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Mimura Y, Tobari Y, Nakajima S, Takano M, Wada M, Honda S, Bun S, Tabuchi H, Ito D, Matsui M, Uchida H, Mimura M, Noda Y. Decreased short-latency afferent inhibition in individuals with mild cognitive impairment: A TMS-EEG study. Prog Neuropsychopharmacol Biol Psychiatry 2024; 132:110967. [PMID: 38354899 DOI: 10.1016/j.pnpbp.2024.110967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 12/03/2023] [Accepted: 02/11/2024] [Indexed: 02/16/2024]
Abstract
TMS combined with EEG (TMS-EEG) is a tool to characterize the neurophysiological dynamics of the cortex. Among the TMS paradigms, short-latency afferent inhibition (SAI) allows the investigation of inhibitory effects mediated by the cholinergic system. The aim of this study was to compare cholinergic function in the DLPFC between individuals with mild cognitive impairment (MCI) and healthy controls (HC) using TMS-EEG with the SAI paradigm. In this study, 30 MCI and 30 HC subjects were included. The SAI paradigm consisted of 80 single pulse TMS and 80 SAI stimulations applied to the left DLPFC. N100 components, global mean field power (GMFP) and total power were calculated. As a result, individuals with MCI showed reduced inhibitory effects on N100 components and GMFP at approximately 100 ms post-stimulation and on β-band activity at 200 ms post-stimulation compared to HC. Individuals with MCI showed reduced SAI, suggesting impaired cholinergic function in the DLPFC compared to the HC group. We conclude that these findings underscore the clinical applicability of the TMS-EEG method as a powerful tool for assessing cholinergic function in individuals with MCI.
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Affiliation(s)
- Yu Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Yui Tobari
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Shinichiro Nakajima
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
| | - Mayuko Takano
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan; TEIJIN PHARMA LIMITED, Tokyo 100-8585, Japan
| | - Masataka Wada
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Shiori Honda
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Shogyoku Bun
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Hajime Tabuchi
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Daisuke Ito
- Department of Physiology/Memory Center, Keio University School of Medicine, Tokyo, Japan
| | - Mie Matsui
- Laboratory of Clinical Cognitive Neuroscience, Graduate School of Medical Science, Kanazawa University, Ishikawa 920-0934, Japan
| | - Hiroyuki Uchida
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Masaru Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Yoshihiro Noda
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
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Das A, Dhillon P. Application of machine learning in measurement of ageing and geriatric diseases: a systematic review. BMC Geriatr 2023; 23:841. [PMID: 38087195 PMCID: PMC10717316 DOI: 10.1186/s12877-023-04477-x] [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: 05/09/2023] [Accepted: 11/10/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND As the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare providers are exploring novel methods to enhance the quality of life for the elderly. Over the last decade, there has been a remarkable surge in the use of machine learning in geriatric diseases and care. Machine learning has emerged as a promising tool for the diagnosis, treatment, and management of these conditions. Hence, our study aims to find out the present state of research in geriatrics and the application of machine learning methods in this area. METHODS This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and focused on healthy ageing in individuals aged 45 and above, with a specific emphasis on the diseases that commonly occur during this process. The study mainly focused on three areas, that are machine learning, the geriatric population, and diseases. Peer-reviewed articles were searched in the PubMed and Scopus databases with inclusion criteria of population above 45 years, must have used machine learning methods, and availability of full text. To assess the quality of the studies, Joanna Briggs Institute's (JBI) critical appraisal tool was used. RESULTS A total of 70 papers were selected from the 120 identified papers after going through title screening, abstract screening, and reference search. Limited research is available on predicting biological or brain age using deep learning and different supervised machine learning methods. Neurodegenerative disorders were found to be the most researched disease, in which Alzheimer's disease was focused the most. Among non-communicable diseases, diabetes mellitus, hypertension, cancer, kidney diseases, and cardiovascular diseases were included, and other rare diseases like oral health-related diseases and bone diseases were also explored in some papers. In terms of the application of machine learning, risk prediction was the most common approach. Half of the studies have used supervised machine learning algorithms, among which logistic regression, random forest, XG Boost were frequently used methods. These machine learning methods were applied to a variety of datasets including population-based surveys, hospital records, and digitally traced data. CONCLUSION The review identified a wide range of studies that employed machine learning algorithms to analyse various diseases and datasets. While the application of machine learning in geriatrics and care has been well-explored, there is still room for future development, particularly in validating models across diverse populations and utilizing personalized digital datasets for customized patient-centric care in older populations. Further, we suggest a scope of Machine Learning in generating comparable ageing indices such as successful ageing index.
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Affiliation(s)
- Ayushi Das
- International Institute for Population Sciences, Deonar, Mumbai, 400088, India
| | - Preeti Dhillon
- Department of Survey Research and Data Analytics, International Institute for Population Sciences, Deonar, Mumbai, 400088, India.
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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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Valdes-Hernandez P, Nodarse CL, Peraza J, Cole J, Cruz-Almeida Y. Toward MR protocol-agnostic, bias-corrected brain age predicted from clinical-grade MRIs. RESEARCH SQUARE 2023:rs.3.rs-3229072. [PMID: 37609150 PMCID: PMC10441510 DOI: 10.21203/rs.3.rs-3229072/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
The predicted brain age minus the chronological age ('brain-PAD') could become a clinical biomarker. However, most brain age methods were developed to use research-grade high-resolution T1-weighted MRIs, limiting their applicability to clinical-grade MRIs from multiple protocols. To overcome this, we adopted a double transfer learning approach to develop a brain age model agnostic to modality, resolution, or slice orientation. Using 6,224 clinical MRIs among 7 modalities, scanned from 1,540 patients using 8 scanners among 15 + facilities of the University of Florida's Health System, we retrained a convolutional neural network (CNN) to predict brain age from synthetic research-grade magnetization-prepared rapid gradient-echo MRIs (MPRAGEs) generated by a deep learning-trained 'super-resolution' method. We also modeled the "regression dilution bias", a typical overestimation of younger ages and underestimation of older ages, which correction is paramount for personalized brain age-based biomarkers. This bias was independent of modality or scanner and generalizable to new samples, allowing us to add a bias-correction layer to the CNN. The mean absolute error in test samples was 4.67-6.47 years across modalities, with similar accuracy between original MPRAGEs and their synthetic counterparts. Brain-PAD was also reliable across modalities. We demonstrate the feasibility of clinical-grade brain age predictions, contributing to personalized medicine.
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Korbmacher M, Gurholt TP, de Lange AMG, van der Meer D, Beck D, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Bio-psycho-social factors' associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants. Front Psychol 2023; 14:1117732. [PMID: 37359862 PMCID: PMC10288151 DOI: 10.3389/fpsyg.2023.1117732] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/27/2023] [Indexed: 06/28/2023] Open
Abstract
Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6-82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions' influence on brain age in future studies.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
| | - Tiril P. Gurholt
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Ann-Marie G. de Lange
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Dani Beck
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
| | - Arvid Lundervold
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I. Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
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Shigemoto Y, Sato N, Maikusa N, Sone D, Ota M, Kimura Y, Chiba E, Okita K, Yamao T, Nakaya M, Maki H, Arizono E, Matsuda H. Age and Sex-Related Effects on Single-Subject Gray Matter Networks in Healthy Participants. J Pers Med 2023; 13:jpm13030419. [PMID: 36983603 PMCID: PMC10057933 DOI: 10.3390/jpm13030419] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/22/2023] [Accepted: 02/24/2023] [Indexed: 03/03/2023] Open
Abstract
Recent developments in image analysis have enabled an individual’s brain network to be evaluated and brain age to be predicted from gray matter images. Our study aimed to investigate the effects of age and sex on single-subject gray matter networks using a large sample of healthy participants. We recruited 812 healthy individuals (59.3 ± 14.0 years, 407 females, and 405 males) who underwent three-dimensional T1-weighted magnetic resonance imaging. Similarity-based gray matter networks were constructed, and the following network properties were calculated: normalized clustering, normalized path length, and small-world coefficients. The predicted brain age was computed using a support-vector regression model. We evaluated the network alterations related to age and sex. Additionally, we examined the correlations between the network properties and predicted brain age and compared them with the correlations between the network properties and chronological age. The brain network retained efficient small-world properties regardless of age; however, reduced small-world properties were observed with advancing age. Although women exhibited higher network properties than men and similar age-related network declines as men in the subjects aged < 70 years, faster age-related network declines were observed in women, leading to no differences in sex among the participants aged ≥ 70 years. Brain age correlated well with network properties compared to chronological age in participants aged ≥ 70 years. Although the brain network retained small-world properties, it moved towards randomized networks with aging. Faster age-related network disruptions in women were observed than in men among the elderly. Our findings provide new insights into network alterations underlying aging.
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Affiliation(s)
- Yoko Shigemoto
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Noriko Sato
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo 153-8902, Japan
| | - Daichi Sone
- Department of Psychiatry, Jikei University School of Medicine, Tokyo 105-8461, Japan
| | - Miho Ota
- Department of Neuropsychiatry, University of Tsukuba, Tsukuba 305-8576, Japan
| | - Yukio Kimura
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Emiko Chiba
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Kyoji Okita
- Department of Drug Dependence Research, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
- Department of Psychiatry, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Tensho Yamao
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, Fukushima 960-8516, Japan
| | - Moto Nakaya
- Department of Radiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Hiroyuki Maki
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Elly Arizono
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Hiroshi Matsuda
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
- Department of Biofunctional Imaging, Fukushima Medical University, Fukushima 960-1295, Japan
- Drug Discovery and Cyclotron Research Center, Southern Tohoku Research Institute for Neuroscience, Fukushima 963-8052, Japan
- Correspondence: ; Tel.: +81-3-6271-8507
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Sone D, Beheshti I. Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry. J Pers Med 2022; 12:jpm12111850. [PMID: 36579560 PMCID: PMC9695293 DOI: 10.3390/jpm12111850] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/01/2022] [Accepted: 11/01/2022] [Indexed: 11/10/2022] Open
Abstract
It is now possible to estimate an individual's brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, Jikei University School of Medicine, Tokyo 105-8461, Japan
- Correspondence: ; Tel.: +81-03-3433
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB R3E 3P5, Canada
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