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Gaser C, Kalc P, Cole JH. A perspective on brain-age estimation and its clinical promise. NATURE COMPUTATIONAL SCIENCE 2024:10.1038/s43588-024-00659-8. [PMID: 39048692 DOI: 10.1038/s43588-024-00659-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 06/12/2024] [Indexed: 07/27/2024]
Abstract
Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings.
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Affiliation(s)
- Christian Gaser
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany.
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.
- German Centre for Mental Health (DZPG), Jena-Halle-Magdeburg, Jena, Germany.
| | - Polona Kalc
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
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Demuth S, Paris J, Faddeenkov I, De Sèze J, Gourraud PA. Clinical applications of deep learning in neuroinflammatory diseases: A scoping review. Rev Neurol (Paris) 2024:S0035-3787(24)00522-8. [PMID: 38772806 DOI: 10.1016/j.neurol.2024.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 03/26/2024] [Accepted: 04/09/2024] [Indexed: 05/23/2024]
Abstract
BACKGROUND Deep learning (DL) is an artificial intelligence technology that has aroused much excitement for predictive medicine due to its ability to process raw data modalities such as images, text, and time series of signals. OBJECTIVES Here, we intend to give the clinical reader elements to understand this technology, taking neuroinflammatory diseases as an illustrative use case of clinical translation efforts. We reviewed the scope of this rapidly evolving field to get quantitative insights about which clinical applications concentrate the efforts and which data modalities are most commonly used. METHODS We queried the PubMed database for articles reporting DL algorithms for clinical applications in neuroinflammatory diseases and the radiology.healthairegister.com website for commercial algorithms. RESULTS The review included 148 articles published between 2018 and 2024 and five commercial algorithms. The clinical applications could be grouped as computer-aided diagnosis, individual prognosis, functional assessment, the segmentation of radiological structures, and the optimization of data acquisition. Our review highlighted important discrepancies in efforts. The segmentation of radiological structures and computer-aided diagnosis currently concentrate most efforts with an overrepresentation of imaging. Various model architectures have addressed different applications, relatively low volume of data, and diverse data modalities. We report the high-level technical characteristics of the algorithms and synthesize narratively the clinical applications. Predictive performances and some common a priori on this topic are finally discussed. CONCLUSION The currently reported efforts position DL as an information processing technology, enhancing existing modalities of paraclinical investigations and bringing perspectives to make innovative ones actionable for healthcare.
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Affiliation(s)
- S Demuth
- Inserm U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 44000 Nantes, France; Inserm U1119 : biopathologie de la myéline, neuroprotection et stratégies thérapeutiques, University of Strasbourg, 1, rue Eugène-Boeckel - CS 60026, 67084 Strasbourg, France.
| | - J Paris
- Inserm U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 44000 Nantes, France
| | - I Faddeenkov
- Inserm U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 44000 Nantes, France
| | - J De Sèze
- Inserm U1119 : biopathologie de la myéline, neuroprotection et stratégies thérapeutiques, University of Strasbourg, 1, rue Eugène-Boeckel - CS 60026, 67084 Strasbourg, France; Department of Neurology, University Hospital of Strasbourg, 1, avenue Molière, 67200 Strasbourg, France; Inserm CIC 1434 Clinical Investigation Center, University Hospital of Strasbourg, 1, avenue Molière, 67200 Strasbourg, France
| | - P-A Gourraud
- Inserm U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 44000 Nantes, France; "Data clinic", Department of Public Health, University Hospital of Nantes, Nantes, France
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Ma H, Zhu Y, Liang X, Wu L, Wang Y, Li X, Qian L, Cheung GL, Zhou F. In patients with mild disability NMOSD: is the alteration in the cortical morphological or functional network topological properties more significant. Front Immunol 2024; 15:1345843. [PMID: 38375481 PMCID: PMC10875087 DOI: 10.3389/fimmu.2024.1345843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/15/2024] [Indexed: 02/21/2024] Open
Abstract
Objective To assess the alteration of individual brain morphological and functional network topological properties and their clinical significance in patients with neuromyelitis optica spectrum disorder (NMOSD). Materials and methods Eighteen patients with NMOSD and twenty-two healthy controls (HCs) were included. The clinical assessment of NMOSD patients involved evaluations of disability status, cognitive function, and fatigue impact. For each participant, brain images, including high-resolution T1-weighted images for individual morphological brain networks (MBNs) and resting-state functional MR images for functional brain networks (FBNs) were obtained. Topological properties were calculated and compared for both MBNs and FBNs. Then, partial correlation analysis was performed to investigate the relationships between the altered network properties and clinical variables. Finally, the altered network topological properties were used to classify NMOSD patients from HCs and to analyses time- to-progression of the patients. Results The average Expanded Disability Status Scale score of NMOSD patients was 1.05 (range from 0 to 2), indicating mild disability. Compared to HCs, NMOSD patients exhibited a higher normalized characteristic path length (λ) in their MBNs (P = 0.0118, FDR corrected) but showed no significant differences in the global properties of FBNs (p: 0.405-0.488). Network-based statistical analysis revealed that MBNs had more significantly altered connections (P< 0.01, NBS corrected) than FBNs. Altered nodal properties of MBNs were correlated with disease duration or fatigue scores (P< 0.05/6 with Bonferroni correction). Using the altered nodal properties of MBNs, the accuracy of classification of NMOSD patients versus HCs was 96.4%, with a sensitivity of 93.3% and a specificity of 100%. This accuracy was better than that achieved using the altered nodal properties of FBNs. Nodal properties of MBN significantly predicted Expanded Disability Status Scale worsening in patients with NMOSD. Conclusion The results indicated that patients with mild disability NMOSD exhibited compensatory increases in local network properties to maintain overall stability. Furthermore, the alterations in the morphological network nodal properties of NMOSD patients not only had better relevance for clinical assessments compared with functional network nodal properties, but also exhibited predictive values of EDSS worsening.
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Affiliation(s)
- Haotian Ma
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Queen Mary College, Nanchang University, Nanchang, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Xiao Liang
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Lin Wu
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Yao Wang
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Xiaoxing Li
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Long Qian
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China
| | | | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Neuroimaging Laboratory, Jiangxi Medical Imaging Research Institute, Nanchang, China
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Valdes-Hernandez PA, Laffitte Nodarse C, Cole JH, Cruz-Almeida Y. Feasibility of brain age predictions from clinical T1-weighted MRIs. Brain Res Bull 2023; 205:110811. [PMID: 37952679 DOI: 10.1016/j.brainresbull.2023.110811] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 11/14/2023]
Abstract
An individual's brain predicted age minus chronological age (brain-PAD) obtained from MRIs could become a biomarker of disease in research studies. However, brain age reports from clinical MRIs are scant despite the rich clinical information hospitals provide. Since clinical MRI protocols are meant for specific clinical purposes, performance of brain age predictions on clinical data need to be tested. We explored the feasibility of using DeepBrainNet, a deep network previously trained on research-oriented MRIs, to predict the brain ages of 840 patients who visited 15 facilities of a health system in Florida. Anticipating a strong prediction bias in our clinical sample, we characterized it to propose a covariate model in group-level regressions of brain-PAD (recommended to avoid Type I, II errors), and tested its generalizability, a requirement for meaningful brain age predictions in new single clinical cases. The best bias-related covariate model was scanner-independent and linear in age, while the best method to estimate bias-free brain ages was the inverse of a scanner-independent and quadratic in brain age function. We demonstrated the feasibility to detect sex-related differences in brain-PAD using group-level regression accounting for the selected covariate model. These differences were preserved after bias correction. The Mean-Average Error (MAE) of the predictions in independent data was ∼8 years, 2-3 years greater than reports for research-oriented MRIs using DeepBrainNet, whereas an R2 (assuming no bias) was 0.33 and 0.76 for the uncorrected and corrected brain ages, respectively. DeepBrainNet on clinical populations seems feasible, but more accurate algorithms or transfer-learning retraining is needed.
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Affiliation(s)
- Pedro A Valdes-Hernandez
- Department of Community Dentistry and Behavioral Science, University of Florida, USA; Pain Research and Intervention Center of Excellence, University of Florida, USA; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - Chavier Laffitte Nodarse
- Department of Community Dentistry and Behavioral Science, University of Florida, USA; Pain Research and Intervention Center of Excellence, University of Florida, USA; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK; Dementia Research Centre, Queen Square Institute of Neurology, University College London, UK
| | - Yenisel Cruz-Almeida
- Department of Community Dentistry and Behavioral Science, University of Florida, USA; Pain Research and Intervention Center of Excellence, University of Florida, USA; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA; Department of Neuroscience, College of Medicine, University of Florida, USA.
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Valdes-Hernandez PA, Laffitte Nodarse C, Peraza JA, Cole JH, Cruz-Almeida Y. Toward MR protocol-agnostic, unbiased brain age predicted from clinical-grade MRIs. Sci Rep 2023; 13:19570. [PMID: 37950024 PMCID: PMC10638359 DOI: 10.1038/s41598-023-47021-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/08/2023] [Indexed: 11/12/2023] Open
Abstract
The difference between the estimated brain age and the chronological age ('brain-PAD') could become a clinical biomarker. However, most brain age models were developed for research-grade high-resolution T1-weighted MRIs, limiting their applicability to clinical-grade MRIs from various protocols. We adopted a dual-transfer learning strategy to develop a model agnostic to modality, resolution, or slice orientation. We retrained a convolutional neural network (CNN) using 6281 clinical MRIs from 1559 patients, among 7 modalities and 8 scanner models. The CNN was trained to estimate brain age from synthetic research-grade magnetization-prepared rapid gradient-echo MRIs (MPRAGEs) generated by a 'super-resolution' method. The model failed with T2-weighted Gradient-Echo MRIs. The mean absolute error (MAE) was 5.86-8.59 years across the other modalities, still higher than for research-grade MRIs, but comparable between actual and synthetic MPRAGEs for some modalities. We modeled the "regression bias" in brain age, for its correction is crucial for providing unbiased summary statistics of brain age or for personalized brain age-based biomarkers. The bias model was generalizable as its correction eliminated any correlation between brain-PAD and chronological age in new samples. Brain-PAD was reliable across modalities. We demonstrate the feasibility of brain age predictions from arbitrary clinical-grade MRIs, thereby contributing to personalized medicine.
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Affiliation(s)
- Pedro A Valdes-Hernandez
- Department of Community Dentistry and Behavioral Science, University of Florida, 1329 SW 16th Street, Ste. 5180, Gainesville, FL, 32610, USA.
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA.
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.
| | - Chavier Laffitte Nodarse
- Department of Community Dentistry and Behavioral Science, University of Florida, 1329 SW 16th Street, Ste. 5180, Gainesville, FL, 32610, USA
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | - Julio A Peraza
- Department of Physics, Florida International University, Miami, FL, USA
| | - James H Cole
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, UK
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
| | - Yenisel Cruz-Almeida
- Department of Community Dentistry and Behavioral Science, University of Florida, 1329 SW 16th Street, Ste. 5180, Gainesville, FL, 32610, USA
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USA
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