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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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2
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Punzi M, Sestieri C, Picerni E, Chiarelli AM, Padulo C, Delli Pizzi A, Tullo MG, Tosoni A, Granzotto A, Della Penna S, Onofrj M, Ferretti A, Delli Pizzi S, Sensi SL. Atrophy of hippocampal subfields and amygdala nuclei in subjects with mild cognitive impairment progressing to Alzheimer's disease. Heliyon 2024; 10:e27429. [PMID: 38509925 PMCID: PMC10951508 DOI: 10.1016/j.heliyon.2024.e27429] [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: 09/18/2023] [Revised: 02/27/2024] [Accepted: 02/28/2024] [Indexed: 03/22/2024] Open
Abstract
The hippocampus and amygdala are the first brain regions to show early signs of Alzheimer's Disease (AD) pathology. AD is preceded by a prodromal stage known as Mild Cognitive Impairment (MCI), a crucial crossroad in the clinical progression of the disease. The topographical development of AD has been the subject of extended investigation. However, it is still largely unknown how the transition from MCI to AD affects specific hippocampal and amygdala subregions. The present study is set to answer that question. We analyzed data from 223 subjects: 75 healthy controls, 52 individuals with MCI, and 96 AD patients obtained from the ADNI. The MCI group was further divided into two subgroups depending on whether individuals in the 48 months following the diagnosis either remained stable (N = 21) or progressed to AD (N = 31). A MANCOVA test evaluated group differences in the volume of distinct amygdala and hippocampal subregions obtained from magnetic resonance images. Subsequently, a stepwise linear discriminant analysis (LDA) determined which combination of magnetic resonance imaging parameters was most effective in predicting the conversion from MCI to AD. The predictive performance was assessed through a Receiver Operating Characteristic analysis. AD patients displayed widespread subregional atrophy. MCI individuals who progressed to AD showed selective atrophy of the hippocampal subiculum and tail compared to stable MCI individuals, who were undistinguishable from healthy controls. Converter MCI showed atrophy of the amygdala's accessory basal, central, and cortical nuclei. The LDA identified the hippocampal subiculum and the amygdala's lateral and accessory basal nuclei as significant predictors of MCI conversion to AD. The analysis returned a sensitivity value of 0.78 and a specificity value of 0.62. These findings highlight the importance of targeted assessments of distinct amygdala and hippocampus subregions to help dissect the clinical and pathophysiological development of the MCI to AD transition.
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Affiliation(s)
- Miriam Punzi
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Molecular Neurology Unit, Center for Advanced Studies and Technology (CAST), University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Carlo Sestieri
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Eleonora Picerni
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Antonio Maria Chiarelli
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Caterina Padulo
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Department of Humanities, University of Naples Federico II, Naples, 80133, Italy
| | - Andrea Delli Pizzi
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Maria Giulia Tullo
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Annalisa Tosoni
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Alberto Granzotto
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Molecular Neurology Unit, Center for Advanced Studies and Technology (CAST), University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Stefania Della Penna
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Marco Onofrj
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Antonio Ferretti
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- UdA-TechLab, Research Center, University “G. D’Annunzio” of Chieti-Pescara, 66100, Chieti, Italy
| | - Stefano Delli Pizzi
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Molecular Neurology Unit, Center for Advanced Studies and Technology (CAST), University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
| | - Stefano L. Sensi
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
- Molecular Neurology Unit, Center for Advanced Studies and Technology (CAST), University “G. D'Annunzio of Chieti-Pescara”, Chieti, 66100, Italy
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Feng Q, Wang L, Tang X, Ge X, Hu H, Liao Z, Ding Z. Machine learning classifiers and associations of cognitive performance with hippocampal subfields in amnestic mild cognitive impairment. Front Aging Neurosci 2023; 15:1273658. [PMID: 38099266 PMCID: PMC10719844 DOI: 10.3389/fnagi.2023.1273658] [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: 08/07/2023] [Accepted: 11/10/2023] [Indexed: 12/17/2023] Open
Abstract
Background Neuroimaging studies have demonstrated alterations in hippocampal volume and hippocampal subfields among individuals with amnestic mild cognitive impairment (aMCI). However, research on using hippocampal subfield volume modeling to differentiate aMCI from normal controls (NCs) is limited, and the relationship between hippocampal volume and overall cognitive scores remains unclear. Methods We enrolled 50 subjects with aMCI and 44 NCs for this study. Initially, a univariate general linear model was employed to analyze differences in the volumes of hippocampal subfields. Subsequently, two sets of dimensionality reduction methods and four machine learning techniques were applied to distinguish aMCI from NCs based on hippocampal subfield volumes. Finally, we assessed the correlation between the relative volumes of hippocampal subfields and cognitive test variables (Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA)). Results Significant volume differences were observed in several hippocampal subfields, notably in the left hippocampus. Specifically, the volumes of the hippocampal tail, subiculum, CA1, presubiculum, molecular layer, GC-ML-DG, CA3, CA4, and fimbria differed significantly between the two groups. The highest area under the curve (AUC) values for left and right hippocampal machine learning classifiers were 0.678 and 0.701, respectively. Moreover, the volumes of the left subiculum, left molecular layer, right subiculum, right CA1, right molecular layer, right GC-ML-DG, and right CA4 exhibited the strongest and most consistent correlations with MoCA scores. Conclusion Hippocampal subfield volume may serve as a predictive marker for aMCI. These findings underscore the sensitivity of hippocampal subfield volume to overall cognitive performance.
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Affiliation(s)
- Qi Feng
- Department of Radiology, Hangzhou First People’s Hospital, Hangzhou, China
| | - Luoyu Wang
- Department of Radiology, Hangzhou First People’s Hospital, Hangzhou, China
| | - Xue Tang
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, China
| | - Xiuhong Ge
- Department of Radiology, Hangzhou First People’s Hospital, Hangzhou, China
| | - Hanjun Hu
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhengluan Liao
- Department of Psychiatry, Zhejiang Provincial People’s Hospital/People’s Hospital of Hangzhou Medical College, Hangzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Hangzhou First People’s Hospital, Hangzhou, China
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Zhang Q, Sheng J, Zhang Q, Wang L, Yang Z, Xin Y. Enhanced Harris hawks optimization-based fuzzy k-nearest neighbor algorithm for diagnosis of Alzheimer's disease. Comput Biol Med 2023; 165:107392. [PMID: 37669585 DOI: 10.1016/j.compbiomed.2023.107392] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/30/2023] [Accepted: 08/25/2023] [Indexed: 09/07/2023]
Abstract
In order to stop deterioration and give patients with Alzheimer's disease (AD) early therapy, it is crucial to correctly diagnose AD and its early stage, mild cognitive impairment (MCI). A framework for diagnosing AD is presented in this paper, which includes magnetic resonance imaging (MRI) image preprocessing, feature extraction, and the Fuzzy k-nearest neighbor algorithm (FKNN) model. In particular, the framework's novelty lies in the use of an improved Harris Hawks Optimization (HHO) algorithm named SSFSHHO, which integrates the Sobol sequence and Stochastic Fractal Search (SFS) mechanisms for optimizing the parameters of FKNN. The HHO method improves the quality of the initial population overall by incorporating the Sobol sequence, and the SFS mechanism increases the algorithm's capacity to get out of the local optimum solution. Comparisons with other classical meta-heuristic algorithms, state-of-the-art HHO variants in low and high dimensions, and enhanced meta-heuristic algorithms on 30 typical IEEE CEC2014 benchmark test problems show that the overall performance of SSFSHHO is significantly better than other comparative algorithms. Moreover, the created framework based on the SSFSHHO-FKNN model is employed to classify AD and MCI using MRI scans from the ADNI dataset, achieving high classification performance for 6 representative cases. Experimental findings indicate that the proposed algorithm performs better than a number of high-performance optimization algorithms and classical machine learning algorithms, thus offering a promising approach for AD classification. Additionally, the proposed strategy can successfully identify relevant features and enhance classification performance for AD diagnosis.
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Affiliation(s)
- Qian Zhang
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China; School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, Zhejiang, 325035, China
| | - Jinhua Sheng
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China.
| | - Qiao Zhang
- Beijing Hospital, Beijing, 100730, China; National Center of Gerontology, Beijing, 100730, China; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Luyun Wang
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
| | - Ze Yang
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
| | - Yu Xin
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
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5
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Pommy J, Conant L, Butts AM, Nencka A, Wang Y, Franczak M, Glass-Umfleet L. A graph theoretic approach to neurodegeneration: five data-driven neuropsychological subtypes in mild cognitive impairment. NEUROPSYCHOLOGY, DEVELOPMENT, AND COGNITION. SECTION B, AGING, NEUROPSYCHOLOGY AND COGNITION 2023; 30:903-922. [PMID: 36648118 DOI: 10.1080/13825585.2022.2163973] [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: 04/20/2022] [Accepted: 12/26/2022] [Indexed: 01/18/2023]
Abstract
Mild cognitive Impairment (MCI) is notoriously heterogenous in terms of clinical presentation, neuroimaging correlates, and subsequent progression. Predicting who will progress to dementia, which type of dementia, and over what timeframe is challenging. Previous work has attempted to identify MCI subtypes using neuropsychological measures in an effort to address this challenge; however, there is no consensus on approach, which may account for some of the variability. Using a hierarchical community detection approach, we examined cognitive subtypes within an MCI sample (from the Alzheimer's Disease Neuroimaging Initiative [ADNI] study). We then examined whether these subtypes were related to biomarkers (e.g., cortical volumes, fluorodeoxyglucose (FDG)-positron emission tomography (PET) hypometabolism) or clinical progression. We identified five communities (i.e., cognitive subtypes) within the MCI sample: 1) predominantly memory impairment, 2) predominantly language impairment, 3) cognitively normal, 4) multidomain, with notable executive dysfunction, 5) multidomain, with notable processing speed impairment. Community membership was significantly associated with 1) cortical volume in the hippocampus, entorhinal cortex, and fusiform cortex; 2) FDG PET hypometabolism in the posterior cingulate, angular gyrus, and inferior/middle temporal gyrus; and 3) conversion to dementia at follow up. Overall, community detection as an approach appears a viable method for identifying unique cognitive subtypes in a neurodegenerative sample that were linked to several meaningful biomarkers and modestly with progression at one year follow up.
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Affiliation(s)
- Jessica Pommy
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
| | - L Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
| | - A M Butts
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
| | - A Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, United States
| | - Y Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, United States
| | - M Franczak
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
| | - L Glass-Umfleet
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
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6
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Cui D, Wang D, Jin J, Liu X, Wang Y, Cao W, Liu Z, Yin T. Age- and sex-related differences in cortical morphology and their relationships with cognitive performance in healthy middle-aged and older adults. Quant Imaging Med Surg 2023; 13:1083-1099. [PMID: 36819243 PMCID: PMC9929420 DOI: 10.21037/qims-22-583] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 11/16/2022] [Indexed: 12/15/2022]
Abstract
Background The impacts of age and sex on brain structures related to cognitive function may be important for understanding the role of aging in Alzheimer disease for both sexes. We intended to investigate the age and sex differences of cortical morphology in middle-aged and older adults and their relationships with the decline of cognitive function. Methods In this cross-sectional study, we examined the cortical morphology in 204 healthy middle-aged and older adult participants aged 45 to 89 years using structural magnetic resonance imaging (sMRI) data from the Dallas Lifespan Brain Study data set. Brain cortical thickness, surface complexity, and gyrification index were analyzed through a completely automated surface-based morphometric analysis using the CAT12 toolbox. Furthermore, we explored the correlation between cortical morphology differences and test scores for processing speed and working memory. Results There were no significant interactions of age and sex with cortical thickness, fractal dimension, or gyrification index. Rather, we found that both males and females showed age-related decreases in cortical thickness, fractal dimension, and gyrification index. There were significant sex differences in the fractal dimension in middle-aged participants and the gyrification index in older adult participants. In addition, there were significant positive correlations between the cortical thickness of the right superior frontal gyrus and Wechsler Adult Intelligence Scale (WAIS)-III Letter-Number Sequencing test scores in males (r=0.394; P<0.001; 95% CI for r values 0.216-0.577) and females (r=0.344; P<0.001; 95% CI for r values 0.197-0.491), respectively. Furthermore, a significant relationship between the gyrification index of the right supramarginal gyrus (SupraMG) and WAIS-III Digit Symbol test scores was observed in older adult participants (r=0.375; P<0.001; 95% CI for r values 0.203-0.522). Conclusions The results suggest that, compared with males, females have more extensive differences in cortical morphology. The gyrification index of the right SupraMG can be used as an imaging marker of sexual cognitive differences between males and females in older adults. This study helps to further understand sex differences in the aging of the brain and cognition.
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Affiliation(s)
- Dong Cui
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China;,School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Dianyu Wang
- Institute of Radiation Medicine, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Jingna Jin
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Xu Liu
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Yuheng Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Weifang Cao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China;,School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Zhipeng Liu
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Tao Yin
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China;,Neuroscience Center, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China
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7
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Spence H, McNeil CJ, Waiter GD. Cognition and brain iron deposition in whole grey matter regions and hippocampal subfields. Eur J Neurosci 2022; 56:6039-6054. [PMID: 36215153 PMCID: PMC10092357 DOI: 10.1111/ejn.15838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/26/2022] [Accepted: 10/03/2022] [Indexed: 12/29/2022]
Abstract
Regional brain iron accumulation is observed in many neurodegenerative diseases, including Alzheimer's disease and Parkinson's disease, and is associated with cognitive decline. We explored associations between age, cognition and iron content in grey matter regions and hippocampal subfields in 380 participants of the Aberdeen children of the 1950s cohort and their first-generation relatives (aged 26-72 years). Participants underwent cognitive assessment at the time of MRI scanning. Quantitative susceptibility mapping of these MRI data was used to assess iron content in grey matter regions and in hippocampal subfields. Principle component analysis was performed on cognitive test scores to create a general cognition score. Spline analysis was used with the Akaike information criterion to determine if order 1, 2 or 3 natural splines were optimal for assessing non-linear relationships between regional iron and age. Multivariate linear models were used to assess associations between regional iron and cognition. Higher iron correlated with older age in the left putamen across all ages and in the right putamen of only participants over 58. Whereas a decrease in iron with older age was observed in the right thalamus and left pallidum across all ages. Right amygdala iron levels were associated with poorer general cognition scores and poorer immediate recall scores. Iron was not associated with any measures of cognitive performance in other regions of interest. Our results suggest that, whilst iron in some regions was associated with cognitive performance, there is an overall lack of association between regional iron content and cognitive ability in cognitively healthy individuals.
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Affiliation(s)
- Holly Spence
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Chris J McNeil
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Gordon D Waiter
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
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8
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Bi XA, Li L, Wang Z, Wang Y, Luo X, Xu L. IHGC-GAN: influence hypergraph convolutional generative adversarial network for risk prediction of late mild cognitive impairment based on imaging genetic data. Brief Bioinform 2022; 23:6554128. [PMID: 35348583 DOI: 10.1093/bib/bbac093] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 01/28/2022] [Accepted: 02/23/2022] [Indexed: 11/13/2022] Open
Abstract
Predicting disease progression in the initial stage to implement early intervention and treatment can effectively prevent the further deterioration of the condition. Traditional methods for medical data analysis usually fail to perform well because of their incapability for mining the correlation pattern of pathogenies. Therefore, many calculation methods have been excavated from the field of deep learning. In this study, we propose a novel method of influence hypergraph convolutional generative adversarial network (IHGC-GAN) for disease risk prediction. First, a hypergraph is constructed with genes and brain regions as nodes. Then, an influence transmission model is built to portray the associations between nodes and the transmission rule of disease information. Third, an IHGC-GAN method is constructed based on this model. This method innovatively combines the graph convolutional network (GCN) and GAN. The GCN is used as the generator in GAN to spread and update the lesion information of nodes in the brain region-gene hypergraph. Finally, the prediction accuracy of the method is improved by the mutual competition and repeated iteration between generator and discriminator. This method can not only capture the evolutionary pattern from early mild cognitive impairment (EMCI) to late MCI (LMCI) but also extract the pathogenic factors and predict the deterioration risk from EMCI to LMCI. The results on the two datasets indicate that the IHGC-GAN method has better prediction performance than the advanced methods in a variety of indicators.
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Affiliation(s)
- Xia-An Bi
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, and the College of Information Science and Engineering in Hunan Normal University, Changsha 410081, P.R. China
| | - Lou Li
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Zizheng Wang
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Yu Wang
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Xun Luo
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, and the College of Information Science and Engineering in Hunan Normal University, Changsha 410081, P.R. China
| | - Luyun Xu
- College of Business, Hunan Normal University, Changsha 410081, P.R. China
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Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain. Neuroinformatics 2022; 20:109-137. [PMID: 33974213 PMCID: PMC8111663 DOI: 10.1007/s12021-021-09519-6] [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] [Subscribe] [Scholar Register] [Accepted: 03/07/2021] [Indexed: 02/06/2023]
Abstract
We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.
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10
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Giannakopoulos P, Montandon ML, Rodriguez C, Haller S, Garibotto V, Herrmann FR. Prediction of Subtle Cognitive Decline in Normal Aging: Added Value of Quantitative MRI and PET Imaging. Front Aging Neurosci 2021; 13:664224. [PMID: 34322007 PMCID: PMC8313279 DOI: 10.3389/fnagi.2021.664224] [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: 02/04/2021] [Accepted: 06/21/2021] [Indexed: 11/26/2022] Open
Abstract
Quantitative imaging processing tools have been proposed to improve clinic-radiological correlations but their added value at the initial stages of cognitive decline is still a matter of debate. We performed a longitudinal study in 90 community-dwelling elders with three neuropsychological assessments during a 4.5 year follow-up period, and visual assessment of medial temporal atrophy (MTA), white matter hyperintensities, cortical microbleeds (CMB) as well as amyloid positivity, and presence of abnormal FDG-PET patterns. Quantitative imaging data concerned ROI analysis of MRI volume, amyloid burden, and FDG-PET metabolism in several AD-signature areas. Multiple regression models, likelihood-ratio tests, and areas under the receiver operating characteristic curve (AUC) were used to compare quantitative imaging markers to visual inspection. The presence of more or equal to four CMB at inclusion and slight atrophy of the right MTL at follow-up were the only parameters to be independently related to the worst cognitive score explaining 6% of its variance. This percentage increased to 24.5% when the ROI-defined volume loss in the posterior cingulate cortex, baseline hippocampus volume, and MTL metabolism were also considered. When binary classification of cognition was made, the area under the ROC curve increased from 0.69 for the qualitative to 0.79 for the mixed imaging model. Our data reveal that the inclusion of quantitative imaging data significantly increases the prediction of cognitive changes in elderly controls compared to the single consideration of visual inspection.
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Affiliation(s)
- Panteleimon Giannakopoulos
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Medical Direction, Geneva University Hospitals, Geneva, Switzerland
| | - Marie-Louise Montandon
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Cristelle Rodriguez
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Medical Direction, Geneva University Hospitals, Geneva, Switzerland
| | - Sven Haller
- Department of Neuroradiology, Faculty of Medicine of the University of Geneva, Geneva, Switzerland.,CIRD-Centre d'Imagerie Rive Droite, Geneva, Switzerland.,Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | - Valentina Garibotto
- Department of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - François R Herrmann
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
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Shang S, Wu J, Chen YC, Chen H, Zhang H, Dou W, Wang P, Cao X, Yin X. Aberrant cerebral perfusion pattern in amnestic mild cognitive impairment and Parkinson's disease with mild cognitive impairment: a comparative arterial spin labeling study. Quant Imaging Med Surg 2021; 11:3082-3097. [PMID: 34249637 DOI: 10.21037/qims-20-1259] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 03/15/2021] [Indexed: 11/06/2022]
Abstract
Background Mild cognitive impairment (MCI) has been defined as the prodromal stage of Alzheimer's disease and Parkinson's disease (PD) with dementia. We investigated the differences in regional perfusion properties among MCI subtypes and healthy control (HC) subjects by using arterial spin labeling (ASL). Methods Regional normalized CBF (z-CBF) and CBF-connectivity were analyzed from ASL data in 44 amnestic MCI (aMCI) patients, 42 PD-MCI patients, and 50 matched HC participants. The correlations between these significant regions and clinical performance were investigated separately using Spearman correlation analysis. Receiver operating characteristic analysis was generated to determine the differentiating ability of z-CBF values. z-CBF values in disease-related specific regions were extracted for group comparison. Results MCI subgroups showed overlapped impaired regions, aMCI group seemed more extensive than the PD-MCI group. PD-MCI patients had reduced z-CBF in the bilateral putamen, left precentral gyrus, left middle cingulate gyrus, and right middle frontal gyrus compared to aMCI group. Correlations to executive performance and motor severity were found in PD-MCI group, and correlations were to memory performance found in aMCI group. CBF-connectivity in left precentral gyrus, left middle cingulate gyrus, and right middle frontal gyrus were significantly altered. All of the significant clusters had good discriminatory ability. Conclusions Normalized CBF as measured by ASL revealed different patterns of perfusion between aMCI and PD-MCI, which were probably linked to distinct neural mechanisms. The present study indicates that z-CBF can provide specific perfusion information for further pathological and neuropsychological studies.
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Affiliation(s)
- Song'an Shang
- Department of Radiology, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Jingtao Wu
- Department of Radiology, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Hongri Chen
- Department of Radiology, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Hongying Zhang
- Department of Radiology, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Weiqiang Dou
- MR Research China, GE Healthcare, Beijing, China
| | - Peng Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xin Cao
- Department of Medical Genetics, School of Basic Medical Science, Nanjing Medical University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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12
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Odusami M, Maskeliūnas R, Damaševičius R, Krilavičius T. Analysis of Features of Alzheimer's Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network. Diagnostics (Basel) 2021; 11:1071. [PMID: 34200832 PMCID: PMC8230447 DOI: 10.3390/diagnostics11061071] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/04/2021] [Accepted: 06/08/2021] [Indexed: 12/12/2022] Open
Abstract
One of the first signs of Alzheimer's disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer's Disease Neuroimaging Initiative (ADNI) fMRI dataset consisting of 138 subjects was used for evaluation. The finetuned ResNet18 network achieved a classification accuracy of 99.99%, 99.95%, and 99.95% on EMCI vs. AD, LMCI vs. AD, and MCI vs. EMCI classification scenarios, respectively. The proposed model performed better than other known models in terms of accuracy, sensitivity, and specificity.
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Affiliation(s)
- Modupe Odusami
- Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania; (M.O.); (R.M.)
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania; (M.O.); (R.M.)
| | - Robertas Damaševičius
- Department of Applied Informatics, Vytautas Magnus University, 44248 Kaunas, Lithuania;
| | - Tomas Krilavičius
- Department of Applied Informatics, Vytautas Magnus University, 44248 Kaunas, Lithuania;
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13
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Wang M, Yan Z, Zhang H, Lu J, Li L, Yu J, Wang J, Matsuda H, Zuo C, Jiang J. Parametric estimation of reference signal intensity in the quantification of amyloid-beta deposition: an 18F-AV-45 study. Quant Imaging Med Surg 2021; 11:249-263. [PMID: 33392026 PMCID: PMC7719939 DOI: 10.21037/qims-20-110] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 09/07/2020] [Indexed: 12/23/2022]
Abstract
BACKGROUND Positron emission tomography (PET) with the radiotracer florbetapir (18F-AV-45) allows the pathophysiology of Alzheimer's disease (AD) to be tracked in vivo. The semi-quantification of amyloid-beta (Aβ) has been extensively evaluated with the standardized uptake value ratio (SUVR) but is susceptible to disturbance from the candidate reference region and the partial volume effect (PVE). In the present study, we applied the parametric estimation of reference signal intensity (PERSI) method to 18F-AV-45 PET images for intensity normalization. METHODS We enrolled 479 people with 18F-AV-45 images from the Alzheimer's Disease Neuroimaging Initiative database: 261 healthy controls (HCs), 102 patients with mild cognitive impairment (MCI), and 116 AD patients. We used white matter post-processed by PERSI (PERSI-WM) as the reference region and compared our proposed method with the traditional method for semi-quantification. SUVRs were calculated for eight regions of interest: the frontal lobe, the parietal lobe, the temporal lobe, the occipital lobe, the anterior cingulate cortex, the posterior cingulate cortex, the precuneus, and the global cortex. The SUVRs derived from PERSI-WM and other reference regions were evaluated by effect size and receiver-operator characteristic curve analyses. RESULTS The SUVRs derived from PERSI-WM showed significantly higher trace retention in the frontal, parietal, temporal, and occipital lobes, as well as in the anterior cingulate, posterior cingulate, precuneus, and global cortex in the AD Aβ-positive (+) group (mean: +43.3%±5.4%, P<0.01) and MCI Aβ+ group (mean: +29.6%±5.3%, P<0.01). For the global cortex, PERSI-WM had the greatest Cohen's d effect size compared with the HC Aβ-negative (-) group (AD Aβ+ and MCI Aβ+: 3.02, AD Aβ+: 3.56, MCI Aβ+: 2.34), and the highest area under the curve (AUC) between the HC Aβ- and AD Aβ+ groups (AUC: 0.983, 95% confidence interval: 0.978-0.998). CONCLUSIONS PERSI-WM could mitigate the influence of PVE and improve the semi-quantification of 18F-AV-45 images; therefore, it could be used for large-scale clinical application in the nuclear medicine domain.
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Affiliation(s)
- Min Wang
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Zhuangzhi Yan
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Huiwei Zhang
- PET Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiaying Lu
- PET Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ling Li
- PET Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jintai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian Wang
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - the Alzheimer’s Disease Neuroimaging Initiative
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- PET Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
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