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Zhang W, Zhai X, Zhang C, Cheng S, Zhang C, Bai J, Deng X, Ji J, Li T, Wang Y, Tong HHY, Li J, Li K. Regional brain structural network topology mediates the associations between white matter damage and disease severity in first-episode, Treatment-naïve pubertal children with major depressive disorder. Psychiatry Res Neuroimaging 2024; 344:111862. [PMID: 39153232 DOI: 10.1016/j.pscychresns.2024.111862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/22/2024] [Accepted: 07/31/2024] [Indexed: 08/19/2024]
Abstract
Puberty is a vulnerable period for the onset of major depressive disorder (MDD) due to considerable neurodevelopmental changes. Prior diffusion tensor imaging (DTI) studies in depressed youth have had heterogeneous participants, making assessment of early pathology challenging due to illness chronicity and medication confounds. This study leveraged whole-brain DTI and graph theory approaches to probe white matter (WM) abnormalities and disturbances in structural network topology related to first-episode, treatment-naïve pediatric MDD. Participants included 36 first-episode, unmedicated adolescents with MDD (mean age 15.8 years) and 29 age- and sex-matched healthy controls (mean age 15.2 years). Compared to controls, the MDD group showed reduced fractional anisotropy in the internal and external capsules, unveiling novel regions of WM disruption in early-onset depression. The right thalamus and superior temporal gyrus were identified as network hubs where betweenness centrality changes mediated links between WM anomalies and depression severity. A diagnostic model incorporating demographics, DTI, and network metrics achieved an AUROC of 0.88 and a F1 score of 0.80 using a neural network algorithm. By examining first-episode, treatment-naïve patients, this work identified novel WM abnormalities and a potential causal pathway linking WM damage to symptom severity via regional structural network alterations in brain hubs.
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Affiliation(s)
- Wenjie Zhang
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Xiaobing Zhai
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
| | - Chan Zhang
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Song Cheng
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Chaoqing Zhang
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Jinji Bai
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Xuan Deng
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Junjun Ji
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
| | - Ting Li
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Yu Wang
- Department of Psychiatry, Changzhi Mental Health Center, Changzhi, Shanxi, China
| | - Henry H Y Tong
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
| | - Junfeng Li
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Kefeng Li
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China.
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Wang C, Zhou L, Zhou F, Fu T. The application value of Rs-fMRI-based machine learning models for differentiating mild cognitive impairment from Alzheimer's disease: a systematic review and meta-analysis. Neurol Sci 2024:10.1007/s10072-024-07731-1. [PMID: 39225837 DOI: 10.1007/s10072-024-07731-1] [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: 05/05/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND Various machine learning (ML) models based on resting-state functional MRI (Rs-fMRI) have been developed to facilitate differential diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, the diagnostic accuracy of such models remains understudied. Therefore, we conducted this systematic review and meta-analysis to explore the diagnostic accuracy of Rs-fMRI-based radiomics in differentiating MCI from AD. METHODS PubMed, Embase, Cochrane, and Web of Science were searched from inception up to February 8, 2024, to identify relevant studies. Meta-analysis was conducted using a bivariate mixed-effects model, and sub-group analyses were carried out by the types of ML tasks (binary classification and multi-class classification tasks). FINDINGS In total, 23 studies, comprising 5,554 participants were enrolled in the study. In the binary classification tasks (twenty studies), the diagnostic accuracy of the ML model for AD was 0.99 (95%CI: 0.34 ~ 1.00), with a sensitivity of 0.94 (95%CI: 0.89 ~ 0.97) and a specificity of 0.98 (95%CI: 0.95 ~ 1.00). In the multi-class classification tasks (six studies), the diagnostic accuracy of the ML model was 0.98 (95%CI: 0.98 ~ 0.99) for NC, 0.96 (95%CI: 0.96 ~ 0.96) for early mild cognitive impairment (EMCI), 0.97 (95%CI: 0.96 ~ 0.97) for late mild cognitive impairment (LMCI), and 0.95 (95%CI: 0.95 ~ 0.95) for AD. CONCLUSIONS The Rs-fMRI-based ML model can be adapted to multi-class classification tasks. Therefore, multi-center studies with large samples are needed to develop intelligent application tools to promote the development of intelligent ML models for disease diagnosis.
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Affiliation(s)
- Chentong Wang
- Rheumatology Immunology Department, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang, 315000, China
| | - Li Zhou
- Rheumatology Immunology Department, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang, 315000, China.
- Ningbo Medical Center Lihuili Hospital, 1111 Jiangnan Road, Yinzhou District, Ningbo, Zhejiang, China.
| | - Feng Zhou
- Rheumatology Immunology Department, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang, 315000, China
| | - Tingting Fu
- Rheumatology Immunology Department, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang, 315000, China
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Odusami M, Damaševičius R, Milieškaitė-Belousovienė E, Maskeliūnas R. Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization. Heliyon 2024; 10:e34402. [PMID: 39145034 PMCID: PMC11320145 DOI: 10.1016/j.heliyon.2024.e34402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 07/06/2024] [Accepted: 07/09/2024] [Indexed: 08/16/2024] Open
Abstract
The threat posed by Alzheimer's disease (AD) to human health has grown significantly. However, the precise diagnosis and classification of AD stages remain a challenge. Neuroimaging methods such as structural magnetic resonance imaging (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to diagnose and categorize AD. However, feature selection approaches that are frequently used to extract additional data from multimodal imaging are prone to errors. This paper suggests using a static pulse-coupled neural network and a Laplacian pyramid to combine sMRI and FDG-PET data. After that, the fused images are used to train the Mobile Vision Transformer (MViT), optimized with Pareto-Optimal Quantum Dynamic Optimization for Neural Architecture Search, while the fused images are augmented to avoid overfitting and then classify unfused MRI and FDG-PET images obtained from the AD Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets into various stages of AD. The architectural hyperparameters of MViT are optimized using Quantum Dynamic Optimization, which ensures a Pareto-optimal solution. The Peak Signal-to-Noise Ratio (PSNR), the Mean Squared Error (MSE), and the Structured Similarity Indexing Method (SSIM) are used to measure the quality of the fused image. We found that the fused image was consistent in all metrics, having 0.64 SIMM, 35.60 PSNR, and 0.21 MSE for the FDG-PET image. In the classification of AD vs. cognitive normal (CN), AD vs. mild cognitive impairment (MCI), and CN vs. MCI, the precision of the proposed method is 94.73%, 92.98% and 89.36%, respectively. The sensitivity is 90. 70%, 90. 70%, and 90. 91% while the specificity is 100%, 100%, and 85. 71%, respectively, in the ADNI MRI test data.
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Affiliation(s)
- Modupe Odusami
- Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania
| | | | | | - Rytis Maskeliūnas
- Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania
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Heyer S, Simon M, Doyen M, Mortada A, Roch V, Jeanbert E, Thilly N, Malaplate C, Kearney-Schwartz A, Jonveaux T, Bannay A, Verger A. 18F-FDG PET can effectively rule out conversion to dementia and the presence of CSF biomarker of neurodegeneration: a real-world data analysis. Alzheimers Res Ther 2024; 16:182. [PMID: 39135067 PMCID: PMC11320856 DOI: 10.1186/s13195-024-01535-3] [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: 03/05/2024] [Accepted: 07/16/2024] [Indexed: 08/15/2024]
Abstract
BACKGROUND Precisely defining the delay in onset of dementia is a particular challenge for early diagnosis. Brain [18F] fluoro-2-deoxy-2-D-glucose (18F-FDG) Positron Emission Tomography (PET) is a particularly interesting tool for the early diagnosis of neurodegenerative diseases, through the measurement of the cerebral glucose metabolic rate. There is currently a lack of longitudinal studies under real-life conditions, with sufficient patients, to accurately evaluate the predictive values of brain 18F-FDG PET scans. Here, we aimed to estimate the value of brain 18F-FDG PET for predicting the risk of dementia conversion and the risk of occurrence of a neurodegenerative pathology. METHODS Longitudinal data for a cohort of patients with no diagnosis of dementia at the time of recruitment referred by a tertiary memory clinic for brain 18F-FDG PET were matched with (Prince M, Wimo A, Guerchet Maëlenn, Ali G-C, Wu Y-T et al. World Alzheimer Report 2015. The Global Impact of Dementia: An analysis of prevalence, incidence, cost and trends. [Research Report] Alzheimer's Disease International. 2015. 2015.) data from the French National Health Data System (NHDS), (Jack CR, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14(4):535-62.) data from the National Alzheimer Bank (NAB), and (Davis M, O`Connell T, Johnson S, Cline S, Merikle E, Martenyi F, et al. Estimating Alzheimer's Disease Progression Rates from Normal Cognition Through Mild Cognitive Impairment and Stages of Dementia. CAR. 2018;15(8):777-88.) lumbar puncture (LP) biomarker data. The criteria for dementia conversion were the designation, within the three years after the brain 18F-FDG PET scan, of a long-term condition for dementia in the NHDS and a dementia stage of cognitive impairment in the NAB. The criterion for the identification of a neurodegenerative disease in the medical records was the determination of LP biomarker levels. RESULTS Among the 403 patients (69.9 ± 11.4 years old, 177 women) from the initial cohort with data matched with the NHDS data, 137 were matched with the NAB data, and 61 were matched with LP biomarker data. Within three years of the scan, a 18F-FDG PET had negative predictive values of 85% for dementia conversion (according to the NHDS and NAB datasets) and 95% for the presence of LP neurodegeneration biomarkers. CONCLUSION A normal brain 18F-FDG PET scan can help rule out the risk of dementia conversion and the presence of cerebrospinal fluid (CSF) biomarker of neurodegeneration early with high certainty, allowing modifications to patient management regimens in the short term. TRIAL REGISTRATION Clinical Trials database (NCT04804722). March 18, 2021. Retrospectively registered.
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Affiliation(s)
- Sébastien Heyer
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, Université de Lorraine, CHRU Nancy, Nancy, F-54000, France
| | - Maïa Simon
- Department of Methodology, Promotion and Investigation, Université de Lorraine, CHRU-Nancy, Nancy, F-54000, France
| | - Matthieu Doyen
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, Université de Lorraine, CHRU Nancy, Nancy, F-54000, France
- Université de Lorraine, IADI, INSERM U1254, Nancy, F-54000, France
| | - Ali Mortada
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, Université de Lorraine, CHRU Nancy, Nancy, F-54000, France
| | - Véronique Roch
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, Université de Lorraine, CHRU Nancy, Nancy, F-54000, France
| | - Elodie Jeanbert
- Department of Methodology, Promotion and Investigation, Université de Lorraine, CHRU-Nancy, Nancy, F-54000, France
| | - Nathalie Thilly
- Department of Methodology, Promotion and Investigation, Université de Lorraine, CHRU-Nancy, Nancy, F-54000, France
| | - Catherine Malaplate
- Department of Biochemistry, Université de Lorraine, CHRU-Nancy, Nancy, F-54000, France
| | - Anna Kearney-Schwartz
- Department of Geriatrics, Université de Lorraine, CHRU-Nancy, Nancy, F-54000, France
- CMRR, University Hospital Nancy, Nancy, F-54000, France
| | - Thérèse Jonveaux
- CMRR, University Hospital Nancy, Nancy, F-54000, France
- Department of Neurology, University Hospital Nancy, Nancy, F-54000, France
| | - Aurélie Bannay
- Medical Assessment and Information Department, Université de Lorraine, CHRU-Nancy, Nancy, 54000, France
| | - Antoine Verger
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, Université de Lorraine, CHRU Nancy, Nancy, F-54000, France.
- Université de Lorraine, IADI, INSERM U1254, Nancy, F-54000, France.
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Lai PH, Hu RY, Huang X. Alterations in dynamic regional homogeneity within default mode network in patients with thyroid-associated ophthalmopathy. Neuroreport 2024; 35:702-711. [PMID: 38829952 DOI: 10.1097/wnr.0000000000002056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Thyroid-associated ophthalmopathy (TAO) is a significant autoimmune eye disease known for causing exophthalmos and substantial optic nerve damage. Prior investigations have solely focused on static functional MRI (fMRI) scans of the brain in TAO patients, neglecting the assessment of temporal variations in local brain activity. This study aimed to characterize alterations in dynamic regional homogeneity (dReHo) in TAO patients and differentiate between TAO patients and healthy controls using support vector machine (SVM) classification. Thirty-two patients with TAO and 32 healthy controls underwent resting-state fMRI scans. We calculated dReHo using sliding-window methods to evaluate changes in regional brain activity and compared these findings between the two groups. Subsequently, we employed SVM, a machine learning algorithm, to investigate the potential use of dReHo maps as diagnostic markers for TAO. Compared to healthy controls, individuals with active TAO demonstrated significantly higher dReHo values in the right angular gyrus, left precuneus, right inferior parietal as well as the left superior parietal gyrus. The SVM model demonstrated an accuracy ranging from 65.62 to 68.75% in distinguishing between TAO patients and healthy controls based on dReHo variability in these identified brain regions, with an area under the curve of 0.70 to 0.76. TAO patients showed increased dReHo in default mode network-related brain regions. The accuracy of classifying TAO patients and healthy controls based on dReHo was notably high. These results offer new insights for investigating the pathogenesis and clinical diagnostic classification of individuals with TAO.
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Affiliation(s)
- Ping-Hong Lai
- Department of Ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Rui-Yang Hu
- School of Ophthalmology and Optometry, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Xin Huang
- Department of Ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
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Cotta Ramusino M, Massa F, Festari C, Gandolfo F, Nicolosi V, Orini S, Nobili F, Frisoni GB, Morbelli S, Garibotto V. Diagnostic performance of molecular imaging methods in predicting the progression from mild cognitive impairment to dementia: an updated systematic review. Eur J Nucl Med Mol Imaging 2024; 51:1876-1890. [PMID: 38355740 DOI: 10.1007/s00259-024-06631-y] [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: 11/13/2023] [Accepted: 01/27/2024] [Indexed: 02/16/2024]
Abstract
PURPOSE Epidemiological and logistical reasons are slowing the clinical validation of the molecular imaging biomarkers in the initial stages of neurocognitive disorders. We provide an updated systematic review of the recent advances (2017-2022), highlighting methodological shortcomings. METHODS Studies reporting the diagnostic accuracy values of the molecular imaging techniques (i.e., amyloid-, tau-, [18F]FDG-PETs, DaT-SPECT, and cardiac [123I]-MIBG scintigraphy) in predicting progression from mild cognitive impairment (MCI) to dementia were selected according to the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) method and evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Main eligibility criteria were as follows: (1) ≥ 50 subjects with MCI, (2) follow-up ≥ 3 years, (3) gold standard: progression to dementia or diagnosis on pathology, and (4) measures of prospective accuracy. RESULTS Sensitivity (SE) and specificity (SP) in predicting progression to dementia, mainly to Alzheimer's dementia were 43-100% and 63-94% for [18F]FDG-PET and 64-94% and 48-93% for amyloid-PET. Longitudinal studies were lacking for less common disorders (Dementia with Lewy bodies-DLB and Frontotemporal lobe degeneration-FTLD) and for tau-PET, DaT-SPECT, and [123I]-MIBG scintigraphy. Therefore, the accuracy values from cross-sectional studies in a smaller sample of subjects (n > 20, also including mild dementia stage) were chosen as surrogate outcomes. DaT-SPECT showed 47-100% SE and 71-100% SP in differentiating Lewy body disease (LBD) from non-LBD conditions; tau-PET: 88% SE and 100% SP in differentiating DLB from Posterior Cortical Atrophy. [123I]-MIBG scintigraphy differentiated LBD from non-LBD conditions with 47-100% SE and 71-100% SP. CONCLUSION Molecular imaging has a moderate-to-good accuracy in predicting the progression of MCI to Alzheimer's dementia. Longitudinal studies are sparse in non-AD conditions, requiring additional efforts in these settings.
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Affiliation(s)
- Matteo Cotta Ramusino
- Unit of Behavior Neurology and Dementia Research Center, IRCCS Mondino Foundation, via Mondino 2, 27100, Pavia, Italy.
| | - Federico Massa
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Festari
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni Di Dio Fatebenefratelli, Brescia, Italy
| | - Federica Gandolfo
- Department of Geriatric Care, Orthogeriatrics and Rehabilitation, E.O. Galliera Hospital, Genoa, Italy
| | - Valentina Nicolosi
- UOC Neurologia Ospedale Magalini Di Villafranca Di Verona (VR) ULSS 9, Verona, Italy
| | - Stefania Orini
- Alzheimer's Unit-Memory Clinic, IRCCS Istituto Centro San Giovanni Di Dio Fatebenefratelli, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Flavio Nobili
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University and University Hospitals, Geneva, Switzerland
| | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, University Hospitals of Geneva, Geneva, Switzerland
- NIMTLab, Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Geneva, Switzerland
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Odusami M, Maskeliūnas R, Damaševičius R, Misra S. Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: a systematic review and meta-analysis. Cogn Neurodyn 2024; 18:775-794. [PMID: 38826669 PMCID: PMC11143094 DOI: 10.1007/s11571-023-09993-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/23/2023] [Accepted: 07/17/2023] [Indexed: 06/04/2024] Open
Abstract
In recent years, Alzheimer's disease (AD) has been a serious threat to human health. Researchers and clinicians alike encounter a significant obstacle when trying to accurately identify and classify AD stages. Several studies have shown that multimodal neuroimaging input can assist in providing valuable insights into the structural and functional changes in the brain related to AD. Machine learning (ML) algorithms can accurately categorize AD phases by identifying patterns and linkages in multimodal neuroimaging data using powerful computational methods. This study aims to assess the contribution of ML methods to the accurate classification of the stages of AD using multimodal neuroimaging data. A systematic search is carried out in IEEE Xplore, Science Direct/Elsevier, ACM DigitalLibrary, and PubMed databases with forward snowballing performed on Google Scholar. The quantitative analysis used 47 studies. The explainable analysis was performed on the classification algorithm and fusion methods used in the selected studies. The pooled sensitivity and specificity, including diagnostic efficiency, were evaluated by conducting a meta-analysis based on a bivariate model with the hierarchical summary receiver operating characteristics (ROC) curve of multimodal neuroimaging data and ML methods in the classification of AD stages. Wilcoxon signed-rank test is further used to statistically compare the accuracy scores of the existing models. With a 95% confidence interval of 78.87-87.71%, the combined sensitivity for separating participants with mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%; for separating participants with AD from NC, it was 94.60% (90.76%, 96.89%); for separating participants with progressive MCI (pMCI) from stable MCI (sMCI), it was 80.41% (74.73%, 85.06%). With a 95% confidence interval (78.87%, 87.71%), the Pooled sensitivity for distinguishing mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%, with a 95% confidence interval (90.76%, 96.89%), the Pooled sensitivity for distinguishing AD from NC was 94.60%, likewise (MCI) from healthy control (NC) participants was 83.77% progressive MCI (pMCI) from stable MCI (sMCI) was 80.41% (74.73%, 85.06%), and early MCI (EMCI) from NC was 86.63% (82.43%, 89.95%). Pooled specificity for differentiating MCI from NC was 79.16% (70.97%, 87.71%), AD from NC was 93.49% (91.60%, 94.90%), pMCI from sMCI was 81.44% (76.32%, 85.66%), and EMCI from NC was 85.68% (81.62%, 88.96%). The Wilcoxon signed rank test showed a low P-value across all the classification tasks. Multimodal neuroimaging data with ML is a promising future in classifying the stages of AD but more research is required to increase the validity of its application in clinical practice.
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Affiliation(s)
- Modupe Odusami
- Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania
| | | | - Sanjay Misra
- Department of Applied Data Science, Institute for Energy Technology, Halden, Norway
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Chattopadhyay T, Joshy NA, Ozarkar SS, Buwa KS, Feng Y, Laltoo E, Thomopoulos SI, Villalon-Reina JE, Joshi H, Venkatasubramanian G, John JP, Thompson PM. Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.04.578829. [PMID: 38370641 PMCID: PMC10871286 DOI: 10.1101/2024.02.04.578829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMRI) as an input to these models. Much research in this area focuses on specific datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), which assesses people of North American, largely European ancestry, so we examine how models trained on ADNI, generalize to a new population dataset from India (the NIMHANS cohort). We first benchmark our models by predicting 'brain age' - the task of predicting a person's chronological age from their MRI scan and proceed to AD classification. We also evaluate the benefit of using a 3D CycleGAN approach to harmonize the imaging datasets before training the CNN models. Our experiments show that classification performance improves after harmonization in most cases, as well as better performance for dMRI as input.
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Mai Y, Cao Z, Zhao L, Yu Q, Xu J, Liu W, Liu B, Tang J, Luo Y, Liao W, Fang W, Ruan Y, Lei M, Mok VCT, Shi L, Liu J. The role of visual rating and automated brain volumetry in early detection and differential diagnosis of Alzheimer's disease. CNS Neurosci Ther 2024; 30:e14492. [PMID: 37864441 PMCID: PMC11017425 DOI: 10.1111/cns.14492] [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: 12/15/2022] [Revised: 09/07/2023] [Accepted: 09/26/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Medial temporal lobe atrophy (MTA) is a diagnostic marker for mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the accuracy of quantitative MTA (QMTA) in diagnosing early AD is unclear. This study aimed to investigate the accuracy of QMTA and its related components (inferior lateral ventricle [ILV] and hippocampus) with MTA in the early diagnosis of MCI and AD. METHODS This study included four groups: normal (NC), MCI stable (MCIs), MCI converted to AD (MCIs), and mild AD (M-AD) groups. Magnetic resonance image analysis software was used to quantify the hippocampus, ILV, and QMTA. MTA was rated by two experienced neurologists. Receiver operating characteristic area under the curve (AUC) analysis was performed to compare their capability in differentiating AD from NC and MCI, and optimal thresholds were determined using the Youden index. RESULTS QMTA distinguished M-AD from NC and MCI with higher diagnostic accuracy than MTA, hippocampus, and ILV (AUCNC = 0.976, AUCMCI = 0.836, AUCMCIs = 0.894, AUCMCIc = 0.730). The diagnostic accuracy of QMTA was superior to that of MTA, the hippocampus, and ILV in differentiating MCI from AD. The diagnostic accuracy of QMTA was found to remain the best across age, sex, and pathological subgroups analyzed. The sensitivity (92.45%) and specificity (90.64%) were higher in this study when a cutoff value of 0.635 was chosen for QMTA. CONCLUSIONS QMTA may be a better choice than the MTA scale or the associated quantitative components alone in identifying AD patients and MCI individuals with higher progression risk.
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Affiliation(s)
- Yingren Mai
- Department of NeurologyThe Second Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Zhiyu Cao
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Lei Zhao
- BrainNow Research InstituteShenzhenChina
| | - Qun Yu
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Jiaxin Xu
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Wenyan Liu
- BrainNow Research InstituteShenzhenChina
| | - Bowen Liu
- Department of Statistics, College of Liberal Art and SciencesUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
| | - Jingyi Tang
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Yishan Luo
- BrainNow Research InstituteShenzhenChina
| | - Wang Liao
- Department of NeurologyThe Second Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Wenli Fang
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Yuting Ruan
- Department of RehabilitationThe Second Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Ming Lei
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Vincent C. T. Mok
- BrainNow Research InstituteShenzhenChina
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative MedicineThe Chinese University of Hong KongHong Kong, SARChina
| | - Lin Shi
- BrainNow Research InstituteShenzhenChina
- Department of Imaging and Interventional RadiologyThe Chinese University of Hong KongHong Kong, SARChina
| | - Jun Liu
- Department of NeurologyThe Second Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
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Chen Z, Chen K, Li Y, Geng D, Li X, Liang X, Lu H, Ding S, Xiao Z, Ma X, Zheng L, Ding D, Zhao Q, Yang L. Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter-cohort validation of Shanghai Memory Study and ADNI. Hum Brain Mapp 2024; 45:e26529. [PMID: 37991144 PMCID: PMC10789213 DOI: 10.1002/hbm.26529] [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: 09/30/2022] [Revised: 10/06/2023] [Accepted: 10/23/2023] [Indexed: 11/23/2023] Open
Abstract
Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter-cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study-specific imaging indices. We proposed a novel framework for inter-cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting-state functional MRI (fMRI) between MCI converters (MCI_C) and non-converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3-year follow-up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross-validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter-network hypo-connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging-based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi-modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter-cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.
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Affiliation(s)
- Zhihan Chen
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Academy for Engineering & TechnologyFudan UniversityShanghaiChina
| | - Keliang Chen
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Yuxin Li
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
| | - Daoying Geng
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Academy for Engineering & TechnologyFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
| | - Xiantao Li
- Department of Critical Care MedicineHuashan Hospital, Fudan UniversityShanghaiChina
| | - Xiaoniu Liang
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Huimeng Lu
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Saineng Ding
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Zhenxu Xiao
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Xiaoxi Ma
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Li Zheng
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Ding Ding
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Qianhua Zhao
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
- National Center for Neurological DisordersHuashan Hospital, Fudan UniversityShanghaiChina
- MOE Frontiers Center for Brain ScienceFudan UniversityShanghaiChina
- National Clinical Research Center for Aging and MedicineHuashan Hospital, Fudan UniversityShanghaiChina
| | - Liqin Yang
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
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11
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Hassouneh A, Bazuin B, Danna-dos-Santos A, Acar I, Abdel-Qader I. Feature Importance Analysis and Machine Learning for Alzheimer's Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio. Digit Biomark 2024; 8:59-74. [PMID: 38650695 PMCID: PMC11034932 DOI: 10.1159/000538486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 03/10/2024] [Indexed: 04/25/2024] Open
Abstract
Introduction Alzheimer's disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the USA, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention as the neurodegenerative process often starts 15-20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D magnetic resonance imaging hippocampal and entorhinal cortex and standardized uptake value ratio (SUVR) derived from positron emission tomography (PET) images. Methods To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification, Linear Discriminant Analysis, Logistic Regression, and Logistic Regression Classifier with Stochastic Gradient Descent Learning). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes, AD-negative (or mild cognitive impairment stable [MCIs]) and AD-positive (or MCI conversion [MCIc]), using a probabilistic neural network classifier and the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the receiver operating characteristic curves validate the GLCMs' superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single feature category. Conclusion Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer's detection.
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Affiliation(s)
- Aya Hassouneh
- Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI, USA
| | - Bradley Bazuin
- Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI, USA
| | | | - Ilgin Acar
- Department of Industrial and Entrepreneurial Engineering and Engineering Management, Western Michigan University, Kalamazoo, MI, USA
| | - Ikhlas Abdel-Qader
- Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI, USA
| | - The Alzheimer’s Disease Neuroimaging Initiative
- Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI, USA
- Department of Physical Therapy, Western Michigan University, Kalamazoo, MI, USA
- Department of Industrial and Entrepreneurial Engineering and Engineering Management, Western Michigan University, Kalamazoo, MI, USA
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12
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Camargo-Marín L, Guzmán-Huerta M, Piña-Ramirez O, Perez-Gonzalez J. Multimodal Early Birth Weight Prediction Using Multiple Kernel Learning. SENSORS (BASEL, SWITZERLAND) 2023; 24:2. [PMID: 38202864 PMCID: PMC10780741 DOI: 10.3390/s24010002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/08/2023] [Accepted: 12/14/2023] [Indexed: 01/12/2024]
Abstract
In this work, a novel multimodal learning approach for early prediction of birth weight is presented. Fetal weight is one of the most relevant indicators in the assessment of fetal health status. The aim is to predict early birth weight using multimodal maternal-fetal variables from the first trimester of gestation (Anthropometric data, as well as metrics obtained from Fetal Biometry, Doppler and Maternal Ultrasound). The proposed methodology starts with the optimal selection of a subset of multimodal features using an ensemble-based approach of feature selectors. Subsequently, the selected variables feed the nonparametric Multiple Kernel Learning regression algorithm. At this stage, a set of kernels is selected and weighted to maximize performance in birth weight prediction. The proposed methodology is validated and compared with other computational learning algorithms reported in the state of the art. The obtained results (absolute error of 234 g) suggest that the proposed methodology can be useful as a tool for the early evaluation and monitoring of fetal health status through indicators such as birth weight.
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Affiliation(s)
- Lisbeth Camargo-Marín
- Departamento de Medicina Traslacional, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Montes Urales 800, Lomas de Virreyes, Miguel Hidalgo, Mexico City 11000, Mexico; (L.C.-M.); (M.G.-H.)
| | - Mario Guzmán-Huerta
- Departamento de Medicina Traslacional, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Montes Urales 800, Lomas de Virreyes, Miguel Hidalgo, Mexico City 11000, Mexico; (L.C.-M.); (M.G.-H.)
| | - Omar Piña-Ramirez
- Departamento de Bioinformática y Análisis Estadístico, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Montes Urales 800, Lomas de Virreyes, Miguel Hidalgo, Mexico City 11000, Mexico;
| | - Jorge Perez-Gonzalez
- Unidad Académica del Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Km 4.5 Carretera Mérida-Tetiz, Municipio de Ucú, Yucatán 97357, Mexico
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13
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Qiang YR, Zhang SW, Li JN, Li Y, Zhou QY. Diagnosis of Alzheimer's disease by joining dual attention CNN and MLP based on structural MRIs, clinical and genetic data. Artif Intell Med 2023; 145:102678. [PMID: 37925204 DOI: 10.1016/j.artmed.2023.102678] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 07/12/2023] [Accepted: 10/03/2023] [Indexed: 11/06/2023]
Abstract
Alzheimer's disease (AD) is an irreversible central nervous degenerative disease, while mild cognitive impairment (MCI) is a precursor state of AD. Accurate early diagnosis of AD is conducive to the prevention and early intervention treatment of AD. Although some computational methods have been developed for AD diagnosis, most employ only neuroimaging, ignoring other data (e.g., genetic, clinical) that may have potential disease information. In addition, the results of some methods lack interpretability. In this work, we proposed a novel method (called DANMLP) of joining dual attention convolutional neural network (CNN) and multilayer perceptron (MLP) for computer-aided AD diagnosis by integrating multi-modality data of the structural magnetic resonance imaging (sMRI), clinical data (i.e., demographics, neuropsychology), and APOE genetic data. Our DANMLP consists of four primary components: (1) the Patch-CNN for extracting the image characteristics from each local patch, (2) the position self-attention block for capturing the dependencies between features within a patch, (3) the channel self-attention block for capturing dependencies of inter-patch features, (4) two MLP networks for extracting the clinical features and outputting the AD classification results, respectively. Compared with other state-of-the-art methods in the 5CV test, DANMLP achieves 93% and 82.4% classification accuracy for the AD vs. MCI and MCI vs. NC tasks on the ADNI database, which is 0.2%∼15.2% and 3.4%∼26.8% higher than that of other five methods, respectively. The individualized visualization of focal areas can also help clinicians in the early diagnosis of AD. These results indicate that DANMLP can be effectively used for diagnosing AD and MCI patients.
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Affiliation(s)
- Yan-Rui Qiang
- Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Jia-Ni Li
- Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Yan Li
- Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Qin-Yi Zhou
- Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
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14
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Zhao R, Wang P, Liu L, Zhang F, Hu P, Wen J, Li H, Biswal BB. Whole-brain structure-function coupling abnormalities in mild cognitive impairment: a study combining amplitude of low-frequency fluctuations and voxel-based morphometry. Front Neurosci 2023; 17:1236221. [PMID: 37583417 PMCID: PMC10424122 DOI: 10.3389/fnins.2023.1236221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/12/2023] [Indexed: 08/17/2023] Open
Abstract
Alzheimer's disease (AD), one of the leading diseases of the nervous system, is accompanied by symptoms such as loss of memory, thinking and language skills. Both mild cognitive impairment (MCI) and very mild cognitive impairment (VMCI) are the transitional pathological stages between normal aging and AD. While the changes in whole-brain structural and functional information have been extensively investigated in AD, The impaired structure-function coupling remains unknown. The current study employed the OASIS-3 dataset, which includes 53 MCI, 90 VMCI, and 100 Age-, gender-, and education-matched normal controls (NC). Several structural and functional parameters, such as the amplitude of low-frequency fluctuations (ALFF), voxel-based morphometry (VBM), and The ALFF/VBM ratio, were used To estimate The whole-brain neuroimaging changes In MCI, VMCI, and NC. As disease symptoms became more severe, these regions, distributed in the frontal-inf-orb, putamen, and paracentral lobule in the white matter (WM), exhibited progressively increasing ALFF (ALFFNC < ALFFVMCI < ALFFMCI), which was similar to the tendency for The cerebellum and putamen in the gray matter (GM). Additionally, as symptoms worsened in AD, the cuneus/frontal lobe in the WM and the parahippocampal gyrus/hippocampus in the GM showed progressively decreasing structure-function coupling. As the typical focal areas in AD, The parahippocampal gyrus and hippocampus showed significant positive correlations with the severity of cognitive impairment, suggesting the important applications of the ALFF/VBM ratio in brain disorders. On the other hand, these findings from WM functional signals provided a novel perspective for understanding the pathophysiological mechanisms involved In cognitive decline in AD.
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Affiliation(s)
- Rong Zhao
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Pan Wang
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Lin Liu
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Fanyu Zhang
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Hu
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiaping Wen
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongyi Li
- The Fourth People’s Hospital of Chengdu, Chengdu, China
| | - Bharat B. Biswal
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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15
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Chattopadhyay T, Ozarkar SS, Buwa K, Thomopoulos SI, Thompson PM. Predicting Brain Amyloid Positivity from T1 weighted brain MRI and MRI-derived Gray Matter, White Matter and CSF maps using Transfer Learning on 3D CNNs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.15.528705. [PMID: 36824826 PMCID: PMC9949045 DOI: 10.1101/2023.02.15.528705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer's disease and practical tests could help identify patients who could respond to treatment, now that promising anti-amyloid drugs are available. Even so, Aβ positivity (Aβ+) is assessed using PET or CSF assays, both highly invasive procedures. Here, we investigate how well Aβ+ can be predicted from T1 weighted brain MRI and gray matter, white matter and cerebrospinal fluid segmentations from T1-weighted brain MRI (T1w), a less invasive alternative. We used 3D convolutional neural networks to predict Aβ+ based on 3D brain MRI data, from 762 elderly subjects (mean age: 75.1 yrs. ± 7.6SD; 394F/368M; 459 healthy controls, 67 with MCI and 236 with dementia) scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We also tested whether the accuracy increases when using transfer learning from the larger UK Biobank dataset. Overall, the 3D CNN predicted Aβ+ with 76% balanced accuracy from T1w scans. The closest performance to this was using white matter maps alone when the model was pre-trained on an age prediction in the UK Biobank. The performance of individual tissue maps was less than the T1w, but transfer learning helped increase the accuracy. Although tests on more diverse data are warranted, deep learned models from standard MRI show initial promise for Aβ+ estimation, before considering more invasive procedures.
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Affiliation(s)
- Tamoghna Chattopadhyay
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Saket S Ozarkar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Ketaki Buwa
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
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16
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OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer's Disease Using Resting-State fMRI and Structural MRI Data. Brain Sci 2023; 13:brainsci13020260. [PMID: 36831803 PMCID: PMC9954686 DOI: 10.3390/brainsci13020260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 01/19/2023] [Accepted: 02/01/2023] [Indexed: 02/09/2023] Open
Abstract
Advances in applied machine learning techniques for neuroimaging have encouraged scientists to implement models to diagnose brain disorders such as Alzheimer's disease at early stages. Predicting the exact stage of Alzheimer's disease is challenging; however, complex deep learning techniques can precisely manage this. While successful, these complex architectures are difficult to interrogate and computationally expensive. Therefore, using novel, simpler architectures with more efficient pattern extraction capabilities, such as transformers, is of interest to neuroscientists. This study introduced an optimized vision transformer architecture to predict the group membership by separating healthy adults, mild cognitive impairment, and Alzheimer's brains within the same age group (>75 years) using resting-state functional (rs-fMRI) and structural magnetic resonance imaging (sMRI) data aggressively preprocessed by our pipeline. Our optimized architecture, known as OViTAD is currently the sole vision transformer-based end-to-end pipeline and outperformed the existing transformer models and most state-of-the-art solutions. Our model achieved F1-scores of 97%±0.0 and 99.55%±0.39 from the testing sets for the rs-fMRI and sMRI modalities in the triple-class prediction experiments. Furthermore, our model reached these performances using 30% fewer parameters than a vanilla transformer. Furthermore, the model was robust and repeatable, producing similar estimates across three runs with random data splits (we reported the averaged evaluation metrics). Finally, to challenge the model, we observed how it handled increasing noise levels by inserting varying numbers of healthy brains into the two dementia groups. Our findings suggest that optimized vision transformers are a promising and exciting new approach for neuroimaging applications, especially for Alzheimer's disease prediction.
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17
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Li L, Yu X, Sheng C, Jiang X, Zhang Q, Han Y, Jiang J. A review of brain imaging biomarker genomics in Alzheimer’s disease: implementation and perspectives. Transl Neurodegener 2022; 11:42. [PMID: 36109823 PMCID: PMC9476275 DOI: 10.1186/s40035-022-00315-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022] Open
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disease with phenotypic changes closely associated with both genetic variants and imaging pathology. Brain imaging biomarker genomics has been developed in recent years to reveal potential AD pathological mechanisms and provide early diagnoses. This technique integrates multimodal imaging phenotypes with genetic data in a noninvasive and high-throughput manner. In this review, we summarize the basic analytical framework of brain imaging biomarker genomics and elucidate two main implementation scenarios of this technique in AD studies: (1) exploring novel biomarkers and seeking mutual interpretability and (2) providing a diagnosis and prognosis for AD with combined use of machine learning methods and brain imaging biomarker genomics. Importantly, we highlight the necessity of brain imaging biomarker genomics, discuss the strengths and limitations of current methods, and propose directions for development of this research field.
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18
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Li K, Shu Y, Liu X, Xie W, Li P, Kong L, Yu P, Zeng Y, Huang L, Long T, Zeng L, Li H, Peng D. Dynamic regional homogeneity alterations and cognitive impairment in patients with moderate and severe obstructive sleep apnea. Front Neurosci 2022; 16:940721. [PMID: 36090274 PMCID: PMC9459312 DOI: 10.3389/fnins.2022.940721] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background and purposePrevious studies have found that abnormal local spontaneous brain activity in patients with obstructive sleep apnea (OSA) was associated with cognitive impairment, and dynamic functional connections can capture the time changes of functional connections during magnetic resonance imaging acquisition. The purpose of this study was to investigate the dynamic characteristics of regional brain connectivity and its relationship with cognitive function in patients with OSA and to explore whether the dynamic changes can be used to distinguish them from healthy controls (HCs).MethodsSeventy-nine moderate and severe male OSA patients without any treatment and 84 HCs with similar age and education were recruited, and clinical data and resting functional magnetic resonance imaging data were collected. The dynamic regional homogeneity (dReHo) was calculated using a sliding window technique, and a double-sample t-test was used to test the difference in the dReHo map between OSA patients and HCs. We explored the relationship between dReHo and clinical and cognitive function in OSA patients using Pearson correlation analysis. A support vector machine was used to classify the OSA patients and HCs based on abnormal dReHo.ResultCompared with HCs, OSA patients exhibited higher dReHo values in the right medial frontal gyrus and significantly lower dReHo values in the right putamen, right superior temporal gyrus, right cingulate gyrus, left insula and left precuneus. The correlation analysis showed that the abnormal dReHo values in multiple brain regions in patients with OSA were significantly correlated with nadir oxygen saturation, the oxygen depletion index, sleep period time, and Montreal cognitive assessment score. The support vector machine classification accuracy based on the dReHo difference in brain regions was 81.60%, precision was 81.01%, sensitivity was 81.01%, specificity was 82.14%, and area under the curve was 0.89.ConclusionThe results of this study suggested that there was abnormal dynamic regional spontaneous brain activity in patients with OSA, which was related to clinical and cognitive evaluation and can be used to distinguish OSA patients from HCs. The dReHo is a potential objective neuroimaging marker for patients with OSA that can further the understanding of the neuropathological mechanism of patients with OSA.
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Affiliation(s)
- Kunyao Li
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yongqiang Shu
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiang Liu
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wei Xie
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Panmei Li
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Linghong Kong
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Pengfei Yu
- Science and Technology Division, Big Data Research Center, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yaping Zeng
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ling Huang
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ting Long
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Li Zeng
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Haijun Li
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
- PET Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Haijun Li,
| | - Dechang Peng
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
- PET Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
- *Correspondence: Dechang Peng,
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Kikuchi M, Kobayashi K, Itoh S, Kasuga K, Miyashita A, Ikeuchi T, Yumoto E, Kosaka Y, Fushimi Y, Takeda T, Manabe S, Hattori S, Disease Neuroimaging Initiative A, Nakaya A, Kamijo K, Matsumura Y. Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer’s disease using multimodal data. Comput Struct Biotechnol J 2022; 20:5296-5308. [PMID: 36212530 PMCID: PMC9513733 DOI: 10.1016/j.csbj.2022.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 08/03/2022] [Accepted: 08/03/2022] [Indexed: 11/27/2022] Open
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Meng X, Liu J, Fan X, Bian C, Wei Q, Wang Z, Liu W, Jiao Z. Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease. Front Aging Neurosci 2022; 14:911220. [PMID: 35651528 PMCID: PMC9149574 DOI: 10.3389/fnagi.2022.911220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 04/19/2022] [Indexed: 11/29/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source resolution and has poor spatiotemporal sensitivity. In this study, we proposed a novel multi-modal LassoNet framework with a neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups prove that our proposed framework outperforms well in classification performance, generalization, and reproducibility. Also, we found discriminative brain regions, such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research, and the experimental study demonstrates that the framework will further improve our understanding of the mechanisms underlying the development of AD.
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Affiliation(s)
- Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Junlong Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Xiang Fan
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Chenyuan Bian
- Shandong Provincial Key Laboratory of Digital Medicine and Computer-Assisted Surgery, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qingpeng Wei
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Ziwei Wang
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
- *Correspondence: Wenjie Liu,
| | - Zhuqing Jiao
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
- Zhuqing Jiao,
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21
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Tabarestani S, Eslami M, Cabrerizo M, Curiel RE, Barreto A, Rishe N, Vaillancourt D, DeKosky ST, Loewenstein DA, Duara R, Adjouadi M. A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer's Disease. Front Aging Neurosci 2022; 14:810873. [PMID: 35601611 PMCID: PMC9120529 DOI: 10.3389/fnagi.2022.810873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
With the advances in machine learning for the diagnosis of Alzheimer's disease (AD), most studies have focused on either identifying the subject's status through classification algorithms or on predicting their cognitive scores through regression methods, neglecting the potential association between these two tasks. Motivated by the need to enhance the prospects for early diagnosis along with the ability to predict future disease states, this study proposes a deep neural network based on modality fusion, kernelization, and tensorization that perform multiclass classification and longitudinal regression simultaneously within a unified multitask framework. This relationship between multiclass classification and longitudinal regression is found to boost the efficacy of the final model in dealing with both tasks. Different multimodality scenarios are investigated, and complementary aspects of the multimodal features are exploited to simultaneously delineate the subject's label and predict related cognitive scores at future timepoints using baseline data. The main intent in this multitask framework is to consolidate the highest accuracy possible in terms of precision, sensitivity, F1 score, and area under the curve (AUC) in the multiclass classification task while maintaining the highest similarity in the MMSE score as measured through the correlation coefficient and the RMSE for all time points under the prediction task, with both tasks, run simultaneously under the same set of hyperparameters. The overall accuracy for multiclass classification of the proposed KTMnet method is 66.85 ± 3.77. The prediction results show an average RMSE of 2.32 ± 0.52 and a correlation of 0.71 ± 5.98 for predicting MMSE throughout the time points. These results are compared to state-of-the-art techniques reported in the literature. A discovery from the multitasking of this consolidated machine learning framework is that a set of hyperparameters that optimize the prediction results may not necessarily be the same as those that would optimize the multiclass classification. In other words, there is a breakpoint beyond which enhancing further the results of one process could lead to the downgrading in accuracy for the other.
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Affiliation(s)
- Solale Tabarestani
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
| | - Mohammad Eslami
- Harvard Ophthalmology AI Lab and Harvard Medical School, Schepens Eye Research Institute, Massachusetts Eye and Ear, Boston, MA, United States
| | - Mercedes Cabrerizo
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
| | - Rosie E. Curiel
- Center for Cognitive Neuroscience and Aging, Psychiatry and Behavioral Sciences, University of Miami School of Medicine, Miami, FL, United States
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
| | - Armando Barreto
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
| | - Naphtali Rishe
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
| | - David Vaillancourt
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
- Department of Neurology, University of Florida, Gainesville, FL, United States
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | - Steven T. DeKosky
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
- Department of Neurology, University of Florida, Gainesville, FL, United States
| | - David A. Loewenstein
- Center for Cognitive Neuroscience and Aging, Psychiatry and Behavioral Sciences, University of Miami School of Medicine, Miami, FL, United States
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, United States
| | - Ranjan Duara
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, United States
| | - Malek Adjouadi
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
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22
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Ma H, Huang G, Li M, Han Y, Sun J, Zhan L, Wang Q, Jia X, Han X, Li H, Song Y, Lv Y. The Predictive Value of Dynamic Intrinsic Local Metrics in Transient Ischemic Attack. Front Aging Neurosci 2022; 13:808094. [PMID: 35221984 PMCID: PMC8868122 DOI: 10.3389/fnagi.2021.808094] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/30/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Transient ischemic attack (TIA) is known as "small stroke." However, the diagnosis of TIA is currently difficult due to the transient symptoms. Therefore, objective and reliable biomarkers are urgently needed in clinical practice. OBJECTIVE The purpose of this study was to investigate whether dynamic alterations in resting-state local metrics could differentiate patients with TIA from healthy controls (HCs) using the support-vector machine (SVM) classification method. METHODS By analyzing resting-state functional MRI (rs-fMRI) data from 48 patients with and 41 demographically matched HCs, we compared the group differences in three dynamic local metrics: dynamic amplitude of low-frequency fluctuation (d-ALFF), dynamic fractional amplitude of low-frequency fluctuation (d-fALFF), and dynamic regional homogeneity (d-ReHo). Furthermore, we selected the observed alterations in three dynamic local metrics as classification features to distinguish patients with TIA from HCs through SVM classifier. RESULTS We found that TIA was associated with disruptions in dynamic local intrinsic brain activities. Compared with HCs, the patients with TIA exhibited increased d-fALFF, d-fALFF, and d-ReHo in vermis, right calcarine, right middle temporal gyrus, opercular part of right inferior frontal gyrus, left calcarine, left occipital, and left temporal and cerebellum. These alternations in the dynamic local metrics exhibited an accuracy of 80.90%, sensitivity of 77.08%, specificity of 85.37%, precision of 86.05%, and area under curve of 0.8501 for distinguishing the patients from HCs. CONCLUSION Our findings may provide important evidence for understanding the neuropathology underlying TIA and strong support for the hypothesis that these local metrics have potential value in clinical diagnosis.
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Affiliation(s)
- Huibin Ma
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
- Integrated Medical School, Jiamusi University, Jiamusi, China
| | - Guofeng Huang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Mengting Li
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Yu Han
- Department of Neurology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
- Department of Neurology, Anshan Changda Hospital, Anshan, China
| | - Jiawei Sun
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Linlin Zhan
- Faculty of Western Languages, Heilongjiang University, Harbin, China
| | - Qianqian Wang
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
| | - Xize Jia
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Xiujie Han
- Department of Neurology, Anshan Changda Hospital, Anshan, China
| | - Huayun Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
| | - Yulin Song
- Department of Neurology, Anshan Changda Hospital, Anshan, China
| | - Yating Lv
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
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23
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Zhang T, Liao Q, Zhang D, Zhang C, Yan J, Ngetich R, Zhang J, Jin Z, Li L. Predicting MCI to AD Conversation Using Integrated sMRI and rs-fMRI: Machine Learning and Graph Theory Approach. Front Aging Neurosci 2021; 13:688926. [PMID: 34421570 PMCID: PMC8375594 DOI: 10.3389/fnagi.2021.688926] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/23/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Graph theory and machine learning have been shown to be effective ways of classifying different stages of Alzheimer's disease (AD). Most previous studies have only focused on inter-subject classification with single-mode neuroimaging data. However, whether this classification can truly reflect the changes in the structure and function of the brain region in disease progression remains unverified. In the current study, we aimed to evaluate the classification framework, which combines structural Magnetic Resonance Imaging (sMRI) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) metrics, to distinguish mild cognitive impairment non-converters (MCInc)/AD from MCI converters (MCIc) by using graph theory and machine learning. METHODS With the intra-subject (MCInc vs. MCIc) and inter-subject (MCIc vs. AD) design, we employed cortical thickness features, structural brain network features, and sub-frequency (full-band, slow-4, slow-5) functional brain network features for classification. Three feature selection methods [random subset feature selection algorithm (RSFS), minimal redundancy maximal relevance (mRMR), and sparse linear regression feature selection algorithm based on stationary selection (SS-LR)] were used respectively to select discriminative features in the iterative combinations of MRI and network measures. Then support vector machine (SVM) classifier with nested cross-validation was employed for classification. We also compared the performance of multiple classifiers (Random Forest, K-nearest neighbor, Adaboost, SVM) and verified the reliability of our results by upsampling. RESULTS We found that in the classifications of MCIc vs. MCInc, and MCIc vs. AD, the proposed RSFS algorithm achieved the best accuracies (84.71, 89.80%) than the other algorithms. And the high-sensitivity brain regions found with the two classification groups were inconsistent. Specifically, in MCIc vs. MCInc, the high-sensitivity brain regions associated with both structural and functional features included frontal, temporal, caudate, entorhinal, parahippocampal, and calcarine fissure and surrounding cortex. While in MCIc vs. AD, the high-sensitivity brain regions associated only with functional features included frontal, temporal, thalamus, olfactory, and angular. CONCLUSIONS These results suggest that our proposed method could effectively predict the conversion of MCI to AD, and the inconsistency of specific brain regions provides a novel insight for clinical AD diagnosis.
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Affiliation(s)
| | | | | | | | | | | | | | - Zhenlan Jin
- Key Laboratory for NeuroInformation of Ministry of Education, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ling Li
- Key Laboratory for NeuroInformation of Ministry of Education, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
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24
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Perez-Gonzalez J, Jiménez-Ángeles L, Rojas Saavedra K, Barbará Morales E, Medina-Bañuelos V. Mild cognitive impairment classification using combined structural and diffusion imaging biomarkers. Phys Med Biol 2021; 66. [PMID: 34167090 DOI: 10.1088/1361-6560/ac0e77] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 06/24/2021] [Indexed: 11/11/2022]
Abstract
Alzheimer's disease is a multifactorial neurodegenerative disorder preceded by a prodromal stage called mild cognitive impairment (MCI). Early diagnosis of MCI is crucial for delaying the progression and optimizing the treatment. In this study we propose a random forest (RF) classifier to distinguish between MCI and healthy control subjects (HC), identifying the most relevant features computed from structural T1-weighted and diffusion-weighted magnetic resonance images (sMRI and DWI), combined with neuro-psychological scores. To train the RF we used a set of 60 subjects (HC = 30, MCI = 30) drawn from the Alzheimer's disease neuroimaging initiative database, while testing with unseen data was carried out on a 23-subjects Mexican cohort (HC = 12, MCI = 11). Features from hippocampus, thalamus and amygdala, for left and right hemispheres were fed to the RF, with the most relevant being previously selected by applying extra trees classifier and the mean decrease in impurity index. All the analyzed brain structures presented changes in sMRI and DWI features for MCI, but those computed from sMRI contribute the most to distinguish from HC. However, sMRI+DWI improves classification performance in training area under the receiver operating characteristic curve (AUROC = 93.5 ± 8%, accuracy = 88.8 ± 9%) and testing with unseen data (AUROC = 93.79%, accuracy = 91.3%), having a better performance when neuro-psychological scores were included. Compared to other classifiers the proposed RF provide the best performance for HC/MCI discrimination and the application of a feature selection step improves its performance. These findings imply that multimodal analysis gives better results than unimodal analysis and hence may be a useful tool to assist in early MCI diagnosis.
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Affiliation(s)
- Jorge Perez-Gonzalez
- Unidad Académica del Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas en el Estado de Yucatán, UNAM, Yucatán, México
| | - Luis Jiménez-Ángeles
- Department of Biomedical Systems Engineering, Engineering Faculty, UNAM, Mexico City, México
| | - Karla Rojas Saavedra
- Health Sciences Department, Universidad del Valle de México, Mexico City, México
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25
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Wang S, Rao J, Yue Y, Xue C, Hu G, Qi W, Ma W, Ge H, Zhang F, Zhang X, Chen J. Altered Frequency-Dependent Brain Activation and White Matter Integrity Associated With Cognition in Characterizing Preclinical Alzheimer's Disease Stages. Front Hum Neurosci 2021; 15:625232. [PMID: 33664660 PMCID: PMC7921321 DOI: 10.3389/fnhum.2021.625232] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 01/06/2021] [Indexed: 01/21/2023] Open
Abstract
Background Subjective cognitive decline (SCD), non-amnestic mild cognitive impairment (naMCI), and amnestic mild cognitive impairment (aMCI) are regarded to be at high risk of converting to Alzheimer's disease (AD). Amplitude of low-frequency fluctuations (ALFF) can reflect functional deterioration while diffusion tensor imaging (DTI) is capable of detecting white matter integrity. Our study aimed to investigate the structural and functional alterations to further reveal convergence and divergence among SCD, naMCI, and aMCI and how these contribute to cognitive deterioration. Methods We analyzed ALFF under slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) bands and white matter fiber integrity among normal controls (CN), SCD, naMCI, and aMCI groups. Correlation analyses were further utilized among paired DTI alteration, ALFF deterioration, and cognitive decline. Results For ALFF calculation, ascended ALFF values were detected in the lingual gyrus (LING) and superior frontal gyrus (SFG) within SCD and naMCI patients, respectively. Descended ALFF values were presented mainly in the LING, SFG, middle frontal gyrus, and precuneus in aMCI patients compared to CN, SCD, and naMCI groups. For DTI analyses, white matter alterations were detected within the uncinate fasciculus (UF) in aMCI patients and within the superior longitudinal fasciculus (SLF) in naMCI patients. SCD patients presented alterations in both fasciculi. Correlation analyses revealed that the majority of these structural and functional alterations were associated with complicated cognitive decline. Besides, UF alterations were correlated with ALFF deterioration in the SFG within aMCI patients. Conclusions SCD shares structurally and functionally deteriorative characteristics with aMCI and naMCI, and tends to convert to either of them. Furthermore, abnormalities in white matter fibers may be the structural basis of abnormal brain activation in preclinical AD stages. Combined together, it suggests that structural and functional integration may characterize the preclinical AD progression.
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Affiliation(s)
- Siyu Wang
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Jiang Rao
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yingying Yue
- Department of Psychosomatics and Psychiatry, The Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Chen Xue
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenying Ma
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Fuquan Zhang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangrong Zhang
- Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Fourth Clinical College of Nanjing Medical University, Nanjing, China
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