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Nakaya M, Sato N, Suzuki F, Maikusa N, Matsuda H, Kimura Y, Shigemoto Y, Chiba E, Ota M, Yamamura T, Sato W, Okamoto T, Abe O. Multimodal imaging analyses in neuromyelitis optica spectrum disorder with or without visual disturbance. J Neurol Sci 2024; 462:123090. [PMID: 38865876 DOI: 10.1016/j.jns.2024.123090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 05/10/2024] [Accepted: 06/05/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND AND PURPOSE Neuromyelitis optica spectrum disorder is a demyelinating and inflammatory affliction that often leads to visual disturbance. Various imaging techniques, including free-water imaging, have been used to determine neuroinflammation and degeneration. Therefore, this study aimed at determining multimodal imaging differences between patients with neuromyelitis optica spectrum disorder, especially those with visual disturbance, and healthy controls. MATERIALS AND METHODS Eighty-five neuromyelitis optica spectrum disorder patients and 89 age- and sex-matched healthy controls underwent 3-T magnetic resonance imaging (MRI). We analyzed adjusted brain-predicted age difference, voxel-based morphometry, and free-water-corrected diffusion tensor imaging (DTI) by tract-based spatial statistics in each patient group (MRI-positive/negative neuromyelitis optica spectrum disorder patients with or without a history of visual disturbance) compared with the healthy control group. RESULTS MRI-positive neuromyelitis optica spectrum disorder patients exhibited reduced volumes of the bilateral thalamus. Tract-based spatial statistics showed diffuse white matter abnormalities in all DTI metrics in MRI-positive neuromyelitis optica spectrum disorder patients with a history of visual disturbance. In MRI-negative neuromyelitis optica spectrum disorder patients with a history of visual disturbance, voxel-based morphometry showed volume reduction of bilateral thalami and optic radiations, and tract-based spatial statistics revealed significantly lower free-water-corrected fractional anisotropy and higher mean diffusivity in the posterior dominant distributions, including the optic nerve radiation. CONCLUSION Free-water-corrected DTI and voxel-based morphometry analyses may reflect symptoms of visual disturbance in neuromyelitis optica spectrum disorder.
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Affiliation(s)
- Moto Nakaya
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan; Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Noriko Sato
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan.
| | - Fumio Suzuki
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan; Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Norihide Maikusa
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Hiroshi Matsuda
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan; Department of Biofunctional Imaging, Fukushima Medical University, 1 Hikariga-Oka, Fukushima 960-1295, Japan
| | - Yukio Kimura
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Yoko Shigemoto
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Emiko Chiba
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Miho Ota
- Department of Radiology, National Center Hospital of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan; Department of Neuropsychiatry, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki 305-8576, Japan
| | - Takashi Yamamura
- Department of Immunology, Institute of Neuroscience, National Center of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Wakiro Sato
- Department of Immunology, Institute of Neuroscience, National Center of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Tomoko Okamoto
- Department of Neurology, National Center of Neurology and Psychiatry, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
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2
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Wan X, Zeng Y, Wang J, Tian M, Yin X, Zhang J. Structural and functional abnormalities and cognitive profiles in older adults with early-onset and late-onset focal epilepsy. Cereb Cortex 2024; 34:bhae300. [PMID: 39052362 DOI: 10.1093/cercor/bhae300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 06/26/2024] [Accepted: 07/09/2024] [Indexed: 07/27/2024] Open
Abstract
This study aimed to determine the patterns of changes in structure, function, and cognitive ability in early-onset and late-onset older adults with focal epilepsy (OFE). This study first utilized the deformation-based morphometry analysis to identify structural abnormalities, which were used as the seed region to investigate the functional connectivity with the whole brain. Next, a correlation analysis was performed between the altered imaging findings and neuropsychiatry assessments. Finally, the potential role of structural-functional abnormalities in the diagnosis of epilepsy was further explored by using mediation analysis. Compared with healthy controls (n = 28), the area of reduced structural volume was concentrated in the bilateral cerebellum, right thalamus, and right middle cingulate cortex, with frontal, temporal, and occipital lobes also affected in early-onset focal epilepsy (n = 26), while late-onset patients (n = 31) displayed cerebellar, thalamic, and cingulate atrophy. Furthermore, correlation analyses suggest an association between structural abnormalities and cognitive assessments. Dysfunctional connectivity in the cerebellum, cingulate cortex, and frontal gyrus partially mediates the relationship between structural abnormalities and the diagnosis of early-onset focal epilepsy. This study identified structural and functional abnormalities in early-onset and late-onset focal epilepsy and explored characters in cognitive performance. Structural-functional coupling may play a potential role in the diagnosis of epilepsy.
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Affiliation(s)
- Xinyue Wan
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai 200040, China
- Human Phenome Institute, Fudan University, Shanghai 201203, China
| | - Yanwei Zeng
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai 200040, China
| | - Jianhong Wang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Mei Tian
- Human Phenome Institute, Fudan University, Shanghai 201203, China
| | - Xuyang Yin
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai 200040, China
| | - Jun Zhang
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200040, China
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3
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Vakli P, Weiss B, Rozmann D, Erőss G, Nárai Á, Hermann P, Vidnyánszky Z. The effect of head motion on brain age prediction using deep convolutional neural networks. Neuroimage 2024; 294:120646. [PMID: 38750907 DOI: 10.1016/j.neuroimage.2024.120646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/10/2024] [Accepted: 05/12/2024] [Indexed: 05/23/2024] Open
Abstract
Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age-referred to as brain-predicted age difference (brain-PAD)-is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.
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Affiliation(s)
- Pál Vakli
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary.
| | - Béla Weiss
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary; Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Óbuda University, Budapest 1034, Hungary.
| | - Dorina Rozmann
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - György Erőss
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Ádám Nárai
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary; Doctoral School of Biology and Sportbiology, Institute of Biology, Faculty of Sciences, University of Pécs, Pécs 7624, Hungary
| | - Petra Hermann
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary.
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Beheshti I, Perron J, Ko JH. Neuroanatomical Signature of the Transition from Normal Cognition to MCI in Parkinson's Disease. Aging Dis 2024:AD.2024.0323. [PMID: 38913040 DOI: 10.14336/ad.2024.0323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/23/2024] [Indexed: 06/25/2024] Open
Abstract
The progression of Parkinson's disease (PD) is often accompanied by cognitive decline. We had previously developed a brain age estimation program utilizing structural MRI data of 949 healthy individuals from publicly available sources. Structural MRI data of 244 PD patients who were cognitively normal at baseline was acquired from the Parkinson Progression Markers Initiative (PPMI). 192 of these showed stable normal cognitive function from baseline out to 5 years (PD-SNC), and the remaining 52 had unstable normal cognition and developed mild cognitive impairment within 5 years (PD-UNC). 105 healthy controls were also included in the analysis as a reference. First, we examined if there were any baseline differences in regional brain structure between PD-UNC and PD-SNC cohorts utilizing the three most widely used atrophy estimation pipelines, i.e., voxel-based morphometry (VBM), deformation-based morphometry and cortical thickness analyses. We then investigated if accelerated brain age estimation with our multivariate regressive machine learning algorithm was different across these groups (HC, PD-SNC, and PD-UNC). As per the VBM analysis, PD-UNC patients demonstrated a noticeable increase in GM volume in the posterior and anterior lobes of the cerebellum, sub-lobar, extra-nuclear, thalamus, and pulvinar regions when compared to PD-SNC at baseline. PD-UNC patients were observed to have significantly older brain age compared to both PD-SNC patients (p=0.009) and healthy controls (p<0.009). The increase in GM volume in the PD-UNC group could potentially indicate an inflammatory or neuronal hypertrophy response, which could serve as a biomarker for future cognitive decline among this population.
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Affiliation(s)
- Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- PrairieNeuro Research Centre, Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
| | - Jarrad Perron
- PrairieNeuro Research Centre, Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB, Canada
| | - Ji Hyun Ko
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- PrairieNeuro Research Centre, Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB, Canada
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5
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Wang M, Wei M, Wang L, Song J, Rominger A, Shi K, Jiang J. Tau Protein Accumulation Trajectory-Based Brain Age Prediction in the Alzheimer's Disease Continuum. Brain Sci 2024; 14:575. [PMID: 38928575 PMCID: PMC11201453 DOI: 10.3390/brainsci14060575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 05/22/2024] [Accepted: 05/25/2024] [Indexed: 06/28/2024] Open
Abstract
Clinical cognitive advancement within the Alzheimer's disease (AD) continuum is intimately connected with sustained accumulation of tau protein pathology. The biological brain age and its gap show great potential for pathological risk and disease severity. In the present study, we applied multivariable linear support vector regression to train a normative brain age prediction model using tau brain images. We further assessed the predicted biological brain age and its gap for patients within the AD continuum. In the AD continuum, evaluated pathologic tau binding was found in the inferior temporal, parietal-temporal junction, precuneus/posterior cingulate, dorsal frontal, occipital, and inferior-medial temporal cortices. The biological brain age gaps of patients within the AD continuum were notably higher than those of the normal controls (p < 0.0001). Significant positive correlations were observed between the brain age gap and global tau protein accumulation levels for mild cognitive impairment (r = 0.726, p < 0.001), AD (r = 0.845, p < 0.001), and AD continuum (r = 0.797, p < 0.001). The pathologic tau-based age gap was significantly linked to neuropsychological scores. The proposed pathologic tau-based biological brain age model could track the tau protein accumulation trajectory of cognitive impairment and further provide a comprehensive quantification index for the tau accumulation risk.
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Affiliation(s)
- Min Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Min Wei
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, China
| | - Luyao Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Jun Song
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
- Computer Aided Medical Procedures, School of Computation, Information and Technology, Technical University of Munich, 85748 Munich, Germany
| | - Jiehui Jiang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
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6
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Beheshti I, Booth S, Ko JH. Differences in brain aging between sexes in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:35. [PMID: 38355735 PMCID: PMC10866976 DOI: 10.1038/s41531-024-00646-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 01/22/2024] [Indexed: 02/16/2024] Open
Abstract
Parkinson's disease (PD) is linked to faster brain aging. Male sex is associated with higher prevalence, severe symptoms, and a faster progression rate in PD. There remains a significant gap in understanding the function of sex in the process of brain aging in PD. The structural T1-weighted MRI-driven brain-predicted age difference (i.e., Brain-PAD: the actual age subtracted from the brain-predicted age) was computed in a group of 373 people with PD (mean age ± SD: 61.37 ± 9.81, age range: 33-85, 34% female) from the Parkinson's Progression Marker Initiative database using a robust brain-age estimation framework that was trained on 949 healthy subjects. Linear regression models were used to investigate the association between Brain-PAD and clinical variables in PD, stratified by sex. Males with Parkinson's disease (PD-M) exhibited a significantly higher mean Brain-PAD than their female counterparts (PD-F) (t(256) = 2.50, p = 0.012). In the propensity score-matched PD-M group (PD-M*), Brain-PAD was found to be associated with a decline in general cognition, a worse degree of sleep behavior disorder, reduced visuospatial acuity, and caudate atrophy. Conversely, no significant links were observed between these factors and Brain-PAD in the PD-F group. Having 'older' looking brains in PD-M than PD-F supports the idea that sex plays a vital function in PD, such that the PD mechanism may be different in males and females. This study has the potential to broaden our understanding of dissimilarities in brain aging between sexes in the context of PD.
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Affiliation(s)
- Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada
- Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
| | - Samuel Booth
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada
- Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada
| | - Ji Hyun Ko
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada.
- Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada.
- Graduate Program in Biomedical Engineering, University of Manitoba, Winnipeg, MB, Canada.
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7
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Wu Y, Chen Y, Yang Y, Lin C, Su S, Zhao J, Wu S, Wu G, Liu H, Liu X, Yang Z, Zhang J, Huang B. Predicting brain age using partition modeling strategy and atlas-based attentional enhancement in the Chinese population. Cereb Cortex 2024; 34:bhae030. [PMID: 38342684 DOI: 10.1093/cercor/bhae030] [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: 11/13/2023] [Revised: 01/13/2024] [Accepted: 01/15/2024] [Indexed: 02/13/2024] Open
Abstract
As a biomarker of human brain health during development, brain age is estimated based on subtle differences in brain structure from those under typical developmental. Magnetic resonance imaging (MRI) is a routine diagnostic method in neuroimaging. Brain age prediction based on MRI has been widely studied. However, few studies based on Chinese population have been reported. This study aimed to construct a brain age predictive model for the Chinese population across its lifespan. We developed a partition prediction method based on transfer learning and atlas attention enhancement. The participants were separated into four age groups, and a deep learning model was trained for each group to identify the brain regions most critical for brain age prediction. The Atlas attention-enhancement method was also used to help the models focus only on critical brain regions. The proposed method was validated using 354 participants from domestic datasets. For prediction performance in the testing sets, the mean absolute error was 2.218 ± 1.801 years, and the Pearson correlation coefficient (r) was 0.969, exceeding previous results for wide-range brain age prediction. In conclusion, the proposed method could provide brain age estimation to assist in assessing the status of brain health.
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Affiliation(s)
- Yingtong Wu
- Medical AI Lab, School of Biomedical Engineering, Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, Guangdong Province, China
| | - Yingqian Chen
- Department of Radiology, the First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Second Road, Guangzhou 510080, Guangdong Province, China
| | - Yang Yang
- Department of Radiology, Suining Central Hospital, 127 Desheng West Road, Suining 629099, Sichuan Province, China
- Medical Imaging Center of Guizhou Province, Department of Radiology, The Affiliated Hospital of Zunyi Medical University, 149 Dalian Road, Zunyi 563000, Guizhou Province, China
| | - Chuxuan Lin
- Medical AI Lab, School of Biomedical Engineering, Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
| | - Shu Su
- Department of Radiology, the First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Second Road, Guangzhou 510080, Guangdong Province, China
| | - Jing Zhao
- Department of Radiology, the First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Second Road, Guangzhou 510080, Guangdong Province, China
| | - Songxiong Wu
- Radiology Department, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
| | - Guangyao Wu
- Radiology Department, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
| | - Heng Liu
- Medical Imaging Center of Guizhou Province, Department of Radiology, The Affiliated Hospital of Zunyi Medical University, 149 Dalian Road, Zunyi 563000, Guizhou Province, China
| | - Xia Liu
- Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, 1080 Cuizhu Road, Shenzhen 518118, Guangdong Province, China
| | - Zhiyun Yang
- Department of Radiology, the First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Second Road, Guangzhou 510080, Guangdong Province, China
| | - Jian Zhang
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, 1068 Xueyuan Avenue, Shenzhen 518055, Guangdong Province, China
- School of Pharmaceutical Sciences, Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, Guangdong Province, China
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Li Y, Zhou Z, Zhang Y, Ai H, Liu M, Liu J, Wang L, Qiu J, Rachel Han Z, Zhang Z, Luo YJ, Xu P. Brain development mediates the relationship between self-reported poor parental monitoring and adolescent anxiety. Neuroimage Clin 2023; 40:103514. [PMID: 37778196 PMCID: PMC10542017 DOI: 10.1016/j.nicl.2023.103514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 10/03/2023]
Abstract
Adolescence is the peak period for the onset of generalized anxiety disorder (GAD). Brain networks of cognitive and affective control in adolescents are not well developed when their exposure to external stimuli suddenly increases.Reasonable parental monitoring is especially important during this period.To examine the role of parental monitoring in the development of functional brain networks of GAD, we conducted a cross-validation-based predictive study based on the functional brain networks of 192 participants. We found that a set of functional brain networks, especially the default mode network and its connectivity with the frontoparietal network, could predict the ages of adolescents, which was replicated in three independent samples.Importantly, the difference between predicted age and chronological age significantly mediated the relationship between parental monitoring and anxiety levels. These findings suggest that inadequate parental monitoring plays a crucial role in the delayed development of specific brain networks associated with GAD in adolescents. Our work highlights the important role of parental monitoring in adolescent development.
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Affiliation(s)
- Yiman Li
- School of Psychology, Shenzhen University, Shenzhen, China; Institute for Neuropsychological Rehabilitation, University of Health and Rehabilitation Sciences, Qingdao, China
| | - Zheyi Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Yuqi Zhang
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Hui Ai
- Institute of Applied Psychology, Tianjin University, Tianjin, China; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Mingfang Liu
- Community Health Service Center, Beijing Normal University, Beijing, China
| | - Jing Liu
- The China Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Li Wang
- College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China
| | - Jiang Qiu
- School of Psychology, Southwest University (SWU), Chongqing, China
| | - Zhuo Rachel Han
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yue-Jia Luo
- School of Psychology, Shenzhen University, Shenzhen, China; Institute for Neuropsychological Rehabilitation, University of Health and Rehabilitation Sciences, Qingdao, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
| | - Pengfei Xu
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, China; Center for Emotion and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China.
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Hu L, Wan Q, Huang L, Tang J, Huang S, Chen X, Bai X, Kong L, Deng J, Liang H, Liu G, Liu H, Lu L. MRI-based brain age prediction model for children under 3 years old using deep residual network. Brain Struct Funct 2023; 228:1771-1784. [PMID: 37603065 DOI: 10.1007/s00429-023-02686-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023]
Abstract
Early identification and intervention of abnormal brain development individual subjects are of great significance, especially during the earliest and most active stage of brain development in children aged under 3. Neuroimage-based brain's biological age has been associated with health, ability, and remaining life. However, the existing brain age prediction models based on neuroimage are predominantly adult-oriented. Here, we collected 658 T1-weighted MRI scans from 0 to 3 years old healthy controls and developed an accurate brain age prediction model for young children using deep learning techniques with high accuracy in capturing age-related changes. The performance of the deep learning-based model is comparable to that of the SVR-based model, showcasing remarkable precision and yielding a noteworthy correlation of 91% between the predicted brain age and the chronological age. Our results demonstrate the accuracy of convolutional neural network (CNN) brain-predicted age using raw T1-weighted MRI data with minimum preprocessing necessary. We also applied our model to children with low birth weight, premature delivery history, autism, and ADHD, and discovered that the brain age was delayed in children with extremely low birth weight (less than 1000 g) while ADHD may cause accelerated aging of the brain. Our child-specific brain age prediction model can be a valuable quantitative tool to detect abnormal brain development and can be helpful in the early identification and intervention of age-related brain disorders.
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Affiliation(s)
- Lianting Hu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Qirong Wan
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, 4330060, Hubei, China
| | - Li Huang
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Jiajie Tang
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Shuai Huang
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Xuanhui Chen
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Xiaohe Bai
- School of Physical Sciences, University of California San Diego, La Jolla, San Diego, CA, 92093, USA
| | - Lingcong Kong
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Jingyi Deng
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Huiying Liang
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Guangjian Liu
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Hongsheng Liu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China.
| | - Long Lu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China.
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China.
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10
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Beheshti I. Cocaine Destroys Gray Matter Brain Cells and Accelerates Brain Aging. BIOLOGY 2023; 12:biology12050752. [PMID: 37237564 DOI: 10.3390/biology12050752] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/16/2023] [Accepted: 05/18/2023] [Indexed: 05/28/2023]
Abstract
Introduction: Cocaine use disorder (CUD) is a substance use disorder characterized by a strong desire to obtain, consume, and misuse cocaine. Little is known about how cocaine affects the structure of the brain. In this study, we first investigated the anatomical brain changes in individuals with CUD compared to their matched healthy controls, and then explored whether these anatomical brain abnormalities contribute to considerably accelerated brain aging among this population. Methods: At the first stage, we used anatomical magnetic resonance imaging (MRI) data, voxel-based morphometry (VBM), and deformation-based morphometry techniques to uncover the morphological and macroscopic anatomical brain changes in 74 CUD patients compared to 62 age- and sex-matched healthy controls (HCs) obtained from the SUDMEX CONN dataset, the Mexican MRI dataset of patients with CUD. Then, we computed brain-predicted age difference (i.e., brain-PAD: the brain-predicted age minus the actual age) in CUD and HC groups using a robust brain age estimation framework. Using a multiple regression analysis, we also investigated the regional gray matter (GM) and white matter (WM) changes associated with the brain-PAD. Results: Using a whole-brain VBM analysis, we observed widespread gray matter atrophy in CUD patients located in the temporal lobe, frontal lobe, insula, middle frontal gyrus, superior frontal gyrus, rectal gyrus, and limbic lobe regions compared to the HCs. In contrast, we did not observe any swelling in the GM, changes in the WM, or local brain tissue atrophy or expansion between the CUD and HC groups. Furthermore, we found a significantly higher brain-PAD in CUD patients compared to matched HCs (mean difference = 2.62 years, Cohen's d = 0.54; t-test = 3.16, p = 0.002). The regression analysis showed significant negative changes in GM volume associated with brain-PAD in the CUD group, particularly in the limbic lobe, subcallosal gyrus, cingulate gyrus, and anterior cingulate regions. Discussion: The results of our investigation reveal that chronic cocaine use is linked to significant changes in gray matter, which hasten the process of structural brain aging in individuals who use the drug. These findings offer valuable insights into the impact of cocaine on the composition of the brain.
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Affiliation(s)
- Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, MB R3E 3J7, Canada
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11
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Chu C, Holst SC, Elmenhorst EM, Foerges AL, Li C, Lange D, Hennecke E, Baur DM, Beer S, Hoffstaedter F, Knudsen GM, Aeschbach D, Bauer A, Landolt HP, Elmenhorst D. Total Sleep Deprivation Increases Brain Age Prediction Reversibly in Multisite Samples of Young Healthy Adults. J Neurosci 2023; 43:2168-2177. [PMID: 36804738 PMCID: PMC10039745 DOI: 10.1523/jneurosci.0790-22.2023] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 01/13/2023] [Accepted: 01/18/2023] [Indexed: 02/22/2023] Open
Abstract
Sleep loss pervasively affects the human brain at multiple levels. Age-related changes in several sleep characteristics indicate that reduced sleep quality is a frequent characteristic of aging. Conversely, sleep disruption may accelerate the aging process, yet it is not known what will happen to the age status of the brain if we can manipulate sleep conditions. To tackle this question, we used an approach of brain age to investigate whether sleep loss would cause age-related changes in the brain. We included MRI data of 134 healthy volunteers (mean chronological age of 25.3 between the age of 19 and 39 years, 42 females/92 males) from five datasets with different sleep conditions. Across three datasets with the condition of total sleep deprivation (>24 h of prolonged wakefulness), we consistently observed that total sleep deprivation increased brain age by 1-2 years regarding the group mean difference with the baseline. Interestingly, after one night of recovery sleep, brain age was not different from baseline. We also demonstrated the associations between the change in brain age after total sleep deprivation and the sleep variables measured during the recovery night. By contrast, brain age was not significantly changed by either acute (3 h time-in-bed for one night) or chronic partial sleep restriction (5 h time-in-bed for five continuous nights). Together, the convergent findings indicate that acute total sleep loss changes brain morphology in an aging-like direction in young participants and that these changes are reversible by recovery sleep.SIGNIFICANCE STATEMENT Sleep is fundamental for humans to maintain normal physical and psychological functions. Experimental sleep deprivation is a variable-controlling approach to engaging the brain among different sleep conditions for investigating the responses of the brain to sleep loss. Here, we quantified the response of the brain to sleep deprivation by using the change of brain age predictable with brain morphologic features. In three independent datasets, we consistently found increased brain age after total sleep deprivation, which was associated with the change in sleep variables. Moreover, no significant change in brain age was found after partial sleep deprivation in another two datasets. Our study provides new evidence to explain the brainwide effect of sleep loss in an aging-like direction.
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Affiliation(s)
- Congying Chu
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, 52428 Jülich, Germany
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Sebastian C Holst
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Institute of Pharmacology and Toxicology, University of Zurich, CH-8006 Zurich, Switzerland
| | - Eva-Maria Elmenhorst
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, 51147 Cologne, Germany
- Institute for Occupational, Social and Environmental Medicine, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany
| | - Anna L Foerges
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, 52428 Jülich, Germany
- Department of Neurophysiology, Institute of Zoology (Bio-II), RWTH Aachen University, 52074 Aachen, Germany
| | - Changhong Li
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Denise Lange
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, 51147 Cologne, Germany
| | - Eva Hennecke
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, 51147 Cologne, Germany
| | - Diego M Baur
- Institute of Pharmacology and Toxicology, University of Zurich, CH-8006 Zurich, Switzerland
| | - Simone Beer
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Gitte M Knudsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Institute of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Daniel Aeschbach
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, 51147 Cologne, Germany
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts 02115
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts 02115
- Institute of Experimental Epileptology and Cognition Research, Faculty of Medicine, University of Bonn, 53127, Bonn, Germany
| | - Andreas Bauer
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, 52428 Jülich, Germany
- Neurological Department, Medical Faculty, Heinrich-Heine-University, 40225 Düsseldorf, Germany
| | - Hans-Peter Landolt
- Institute of Pharmacology and Toxicology, University of Zurich, CH-8006 Zurich, Switzerland
- Sleep & Health Zurich, University Center of Competence, University of Zurich, Zurich, Switzerland
| | - David Elmenhorst
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, 52428 Jülich, Germany
- Department of Nuclear Medicine, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
- Division of Medical Psychology, Rheinische Friedrich-Wilhelms-University Bonn, Bonn, 53127 Germany
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12
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Kim E, Jeon S, Yang YS, Jin C, Kim JY, Oh YS, Rah JC, Choi H. A Neurospheroid-Based Microrobot for Targeted Neural Connections in a Hippocampal Slice. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2208747. [PMID: 36640750 DOI: 10.1002/adma.202208747] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 01/10/2023] [Indexed: 06/17/2023]
Abstract
Functional restoration by the re-establishment of cellular or neural connections remains a major challenge in targeted cell therapy and regenerative medicine. Recent advances in magnetically powered microrobots have shown potential for use in controlled and targeted cell therapy. In this study, a magnetic neurospheroid (Mag-Neurobot) that can form both structural and functional connections with an organotypic hippocampal slice (OHS) is assessed using an ex vivo model as a bridge toward in vivo application. The Mag-Neurobot consists of hippocampal neurons and superparamagnetic nanoparticles (SPIONs); it is precisely and skillfully manipulated by an external magnetic field. Furthermore, the results of patch-clamp recordings of hippocampal neurons indicate that neither the neuronal excitabilities nor the synaptic functions of SPION-loaded cells are significantly affected. Analysis of neural activity propagation using high-density multi-electrode arrays shows that the delivered Mag-Neurobot is functionally connected with the OHS. The applications of this study include functional verification for targeted cell delivery through the characterization of novel synaptic connections and the functionalities of transported and transplanted cells. The success of the Mag-Neurobot opens up new avenues of research and application; it offers a test platform for functional neural connections and neural regenerative processes through cell transplantation.
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Affiliation(s)
- Eunhee Kim
- IMsystem Co., Ltd., 333, Technojungang-daero, Hyeonpung-eup, Dalseong-gun, Daegu, 42988, Republic of Korea
| | - Sungwoong Jeon
- IMsystem Co., Ltd., 333, Technojungang-daero, Hyeonpung-eup, Dalseong-gun, Daegu, 42988, Republic of Korea
| | - Yoon-Sil Yang
- Emerging Infectious Disease Vaccines Division, National Institute of Food and Drug Safety Evaluation, 187, Osongsaengmyeong 2-ro, Osong-eup, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do, 28159, Republic of Korea
- Korea Brain Research Institute, 61, Cheomdan-ro, Dong-gu, Daegu, 41062, Republic of Korea
| | - Chaewon Jin
- DGIST-ETH Microrobotics Research Center, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, 42988, Republic of Korea
| | - Jin-Young Kim
- DGIST-ETH Microrobotics Research Center, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, 42988, Republic of Korea
- Department of Robotics and Mechatronics Engineering, DGIST, Daegu, 42988, Republic of Korea
| | - Yong-Seok Oh
- Department of Brain Sciences, DGIST, Daegu, 42988, Republic of Korea
| | - Jong-Cheol Rah
- Korea Brain Research Institute, 61, Cheomdan-ro, Dong-gu, Daegu, 41062, Republic of Korea
- Department of Brain Sciences, DGIST, Daegu, 42988, Republic of Korea
| | - Hongsoo Choi
- DGIST-ETH Microrobotics Research Center, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, 42988, Republic of Korea
- Department of Robotics and Mechatronics Engineering, DGIST, Daegu, 42988, Republic of Korea
- Robotics and Mechatronics Engineering Research Center, DGIST, Daegu, 42988, Republic of Korea
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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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14
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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: 8] [Impact Index Per Article: 4.0] [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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15
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Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants. Transl Psychiatry 2022; 12:397. [PMID: 36130921 PMCID: PMC9492670 DOI: 10.1038/s41398-022-02162-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/04/2022] [Accepted: 09/08/2022] [Indexed: 11/12/2022] Open
Abstract
Major depressive disorder (MDD) is one of the most common mental health conditions that has been intensively investigated for its association with brain atrophy and mortality. Recent studies suggest that the deviation between the predicted and the chronological age can be a marker of accelerated brain aging to characterize MDD. However, current conclusions are usually drawn based on structural MRI information collected from Caucasian participants. The universality of this biomarker needs to be further validated by subjects with different ethnic/racial backgrounds and by different types of data. Here we make use of the REST-meta-MDD, a large scale resting-state fMRI dataset collected from multiple cohort participants in China. We develop a stacking machine learning model based on 1101 healthy controls, which estimates a subject's chronological age from fMRI with promising accuracy. The trained model is then applied to 1276 MDD patients from 24 sites. We observe that MDD patients exhibit a +4.43 years (p < 0.0001, Cohen's d = 0.31, 95% CI: 2.23-3.88) higher brain-predicted age difference (brain-PAD) compared to controls. In the MDD subgroup, we observe a statistically significant +2.09 years (p < 0.05, Cohen's d = 0.134525) brain-PAD in antidepressant users compared to medication-free patients. The statistical relationship observed is further checked by three different machine learning algorithms. The positive brain-PAD observed in participants in China confirms the presence of accelerated brain aging in MDD patients. The utilization of functional brain connectivity for age estimation verifies existing findings from a new dimension.
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16
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Sone D, Sato N, Shigemoto Y, Kimura Y, Matsuda H. Upper cerebellar glucose hypermetabolism in patients with temporal lobe epilepsy and interictal psychosis. Epilepsia Open 2022; 7:657-664. [PMID: 35977826 PMCID: PMC9712471 DOI: 10.1002/epi4.12645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/12/2022] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVE Psychosis is an important comorbidity in epilepsy, but its pathophysiology is still unknown. The imaging modality 18 F-fluorodeoxyglucose-positron emission tomography (18 F-FDG PET) is widely used to measure brain glucose metabolism, and we speculated that 18 F-FDG PET may detect characteristic alteration patterns in individuals with temporal lobe epilepsy (TLE) and psychosis. METHODS We enrolled 13 patients with TLE and interictal psychosis (TLE-P) and 21 patients with TLE without psychosis (TLE-N). All underwent interictal 18 F-FDG-PET scanning. Statistical Parametric Mapping (SPM)12 software was used for the normalization process, and we performed a voxel-wise comparison of the TLE-P and TLE-N groups. RESULTS Cerebral hypometabolic areas were observed in the ipsilateral temporal pole to hippocampus in both patient groups. In the TLE-P group, the voxel-wise comparison revealed significantly increased 18 F-FDG signals in the upper cerebellum, superior cerebellar peduncle, and midbrain. There were no significant between-group metabolic differences around the focus or other cerebral areas. SIGNIFICANCE Our results demonstrated significant hypermetabolism around the upper cerebellum in patients with TLE and interictal psychosis compared to patients with TLE without psychosis. These findings may reflect the involvement of the cerebellum in the underlying neurobiology of interictal psychosis and could contribute to a better understanding of this disorder.
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Affiliation(s)
- Daichi Sone
- Department of RadiologyNational Center of Neurology and PsychiatryTokyoJapan,Department of PsychiatryJikei University School of MedicineTokyoJapan
| | - Noriko Sato
- Department of RadiologyNational Center of Neurology and PsychiatryTokyoJapan
| | - Yoko Shigemoto
- Department of RadiologyNational Center of Neurology and PsychiatryTokyoJapan,Drug Discovery and Cyclotron Research CenterSouthern Tohoku Research Institute for NeuroscienceFukushimaJapan
| | - Yukio Kimura
- Department of RadiologyNational Center of Neurology and PsychiatryTokyoJapan
| | - Hiroshi Matsuda
- Department of RadiologyNational Center of Neurology and PsychiatryTokyoJapan,Drug Discovery and Cyclotron Research CenterSouthern Tohoku Research Institute for NeuroscienceFukushimaJapan
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17
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Ceolini E, Brunner I, Bunschoten J, Majoie MH, Thijs RD, Ghosh A. A model of healthy aging based on smartphone interactions reveals advanced behavioral age in neurological disease. iScience 2022; 25:104792. [PMID: 36039359 PMCID: PMC9418593 DOI: 10.1016/j.isci.2022.104792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/19/2022] [Accepted: 07/14/2022] [Indexed: 12/02/2022] Open
Abstract
Smartphones offer unique opportunities to trace the convoluted behavioral patterns accompanying healthy aging. Here we captured smartphone touchscreen interactions from a healthy population (N = 684, ∼309 million interactions) spanning 16 to 86 years of age and trained a decision tree regression model to estimate chronological age based on the interactions. The interactions were clustered according to their next interval dynamics to quantify diverse smartphone behaviors. The regression model well-estimated the chronological age in health (mean absolute error = 6 years, R2 = 0.8). We next deployed this model on a population of stroke survivors (N = 41) to find larger prediction errors such that the estimated age was advanced by 6 years. A similar pattern was observed in people with epilepsy (N = 51), with prediction errors advanced by 10 years. The smartphone behavioral model trained in health can be used to study altered aging in neurological diseases. A smartphone data-driven model was trained to estimate chronological age The model trained in health performed well to estimate the age The same model estimated advanced aging in stroke and epilepsy Smartphone-based model of healthy behavior may help understand aging in diseases
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18
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Liu X, Beheshti I, Zheng W, Li Y, Li S, Zhao Z, Yao Z, Hu B. Brain age estimation using multi-feature-based networks. Comput Biol Med 2022; 143:105285. [PMID: 35158116 DOI: 10.1016/j.compbiomed.2022.105285] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 02/03/2022] [Accepted: 02/03/2022] [Indexed: 12/17/2022]
Abstract
Studying brain aging improves our understanding in differentiating typical and atypical aging. Directly utilizing traditional morphological features for brain age estimation did not show significant performance in healthy controls (HCs), which may be due to the negligence of the information of structural similarities among cortical regions. For this issue, the multi-feature-based network (MFN) built upon morphological features can be employed to describe these similarities. Based on this, we hypothesized that the MFN is more efficient and robust than traditional morphological features in brain age estimating. In this work, we used six different types of morphological features (i.e., cortical volume, cortical thickness, curvature index, folding index, local gyrification index, and surface area) to build individual MFN for brain age estimation. The efficacy of MFN was estimated on 2501 HCs with T1-weighted structural magnetic resonance imaging (sMRI) data and compared with traditional morphological features. We attained a mean absolute error (MAE) of 3.73 years using the proposed method on an independent test set, whereas a mean absolute error of 5.30 years was derived from morphological features. Our experimental results demonstrated that the MFN is an efficient and robust metric for estimating brain age.
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Affiliation(s)
- Xia Liu
- School of Computer Science, Qinghai Normal University, Xining, Qinghai Province, China
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Canada
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Yongchao Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Shan Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
| | - Bin Hu
- School of Computer Science, Qinghai Normal University, Xining, Qinghai Province, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, China; Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Ministry of Education, Lanzhou, China.
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Rahman MM, Islam F, Afsana Mim S, Khan MS, Islam MR, Haque MA, Mitra S, Emran TB, Rauf A. Multifunctional Therapeutic Approach of Nanomedicines against Inflammation in Cancer and Aging. JOURNAL OF NANOMATERIALS 2022; 2022:1-19. [DOI: 10.1155/2022/4217529] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Cancer is a fatal disorder that affects people across the globe, yet existing therapeutics are ineffective. The development of submicrometer transport for optimizing the biodistribution of systemically provided medications is the focus of nanomedicine. Nanoparticle- (NP-) based treatments may enable the development of novel therapeutic approaches to combat this deadly disorder. In multifunctional, multimodal imaging, and drug delivery carriers, NPs generally play a major role. They have emerged as potential strategies for the invention of innovative therapeutic procedures in the last decade. The exponential growth of nanotechnologies in recent years has increased public awareness of the application of these innovative therapeutic approaches. Many tumor-targeted nanomedicines have been studied in cancer therapy, and there is clear evidence for a significant improvement in the therapeutic index of antineoplastic drugs. Age-related factors such as metabolic and physiological alterations in old age and inadequate animal models are currently understudied in nanomedicine and pharmacology. This review highlighted the most important targeting approaches, as well as public awareness, therapeutic advancements, and future prospects in age-related metabolic variations, and tumor-targeted nanomedicine studies.
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Affiliation(s)
- Md. Mominur Rahman
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh
| | - Fahadul Islam
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh
| | - Sadia Afsana Mim
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh
| | - Md. Shajib Khan
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh
| | - Md. Rezaul Islam
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh
| | - Md. Anamul Haque
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh
| | - Saikat Mitra
- Department of Pharmacy, Faculty of Pharmacy, University of Dhaka, Dhaka 1000, Bangladesh
| | - Talha Bin Emran
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh
| | - Abdur Rauf
- Department of Chemistry, University of Swabi, Anbar, Swabi, Khyber Pakhtunkhwa, Pakistan
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Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults. Neuroimage Clin 2022; 33:102949. [PMID: 35114636 PMCID: PMC8814666 DOI: 10.1016/j.nicl.2022.102949] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 01/20/2022] [Accepted: 01/21/2022] [Indexed: 12/12/2022]
Abstract
There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions. We investigated adipose tissue distribution from body magnetic resonance imaging (MRI) in relation to brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). The results indicated older-appearing brains in people with higher measures of adipose tissue, and accelerated ageing over the course of the study period in people with higher measures of adipose tissue.
There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions. Although research has demonstrated deleterious effects of obesity on brain structure and function, the majority of studies have used conventional measures such as waist-to-hip ratio, waist circumference, and body mass index. While sensitive to gross features of body composition, such global anthropometric features fail to describe regional differences in body fat distribution and composition. The sample consisted of baseline brain magnetic resonance imaging (MRI) acquired from 790 healthy participants aged 18–94 years (mean ± standard deviation (SD) at baseline: 46.8 ± 16.3), and follow-up brain MRI collected from 272 of those individuals (two time-points with 19.7 months interval, on average (min = 9.8, max = 35.6). Of the 790 included participants, cross-sectional body MRI data was available from a subgroup of 286 participants, with age range 19–86 (mean = 57.6, SD = 15.6). Adopting a mixed cross-sectional and longitudinal design, we investigated cross-sectional body magnetic resonance imaging measures of adipose tissue distribution in relation to longitudinal brain structure using MRI-based morphometry (T1) and diffusion tensor imaging (DTI). We estimated tissue-specific brain age at two time points and performed Bayesian multilevel modelling to investigate the associations between adipose measures at follow-up and brain age gap (BAG) – the difference between actual age and the prediction of the brain’s biological age – at baseline and follow-up. We also tested for interactions between BAG and both time and age on each adipose measure. The results showed credible associations between T1-based BAG and liver fat, muscle fat infiltration (MFI), and weight-to-muscle ratio (WMR), indicating older-appearing brains in people with higher measures of adipose tissue. Longitudinal evidence supported interaction effects between time and MFI and WMR on T1-based BAG, indicating accelerated ageing over the course of the study period in people with higher measures of adipose tissue. The results show that specific measures of fat distribution are associated with brain ageing and that different compartments of adipose tissue may be differentially linked with increased brain ageing, with potential to identify key processes involved in age-related transdiagnostic disease processes.
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Beck D, de Lange AG, Pedersen ML, Alnæs D, Maximov II, Voldsbekk I, Richard G, Sanders A, Ulrichsen KM, Dørum ES, Kolskår KK, Høgestøl EA, Steen NE, Djurovic S, Andreassen OA, Nordvik JE, Kaufmann T, Westlye LT. Cardiometabolic risk factors associated with brain age and accelerate brain ageing. Hum Brain Mapp 2022; 43:700-720. [PMID: 34626047 PMCID: PMC8720200 DOI: 10.1002/hbm.25680] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 09/02/2021] [Accepted: 09/25/2021] [Indexed: 11/17/2022] Open
Abstract
The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs) are associated with dementia and other brain disorders. In this mixed cross-sectional and longitudinal study (interval mean = 19.7 months), including 790 healthy individuals (mean age = 46.7 years, 53% women), we investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). We performed tissue specific brain age prediction using machine learning and performed Bayesian multilevel modeling to assess changes in each CMR over time, their respective association with brain age gap (BAG), and their interaction effects with time and age on the tissue-specific BAGs. The results showed credible associations between DTI-based BAG and blood levels of phosphate and mean cell volume (MCV), and between T1-based BAG and systolic blood pressure, smoking, pulse, and C-reactive protein (CRP), indicating older-appearing brains in people with higher cardiometabolic risk (smoking, higher blood pressure and pulse, low-grade inflammation). Longitudinal evidence supported interactions between both BAGs and waist-to-hip ratio (WHR), and between DTI-based BAG and systolic blood pressure and smoking, indicating accelerated ageing in people with higher cardiometabolic risk (smoking, higher blood pressure, and WHR). The results demonstrate that cardiometabolic risk factors are associated with brain ageing. While randomized controlled trials are needed to establish causality, our results indicate that public health initiatives and treatment strategies targeting modifiable cardiometabolic risk factors may also improve risk trajectories and delay brain ageing.
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Affiliation(s)
- Dani Beck
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
- Sunnaas Rehabilitation Hospital HTNesodden
| | - Ann‐Marie G. de Lange
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- LREN, Centre for Research in Neurosciences‐Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Mads L. Pedersen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Bjørknes CollegeOsloNorway
| | - Ivan I. Maximov
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Irene Voldsbekk
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
| | - Geneviève Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
| | - Anne‐Marthe Sanders
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
- Sunnaas Rehabilitation Hospital HTNesodden
| | - Kristine M. Ulrichsen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
- Sunnaas Rehabilitation Hospital HTNesodden
| | - Erlend S. Dørum
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
- Sunnaas Rehabilitation Hospital HTNesodden
| | - Knut K. Kolskår
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
- Sunnaas Rehabilitation Hospital HTNesodden
| | - Einar A. Høgestøl
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
| | - Nils Eiel Steen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
| | - Srdjan Djurovic
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | | | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of Psychiatry and PsychotherapyUniversity of TübingenTubingenGermany
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOslo
- Department of PsychologyUniversity of OsloOslo
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
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Neuroimaging-derived brain age is associated with life satisfaction in cognitively unimpaired elderly: A community-based study. Transl Psychiatry 2022; 12:25. [PMID: 35058431 PMCID: PMC8776862 DOI: 10.1038/s41398-022-01793-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 12/21/2021] [Accepted: 01/10/2022] [Indexed: 12/01/2022] Open
Abstract
With the widespread increase in elderly populations, the quality of life and mental health in old age are issues of great interest. The human brain changes with age, and the brain aging process is biologically complex and varies widely among individuals. In this cross-sectional study, to clarify the effects of mental health, as well as common metabolic factors (e.g., diabetes) on healthy brain aging in late life, we analyzed structural brain MRI findings to examine the relationship between predicted brain age and life satisfaction, depressive symptoms, resilience, and lifestyle-related factors in elderly community-living individuals with unimpaired cognitive function. We extracted data from a community-based cohort study in Arakawa Ward, Tokyo. T1-weighted images of 773 elderly participants aged ≥65 years were analyzed, and the predicted brain age of each subject was calculated by machine learning from anatomically standardized gray-matter images. Specifically, we examined the relationships between the brain-predicted age difference (Brain-PAD: real age subtracted from predicted age) and life satisfaction, depressive symptoms, resilience, alcohol consumption, smoking, diabetes, hypertension, and dyslipidemia. Brain-PAD showed significant negative correlations with life satisfaction (Spearman's rs= -0.102, p = 0.005) and resilience (rs= -0.105, p = 0.004). In a multiple regression analysis, life satisfaction (p = 0.038), alcohol use (p = 0.040), and diabetes (p = 0.002) were independently correlated with Brain-PAD. Thus, in the cognitively unimpaired elderly, higher life satisfaction was associated with a 'younger' brain, whereas diabetes and alcohol use had negative impacts on life satisfaction. Subjective life satisfaction, as well as the prevention of diabetes and alcohol use, may protect the brain from accelerated aging.
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Kuang QJ, Zhou SM, Liu Y, Wu HW, Bi TY, She SL, Zheng YJ. Prediction of Facial Emotion Recognition Ability in Patients With First-Episode Schizophrenia Using Amplitude of Low-Frequency Fluctuation-Based Support Vector Regression Model. Front Psychiatry 2022; 13:905246. [PMID: 35911229 PMCID: PMC9326045 DOI: 10.3389/fpsyt.2022.905246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE There were few studies that had attempted to predict facial emotion recognition (FER) ability at the individual level in schizophrenia patients. In this study, we developed a model for the prediction of FER ability in Chinese Han patients with the first-episode schizophrenia (FSZ). MATERIALS AND METHODS A total of 28 patients with FSZ and 33 healthy controls (HCs) were recruited. All subjects underwent resting-state fMRI (rs-fMRI). The amplitude of low-frequency fluctuation (ALFF) method was selected to analyze voxel-level spontaneous neuronal activity. The visual search experiments were selected to evaluate the FER, while the support vector regression (SVR) model was selected to develop a model based on individual rs-fMRI brain scan. RESULTS Group difference in FER ability showed statistical significance (P < 0.05). In FSZ patients, increased mALFF value were observed in the limbic lobe and frontal lobe, while decreased mALFF value were observed in the frontal lobe, parietal lobe, and occipital lobe (P < 0.05, AlphaSim correction). SVR analysis showed that abnormal spontaneous activity in multiple brain regions, especially in the right posterior cingulate, right precuneus, and left calcarine could effectively predict fearful FER accuracy (r = 0.64, P = 0.011) in patients. CONCLUSION Our study provides an evidence that abnormal spontaneous activity in specific brain regions may serve as a predictive biomarker for fearful FER ability in schizophrenia.
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Affiliation(s)
- Qi-Jie Kuang
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Su-Miao Zhou
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yi Liu
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Hua-Wang Wu
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Tai-Yong Bi
- Centre for Mental Health Research in School of Management, Zunyi Medical University, Zunyi, China
| | - Sheng-Lin She
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Ying-Jun Zheng
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
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Liu L, Liu J, Yang L, Wen B, Zhang X, Cheng J, Han S, Zhang Y, Cheng J. Accelerated Brain Aging in Patients With Obsessive-Compulsive Disorder. Front Psychiatry 2022; 13:852479. [PMID: 35599767 PMCID: PMC9120421 DOI: 10.3389/fpsyt.2022.852479] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 03/28/2022] [Indexed: 12/14/2022] Open
Abstract
Obsessive-compulsive disorder (OCD) may be accompanied by an accelerated structural decline of the brain with age compared to healthy controls (HCs); however, this has yet to be proven. To answer this question, we built a brain age prediction model using mean gray matter volumes of each brain region as features, which were obtained by voxel-based morphometry derived from T1-weighted MRI scans. The prediction model was built using two Chinese Han datasets (dataset 1, N = 106 for HCs and N = 90 for patients with OCD; dataset 2, N = 270 for HCs) to evaluate its performance. Then, a new prediction model was trained using data for HCs in dataset 1 and applied to patients with OCD to investigate the brain aging trajectory. The brain-predicted age difference (brain-PAD) scores, defined as the difference between predicted brain age and chronological age, were calculated for all participants and compared between patients with matched HCs in dataset 1. It was demonstrated that the prediction model performs consistently across different datasets. Patients with OCD presented higher brain-PAD scores than matched HCs, suggesting that patients with OCD presented accelerated brain aging. In addition, brain-PAD scores were negatively correlated with the duration of illness, suggesting that brain-PAD scores might capture progressive structural brain changes. These results identified accelerated brain aging in patients with OCD for the first time and deepened our understanding of the pathogenesis of OCD.
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Affiliation(s)
- Liang Liu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Junhong Liu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Li Yang
- Department of Public Health, School of Medicine, Huanghuai University, Zhumadian, China
| | - Baohong Wen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaopan Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Junying Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Sone D. Neurobiological mechanisms of psychosis in epilepsy: Findings from neuroimaging studies. Front Psychiatry 2022; 13:1079295. [PMID: 36506456 PMCID: PMC9728542 DOI: 10.3389/fpsyt.2022.1079295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/08/2022] [Indexed: 11/24/2022] Open
Abstract
Despite the high prevalence and clinical importance of comorbid psychosis in epilepsy, its neurobiological mechanisms remain understudied. This narrative mini-review aims to provide an overview of recent updates in in vivo neuroimaging studies on psychosis in epilepsy, including structural and diffusion magnetic resonance imaging (MRI) and functional and molecular imaging, and to discuss future directions in this field. While the conventional morphological analysis of structural MRI has provided relatively inconsistent results, advanced methods, including brain network analysis, hippocampal subregion volumetry, and machine learning models, have recently provided novel findings. Diffusion MRI, for example, has revealed a reduction in white matter integrity mainly in the frontal and temporal lobes, as well as a disruption of brain white matter networks. Functional neuroimaging, such as perfusion single-photon emission computed tomography (SPECT) or fluorodeoxyglucose positron emission tomography (FDG-PET), often identifies hyperactivity in various brain regions. The current limitations of these more recent studies may include small and sometimes heterogeneous samples, insufficient control groups, the effects of psychoactive drugs, and the lack of longitudinal analysis. Further investigations are required to establish novel treatments and identify clinical diagnostic or disease-monitoring biomarkers in psychosis in epilepsy.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, Jikei University School of Medicine, Tokyo, Japan
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Baecker L, Garcia-Dias R, Vieira S, Scarpazza C, Mechelli A. Machine learning for brain age prediction: Introduction to methods and clinical applications. EBioMedicine 2021; 72:103600. [PMID: 34614461 PMCID: PMC8498228 DOI: 10.1016/j.ebiom.2021.103600] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 12/19/2022] Open
Abstract
The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as ‘brain-age gap’. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders.
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Affiliation(s)
- Lea Baecker
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - Rafael Garcia-Dias
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; Department of General Psychology, University of Padua, Italy
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
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Fang K, Han S, Li Y, Ding J, Wu J, Zhang W. The Vital Role of Central Executive Network in Brain Age: Evidence From Machine Learning and Transcriptional Signatures. Front Neurosci 2021; 15:733316. [PMID: 34557071 PMCID: PMC8453084 DOI: 10.3389/fnins.2021.733316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/06/2021] [Indexed: 11/24/2022] Open
Abstract
Recent studies combining neuroimaging with machine learning methods successfully infer an individual’s brain age, and its discrepancy with the chronological age is used to identify age-related diseases. However, which brain networks play decisive roles in brain age prediction and the underlying biological basis of brain age remain unknown. To answer these questions, we estimated an individual’s brain age in the Southwest University Adult Lifespan Dataset (N = 492) from the gray matter volumes (GMV) derived from T1-weighted MRI scans by means of Gaussian process regression. Computational lesion analysis was performed to determine the importance of each brain network in brain age prediction. Then, we identified brain age-related genes by using prior brain-wide gene expression data, followed by gene enrichment analysis using Metascape. As a result, the prediction model successfully inferred an individual’s brain age and the computational lesion prediction results identified the central executive network as a vital network in brain age prediction (Steiger’s Z = 2.114, p = 0.035). In addition, the brain age-related genes were enriched in Gene Ontology (GO) processes/Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways grouped into numbers of clusters, such as regulation of iron transmembrane transport, synaptic signaling, synapse organization, retrograde endocannabinoid signaling (e.g., dopaminergic synapse), behavior (e.g., memory and associative learning), neurotransmitter secretion, and dendrite development. In all, these results reveal that the GMV of the central executive network played a vital role in predicting brain age and bridged the gap between transcriptome and neuroimaging promoting an integrative understanding of the pathophysiology of brain age.
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Affiliation(s)
- Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuming Li
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Jing Ding
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Jilian Wu
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Wenzhou Zhang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
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Beheshti I, Sone D, Maikusa N, Kimura Y, Shigemoto Y, Sato N, Matsuda H. Accurate lateralization and classification of MRI-negative 18F-FDG-PET-positive temporal lobe epilepsy using double inversion recovery and machine-learning. Comput Biol Med 2021; 137:104805. [PMID: 34464851 DOI: 10.1016/j.compbiomed.2021.104805] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 08/03/2021] [Accepted: 08/23/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVE The main objective of this study was to determine the ability of double inversion recovery (DIR) data coupled with machine-learning algorithms to distinguish normal individuals from epileptic subjects and to identify the laterality of the focus side in MRI-negative, PET-positive temporal lobe epilepsy (TLE) patients. MATERIALS AND METHODS We used whole-brain DIR data as the input features with which to train a linear support-vector machine model in 63 participants who underwent high-resolution structural MRI and DIR scans. The subjects included 20 left TLE patients, 19 right TLE patients, and 24 healthy controls (HCs). RESULTS Using the DIR data, we achieved a robust accuracy of 87.30% for discriminating among the left TLE, right TLE, and HC groups as well as 84.61%, 97.72%, and 93.02% prediction accuracies for distinguishing left TLE from right TLE, HC from right TLE, and HC from left TLE, respectively. INTERPRETATION Our experimental results suggest that DIR data coupled with machine-learning algorithms provide a promising approach to identifying MRI-negative TLE patients, especially when fluorodeoxyglucose-PET is not available.
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Affiliation(s)
- Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada; Cyclotron and Drug Discovery Research Center, Southern TOHOKU Research Institute for Neuroscience, 7- 61-2, Yatsuyamada, Koriyama, 963-8052, Japan.
| | - Daichi Sone
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo, 187-8551, Japan; Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, United Kingdom; Department of Psychiatry, The Jikei University School of Medicine, 3-25-8, Nishishimbashi, Minato, Tokyo, 105-8461, Japan
| | - Norihide Maikusa
- Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo, 187-8551, Japan
| | - Yukio Kimura
- Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo, 187-8551, Japan
| | - Yoko Shigemoto
- Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo, 187-8551, Japan
| | - Noriko Sato
- Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo, 187-8551, Japan
| | - Hiroshi Matsuda
- Cyclotron and Drug Discovery Research Center, Southern TOHOKU Research Institute for Neuroscience, 7- 61-2, Yatsuyamada, Koriyama, 963-8052, Japan; Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo, 187-8551, Japan
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Wrigglesworth J, Ward P, Harding IH, Nilaweera D, Wu Z, Woods RL, Ryan J. Factors associated with brain ageing - a systematic review. BMC Neurol 2021; 21:312. [PMID: 34384369 PMCID: PMC8359541 DOI: 10.1186/s12883-021-02331-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/24/2021] [Indexed: 11/10/2022] Open
Abstract
Background Brain age is a biomarker that predicts chronological age using neuroimaging features. Deviations of this predicted age from chronological age is considered a sign of age-related brain changes, or commonly referred to as brain ageing. The aim of this systematic review is to identify and synthesize the evidence for an association between lifestyle, health factors and diseases in adult populations, with brain ageing. Methods This systematic review was undertaken in accordance with the PRISMA guidelines. A systematic search of Embase and Medline was conducted to identify relevant articles using search terms relating to the prediction of age from neuroimaging data or brain ageing. The tables of two recent review papers on brain ageing were also examined to identify additional articles. Studies were limited to adult humans (aged 18 years and above), from clinical or general populations. Exposures and study design of all types were also considered eligible. Results A systematic search identified 52 studies, which examined brain ageing in clinical and community dwelling adults (mean age between 21 to 78 years, ~ 37% were female). Most research came from studies of individuals diagnosed with schizophrenia or Alzheimer’s disease, or healthy populations that were assessed cognitively. From these studies, psychiatric and neurologic diseases were most commonly associated with accelerated brain ageing, though not all studies drew the same conclusions. Evidence for all other exposures is nascent, and relatively inconsistent. Heterogenous methodologies, or methods of outcome ascertainment, were partly accountable. Conclusion This systematic review summarised the current evidence for an association between genetic, lifestyle, health, or diseases and brain ageing. Overall there is good evidence to suggest schizophrenia and Alzheimer’s disease are associated with accelerated brain ageing. Evidence for all other exposures was mixed or limited. This was mostly due to a lack of independent replication, and inconsistency across studies that were primarily cross sectional in nature. Future research efforts should focus on replicating current findings, using prospective datasets. Trial registration A copy of the review protocol can be accessed through PROSPERO, registration number CRD42020142817. Supplementary Information The online version contains supplementary material available at 10.1186/s12883-021-02331-4.
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Affiliation(s)
- Jo Wrigglesworth
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Phillip Ward
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, 3168, Australia.,Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, 3800, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Victoria , 3800, , Australia
| | - Ian H Harding
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, 3168, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, 3004, Australia
| | - Dinuli Nilaweera
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Zimu Wu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Robyn L Woods
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Joanne Ryan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia.
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de Bézenac CE, Adan G, Weber B, Keller SS. Association of Epilepsy Surgery With Changes in Imaging-Defined Brain Age. Neurology 2021; 97:e554-e563. [PMID: 34261787 PMCID: PMC8424496 DOI: 10.1212/wnl.0000000000012289] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 05/03/2021] [Indexed: 11/15/2022] Open
Abstract
Objective To determine whether surgery in patients with mesial temporal lobe epilepsy (mTLE) is associated with reduced brain-predicted age as a neural marker overall brain health, we compared brain-predicted and chronologic age difference (brain age gap estimation [BrainAGE]) in patients before and after surgery with healthy controls. Methods We acquired 3D T1-weighted MRI scans for 48 patients with mTLE before and after temporal lobe surgery to estimate brain age using a gaussian processes regression model. We examined BrainAGE before and after surgery controlling for brain volume change, comparing patients to 37 age- and sex-matched controls. Results Preoperatively, patients showed an increased BrainAGE of more than 7 years compared to controls. However, surgery was associated with a mean BrainAGE reduction of 5 years irrespective of whether or not surgery resulted in complete seizure freedom. We observed a lateralization effect as patients with left mTLE had BrainAGE values that more closely resembled control group values following surgery. Conclusions Our findings suggest that while morphologic brain alterations linked to accelerated aging have been observed in mTLE, surgery may be associated with changes that reverse such alterations in some patients. This work highlights the advantages of resective surgery on overall brain health in patients with refractory focal epilepsy.
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Affiliation(s)
- Christophe E de Bézenac
- From the Department of Pharmacology and Therapeutics (C.E.d.B., G.A., S.S.K.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool; The Walton Centre NHS Foundation Trust (C.E.d.B., G.A., S.S.K.), Liverpool, UK; and Institute of Experimental Epileptology and Cognition Research (B.W.), University of Bonn, Germany.
| | - Guleed Adan
- From the Department of Pharmacology and Therapeutics (C.E.d.B., G.A., S.S.K.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool; The Walton Centre NHS Foundation Trust (C.E.d.B., G.A., S.S.K.), Liverpool, UK; and Institute of Experimental Epileptology and Cognition Research (B.W.), University of Bonn, Germany
| | - Bernd Weber
- From the Department of Pharmacology and Therapeutics (C.E.d.B., G.A., S.S.K.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool; The Walton Centre NHS Foundation Trust (C.E.d.B., G.A., S.S.K.), Liverpool, UK; and Institute of Experimental Epileptology and Cognition Research (B.W.), University of Bonn, Germany
| | - Simon S Keller
- From the Department of Pharmacology and Therapeutics (C.E.d.B., G.A., S.S.K.), Institute of Systems, Molecular and Integrative Biology, University of Liverpool; The Walton Centre NHS Foundation Trust (C.E.d.B., G.A., S.S.K.), Liverpool, UK; and Institute of Experimental Epileptology and Cognition Research (B.W.), University of Bonn, Germany
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32
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Sone D, Beheshti I. Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review. Front Neurosci 2021; 15:684825. [PMID: 34239413 PMCID: PMC8258163 DOI: 10.3389/fnins.2021.684825] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/31/2021] [Indexed: 12/13/2022] Open
Abstract
Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address 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, The Jikei University School of Medicine, Tokyo, Japan.,Department of Clinical and Experimental Epilepsy, University College London Institute of Neurology, London, United Kingdom
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
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33
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Beheshti I, Ganaie MA, Paliwal V, Rastogi A, Razzak I, Tanveer M. Predicting brain age using machine learning algorithms: A comprehensive evaluation. IEEE J Biomed Health Inform 2021; 26:1432-1440. [PMID: 34029201 DOI: 10.1109/jbhi.2021.3083187] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Machine learning (ML) algorithms play a vital role in brain age estimation frameworks. The impact of regression algorithms on prediction accuracy in the brain age estimation frameworks have not been comprehensively evaluated. Here, we sought to assess the efficiency of different regression algorithms on brain age estimation. To this end, we built a brain age estimation framework based on a large set of cognitively healthy (CH) individuals (N = 788) as a training set followed by different regression algorithms (18 different algorithms in total). We then quantified each regression-algorithm on independent test sets composed of 88 CH individuals, 70 mild cognitive impairment patients as well as 30 Alzheimers disease patients. The prediction accuracy in the independent test set (i.e., CH set) varied in regression algorithms (mean absolute error (MAE) from 4.63 to 7.14 yrs, R2 from 0.76 to 0.88). The highest and lowest prediction accuracies were achieved by Quadratic Support Vector Regression algorithm (MAE = 4.63 yrs, R2 = 0.88, 95% CI = [-1.26, 1.42]) and Binary Decision Tree algorithm (MAE = 7.14 yrs, R2 = 0.76, 95% CI = [-1.50, 2.62]), respectively. Our experimental results demonstrate that prediction accuracy in brain age frameworks is affected by regression algorithms, indicating that advanced machine learning algorithms can lead to more accurate brain age predictions in clinical settings.
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34
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Delayed brain development of Rolandic epilepsy profiled by deep learning-based neuroanatomic imaging. Eur Radiol 2021; 31:9628-9637. [PMID: 34018056 DOI: 10.1007/s00330-021-08048-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/30/2021] [Accepted: 05/05/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVES Although Rolandic epilepsy (RE) has been regarded as a brain developmental disorder, neuroimaging studies have not yet ascertained whether RE has brain developmental delay. This study employed deep learning-based neuroanatomic biomarker to measure the changed feature of "brain age" in RE. METHODS The study constructed a 3D-CNN brain age prediction model through 1155 cases of typically developing children's morphometric brain MRI from open-source datasets and further applied to a local dataset of 167 RE patients and 107 typically developing children. The brain-predicted age difference was measured to quantitatively estimate brain age changes in RE and further investigated the relevancies with cognitive and clinical variables. RESULTS The brain age estimation network model presented a good performance for brain age prediction in typically developing children. The children with RE showed a 0.45-year delay of brain age by contrast with typically developing children. Delayed brain age was associated with neuroanatomic changes in the Rolandic regions and also associated with cognitive dysfunction of attention. CONCLUSION This study provided neuroimaging evidence to support the notion that RE has delayed brain development. KEY POINTS • The children with Rolandic epilepsy showed imaging phenotypes of delayed brain development with increased GM volume and decreased WM volume in the Rolandic regions. • The children with Rolandic epilepsy had a 0.45-year delay of brain-predicted age by comparing with typically developing children, using 3D-CNN-based brain age prediction model. • The delayed brain age was associated with morphometric changes in the Rolandic regions and attentional deficit in Rolandic epilepsy.
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35
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Han S, Chen Y, Zheng R, Li S, Jiang Y, Wang C, Fang K, Yang Z, Liu L, Zhou B, Wei Y, Pang J, Li H, Zhang Y, Cheng J. The stage-specifically accelerated brain aging in never-treated first-episode patients with depression. Hum Brain Mapp 2021; 42:3656-3666. [PMID: 33932251 PMCID: PMC8249899 DOI: 10.1002/hbm.25460] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 04/02/2021] [Accepted: 04/15/2021] [Indexed: 12/29/2022] Open
Abstract
Depression associated with structural brain abnormalities is hypothesized to be related with accelerated brain aging. However, there is far from a unified conclusion because of clinical variations such as medication status, cumulative illness burden. To explore whether brain age is accelerated in never‐treated first‐episode patients with depression and its association with clinical characteristics, we constructed a prediction model where gray matter volumes measured by voxel‐based morphometry derived from T1‐weighted MRI scans were treated as features. The prediction model was first validated using healthy controls (HCs) in two Chinese Han datasets (Dataset 1, N = 130 for HCs and N = 195 for patients with depression; Dataset 2, N = 270 for HCs) separately or jointly, then the trained prediction model using HCs (N = 400) was applied to never‐treated first‐episode patients with depression (N = 195). The brain‐predicted age difference (brain‐PAD) scores defined as the difference between predicted brain age and chronological age, were calculated for all participants and compared between patients with age‐, gender‐, educational level‐matched HCs in Dataset 1. Overall, patients presented higher brain‐PAD scores suggesting patients with depression having an “older” brain than expected. More specially, this difference occurred at illness onset (illness duration <3 months) and following 2 years then disappeared as the illness further advanced (>2 years) in patients. This phenomenon was verified by another data‐driven method and significant correlation between brain‐PAD scores and illness duration in patients. Our results reveal that accelerated brain aging occurs at illness onset and suggest it is a stage‐dependent phenomenon in depression.
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Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Shuying Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yu Jiang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Keke Fang
- Phase I Clinical Research Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Zhengui Yang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Liang Liu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Jianyue Pang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hengfen Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
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Wang F, Yin Y, Yang Y, Liang T, Huang T, He C, Hu J, Zhang J, Yang Y, Xing Q, Zhang T, Liu H. Connectome-based prediction of brain age in Rolandic epilepsy: a protocol for a multicenter cross-sectional study. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:511. [PMID: 33850908 PMCID: PMC8039653 DOI: 10.21037/atm-21-574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Rolandic epilepsy (RE) is a common pediatric idiopathic partial epilepsy syndrome. Children with RE display varying degrees of cognitive impairment. In epilepsy, age-related neuroanatomic and cognitive changes differ greatly from those observed in the healthy brain, and may be defined as accelerated brain aging. Connectome-based predictive modeling (CPM) is a recently developed machine learning approach that uses whole-brain connectivity measured with neuroimaging data ("neural fingerprints") to predict brain-behavior relationships. The aim of the study will be to develop and validate a CPM for predicting brain age in patients with RE. METHODS A multicenter, cross-sectional study will be conducted in 5 Chinese hospitals. A total of 100 RE patients (including 50 patients receiving anti-epileptic drugs and 50 drug-naïve patients) and 100 healthy children will be recruited to undergo a neuropsychological test using the Wechsler Intelligence Scale. Magnetic resonance images will also be collected. CPM will be applied to predict the brain age of children with RE based on brain functional connectivity. DISCUSSION The findings of the study will facilitate our understanding of developmental changes in the brain in children with RE and could also be an important milestone in the journey toward developing effective early interventions for this disorder. TRIAL REGISTRATION The study has been registered with Chinese Clinical Trial Registry (ChiCTR2000032984).
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Affiliation(s)
- Fuqin Wang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Yu Yin
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Yang Yang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Ting Liang
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Tingting Huang
- Department of Radiology, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Cheng He
- Department of Radiology, Chongqing University Central Hospital, Chongqing, China
| | - Jie Hu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Jingjing Zhang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Yanli Yang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Qianlu Xing
- Department of Pediatrics, the Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Tijiang Zhang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Heng Liu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
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Hogan J, Sun H, Paixao L, Westmeijer M, Sikka P, Jin J, Tesh R, Cardoso M, Cash SS, Akeju O, Thomas R, Westover MB. Night-to-night variability of sleep electroencephalography-based brain age measurements. Clin Neurophysiol 2021; 132:1-12. [PMID: 33248430 PMCID: PMC7855943 DOI: 10.1016/j.clinph.2020.09.029] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 08/21/2020] [Accepted: 09/18/2020] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Brain Age Index (BAI), calculated from sleep electroencephalography (EEG), has been proposed as a biomarker of brain health. This study quantifies night-to-night variability of BAI and establishes probability thresholds for inferring underlying brain pathology based on a patient's BAI. METHODS 86 patients with multiple nights of consecutive EEG recordings were selected from Epilepsy Monitoring Unit patients whose EEGs reported as within normal limits. While EEGs with epileptiform activity were excluded, the majority of patients included in the study had a diagnosis of chronic epilepsy. BAI was calculated for each 12-hour segment of patient data using a previously established algorithm, and the night-to-night variability in BAI was measured. RESULTS The within-patient night-to-night standard deviation in BAI was 7.5 years. Estimates of BAI derived by averaging over 2, 3, and 4 nights had standard deviations of 4.7, 3.7, and 3.0 years, respectively. CONCLUSIONS Averaging BAI over n nights reduces night-to-night variability of BAI by a factor of n, rendering BAI a more suitable biomarker of brain health at the individual level. A brain age risk lookup table of results provides thresholds above which a patient has a high probability of excess BAI. SIGNIFICANCE With increasing ease of EEG acquisition, including wearable technology, BAI has the potential to track brain health and detect deviations from normal physiologic function. The measure of night-to-night variability and how this is reduced by averaging across multiple nights provides a basis for using BAI in patients' homes to identify patients who should undergo further investigation or monitoring.
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Affiliation(s)
- Jacob Hogan
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Luis Paixao
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Mike Westmeijer
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Pooja Sikka
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Jing Jin
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Ryan Tesh
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Madalena Cardoso
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Oluwaseun Akeju
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Robert Thomas
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
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Sone D, Sato N, Shigemoto Y, Kimura Y, Maikusa N, Ota M, Foong J, Koepp M, Matsuda H. Disrupted White Matter Integrity and Structural Brain Networks in Temporal Lobe Epilepsy With and Without Interictal Psychosis. Front Neurol 2020; 11:556569. [PMID: 33071943 PMCID: PMC7542674 DOI: 10.3389/fneur.2020.556569] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 08/20/2020] [Indexed: 01/05/2023] Open
Abstract
Background: Despite the importance of psychosis as a comorbidity of temporal lobe epilepsy (TLE), the underlying neural mechanisms are still unclear. We aimed to investigate abnormalities specific to psychosis in TLE, using diffusion MRI parameters and graph-theoretical network analysis. Material and Methods: We recruited 49 patients with TLE (20 with and 29 without interictal schizophrenia-like psychosis) and 42 age-/gender-matched healthy controls. We performed 3-tesla MRI scans including 3D T1-weighted imaging and diffusion tensor imaging in all participants. Among the three groups, fractional anisotropy (FA), mean diffusivity (MD), and global network metrics were compared by analyses of covariance. Regional connectivity strength was compared by network-based statistics. Results: Compared to controls, TLE patients showed significant temporal and extra-temporal changes in FA, and MD, which were more severe and widespread in patients with than without psychosis. We observed distinct differences between TLE patients with and without psychosis in the anterior thalamic radiation (ATR), inferior fronto-occipital fasciculus (IFOF), and inferior longitudinal fasciculus (ILF). Similarly, for network metrics, global, and local efficiency and increased path length were significantly reduced in TLE patients compared to controls, but with more severe changes in TLE with psychosis than without psychosis. Network-based statistics detected significant differences between TLE with and without psychosis mainly involving the left limbic and prefrontal areas. Conclusion: TLE patients with interictal schizophrenia-like psychosis showed more widespread and severe white matter impairment, involving the ATR, IFOF and ILF, as well as disrupted network connectivity, particularly in the left limbic and prefrontal cortex, than patients without psychosis.
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Affiliation(s)
- Daichi Sone
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan.,Department of Clinical and Experimental Epilepsy, University College London Institute of Neurology, London, United Kingdom
| | - Noriko Sato
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Yoko Shigemoto
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Yukio Kimura
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Norihide Maikusa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Miho Ota
- Division of Clinical Medicine, Department of Neuropsychiatry, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Jacqueline Foong
- Department of Clinical and Experimental Epilepsy, University College London Institute of Neurology, London, United Kingdom
| | - Matthias Koepp
- Department of Clinical and Experimental Epilepsy, University College London Institute of Neurology, London, United Kingdom
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
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39
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Palma M, Tavakoli S, Brettschneider J, Nichols TE. Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression. Neuroimage 2020; 219:116938. [PMID: 32502669 PMCID: PMC7443707 DOI: 10.1016/j.neuroimage.2020.116938] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 05/07/2020] [Accepted: 05/08/2020] [Indexed: 12/12/2022] Open
Abstract
Prediction of subject age from brain anatomical MRI has the potential to provide a sensitive summary of brain changes, indicative of different neurodegenerative diseases. However, existing studies typically neglect the uncertainty of these predictions. In this work we take into account this uncertainty by applying methods of functional data analysis. We propose a penalised functional quantile regression model of age on brain structure with cognitively normal (CN) subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI), and use it to predict brain age in Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) subjects. Unlike the machine learning approaches available in the literature of brain age prediction, which provide only point predictions, the outcome of our model is a prediction interval for each subject.
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Affiliation(s)
- Marco Palma
- Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom.
| | - Shahin Tavakoli
- Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - Julia Brettschneider
- Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom; The Alan Turing Institute, London, NW1 2DB, United Kingdom
| | - Thomas E Nichols
- Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom; Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, United Kingdom
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40
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Zhao X, Zhao XM. Deep learning of brain magnetic resonance images: A brief review. Methods 2020; 192:131-140. [PMID: 32931932 DOI: 10.1016/j.ymeth.2020.09.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 08/22/2020] [Accepted: 09/09/2020] [Indexed: 01/24/2023] Open
Abstract
Magnetic resonance imaging (MRI) is one of the most popular techniques in brain science and is important for understanding brain function and neuropsychiatric disorders. However, the processing and analysis of MRI is not a trivial task with lots of challenges. Recently, deep learning has shown superior performance over traditional machine learning approaches in image analysis. In this survey, we give a brief review of the recent popular deep learning approaches and their applications in brain MRI analysis. Furthermore, popular brain MRI databases and deep learning tools are also introduced. The strength and weaknesses of different approaches are addressed, and challenges as well as future directions are also discussed.
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Affiliation(s)
- Xingzhong Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, China; Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China.
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41
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Kuo CY, Lee PL, Hung SC, Liu LK, Lee WJ, Chung CP, Yang AC, Tsai SJ, Wang PN, Chen LK, Chou KH, Lin CP. Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker. Cereb Cortex 2020; 30:5844-5862. [PMID: 32572452 DOI: 10.1093/cercor/bhaa161] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 05/05/2020] [Accepted: 05/21/2020] [Indexed: 12/31/2022] Open
Abstract
The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.
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Affiliation(s)
- Chen-Yuan Kuo
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming University, Taipei 11221, Taiwan
| | - Pei-Lin Lee
- Institute of Neuroscience, National Yang Ming University, Taipei 11221, Taiwan
| | - Sheng-Che Hung
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Li-Kuo Liu
- Aging and Health Research Center, National Yang Ming University, Taipei 11221, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Wei-Ju Lee
- Aging and Health Research Center, National Yang Ming University, Taipei 11221, Taiwan.,Department of Family Medicine, Yuanshan Branch, Taipei Veterans General Hospital, Yi-Lan 264, Taiwan
| | - Chih-Ping Chung
- Department of Neurology, School of Medicine, National Yang Ming University, Taipei 11221, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Albert C Yang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Pei-Ning Wang
- Department of Neurology, School of Medicine, National Yang Ming University, Taipei 11221, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 11217, Taiwan.,Brain Research Center, National Yang Ming University, Taipei 11221, Taiwan
| | - Liang-Kung Chen
- Aging and Health Research Center, National Yang Ming University, Taipei 11221, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Kun-Hsien Chou
- Institute of Neuroscience, National Yang Ming University, Taipei 11221, Taiwan.,Brain Research Center, National Yang Ming University, Taipei 11221, Taiwan
| | - Ching-Po Lin
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming University, Taipei 11221, Taiwan.,Institute of Neuroscience, National Yang Ming University, Taipei 11221, Taiwan.,Aging and Health Research Center, National Yang Ming University, Taipei 11221, Taiwan.,Brain Research Center, National Yang Ming University, Taipei 11221, Taiwan
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42
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Beheshti I, Mishra S, Sone D, Khanna P, Matsuda H. T1-weighted MRI-driven Brain Age Estimation in Alzheimer's Disease and Parkinson's Disease. Aging Dis 2020; 11:618-628. [PMID: 32489706 PMCID: PMC7220281 DOI: 10.14336/ad.2019.0617] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 06/17/2019] [Indexed: 11/24/2022] Open
Abstract
Neuroimaging-driven brain age estimation has introduced a robust (reliable and heritable) biomarker for detecting and monitoring neurodegenerative diseases. Here, we computed and compared brain age in Alzheimer's disease (AD) and Parkinson's disease (PD) patients using an advanced machine learning procedure involving T1-weighted MRI scans and gray matter (GM) and white matter (WM) models. Brain age estimation frameworks were built using 839 healthy individuals and then the brain estimated age difference (Brain-EAD: chronological age subtracted from brain estimated age) was assessed in a large sample of PD patients (n = 160) and AD patients (n = 129), respectively. The mean Brain-EADs for GM were +9.29 ± 6.43 years for AD patients versus +1.50 ± 6.03 years for PD patients. For WM, the mean Brain-EADs were +8.85 ± 6.62 years for AD patients versus +2.47 ± 5.85 years for PD patients. In addition, PD patients showed a significantly higher WM Brain-EAD than GM Brain-EAD. In a direct comparison between PD and AD patients, we observed significantly higher Brain-EAD values in AD patients for both GM and WM. A comparison of the Brain-EAD between PD and AD patients revealed that AD patients may have a significantly "older-appearing" brain than PD patients.
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Affiliation(s)
- Iman Beheshti
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan.
| | - Shiwangi Mishra
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India.
| | - Daichi Sone
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan.
| | - Pritee Khanna
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India.
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan.
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43
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Hwang G, Hermann B, Nair VA, Conant LL, Dabbs K, Mathis J, Cook CJ, Rivera-Bonet CN, Mohanty R, Zhao G, Almane DN, Nencka A, Felton E, Struck AF, Birn R, Maganti R, Humphries CJ, Raghavan M, DeYoe EA, Bendlin BB, Prabhakaran V, Binder JR, Meyerand ME. Brain aging in temporal lobe epilepsy: Chronological, structural, and functional. NEUROIMAGE-CLINICAL 2020; 25:102183. [PMID: 32058319 PMCID: PMC7016276 DOI: 10.1016/j.nicl.2020.102183] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 12/03/2019] [Accepted: 01/13/2020] [Indexed: 10/26/2022]
Abstract
The association of epilepsy with structural brain changes and cognitive abnormalities in midlife has raised concern regarding the possibility of future accelerated brain and cognitive aging and increased risk of later life neurocognitive disorders. To address this issue we examined age-related processes in both structural and functional neuroimaging among individuals with temporal lobe epilepsy (TLE, N = 104) who were participants in the Epilepsy Connectome Project (ECP). Support vector regression (SVR) models were trained from 151 healthy controls and used to predict TLE patients' brain ages. It was found that TLE patients on average have both older structural (+6.6 years) and functional (+8.3 years) brain ages compared to healthy controls. Accelerated functional brain age (functional - chronological age) was mildly correlated (corrected P = 0.07) with complex partial seizure frequency and the number of anti-epileptic drug intake. Functional brain age was a significant correlate of declining cognition (fluid abilities) and partially mediated chronological age-fluid cognition relationships. Chronological age was the only positive predictor of crystallized cognition. Accelerated aging is evident not only in the structural brains of patients with TLE, but also in their functional brains. Understanding the causes of accelerated brain aging in TLE will be clinically important in order to potentially prevent or mitigate their cognitive deficits.
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Affiliation(s)
- Gyujoon Hwang
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.
| | - Bruce Hermann
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Veena A Nair
- Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Lisa L Conant
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Kevin Dabbs
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Jed Mathis
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Cole J Cook
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Rosaleena Mohanty
- Electrical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Gengyan Zhao
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | - Dace N Almane
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Andrew Nencka
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Aaron F Struck
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Rasmus Birn
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Rama Maganti
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Manoj Raghavan
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Edgar A DeYoe
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Vivek Prabhakaran
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Radiology, University of Wisconsin-Madison, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA; Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Mary E Meyerand
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Radiology, University of Wisconsin-Madison, Madison, WI, USA; Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
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44
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Sone D, Sato N, Ota M, Kimura Y, Matsuda H. Widely Impaired White Matter Integrity and Altered Structural Brain Networks in Psychogenic Non-Epileptic Seizures. Neuropsychiatr Dis Treat 2019; 15:3549-3555. [PMID: 31920315 PMCID: PMC6939397 DOI: 10.2147/ndt.s235159] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 12/06/2019] [Indexed: 01/05/2023] Open
Abstract
OBJECTIVE The underlying neural correlates of psychogenic non-epileptic seizures (PNES) are still unknown and their identification would be helpful for clinicians and patients. This study aimed to reveal details of white matter microstructure and alterations in brain structural networks in patients with PNES by using diffusion tensor imaging (DTI) and graph theoretical connectivity analysis. METHODS Seventeen patients with PNES and 26 age- and sex-matched healthy controls were enrolled. All participants underwent DTI on a 3.0-T MRI scanner, and fractional anisotropy (FA) and mean diffusivity (MD) maps were compared by tract-based spatial statistics. Additionally, the structural networks derived from DTI data were analyzed using graph theory and two different parcellation schemes. RESULTS Patients with PNES showed widespread decreases in FA and increases in MD, particularly in the deep white matter. In addition, graph theoretical analysis revealed impaired brain networks in PNES, including increased path length, decreased network efficiency, altered nodal topology, and reduced regional connectivity in the right posterior areas. CONCLUSION We found widely impaired white matter integrity and impaired brain structural networks in Japanese patients with PNES. These findings contribute to the accumulation of evidence on PNES and may improve understanding of this condition.
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Affiliation(s)
- Daichi Sone
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Noriko Sato
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Miho Ota
- Department of Neuropsychiatry, Division of Clinical Medicine, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Yukio Kimura
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
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