a convolutional variational autoencoder, performs genome wide association studies (GWAS) on each latent factor, then combines the results to study the genetics of the underlying system. To validate the advantages of M-REGLE and multimodal learning, we apply it to common cardiovascular modalities (PPG and ECG), and compare its results to unimodal learning methods in which representations are learned from each data modality separately, but the downstream genetic analyses are performed on the combined unimodal representations. M-REGLE identifies 19.3% more loci on the 12-lead ECG dataset, 13.0% more loci on the ECG lead I + PPG dataset, and its genetic risk score significantly outperforms the unimodal risk score at predicting cardiac phenotypes, such as atrial fibrillation (Afib), in multiple biobanks.
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Affiliation(s)
| | | | | | - Mahantesh I Biradar
- NIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
| | | | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Tae-Hwi Schwantes-An
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Robert Luben
- NIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
| | - Zachary McCaw
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jorgen Engmann
- Center for Translational Genomics, Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, UK
| | - Rui Providencia
- Institute of Health Informatics Research, University College London, London, UK
- Electrophysiology Department, Barts Heart Centre, St. Bartholomew's Hospital, London, UK
| | - Amand Floriaan Schmidt
- Department of Cardiology; Amsterdam University Medical Centres, Amsterdam, The Netherlands
- Institute of Cardiovascular Science; University College London, London, UK
- Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, Netherlands
| | - Patricia Munroe
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Howard Yang
- Google Research, San Francisco CA, 94105 USA
| | | | - Anthony P Khawaja
- NIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
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15
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Lim H, Joo Y, Ha E, Song Y, Yoon S, Shin T. Brain Age Prediction Using Multi-Hop Graph Attention Combined with Convolutional Neural Network. Bioengineering (Basel) 2024; 11:265. [PMID: 38534539 DOI: 10.3390/bioengineering11030265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 03/28/2024] Open
Abstract
Convolutional neural networks (CNNs) have been used widely to predict biological brain age based on brain magnetic resonance (MR) images. However, CNNs focus mainly on spatially local features and their aggregates and barely on the connective information between distant regions. To overcome this issue, we propose a novel multi-hop graph attention (MGA) module that exploits both the local and global connections of image features when combined with CNNs. After insertion between convolutional layers, MGA first converts the convolution-derived feature map into graph-structured data by using patch embedding and embedding-distance-based scoring. Multi-hop connections between the graph nodes are modeled by using the Markov chain process. After performing multi-hop graph attention, MGA re-converts the graph into an updated feature map and transfers it to the next convolutional layer. We combined the MGA module with sSE (spatial squeeze and excitation)-ResNet18 for our final prediction model (MGA-sSE-ResNet18) and performed various hyperparameter evaluations to identify the optimal parameter combinations. With 2788 three-dimensional T1-weighted MR images of healthy subjects, we verified the effectiveness of MGA-sSE-ResNet18 with comparisons to four established, general-purpose CNNs and two representative brain age prediction models. The proposed model yielded an optimal performance with a mean absolute error of 2.822 years and Pearson's correlation coefficient (PCC) of 0.968, demonstrating the potential of the MGA module to improve the accuracy of brain age prediction.
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Affiliation(s)
- Heejoo Lim
- Division of Mechanical and Biomedical Engineering, Ewha W. University, Seoul 03760, Republic of Korea
- Graduate Program in Smart Factory, Ewha W. University, Seoul 03760, Republic of Korea
| | - Yoonji Joo
- Ewha Brain Institute, Ewha W. University, Seoul 03760, Republic of Korea
| | - Eunji Ha
- Ewha Brain Institute, Ewha W. University, Seoul 03760, Republic of Korea
| | - Yumi Song
- Ewha Brain Institute, Ewha W. University, Seoul 03760, Republic of Korea
- Department of Brain and Cognitive Sciences, Ewha W. University, Seoul 03760, Republic of Korea
| | - Sujung Yoon
- Ewha Brain Institute, Ewha W. University, Seoul 03760, Republic of Korea
- Department of Brain and Cognitive Sciences, Ewha W. University, Seoul 03760, Republic of Korea
| | - Taehoon Shin
- Division of Mechanical and Biomedical Engineering, Ewha W. University, Seoul 03760, Republic of Korea
- Graduate Program in Smart Factory, Ewha W. University, Seoul 03760, Republic of Korea
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16
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Wood DA, Townend M, Guilhem E, Kafiabadi S, Hammam A, Wei Y, Al Busaidi A, Mazumder A, Sasieni P, Barker GJ, Ourselin S, Cole JH, Booth TC. Optimising brain age estimation through transfer learning: A suite of pre-trained foundation models for improved performance and generalisability in a clinical setting. Hum Brain Mapp 2024; 45:e26625. [PMID: 38433665 PMCID: PMC10910262 DOI: 10.1002/hbm.26625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 12/27/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024] Open
Abstract
Estimated age from brain MRI data has emerged as a promising biomarker of neurological health. However, the absence of large, diverse, and clinically representative training datasets, along with the complexity of managing heterogeneous MRI data, presents significant barriers to the development of accurate and generalisable models appropriate for clinical use. Here, we present a deep learning framework trained on routine clinical data (N up to 18,890, age range 18-96 years). We trained five separate models for accurate brain age prediction (all with mean absolute error ≤4.0 years, R2 ≥ .86) across five different MRI sequences (T2 -weighted, T2 -FLAIR, T1 -weighted, diffusion-weighted, and gradient-recalled echo T2 *-weighted). Our trained models offer dual functionality. First, they have the potential to be directly employed on clinical data. Second, they can be used as foundation models for further refinement to accommodate a range of other MRI sequences (and therefore a range of clinical scenarios which employ such sequences). This adaptation process, enabled by transfer learning, proved effective in our study across a range of MRI sequences and scan orientations, including those which differed considerably from the original training datasets. Crucially, our findings suggest that this approach remains viable even with limited data availability (as low as N = 25 for fine-tuning), thus broadening the application of brain age estimation to more diverse clinical contexts and patient populations. By making these models publicly available, we aim to provide the scientific community with a versatile toolkit, promoting further research in brain age prediction and related areas.
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Affiliation(s)
- David A. Wood
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | - Matthew Townend
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | - Emily Guilhem
- King's College Hospital NHS Foundation TrustLondonUK
| | | | - Ahmed Hammam
- King's College Hospital NHS Foundation TrustLondonUK
| | - Yiran Wei
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | | | | | - Peter Sasieni
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology, and NeuroscienceKing's College LondonLondonUK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | - James H. Cole
- Dementia Research Centre, Institute of NeurologyUniversity College LondonLondonUK
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
| | - Thomas C. Booth
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
- King's College Hospital NHS Foundation TrustLondonUK
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17
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Georgievskaya A, Tlyachev T, Kiselev K, Hillebrand G, Chekanov K, Danko D, Golodyaev A, Majmudar G. Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females. Exp Dermatol 2024; 33:e15045. [PMID: 38509744 DOI: 10.1111/exd.15045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/11/2024] [Accepted: 02/18/2024] [Indexed: 03/22/2024]
Abstract
Predicting a person's chronological age (CA) from visible skin features using artificial intelligence (AI) is now commonplace. Often, convolutional neural network (CNN) models are built using images of the face as biometric data. However, hands hold telltale signs of a person's age. To determine the utility of using only hand images in predicting CA, we developed two deep CNNs based on 1) dorsal hand images (H) and 2) frontal face images (F). Subjects (n = 1454) were Indian women, 20-80 years, across three geographic cohorts (Mumbai, New Delhi and Bangalore) and having a broad variation in skin tones. Images were randomised: 70% of F and 70% of H were used to train CNNs. The remaining 30% of F and H were retained for validation. CNN validation showed mean absolute error for predicting CA using F and H of 4.1 and 4.7 years, respectively. In both cases correlations of predicted and actual age were statistically significant (r(F) = 0.93, r(H) = 0.90). The CNNs for F and H were validated for dark and light skin tones. Finally, by blurring or accentuating visible features on specific regions of the hand and face, we identified those features that contributed to the CNN models. For the face, areas of the inner eye corner and around the mouth were most important for age prediction. For the hands, knuckle texture was a key driver for age prediction. Collectively, for AI estimates of CA, CNNs based solely on hand images are a viable alternative and comparable to CNNs based on facial images.
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Affiliation(s)
| | | | | | - Greg Hillebrand
- James L. Winkle College of Pharmacy, University of Cincinnati, Cincinnati, Ohio, USA
| | | | | | | | - Gopa Majmudar
- James L. Winkle College of Pharmacy, University of Cincinnati, Cincinnati, Ohio, USA
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18
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Du J, Pan Y, Jiang J, Lam BCP, Thalamuthu A, Chen R, Tsang IW, Sachdev PS, Wen W. White matter brain age as a biomarker of cerebrovascular burden in the ageing brain. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01758-3. [PMID: 38424358 DOI: 10.1007/s00406-024-01758-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 01/13/2024] [Indexed: 03/02/2024]
Abstract
As the brain ages, it almost invariably accumulates vascular pathology, which differentially affects the cerebral white matter. A rich body of research has investigated the link between vascular risk factors and the brain. One of the less studied questions is that among various modifiable vascular risk factors, which is the most debilitating one for white matter health? A white matter specific brain age was developed to evaluate the overall white matter health from diffusion weighted imaging, using a three-dimensional convolutional neural network deep learning model in both cross-sectional UK biobank participants (n = 37,327) and a longitudinal subset (n = 1409). White matter brain age gap (WMBAG) was the difference between the white matter age and the chronological age. Participants with one, two, and three or more vascular risk factors, compared to those without any, showed an elevated WMBAG of 0.54, 1.23, and 1.94 years, respectively. Diabetes was most strongly associated with an increased WMBAG (1.39 years, p < 0.001) among all risk factors followed by hypertension (0.87 years, p < 0.001) and smoking (0.69 years, p < 0.001). Baseline WMBAG was associated significantly with processing speed, executive and global cognition. Significant associations of diabetes and hypertension with poor processing speed and executive function were found to be mediated through the WMBAG. White matter specific brain age can be successfully targeted for the examination of the most relevant risk factors and cognition, and for tracking an individual's cerebrovascular ageing process. It also provides clinical basis for the better management of specific risk factors.
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Affiliation(s)
- Jing Du
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia.
| | - Yuangang Pan
- Centre for Frontier AI Research (CFAR), A*STAR, Singapore, 138623, Singapore
- Australian Artificial Intelligence Institute (AAII), UTS, Sydney, NSW, 2007, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia
| | - Ben C P Lam
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia
| | - Rory Chen
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia
| | - Ivor W Tsang
- Centre for Frontier AI Research (CFAR), A*STAR, Singapore, 138623, Singapore
- Australian Artificial Intelligence Institute (AAII), UTS, Sydney, NSW, 2007, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia
- Neuropsychiatric Institute (NPI), Euroa Centre, Prince of Wales Hospital, Randwick, NSW, 2031, Australia
| | - Wei Wen
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, Kensington, New South Wales, 2052, Australia.
- Neuropsychiatric Institute (NPI), Euroa Centre, Prince of Wales Hospital, Randwick, NSW, 2031, Australia.
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19
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Lee S, Park JS, Woo H, Yoo YK, Lee D, Chung S, Yoon DS, Lee KB, Lee JH. Rapid deep learning-assisted predictive diagnostics for point-of-care testing. Nat Commun 2024; 15:1695. [PMID: 38402240 PMCID: PMC10894262 DOI: 10.1038/s41467-024-46069-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/12/2024] [Indexed: 02/26/2024] Open
Abstract
Prominent techniques such as real-time polymerase chain reaction (RT-PCR), enzyme-linked immunosorbent assay (ELISA), and rapid kits are currently being explored to both enhance sensitivity and reduce assay time for diagnostic tests. Existing commercial molecular methods typically take several hours, while immunoassays can range from several hours to tens of minutes. Rapid diagnostics are crucial in Point-of-Care Testing (POCT). We propose an approach that integrates a time-series deep learning architecture and AI-based verification, for the enhanced result analysis of lateral flow assays. This approach is applicable to both infectious diseases and non-infectious biomarkers. In blind tests using clinical samples, our method achieved diagnostic times as short as 2 minutes, exceeding the accuracy of human analysis at 15 minutes. Furthermore, our technique significantly reduces assay time to just 1-2 minutes in the POCT setting. This advancement has the potential to greatly enhance POCT diagnostics, enabling both healthcare professionals and non-experts to make rapid, accurate decisions.
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Affiliation(s)
- Seungmin Lee
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul, 02841, Republic of Korea
| | - Jeong Soo Park
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
- School of Mechanical Engineering, Korea University, 145 Anam-ro, Seoungbuk-gu, Seoul, 02841, Republic of Korea
| | - Hyowon Woo
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
| | - Yong Kyoung Yoo
- Department of Electronic Engineering, Catholic Kwandong University, 24, Beomil-ro 579 beon-gil, Gangneung-si, Gangwon-do, 25601, Republic of Korea
| | - Dongho Lee
- CALTH Inc., Changeop-ro 54, Seongnam, Gyeonggi, 13449, Republic of Korea
| | - Seok Chung
- School of Mechanical Engineering, Korea University, 145 Anam-ro, Seoungbuk-gu, Seoul, 02841, Republic of Korea
| | - Dae Sung Yoon
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul, 02841, Republic of Korea
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, 02841, Republic of Korea
- Astrion Inc, Seoul, 02841, Republic of Korea
| | - Ki-Baek Lee
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
| | - Jeong Hoon Lee
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea.
- CALTH Inc., Changeop-ro 54, Seongnam, Gyeonggi, 13449, Republic of Korea.
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20
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Kim MJ, Hong E, Yum MS, Lee YJ, Kim J, Ko TS. Deep learning-based, fully automated, pediatric brain segmentation. Sci Rep 2024; 14:4344. [PMID: 38383725 PMCID: PMC10881508 DOI: 10.1038/s41598-024-54663-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 02/15/2024] [Indexed: 02/23/2024] Open
Abstract
The purpose of this study was to demonstrate the performance of a fully automated, deep learning-based brain segmentation (DLS) method in healthy controls and in patients with neurodevelopmental disorders, SCN1A mutation, under eleven. The whole, cortical, and subcortical volumes of previously enrolled 21 participants, under 11 years of age, with a SCN1A mutation, and 42 healthy controls, were obtained using a DLS method, and compared to volumes measured by Freesurfer with manual correction. Additionally, the volumes which were calculated with the DLS method between the patients and the control group. The volumes of total brain gray and white matter using DLS method were consistent with that volume which were measured by Freesurfer with manual correction in healthy controls. Among 68 cortical parcellated volume analysis, the volumes of only 7 areas measured by DLS methods were significantly different from that measured by Freesurfer with manual correction, and the differences decreased with increasing age in the subgroup analysis. The subcortical volume measured by the DLS method was relatively smaller than that of the Freesurfer volume analysis. Further, the DLS method could perfectly detect the reduced volume identified by the Freesurfer software and manual correction in patients with SCN1A mutations, compared with healthy controls. In a pediatric population, this new, fully automated DLS method is compatible with the classic, volumetric analysis with Freesurfer software and manual correction, and it can also well detect brain morphological changes in children with a neurodevelopmental disorder.
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Affiliation(s)
- Min-Jee Kim
- Department of Pediatrics, Asan Medical Center Children's Hospital, Ulsan University College of Medicine, 88, Olympic-ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea
| | | | - Mi-Sun Yum
- Department of Pediatrics, Asan Medical Center Children's Hospital, Ulsan University College of Medicine, 88, Olympic-ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea.
| | - Yun-Jeong Lee
- Department of Pediatrics, Kyungpook National University Hospital and School of Medicine, Kyungpook National University, Daegu, South Korea
| | | | - Tae-Sung Ko
- Department of Pediatrics, Asan Medical Center Children's Hospital, Ulsan University College of Medicine, 88, Olympic-ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea
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21
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Chen M, Wang Y, Shi Y, Feng J, Feng R, Guan X, Xu X, Zhang Y, Jin C, Wei H. Brain Age Prediction Based on Quantitative Susceptibility Mapping Using the Segmentation Transformer. IEEE J Biomed Health Inform 2024; 28:1012-1021. [PMID: 38090820 DOI: 10.1109/jbhi.2023.3341629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The process of brain aging is intricate, encompassing significant structural and functional changes, including myelination and iron deposition in the brain. Brain age could act as a quantitative marker to evaluate the degree of the individual's brain evolution. Quantitative susceptibility mapping (QSM) is sensitive to variations in magnetically responsive substances such as iron and myelin, making it a favorable tool for estimating brain age. In this study, we introduce an innovative 3D convolutional network named Segmentation-Transformer-Age-Network (STAN) to predict brain age based on QSM data. STAN employs a two-stage network architecture. The first-stage network learns to extract informative features from the QSM data through segmentation training, while the second-stage network predicts brain age by integrating the global and local features. We collected QSM images from 712 healthy participants, with 548 for training and 164 for testing. The results demonstrate that the proposed method achieved a high accuracy brain age prediction with a mean absolute error (MAE) of 4.124 years and a coefficient of determination (R2) of 0.933. Furthermore, the gaps between the predicted brain age and the chronological age of Parkinson's disease patients were significantly higher than those of healthy subjects (P<0.01). We thus believe that using QSM-based predicted brain age offers a more reliable and accurate phenotype, with the potentiality to serve as a biomarker to explore the process of advanced brain aging.
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22
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Guan S, Jiang R, Meng C, Biswal B. Brain age prediction across the human lifespan using multimodal MRI data. GeroScience 2024; 46:1-20. [PMID: 37733220 PMCID: PMC10828281 DOI: 10.1007/s11357-023-00924-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 08/22/2023] [Indexed: 09/22/2023] Open
Abstract
Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu, 610041, China.
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu, 610041, China.
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Medical Equipment Department, Xiangyang No. 1 People's Hospital, Xiangyang, 441000, China
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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23
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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24
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Dartora C, Marseglia A, Mårtensson G, Rukh G, Dang J, Muehlboeck JS, Wahlund LO, Moreno R, Barroso J, Ferreira D, Schiöth HB, Westman E. A deep learning model for brain age prediction using minimally preprocessed T1w images as input. Front Aging Neurosci 2024; 15:1303036. [PMID: 38259636 PMCID: PMC10800627 DOI: 10.3389/fnagi.2023.1303036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/04/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction. Methods We used a multicohort dataset of cognitively healthy individuals (age range = 32.0-95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2-4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset's images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4). Results The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1-4, respectively. The model's performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63-2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction. Discussion We provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.
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Affiliation(s)
- Caroline Dartora
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Anna Marseglia
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Gustav Mårtensson
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Gull Rukh
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Junhua Dang
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - J-Sebastian Muehlboeck
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Lars-Olof Wahlund
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Rodrigo Moreno
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
| | - José Barroso
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas, España
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas, España
| | - Helgi B. Schiöth
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
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25
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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. Pac Symp Biocomput 2024; 29:148-162. [PMID: 38160276 PMCID: PMC10764074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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26
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Shah J, Siddiquee MMR, Su Y, Wu T, Li B. Ordinal Classification with Distance Regularization for Robust Brain Age Prediction. IEEE Winter Conf Appl Comput Vis 2024; 2024:7867-7876. [PMID: 38606366 PMCID: PMC11008505 DOI: 10.1109/wacv57701.2024.00770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Age is one of the major known risk factors for Alzheimer's Disease (AD). Detecting AD early is crucial for effective treatment and preventing irreversible brain damage. Brain age, a measure derived from brain imaging reflecting structural changes due to aging, may have the potential to identify AD onset, assess disease risk, and plan targeted interventions. Deep learning-based regression techniques to predict brain age from magnetic resonance imaging (MRI) scans have shown great accuracy recently. However, these methods are subject to an inherent regression to the mean effect, which causes a systematic bias resulting in an overestimation of brain age in young subjects and underestimation in old subjects. This weakens the reliability of predicted brain age as a valid biomarker for downstream clinical applications. Here, we reformulate the brain age prediction task from regression to classification to address the issue of systematic bias. Recognizing the importance of preserving ordinal information from ages to understand aging trajectory and monitor aging longitudinally, we propose a novel ORdinal Distance Encoded Regularization (ORDER) loss that incorporates the order of age labels, enhancing the model's ability to capture age-related patterns. Extensive experiments and ablation studies demonstrate that this framework reduces systematic bias, outperforms state-of-art methods by statistically significant margins, and can better capture subtle differences between clinical groups in an independent AD dataset. Our implementation is publicly available at https://github.com/jaygshah/Robust-Brain-Age-Prediction.
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Affiliation(s)
- Jay Shah
- Arizona State University
- ASU-Mayo Center for Innovative Imaging
| | | | - Yi Su
- ASU-Mayo Center for Innovative Imaging
- Banner Alzheimer's Institute
| | - Teresa Wu
- Arizona State University
- ASU-Mayo Center for Innovative Imaging
| | - Baoxin Li
- Arizona State University
- ASU-Mayo Center for Innovative Imaging
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27
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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28
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Dular L, Špiclin Ž. BASE: Brain Age Standardized Evaluation. Neuroimage 2024; 285:120469. [PMID: 38065279 DOI: 10.1016/j.neuroimage.2023.120469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/31/2023] [Accepted: 11/20/2023] [Indexed: 01/13/2024] Open
Abstract
Brain age, most commonly inferred from T1-weighted magnetic resonance images (T1w MRI), is a robust biomarker of brain health and related diseases. Superior accuracy in brain age prediction, often falling within a 2-3 year range, is achieved predominantly through deep neural networks. However, comparing study results is difficult due to differences in datasets, evaluation methodologies and metrics. Addressing this, we introduce Brain Age Standardized Evaluation (BASE), which includes (i) a standardized T1w MRI dataset including multi-site, new unseen site, test-retest and longitudinal data, and an associated (ii) evaluation protocol, including repeated model training and upon based comprehensive set of performance metrics measuring accuracy, robustness, reproducibility and consistency aspects of brain age predictions, and (iii) statistical evaluation framework based on linear mixed-effects models for rigorous performance assessment and cross-comparison. To showcase BASE, we comprehensively evaluate four deep learning based brain age models, appraising their performance in scenarios that utilize multi-site, test-retest, unseen site, and longitudinal T1w brain MRI datasets. Ensuring full reproducibility and application in future studies, we have made all associated data information and code publicly accessible at https://github.com/AralRalud/BASE.git.
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Affiliation(s)
- Lara Dular
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana, 1000, Slovenia
| | - Žiga Špiclin
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana, 1000, Slovenia.
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29
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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30
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Moon HS, Mahzarnia A, Stout J, Anderson RJ, Badea CT, Badea A. Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease. bioRxiv 2023:2023.12.13.571574. [PMID: 38168445 PMCID: PMC10760088 DOI: 10.1101/2023.12.13.571574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Alzheimer's disease (AD) remains one of the most extensively researched neurodegenerative disorders due to its widespread prevalence and complex risk factors. Age is a crucial risk factor for AD, which can be estimated by the disparity between physiological age and estimated brain age. To model AD risk more effectively, integrating biological, genetic, and cognitive markers is essential. Here, we utilized mouse models expressing the major APOE human alleles and human nitric oxide synthase 2 to replicate genetic risk for AD and a humanized innate immune response. We estimated brain age employing a multivariate dataset that includes brain connectomes, APOE genotype, subject traits such as age and sex, and behavioral data. Our methodology used Feature Attention Graph Neural Networks (FAGNN) for integrating different data types. Behavioral data were processed with a 2D Convolutional Neural Network (CNN), subject traits with a 1D CNN, brain connectomes through a Graph Neural Network using quadrant attention module. The model yielded a mean absolute error for age prediction of 31.85 days, with a root mean squared error of 41.84 days, outperforming other, reduced models. In addition, FAGNN identified key brain connections involved in the aging process. The highest weights were assigned to the connections between cingulum and corpus callosum, striatum, hippocampus, thalamus, hypothalamus, cerebellum, and piriform cortex. Our study demonstrates the feasibility of predicting brain age in models of aging and genetic risk for AD. To verify the validity of our findings, we compared Fractional Anisotropy (FA) along the tracts of regions with the highest connectivity, the Return-to-Origin Probability (RTOP), Return-to-Plane Probability (RTPP), and Return-to-Axis Probability (RTAP), which showed significant differences between young, middle-aged, and old age groups. Younger mice exhibited higher FA, RTOP, RTAP, and RTPP compared to older groups in the selected connections, suggesting that degradation of white matter tracts plays a critical role in aging and for FAGNN's selections. Our analysis suggests a potential neuroprotective role of APOE2, relative to APOE3 and APOE4, where APOE2 appears to mitigate age-related changes. Our findings highlighted a complex interplay of genetics and brain aging in the context of AD risk modeling.
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Affiliation(s)
- Hae Sol Moon
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Ali Mahzarnia
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Jacques Stout
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Robert J Anderson
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Cristian T. Badea
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Alexandra Badea
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
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31
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Jönemo J, Eklund A. Brain Age Prediction Using 2D Projections Based on Higher-Order Statistical Moments and Eigenslices from 3D Magnetic Resonance Imaging Volumes. J Imaging 2023; 9:271. [PMID: 38132689 PMCID: PMC10743800 DOI: 10.3390/jimaging9120271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Brain age prediction from 3D MRI volumes using deep learning has recently become a popular research topic, as brain age has been shown to be an important biomarker. Training deep networks can be very computationally demanding for large datasets like the U.K. Biobank (currently 29,035 subjects). In our previous work, it was demonstrated that using a few 2D projections (mean and standard deviation along three axes) instead of each full 3D volume leads to much faster training at the cost of a reduction in prediction accuracy. Here, we investigated if another set of 2D projections, based on higher-order statistical central moments and eigenslices, leads to a higher accuracy. Our results show that higher-order moments do not lead to a higher accuracy, but that eigenslices provide a small improvement. We also show that an ensemble of such models provides further improvement.
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Affiliation(s)
- Johan Jönemo
- Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, 581 83 Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 83 Linköping, Sweden
| | - Anders Eklund
- Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, 581 83 Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 83 Linköping, Sweden
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, 581 83 Linköping, Sweden
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32
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Dörfel RP, Arenas‐Gomez JM, Fisher PM, Ganz M, Knudsen GM, Svensson JE, Plavén‐Sigray P. Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test-retest reliability of publicly available software packages. Hum Brain Mapp 2023; 44:6139-6148. [PMID: 37843020 PMCID: PMC10619370 DOI: 10.1002/hbm.26502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/14/2023] [Accepted: 09/08/2023] [Indexed: 10/17/2023] Open
Abstract
Brain age prediction algorithms using structural magnetic resonance imaging (MRI) aim to assess the biological age of the human brain. The difference between a person's chronological age and the estimated brain age is thought to reflect deviations from a normal aging trajectory, indicating a slower or accelerated biological aging process. Several pre-trained software packages for predicting brain age are publicly available. In this study, we perform a comparison of such packages with respect to (1) predictive accuracy, (2) test-retest reliability, and (3) the ability to track age progression over time. We evaluated the six brain age prediction packages: brainageR, DeepBrainNet, brainage, ENIGMA, pyment, and mccqrnn. The accuracy and test-retest reliability were assessed on MRI data from 372 healthy people aged between 18.4 and 86.2 years (mean 38.7 ± 17.5 years). All packages showed significant correlations between predicted brain age and chronological age (r = 0.66-0.97, p < 0.001), with pyment displaying the strongest correlation. The mean absolute error was between 3.56 (pyment) and 9.54 years (ENIGMA). brainageR, pyment, and mccqrnn were superior in terms of reliability (ICC values between 0.94-0.98), as well as predicting age progression over a longer time span. Of the six packages, pyment and brainageR consistently showed the highest accuracy and test-retest reliability.
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Affiliation(s)
- Ruben P. Dörfel
- Neurobiology Research UnitCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
- Centre for Psychiatry Research, Department of Clinical NeuroscienceKarolinska Institutet & Stockholm Health Care Services, Region StockholmStockholmSweden
| | - Joan M. Arenas‐Gomez
- Neurobiology Research UnitCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
| | - Patrick M. Fisher
- Neurobiology Research UnitCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
- Department of Drug Design and PharmacologyUniversity of CopenhagenCopenhagenDenmark
| | - Melanie Ganz
- Neurobiology Research UnitCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
- Department of Computer ScienceUniversity of CopenhagenCopenhagenDenmark
| | - Gitte M. Knudsen
- Neurobiology Research UnitCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
- Department of Clinical MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Jonas E. Svensson
- Neurobiology Research UnitCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
- Centre for Psychiatry Research, Department of Clinical NeuroscienceKarolinska Institutet & Stockholm Health Care Services, Region StockholmStockholmSweden
| | - Pontus Plavén‐Sigray
- Neurobiology Research UnitCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
- Centre for Psychiatry Research, Department of Clinical NeuroscienceKarolinska Institutet & Stockholm Health Care Services, Region StockholmStockholmSweden
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33
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Wang H, Hu Z, Jiang D, Lin R, Zhao C, Zhao X, Zhou Y, Zhu Y, Zeng H, Liang D, Liao J, Li Z. Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex-Related Epilepsy Using Deep Learning. AJNR Am J Neuroradiol 2023; 44:1373-1383. [PMID: 38081677 PMCID: PMC10714846 DOI: 10.3174/ajnr.a8053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 10/03/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND AND PURPOSE Tuberous sclerosis complex disease is a rare, multisystem genetic disease, but appropriate drug treatment allows many pediatric patients to have positive outcomes. The purpose of this study was to predict the effectiveness of antiseizure medication treatment in children with tuberous sclerosis complex-related epilepsy. MATERIALS AND METHODS We conducted a retrospective study involving 300 children with tuberous sclerosis complex-related epilepsy. The study included the analysis of clinical data and T2WI and FLAIR images. The clinical data consisted of sex, age of onset, age at imaging, infantile spasms, and antiseizure medication numbers. To forecast antiseizure medication treatment, we developed a multitechnique deep learning method called WAE-Net. This method used multicontrast MR imaging and clinical data. The T2WI and FLAIR images were combined as FLAIR3 to enhance the contrast between tuberous sclerosis complex lesions and normal brain tissues. We trained a clinical data-based model using a fully connected network with the above-mentioned variables. After that, a weighted-average ensemble network built from the ResNet3D architecture was created as the final model. RESULTS The experiments had shown that age of onset, age at imaging, infantile spasms, and antiseizure medication numbers were significantly different between the 2 drug-treatment outcomes (P < .05). The hybrid technique of FLAIR3 could accurately localize tuberous sclerosis complex lesions, and the proposed method achieved the best performance (area under the curve = 0.908 and accuracy of 0.847) in the testing cohort among the compared methods. CONCLUSIONS The proposed method could predict antiseizure medication treatment of children with rare tuberous sclerosis complex-related epilepsy and could be a strong baseline for future studies.
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Affiliation(s)
- Haifeng Wang
- From the Research Center for Medical Artificial Intelligence (H.W., D.J., Y. Zhou, D.L., Z.L.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology (H.W., D.J., Y.Zhu, D.L., Z.L.), University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Zhanqi Hu
- Department of Neurology (Z.H., R.L., X.Z., J.L.), Shenzhen Children's Hospital, Shenzhen, Guangdong, China
- Department of Pediatric Neurology (Z.H.), Boston Children's Hospital, Boston, Massachusetts
| | - Dian Jiang
- From the Research Center for Medical Artificial Intelligence (H.W., D.J., Y. Zhou, D.L., Z.L.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology (H.W., D.J., Y.Zhu, D.L., Z.L.), University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Rongbo Lin
- Department of Neurology (Z.H., R.L., X.Z., J.L.), Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Cailei Zhao
- Department of Radiology (C.Z., H.Z.), Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Xia Zhao
- Department of Neurology (Z.H., R.L., X.Z., J.L.), Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Yihang Zhou
- From the Research Center for Medical Artificial Intelligence (H.W., D.J., Y. Zhou, D.L., Z.L.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Research Department (Y. Zhou), Hong Kong Sanatorium and Hospital, Hong Kong, China
| | - Yanjie Zhu
- Shenzhen College of Advanced Technology (H.W., D.J., Y.Zhu, D.L., Z.L.), University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Paul C. Lauterbur Research Center for Biomedical Imaging (Y.Zhu, D.L.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Hongwu Zeng
- Department of Radiology (C.Z., H.Z.), Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Dong Liang
- From the Research Center for Medical Artificial Intelligence (H.W., D.J., Y. Zhou, D.L., Z.L.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology (H.W., D.J., Y.Zhu, D.L., Z.L.), University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Paul C. Lauterbur Research Center for Biomedical Imaging (Y.Zhu, D.L.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Jianxiang Liao
- Department of Neurology (Z.H., R.L., X.Z., J.L.), Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Zhicheng Li
- From the Research Center for Medical Artificial Intelligence (H.W., D.J., Y. Zhou, D.L., Z.L.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology (H.W., D.J., Y.Zhu, D.L., Z.L.), University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
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Marx GA, Kauffman J, McKenzie AT, Koenigsberg DG, McMillan CT, Morgello S, Karlovich E, Insausti R, Richardson TE, Walker JM, White CL, Babrowicz BM, Shen L, McKee AC, Stein TD, Farrell K, Crary JF. Histopathologic brain age estimation via multiple instance learning. Acta Neuropathol 2023; 146:785-802. [PMID: 37815677 PMCID: PMC10627911 DOI: 10.1007/s00401-023-02636-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/14/2023] [Accepted: 09/18/2023] [Indexed: 10/11/2023]
Abstract
Understanding age acceleration, the discordance between biological and chronological age, in the brain can reveal mechanistic insights into normal physiology as well as elucidate pathological determinants of age-related functional decline and identify early disease changes in the context of Alzheimer's and other disorders. Histopathological whole slide images provide a wealth of pathologic data on the cellular level that can be leveraged to build deep learning models to assess age acceleration. Here, we used a collection of digitized human post-mortem hippocampal sections to develop a histological brain age estimation model. Our model predicted brain age within a mean absolute error of 5.45 ± 0.22 years, with attention weights corresponding to neuroanatomical regions vulnerable to age-related changes. We found that histopathologic brain age acceleration had significant associations with clinical and pathologic outcomes that were not found with epigenetic based measures. Our results indicate that histopathologic brain age is a powerful, independent metric for understanding factors that contribute to brain aging.
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Affiliation(s)
- Gabriel A Marx
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Justin Kauffman
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Andrew T McKenzie
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel G Koenigsberg
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Cory T McMillan
- Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan Morgello
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, New York, NY, USA
| | - Esma Karlovich
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, School of Medicine, University of Castilla-La Mancha, Albacete, Spain
| | - Timothy E Richardson
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Jamie M Walker
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Charles L White
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Bergan M Babrowicz
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Li Shen
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, New York, NY, USA
| | - Ann C McKee
- Department of Pathology, Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA
- Department of Veterans Affairs Medical Center, Bedford, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | - Thor D Stein
- Department of Pathology, Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA
- Department of Veterans Affairs Medical Center, Bedford, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | - Kurt Farrell
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.
| | - John F Crary
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.
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Yang Z, Zhao W, Linli Z, Guo S, Feng J. Associations between polygenic risk scores and accelerated brain ageing in smokers. Psychol Med 2023; 53:7785-7794. [PMID: 37555321 PMCID: PMC10755245 DOI: 10.1017/s0033291723001812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 08/10/2023]
Abstract
BACKGROUND Smoking contributes to a variety of neurodegenerative diseases and neurobiological abnormalities, suggesting that smoking is associated with accelerated brain aging. However, the neurobiological mechanisms affected by smoking, and whether they are genetically influenced, remain to be investigated. METHODS Using structural magnetic resonance imaging data from the UK Biobank (n = 33 293), a brain age predictor was trained on non-smoking healthy groups and tested on smokers to obtain the BrainAge Gap (BAG). The cumulative effect of multiple common genetic variants associated with smoking was then calculated to acquire a polygenic risk score (PRS). The relationship between PRS, BAG, total gray matter volume (tGMV), and smoking parameters was explored and further genes included in the PRS were annotated to identify potential molecular mechanisms affected by smoking. RESULTS The BrainAge in smokers was predicted with very high accuracy (r = 0.725, MAE = 4.16). Smokers had a greater BAG (Cohen's d = 0.074, p < 0.0001) and higher PRS (Cohen's d = 0.63, p < 0.0001) than non-smokers. A higher PRS was associated with increased amount of smoking, mediated by BAG and tGMV. Several neurotransmitters and ion channel pathways were enriched in the group of smoking-related genes involved in addiction, brain synaptic plasticity, and some neurological disorders. CONCLUSION By using a simplified single indicator of the entire brain (BAG) in combination with the PRS, this study highlights the greater BAG in smokers and its linkage with genes and smoking behavior, providing insight into the neurobiological underpinnings and potential features of smoking-related aging.
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Affiliation(s)
- Zeyu Yang
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Zeqiang Linli
- School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou, 510006, P.R.China
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Jianfeng Feng
- Centre for Computational Systems Biology, Fudan University, Shanghai 200433, P.R.China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, England
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Liu M, Lu M, Kim SY, Lee HJ, Duffy BA, Yuan S, Chai Y, Cole JH, Wu X, Toga AW, Jahanshad N, Gano D, Barkovich AJ, Xu D, Kim H. Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates. Eur Radiol 2023:10.1007/s00330-023-10414-8. [PMID: 37957363 DOI: 10.1007/s00330-023-10414-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 09/06/2023] [Accepted: 09/16/2023] [Indexed: 11/15/2023]
Abstract
OBJECTIVES Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates.
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Affiliation(s)
- Mengting Liu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518107, China
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - Minhua Lu
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, 518060, China
| | - Sharon Y Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - Hyun Ju Lee
- Division of Neonatology, Department of Pediatrics, Hanyang University, Seoul, Korea
| | - Ben A Duffy
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - Shiyu Yuan
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - Yaqiong Chai
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Xiaotong Wu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518107, China
| | - Arthur W Toga
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - Neda Jahanshad
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA
| | - Dawn Gano
- Departments of Neurology and Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - Anthony James Barkovich
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Duan Xu
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Hosung Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA.
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Dular L, Pernuš F, Špiclin Ž. Extensive T1-weighted MRI Preprocessing Improves Generalizability of Deep Brain Age Prediction Models. bioRxiv 2023:2023.05.10.540134. [PMID: 37214863 PMCID: PMC10197652 DOI: 10.1101/2023.05.10.540134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Brain age is an estimate of chronological age obtained from T1-weighted magnetic resonance images (T1w MRI) and represents a simple diagnostic biomarker of brain ageing and associated diseases. While the current best accuracy of brain age predictions on T1w MRIs of healthy subjects ranges from two to three years, comparing results from different studies is challenging due to differences in the datasets, T1w preprocessing pipelines, and performance metrics used. This paper investigates the impact of T1w image preprocessing on the performance of four deep learning brain age models presented in recent literature. Four preprocessing pipelines were evaluated, differing in terms of registration, grayscale correction, and software implementation. The results showed that the choice of software or preprocessing steps can significantly affect the prediction error, with a maximum increase of 0.7 years in mean absolute error (MAE) for the same model and dataset. While grayscale correction had no significant impact on MAE, the affine registration, compared to the rigid registration of T1w images to brain atlas was shown to statistically significantly improve MAE. Models trained on 3D images with isotropic 1 mm3 resolution exhibited less sensitivity to the T1w preprocessing variations compared to 2D models or those trained on downsampled 3D images. Some proved invariant to the preprocessing pipeline, however only after offset correction. Our findings generally indicate that extensive T1w preprocessing enhances the MAE, especially when applied to a new dataset. This runs counter to prevailing research literature which suggests that models trained on minimally preprocessed T1w scans are better poised for age predictions on MRIs from unseen scanners. Regardless of model or T1w preprocessing used, we show that to enable generalization of model's performance on a new dataset with either the same or different T1w preprocessing than the one applied in model training, some form of offset correction should be applied.
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Affiliation(s)
- Lara Dular
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
| | - Franjo Pernuš
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
| | - Žiga Špiclin
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
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Fernandez-Quilez A, Nordström T, Jäderling F, Kjosavik SR, Eklund M. Prostate Age Gap: An MRI Surrogate Marker of Aging for Prostate Cancer Detection. J Magn Reson Imaging 2023. [PMID: 37855699 DOI: 10.1002/jmri.29090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Aging is the most important risk factor for prostate cancer (PC). Imaging techniques can be useful to measure age-related changes associated with the transition to diverse pathological states. However, biomarkers of aging from prostate magnetic resonance imaging (MRI) remain to be explored. PURPOSE To develop an aging biomarker from prostate MRI and to examine its relationship with clinically significant PC (csPC, Gleason score ≥7) risk occurrence. STUDY TYPE Retrospective. POPULATION Four hundred and sixty-eight (65.97 ± 6.91 years) biopsied males, contributing 7243 prostate MRI slices. A deep learning (DL) model was trained on 3223 MRI slices from 81 low-grade PC (Gleason score ≤6) and 131 negative patients, defined as non-csPC. The model was tested on 90 negative, 52 low-grade (142 non-csPC), and 114 csPC patients. FIELD STRENGTH/SEQUENCE 3-T, axial T2-weighted spin sequence. ASSESSMENT Chronological age was defined as the age of the participant at the time of the visit. Prostate-specific antigen (PSA), prostate volume, Gleason, and Prostate Imaging-Reporting and Data System (PI-RADS) scores were also obtained. Manually annotated prostate masks were used to crop the MRI slices, and a DL model was trained with those from non-csPC patients to estimate the age of the patients. Following, we obtained the prostate age gap (PAG) on previously unseen csPC and non-csPC cropped MRI exams. PAG was defined as the estimated model age minus the patient's age. Finally, the relationship between PAG and csPC risk occurrence was assessed through an adjusted multivariate logistic regression by PSA levels, age, prostate volume, and PI-RADS ≥ 3 score. STATISTICAL TESTS T-test, Mann-Whitney U test, permutation test, receiver operating characteristics (ROC), area under the curve (AUC), and odds ratio (OR). A P value <0.05 was considered statistically significant. RESULTS After adjusting, there was a significant difference in the odds of csPC (OR = 3.78, 95% confidence interval [CI]: 2.32-6.16). Further, PAG showed a significantly larger bootstrapped AUC to discriminate between csPC and non-csPC than that of adjusted PI-RADS ≥ 3 (AUC = 0.981, 95% CI: 0.975-0.987). DATA CONCLUSION PAG may be associated with the risk of csPC and could outperform other PC risk factors. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Alvaro Fernandez-Quilez
- Department of Computer Science and Electrical Engineering, University of Stavanger, Stavanger, Norway
- SMIL, Department of Radiology, Stavanger University Hospital, Stavanger, Norway
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Tobias Nordström
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Fredrik Jäderling
- Department of Radiology, Capio Saint Göran Hospital, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Svein Reidar Kjosavik
- General Practice and Care Coordination Research Group, Stavanger University Hospital, Stavanger, Norway
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Choi US, Park JY, Lee JJ, Choi KY, Won S, Lee KH. Predicting mild cognitive impairments from cognitively normal brains using a novel brain age estimation model based on structural magnetic resonance imaging. Cereb Cortex 2023; 33:10858-10866. [PMID: 37718166 DOI: 10.1093/cercor/bhad331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/20/2023] [Accepted: 08/22/2023] [Indexed: 09/19/2023] Open
Abstract
Brain age prediction is a practical method used to quantify brain aging and detect neurodegenerative diseases such as Alzheimer's disease (AD). However, very few studies have considered brain age prediction as a biomarker for the conversion of cognitively normal (CN) to mild cognitive impairment (MCI). In this study, we developed a novel brain age prediction model using brain volume and cortical thickness features. We calculated an acceleration of brain age (ABA) derived from the suggested model to estimate different diagnostic groups (CN, MCI, and AD) and to classify CN to MCI and MCI to AD conversion groups. We observed a strong association between ABA and the 3 diagnostic groups. Additionally, the classification models for CN to MCI conversion and MCI to AD conversion exhibited acceptable and robust performances, with area under the curve values of 0.66 and 0.76, respectively. We believe that our proposed model provides a reliable estimate of brain age for elderly individuals and can identify those at risk of progressing from CN to MCI. This model has great potential to reveal a diagnosis associated with a change in cognitive decline.
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Affiliation(s)
- Uk-Su Choi
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation, Daegu 41061, Republic of Korea
| | - Jun Young Park
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Republic of Korea
- Neurozen Inc., Seoul 06168, Republic of Korea
| | - Jang Jae Lee
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
| | - Kyu Yeong Choi
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
| | - Sungho Won
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Republic of Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Republic of Korea
- Institute of Health and Environment, Seoul National University, Seoul 08826, Republic of Korea
| | - Kun Ho Lee
- Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea
- Department of Biomedical Sciences, Chosun University, Gwangju 61452, Republic of Korea
- Korea Brain Research Institute, Daegu 41061, Republic of Korea
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Statsenko Y, Kuznetsov NV, Morozova D, Liaonchyk K, Simiyu GL, Smetanina D, Kashapov A, Meribout S, Gorkom KNV, Hamoudi R, Ismail F, Ansari SA, Emerald BS, Ljubisavljevic M. Reappraisal of the Concept of Accelerated Aging in Neurodegeneration and Beyond. Cells 2023; 12:2451. [PMID: 37887295 PMCID: PMC10605227 DOI: 10.3390/cells12202451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 09/01/2023] [Accepted: 09/06/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Genetic and epigenetic changes, oxidative stress and inflammation influence the rate of aging, which diseases, lifestyle and environmental factors can further accelerate. In accelerated aging (AA), the biological age exceeds the chronological age. OBJECTIVE The objective of this study is to reappraise the AA concept critically, considering its weaknesses and limitations. METHODS We reviewed more than 300 recent articles dealing with the physiology of brain aging and neurodegeneration pathophysiology. RESULTS (1) Application of the AA concept to individual organs outside the brain is challenging as organs of different systems age at different rates. (2) There is a need to consider the deceleration of aging due to the potential use of the individual structure-functional reserves. The latter can be restored by pharmacological and/or cognitive therapy, environment, etc. (3) The AA concept lacks both standardised terminology and methodology. (4) Changes in specific molecular biomarkers (MBM) reflect aging-related processes; however, numerous MBM candidates should be validated to consolidate the AA theory. (5) The exact nature of many potential causal factors, biological outcomes and interactions between the former and the latter remain largely unclear. CONCLUSIONS Although AA is commonly recognised as a perspective theory, it still suffers from a number of gaps and limitations that assume the necessity for an updated AA concept.
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Affiliation(s)
- Yauhen Statsenko
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Big Data Analytic Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Nik V. Kuznetsov
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
| | - Daria Morozova
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
| | - Katsiaryna Liaonchyk
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
| | - Gillian Lylian Simiyu
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Darya Smetanina
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Aidar Kashapov
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Sarah Meribout
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Klaus Neidl-Van Gorkom
- Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (Y.S.); (G.L.S.); (D.S.); (A.K.); (S.M.); (K.N.-V.G.)
| | - Rifat Hamoudi
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah 27272, United Arab Emirates
- Division of Surgery and Interventional Science, University College London, London NW3 2PS, UK
| | - Fatima Ismail
- Department of Pediatrics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates;
| | - Suraiya Anjum Ansari
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Department of Biochemistry and Molecular Biology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Bright Starling Emerald
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Department of Anatomy, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Milos Ljubisavljevic
- ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain 27272, United Arab Emirates; (D.M.); (K.L.); (R.H.); (S.A.A.); (B.S.E.); (M.L.)
- Department of Physiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
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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 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Bottani S, Burgos N, Maire A, Saracino D, Ströer S, Dormont D, Colliot O. Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse. Med Image Anal 2023; 89:102903. [PMID: 37523918 DOI: 10.1016/j.media.2023.102903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 06/01/2023] [Accepted: 07/12/2023] [Indexed: 08/02/2023]
Abstract
A variety of algorithms have been proposed for computer-aided diagnosis of dementia from anatomical brain MRI. These approaches achieve high accuracy when applied to research data sets but their performance on real-life clinical routine data has not been evaluated yet. The aim of this work was to study the performance of such approaches on clinical routine data, based on a hospital data warehouse, and to compare the results to those obtained on a research data set. The clinical data set was extracted from the hospital data warehouse of the Greater Paris area, which includes 39 different hospitals. The research set was composed of data from the Alzheimer's Disease Neuroimaging Initiative data set. In the clinical set, the population of interest was identified by exploiting the diagnostic codes from the 10th revision of the International Classification of Diseases that are assigned to each patient. We studied how the imbalance of the training sets, in terms of contrast agent injection and image quality, may bias the results. We demonstrated that computer-aided diagnosis performance was strongly biased upwards (over 17 percent points of balanced accuracy) by the confounders of image quality and contrast agent injection, a phenomenon known as the Clever Hans effect or shortcut learning. When these biases were removed, the performance was very poor. In any case, the performance was considerably lower than on the research data set. Our study highlights that there are still considerable challenges for translating dementia computer-aided diagnosis systems to clinical routine.
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Affiliation(s)
- Simona Bottani
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France
| | - Ninon Burgos
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France
| | | | - Dario Saracino
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France; IM2A, Reference Centre for Rare or Early-Onset Dementias, Département de Neurologie, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, 75013, France
| | - Sebastian Ströer
- AP-HP, Hôpital de la Pitié Salpêtrière, Department of Neuroradiology, Paris, 75013, France
| | - Didier Dormont
- AP-HP, Hôpital de la Pitié Salpêtrière, Department of Neuroradiology, Paris, 75013, France; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, DMU DIAMENT, Paris, 75013, France
| | - Olivier Colliot
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France.
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Liu M, Zhu AH, Maiti P, Thomopoulos SI, Gadewar S, Chai Y, Kim H, Jahanshad N. Style transfer generative adversarial networks to harmonize multisite MRI to a single reference image to avoid overcorrection. Hum Brain Mapp 2023; 44:4875-4892. [PMID: 37471702 PMCID: PMC10472922 DOI: 10.1002/hbm.26422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 05/30/2023] [Accepted: 06/25/2023] [Indexed: 07/22/2023] Open
Abstract
Recent work within neuroimaging consortia have aimed to identify reproducible, and often subtle, brain signatures of psychiatric or neurological conditions. To allow for high-powered brain imaging analyses, it is often necessary to pool MR images that were acquired with different protocols across multiple scanners. Current retrospective harmonization techniques have shown promise in removing site-related image variation. However, most statistical approaches may over-correct for technical, scanning-related, variation as they cannot distinguish between confounded image-acquisition based variability and site-related population variability. Such statistical methods often require that datasets contain subjects or patient groups with similar clinical or demographic information to isolate the acquisition-based variability. To overcome this limitation, we consider site-related magnetic resonance (MR) imaging harmonization as a style transfer problem rather than a domain transfer problem. Using a fully unsupervised deep-learning framework based on a generative adversarial network (GAN), we show that MR images can be harmonized by inserting the style information encoded from a single reference image, without knowing their site/scanner labels a priori. We trained our model using data from five large-scale multisite datasets with varied demographics. Results demonstrated that our style-encoding model can harmonize MR images, and match intensity profiles, without relying on traveling subjects. This model also avoids the need to control for clinical, diagnostic, or demographic information. We highlight the effectiveness of our method for clinical research by comparing extracted cortical and subcortical features, brain-age estimates, and case-control effect sizes before and after the harmonization. We showed that our harmonization removed the site-related variances, while preserving the anatomical information and clinical meaningful patterns. We further demonstrated that with a diverse training set, our method successfully harmonized MR images collected from unseen scanners and protocols, suggesting a promising tool for ongoing collaborative studies. Source code is released in USC-IGC/style_transfer_harmonization (github.com).
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Affiliation(s)
- Mengting Liu
- School of Biomedical EngineeringSun Yat‐sen UniversityShenzhenChina
- USC Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine of USC, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Alyssa H. Zhu
- USC Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine of USC, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Piyush Maiti
- USC Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine of USC, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Sophia I. Thomopoulos
- USC Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine of USC, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Shruti Gadewar
- USC Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine of USC, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Yaqiong Chai
- USC Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine of USC, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Hosung Kim
- USC Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine of USC, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Neda Jahanshad
- USC Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine of USC, University of Southern CaliforniaLos AngelesCaliforniaUSA
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Antonopoulos G, More S, Raimondo F, Eickhoff SB, Hoffstaedter F, Patil KR. A systematic comparison of VBM pipelines and their application to age prediction. Neuroimage 2023; 279:120292. [PMID: 37572766 PMCID: PMC10529438 DOI: 10.1016/j.neuroimage.2023.120292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 06/23/2023] [Accepted: 07/21/2023] [Indexed: 08/14/2023] Open
Abstract
Voxel-based morphometry (VBM) analysis is commonly used for localized quantification of gray matter volume (GMV). Several alternatives exist to implement a VBM pipeline. However, how these alternatives compare and their utility in applications, such as the estimation of aging effects, remain largely unclear. This leaves researchers wondering which VBM pipeline they should use for their project. In this study, we took a user-centric perspective and systematically compared five VBM pipelines, together with registration to either a general or a study-specific template, utilizing three large datasets (n>500 each). Considering the known effect of aging on GMV, we first compared the pipelines in their ability of individual-level age prediction and found markedly varied results. To examine whether these results arise from systematic differences between the pipelines, we classified them based on their GMVs, resulting in near-perfect accuracy. To gain deeper insights, we examined the impact of different VBM steps using the region-wise similarity between pipelines. The results revealed marked differences, largely driven by segmentation and registration steps. We observed large variability in subject-identification accuracies, highlighting the interpipeline differences in individual-level quantification of GMV. As a biologically meaningful criterion we correlated regional GMV with age. The results were in line with the age-prediction analysis, and two pipelines, CAT and the combination of fMRIPrep for tissue characterization with FSL for registration, reflected age information better.
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Affiliation(s)
- Georgios Antonopoulos
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Shammi More
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Federico Raimondo
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
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Yang Y, Guo X, Ye C, Xiang Y, Ma T. CReg-KD: Model refinement via confidence regularized knowledge distillation for brain imaging. Med Image Anal 2023; 89:102916. [PMID: 37549611 DOI: 10.1016/j.media.2023.102916] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 07/10/2023] [Accepted: 07/25/2023] [Indexed: 08/09/2023]
Abstract
One of the core challenges of deep learning in medical image analysis is data insufficiency, especially for 3D brain imaging, which may lead to model over-fitting and poor generalization. Regularization strategies such as knowledge distillation are powerful tools to mitigate the issue by penalizing predictive distributions and introducing additional knowledge to reinforce the training process. In this paper, we revisit knowledge distillation as a regularization paradigm by penalizing attentive output distributions and intermediate representations. In particular, we propose a Confidence Regularized Knowledge Distillation (CReg-KD) framework, which adaptively transfers knowledge for distillation in light of knowledge confidence. Two strategies are advocated to regularize the global and local dependencies between teacher and student knowledge. In detail, a gated distillation mechanism is proposed to soften the transferred knowledge globally by utilizing the teacher loss as a confidence score. Moreover, the intermediate representations are attentively and locally refined with key semantic context to mimic meaningful features. To demonstrate the superiority of our proposed framework, we evaluated the framework on two brain imaging analysis tasks (i.e. Alzheimer's Disease classification and brain age estimation based on T1-weighted MRI) on the Alzheimer's Disease Neuroimaging Initiative dataset including 902 subjects and a cohort of 3655 subjects from 4 public datasets. Extensive experimental results show that CReg-KD achieves consistent improvements over the baseline teacher model and outperforms other state-of-the-art knowledge distillation approaches, manifesting that CReg-KD as a powerful medical image analysis tool in terms of both promising prediction performance and generalizability.
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Affiliation(s)
- Yanwu Yang
- Electronic & Information Engineering School, Harbin Institute of Technology (Shenzhen), Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China.
| | - Xutao Guo
- Electronic & Information Engineering School, Harbin Institute of Technology (Shenzhen), Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China.
| | - Chenfei Ye
- Peng Cheng Laboratory, Shenzhen, China; International Research Institute for Artificial Intelligence, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
| | | | - Ting Ma
- Electronic & Information Engineering School, Harbin Institute of Technology (Shenzhen), Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China; Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China; International Research Institute for Artificial Intelligence, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
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Guo X, Ding Y, Xu W, Wang D, Yu H, Lin Y, Chang S, Zhang Q, Zhang Y. Predicting brain age gap with radiomics and automl: A Promising approach for age-Related brain degeneration biomarkers. J Neuroradiol 2023:S0150-9861(23)00241-9. [PMID: 37722591 DOI: 10.1016/j.neurad.2023.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 09/14/2023] [Accepted: 09/15/2023] [Indexed: 09/20/2023]
Abstract
The Brain Age Gap (BAG), which refers to the difference between chronological age and predicted neuroimaging age, is proposed as a potential biomarker for age-related brain degeneration. However, existing brain age prediction models usually rely on a single marker and can not discover meaningful hidden information in radiographic images. This study focuses on the application of radiomics, an advanced imaging analysis technique, combined with automated machine learning to predict BAG. Our methods achieve a promising result with a mean absolute error of 1.509 using the Alzheimer's Disease Neuroimaging Initiative dataset. Furthermore, we find that the hippocampus and parahippocampal gyrus play a significant role in predicting age with interpretable method called SHapley Additive exPlanations. Additionally, our investigation of age prediction discrepancies between patients with Alzheimer's disease (AD) and those with mild cognitive impairment (MCI) reveals a notable correlation with clinical cognitive assessment scale scores. This suggests that BAG has the potential to serve as a biomarker to support the diagnosis of AD and MCI. Overall, this study presents valuable insights into the application of neuroimaging models in the diagnosis of neurodegenerative diseases.
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Affiliation(s)
- Xiaoliang Guo
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yanhui Ding
- School of Information Science and Engineering, Shandong Normal University, Jinan, China.
| | - Weizhi Xu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Dong Wang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunication, Beijing, China
| | - Huiying Yu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yongkang Lin
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Shulei Chang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Qiqi Zhang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yongxin Zhang
- School of Mathematics and Statistics, Shandong Normal University, Jinan, China.
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Jönemo J, Akbar MU, Kämpe R, Hamilton JP, Eklund A. Efficient Brain Age Prediction from 3D MRI Volumes Using 2D Projections. Brain Sci 2023; 13:1329. [PMID: 37759930 PMCID: PMC10526282 DOI: 10.3390/brainsci13091329] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 09/09/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Using 3D CNNs on high-resolution medical volumes is very computationally demanding, especially for large datasets like UK Biobank, which aims to scan 100,000 subjects. Here, we demonstrate that using 2D CNNs on a few 2D projections (representing mean and standard deviation across axial, sagittal and coronal slices) of 3D volumes leads to reasonable test accuracy (mean absolute error of about 3.5 years) when predicting age from brain volumes. Using our approach, one training epoch with 20,324 subjects takes 20-50 s using a single GPU, which is two orders of magnitude faster than a small 3D CNN. This speedup is explained by the fact that 3D brain volumes contain a lot of redundant information, which can be efficiently compressed using 2D projections. These results are important for researchers who do not have access to expensive GPU hardware for 3D CNNs.
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Affiliation(s)
- Johan Jönemo
- Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, 581 83 Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 83 Linköping, Sweden
| | - Muhammad Usman Akbar
- Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, 581 83 Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 83 Linköping, Sweden
| | - Robin Kämpe
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 83 Linköping, Sweden
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, 581 83 Linköping, Sweden
| | - J. Paul Hamilton
- Department of Biological and Medical Psychology, University of Bergen, 5020 Bergen, Norway
| | - Anders Eklund
- Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, 581 83 Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 83 Linköping, Sweden
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, 581 83 Linköping, Sweden
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Persson K, Leonardsen EH, Edwin TH, Knapskog AB, Tangen GG, Selbæk G, Wolfers T, Westlye LT, Engedal K. Diagnostic accuracy of brain age prediction in a memory clinic population and comparison with clinically available volumetric measures. Sci Rep 2023; 13:14957. [PMID: 37696909 PMCID: PMC10495330 DOI: 10.1038/s41598-023-42354-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/08/2023] [Indexed: 09/13/2023] Open
Abstract
The aim of this study was to assess the diagnostic validity of a deep learning-based method estimating brain age based on magnetic resonance imaging (MRI) and to compare it with volumetrics obtained using NeuroQuant (NQ) in a clinical cohort. Brain age prediction was performed on minimally processed MRI data using deep convolutional neural networks and an independent training set. The brain age gap (difference between chronological and biological age) was calculated, and volumetrics were performed in 110 patients with dementia (Alzheimer's disease, frontotemporal dementia (FTD), and dementia with Lewy bodies), and 122 with non-dementia (subjective and mild cognitive impairment). Area-under-the-curve (AUC) based on receiver operating characteristics and logistic regression analyses were performed. The mean age was 67.1 (9.5) years and 48.7% (113) were females. The dementia versus non-dementia sensitivity and specificity of the volumetric measures exceeded 80% and yielded higher AUCs compared to BAG. The explained variance of the prediction of diagnostic stage increased when BAG was added to the volumetrics. Further, BAG separated patients with FTD from other dementia etiologies with > 80% sensitivity and specificity. NQ volumetrics outperformed BAG in terms of diagnostic discriminatory power but the two methods provided complementary information, and BAG discriminated FTD from other dementia etiologies.
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Affiliation(s)
- Karin Persson
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway.
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.
| | - Esten H Leonardsen
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Trine Holt Edwin
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Gro Gujord Tangen
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Geir Selbæk
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
- Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Knut Engedal
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
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Sabbagh D, Cartailler J, Touchard C, Joachim J, Mebazaa A, Vallée F, Gayat É, Gramfort A, Engemann DA. Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study. BJA Open 2023; 7:100145. [PMID: 37638087 PMCID: PMC10457469 DOI: 10.1016/j.bjao.2023.100145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 05/16/2023] [Indexed: 08/29/2023]
Abstract
Background Electroencephalography (EEG) is increasingly used for monitoring the depth of general anaesthesia, but EEG data from general anaesthesia monitoring are rarely reused for research. Here, we explored repurposing EEG monitoring from general anaesthesia for brain-age modelling using machine learning. We hypothesised that brain age estimated from EEG during general anaesthesia is associated with perioperative risk. Methods We reanalysed four-electrode EEGs of 323 patients under stable propofol or sevoflurane anaesthesia to study four EEG signatures (95% of EEG power <8-13 Hz) for age prediction: total power, alpha-band power (8-13 Hz), power spectrum, and spatial patterns in frequency bands. We constructed age-prediction models from EEGs of a healthy reference group (ASA 1 or 2) during propofol anaesthesia. Although all signatures were informative, state-of-the-art age-prediction performance was unlocked by parsing spatial patterns across electrodes along the entire power spectrum (mean absolute error=8.2 yr; R2=0.65). Results Clinical exploration in ASA 1 or 2 patients revealed that brain age was positively correlated with intraoperative burst suppression, a risk factor for general anaesthesia complications. Surprisingly, brain age was negatively correlated with burst suppression in patients with higher ASA scores, suggesting hidden confounders. Secondary analyses revealed that age-related EEG signatures were specific to propofol anaesthesia, reflected by limited model generalisation to anaesthesia maintained with sevoflurane. Conclusions Although EEG from general anaesthesia may enable state-of-the-art age prediction, differences between anaesthetic drugs can impact the effectiveness and validity of brain-age models. To unleash the dormant potential of EEG monitoring for clinical research, larger datasets from heterogeneous populations with precisely documented drug dosage will be essential.
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Affiliation(s)
- David Sabbagh
- INSERM, Université de Paris, Paris, France
- Inria, CEA, Université Paris-Saclay, Palaiseau, France
| | - Jérôme Cartailler
- INSERM, Université de Paris, Paris, France
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Cyril Touchard
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Jona Joachim
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Alexandre Mebazaa
- INSERM, Université de Paris, Paris, France
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Fabrice Vallée
- INSERM, Université de Paris, Paris, France
- Inria, CEA, Université Paris-Saclay, Palaiseau, France
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | - Étienne Gayat
- INSERM, Université de Paris, Paris, France
- Department of Anesthesia and Critical Care Medicine, AP-HP, Hôpital Lariboisière, Paris, France
| | | | - Denis A. Engemann
- Inria, CEA, Université Paris-Saclay, Palaiseau, France
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
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50
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Iakunchykova O, Schirmer H, Vangberg T, Wang Y, Benavente ED, van Es R, van de Leur RR, Lindekleiv H, Attia ZI, Lopez-Jimenez F, Leon DA, Wilsgaard T. Machine-learning-derived heart and brain age are independently associated with cognition. Eur J Neurol 2023; 30:2611-2619. [PMID: 37254942 DOI: 10.1111/ene.15902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/03/2023] [Accepted: 05/28/2023] [Indexed: 06/01/2023]
Abstract
BACKGROUND AND PURPOSE A heart age biomarker has been developed using deep neural networks applied to electrocardiograms. Whether this biomarker is associated with cognitive function was investigated. METHODS Using 12-lead electrocardiograms, heart age was estimated for a population-based sample (N = 7779, age 40-85 years, 45.3% men). Associations between heart delta age (HDA) and cognitive test scores were studied adjusted for cardiovascular risk factors. In addition, the relationship between HDA, brain delta age (BDA) and cognitive test scores was investigated in mediation analysis. RESULTS Significant associations between HDA and the Word test, Digit Symbol Coding Test and tapping test scores were found. HDA was correlated with BDA (Pearson's r = 0.12, p = 0.0001). Moreover, 13% (95% confidence interval 3-36) of the HDA effect on the tapping test score was mediated through BDA. DISCUSSION Heart delta age, representing the cumulative effects of life-long exposures, was associated with brain age. HDA was associated with cognitive function that was minimally explained through BDA.
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Affiliation(s)
- Olena Iakunchykova
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Henrik Schirmer
- Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
| | - Torgil Vangberg
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromsø, Norway
- PET Imaging Center, University Hospital of North Norway, Tromsø, Norway
| | - Yunpeng Wang
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Ernest D Benavente
- Department of Experimental Cardiology, University Medical Center, Utrecht, The Netherlands
| | - René van Es
- Department of Cardiology, University Medical Center, Utrecht, The Netherlands
| | | | - Haakon Lindekleiv
- University Hospital of North Norway, Tromsø, Norway
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | - Zachi I Attia
- Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | | | - David A Leon
- Department of Noncommunicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Tom Wilsgaard
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
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