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Panigrahi P, Das S, Chakrabarti S. CCADD: An online webserver for Alzheimer's disease detection from brain MRI. Comput Biol Med 2024; 177:108622. [PMID: 38781645 DOI: 10.1016/j.compbiomed.2024.108622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/26/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024]
Abstract
Alzheimer's disease (AD) imposes a growing burden on public health due to its impact on memory, cognition, behavior, and social skills. Early detection using non-invasive brain magnetic resonance images (MRI) is vital for disease management. We introduce CCADD (Corpus Callosum-based Alzheimer's Disease Detection), a user-friendly webserver that automatically identifies and segments the corpus callosum (CC) region from brain MRI slices. Extracted shape and size-based features of CC are fed into Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) classifiers to predict AD or Mild Cognitive Impairment (MCI). Exhaustive benchmarking on ADNI data reveals high prediction accuracies for different AD severity levels. CCADD empowers clinicians and researchers for AD detection. This server is available at: http://www.hpppi.iicb.res.in/add.
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Affiliation(s)
- Priyanka Panigrahi
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), TRUE Campus, Kolkata, 700091, West Bengal, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, Uttar Pradesh, India
| | - Subhrangshu Das
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), TRUE Campus, Kolkata, 700091, West Bengal, India.
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), TRUE Campus, Kolkata, 700091, West Bengal, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, Uttar Pradesh, India.
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Said A, Göker H. Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals. Cogn Neurodyn 2024; 18:597-614. [PMID: 38699612 PMCID: PMC11061085 DOI: 10.1007/s11571-023-10010-y] [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: 05/17/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 05/05/2024] Open
Abstract
Mild cognitive impairment (MCI) is a neuropsychological syndrome that is characterized by cognitive impairments. It typically affects adults 60 years of age and older. It is a noticeable decline in the cognitive function of the patient, and if left untreated it gets converted to Alzheimer's disease (AD). For that reason, early diagnosis of MCI is important as it slows down the conversion of the disease to AD. Early and accurate diagnosis of MCI requires recognition of the clinical characteristics of the disease, extensive testing, and long-term observations. These observations and tests can be subjective, expensive, incomplete, or inaccurate. Electroencephalography (EEG) is a powerful choice for the diagnosis of diseases with its advantages such as being non-invasive, based on findings, less costly, and getting results in a short time. In this study, a new EEG-based model is developed which can effectively detect MCI patients with higher accuracy. For this purpose, a dataset consisting of EEG signals recorded from a total of 34 subjects, 18 of whom were MCI and 16 control groups was used, and their ages ranged from 40 to 77. To conduct the experiment, the EEG signals were denoised using Multiscale Principal Component Analysis (MSPCA), and to increase the size of the dataset Data Augmentation (DA) method was performed. The tenfold cross-validation method was used to validate the model, moreover, the power spectral density (PSD) of the EEG signals was extracted from the EEG signals using three spectral analysis methods, the periodogram, welch, and multitaper. The PSD graphs of the EEG signals showed signal differences between the subjects of control and the MCI group, indicating that the signal power of MCI patients is lower compared to control groups. To classify the subjects, one of the best classifiers of deep learning algorithms called the Bi-directional long-short-term-memory (Bi-LSTM) was used, and several machine learning algorithms, such as decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN). These algorithms were trained and tested using the extracted feature vectors from the control and the MCI groups. Additionally, the values of the coefficient matrix of those algorithms were compared and evaluated with the performance evaluation matrix to determine which one performed the best overall. According to the experimental results, the proposed deep learning model of multitaper spectral analysis approach with Bi-LSTM deep learning algorithm attained the highest number of correctly classified samples for diagnosing MCI patients and achieved a remarkable accuracy compared to the other proposed models. The achieved classification results of the deep learning model are reported to be 98.97% accuracy, 98.34% sensitivity, 99.67% specificity, 99.70% precision, 99.02% f1 score, and 97.94% Matthews correlation coefficient (MCC).
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Affiliation(s)
- Afrah Said
- Department of Electrical Electronics Engineering, Faculty of Simav Technology, Dumlupınar University, 43500 Kütahya, Turkey
| | - Hanife Göker
- Health Services Vocational College, Gazi University, 06830 Ankara, Turkey
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Duan J, Liu Y, Wu H, Wang J, Chen L, Chen CLP. Broad learning for early diagnosis of Alzheimer's disease using FDG-PET of the brain. Front Neurosci 2023; 17:1137567. [PMID: 36992851 PMCID: PMC10040750 DOI: 10.3389/fnins.2023.1137567] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 02/13/2023] [Indexed: 03/14/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease, and the development of AD is irreversible. However, preventive measures in the presymptomatic stage of AD can effectively slow down deterioration. Fluorodeoxyglucose positron emission tomography (FDG-PET) can detect the metabolism of glucose in patients' brains, which can help to identify changes related to AD before brain damage occurs. Machine learning is useful for early diagnosis of patients with AD using FDG-PET, but it requires a sufficiently large dataset, and it is easy for overfitting to occur in small datasets. Previous studies using machine learning for early diagnosis with FDG-PET have either involved the extraction of elaborately handcrafted features or validation on a small dataset, and few studies have explored the refined classification of early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI). This article presents a broad network-based model for early diagnosis of AD (BLADNet) through PET imaging of the brain; this method employs a novel broad neural network to enhance the features of FDG-PET extracted via 2D CNN. BLADNet can search for information over a broad space through the addition of new BLS blocks without retraining of the whole network, thus improving the accuracy of AD classification. Experiments conducted on a dataset containing 2,298 FDG-PET images of 1,045 subjects from the ADNI database demonstrate that our methods are superior to those used in previous studies on early diagnosis of AD with FDG-PET. In particular, our methods achieved state-of-the-art results in EMCI and LMCI classification with FDG-PET.
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Affiliation(s)
- Junwei Duan
- College of Information Science and Technology, Jinan University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Jinan University, Guangzhou, China
- *Correspondence: Junwei Duan
| | - Yang Liu
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Huanhua Wu
- Department of Nuclear Medicine and PET/CT-MRI Centre, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jing Wang
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
- Jing Wang
| | - Long Chen
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - C. L. Philip Chen
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
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El-Sappagh S, Alonso-Moral JM, Abuhmed T, Ali F, Bugarín-Diz A. Trustworthy artificial intelligence in Alzheimer’s disease: state of the art, opportunities, and challenges. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10415-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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Golovanevsky M, Eickhoff C, Singh R. Multimodal attention-based deep learning for Alzheimer's disease diagnosis. J Am Med Inform Assoc 2022; 29:2014-2022. [PMID: 36149257 PMCID: PMC9667156 DOI: 10.1093/jamia/ocac168] [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: 06/15/2022] [Revised: 08/10/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Alzheimer's disease (AD) is the most common neurodegenerative disorder with one of the most complex pathogeneses, making effective and clinically actionable decision support difficult. The objective of this study was to develop a novel multimodal deep learning framework to aid medical professionals in AD diagnosis. MATERIALS AND METHODS We present a Multimodal Alzheimer's Disease Diagnosis framework (MADDi) to accurately detect the presence of AD and mild cognitive impairment (MCI) from imaging, genetic, and clinical data. MADDi is novel in that we use cross-modal attention, which captures interactions between modalities-a method not previously explored in this domain. We perform multi-class classification, a challenging task considering the strong similarities between MCI and AD. We compare with previous state-of-the-art models, evaluate the importance of attention, and examine the contribution of each modality to the model's performance. RESULTS MADDi classifies MCI, AD, and controls with 96.88% accuracy on a held-out test set. When examining the contribution of different attention schemes, we found that the combination of cross-modal attention with self-attention performed the best, and no attention layers in the model performed the worst, with a 7.9% difference in F1-scores. DISCUSSION Our experiments underlined the importance of structured clinical data to help machine learning models contextualize and interpret the remaining modalities. Extensive ablation studies showed that any multimodal mixture of input features without access to structured clinical information suffered marked performance losses. CONCLUSION This study demonstrates the merit of combining multiple input modalities via cross-modal attention to deliver highly accurate AD diagnostic decision support.
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Affiliation(s)
- Michal Golovanevsky
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
| | - Carsten Eickhoff
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
- Center for Biomedical Informatics, Brown University, Providence, Rhode Island, USA
| | - Ritambhara Singh
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA
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Dynamic ensemble prediction of cognitive performance in spaceflight. Sci Rep 2022; 12:11032. [PMID: 35773291 PMCID: PMC9246897 DOI: 10.1038/s41598-022-14456-8] [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: 03/20/2022] [Accepted: 06/07/2022] [Indexed: 11/08/2022] Open
Abstract
During spaceflight, astronauts face a unique set of stressors, including microgravity, isolation, and confinement, as well as environmental and operational hazards. These factors can negatively impact sleep, alertness, and neurobehavioral performance, all of which are critical to mission success. In this paper, we predict neurobehavioral performance over the course of a 6-month mission aboard the International Space Station (ISS), using ISS environmental data as well as self-reported and cognitive data collected longitudinally from 24 astronauts. Neurobehavioral performance was repeatedly assessed via a 3-min Psychomotor Vigilance Test (PVT-B) that is highly sensitive to the effects of sleep deprivation. To relate PVT-B performance to time-varying and discordantly-measured environmental, operational, and psychological covariates, we propose an ensemble prediction model comprising of linear mixed effects, random forest, and functional concurrent models. An extensive cross-validation procedure reveals that this ensemble outperforms any one of its components alone. We also identify the most important predictors of PVT-B performance, which include an individual's previous PVT-B performance, reported fatigue and stress, and temperature and radiation dose. This method is broadly applicable to settings where the main goal is accurate, individualized prediction of human behavior involving a mixture of person-level traits and irregularly measured time series.
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Zamani J, Sadr A, Javadi AH. Classification of early-MCI patients from healthy controls using evolutionary optimization of graph measures of resting-state fMRI, for the Alzheimer's disease neuroimaging initiative. PLoS One 2022; 17:e0267608. [PMID: 35727837 PMCID: PMC9212187 DOI: 10.1371/journal.pone.0267608] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 04/11/2022] [Indexed: 11/21/2022] Open
Abstract
Identifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer's disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n = 72) and EMCI (n = 68) extracted from the publicly available database of the Alzheimer's disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.
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Affiliation(s)
- Jafar Zamani
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Ali Sadr
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Amir-Homayoun Javadi
- School of Psychology, University of Kent, Canterbury, United Kingdom
- School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran
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El-Sappagh S, Saleh H, Ali F, Amer E, Abuhmed T. Two-stage deep learning model for Alzheimer’s disease detection and prediction of the mild cognitive impairment time. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07263-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Cui W, Yan C, Yan Z, Peng Y, Leng Y, Liu C, Chen S, Jiang X, Zheng J, Yang X. BMNet: A New Region-Based Metric Learning Method for Early Alzheimer's Disease Identification With FDG-PET Images. Front Neurosci 2022; 16:831533. [PMID: 35281501 PMCID: PMC8908419 DOI: 10.3389/fnins.2022.831533] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/11/2022] [Indexed: 12/21/2022] Open
Abstract
18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) reveals altered brain metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Some biomarkers derived from FDG-PET by computer-aided-diagnosis (CAD) technologies have been proved that they can accurately diagnosis normal control (NC), MCI, and AD. However, existing FDG-PET-based researches are still insufficient for the identification of early MCI (EMCI) and late MCI (LMCI). Compared with methods based other modalities, current methods with FDG-PET are also inadequate in using the inter-region-based features for the diagnosis of early AD. Moreover, considering the variability in different individuals, some hard samples which are very similar with both two classes limit the classification performance. To tackle these problems, in this paper, we propose a novel bilinear pooling and metric learning network (BMNet), which can extract the inter-region representation features and distinguish hard samples by constructing the embedding space. To validate the proposed method, we collect 898 FDG-PET images from Alzheimer's disease neuroimaging initiative (ADNI) including 263 normal control (NC) patients, 290 EMCI patients, 147 LMCI patients, and 198 AD patients. Following the common preprocessing steps, 90 features are extracted from each FDG-PET image according to the automatic anatomical landmark (AAL) template and then sent into the proposed network. Extensive fivefold cross-validation experiments are performed for multiple two-class classifications. Experiments show that most metrics are improved after adding the bilinear pooling module and metric losses to the Baseline model respectively. Specifically, in the classification task between EMCI and LMCI, the specificity improves 6.38% after adding the triple metric loss, and the negative predictive value (NPV) improves 3.45% after using the bilinear pooling module. In addition, the accuracy of classification between EMCI and LMCI achieves 79.64% using imbalanced FDG-PET images, which illustrates that the proposed method yields a state-of-the-art result of the classification accuracy between EMCI and LMCI based on PET images.
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Affiliation(s)
- Wenju Cui
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Caiying Yan
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Zhuangzhi Yan
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Yunsong Peng
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yilin Leng
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Chenlu Liu
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Shuangqing Chen
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Xi Jiang
- School of Life Sciences and Technology, The University of Electronic Science and Technology of China, Chengdu, China
| | - Jian Zheng
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xiaodong Yang
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
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Liu Q, Vaci N, Koychev I, Kormilitzin A, Li Z, Cipriani A, Nevado-Holgado A. Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model. BMC Med 2022; 20:45. [PMID: 35101059 PMCID: PMC8805393 DOI: 10.1186/s12916-022-02250-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 01/11/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Donepezil, galantamine, rivastigmine and memantine are potentially effective interventions for cognitive impairment in dementia, but the use of these drugs has not been personalised to individual patients yet. We examined whether artificial intelligence-based recommendations can identify the best treatment using routinely collected patient-level information. METHODS Six thousand eight hundred four patients aged 59-102 years with a diagnosis of dementia from two National Health Service (NHS) Foundation Trusts in the UK were used for model training/internal validation and external validation, respectively. A personalised prescription model based on the Recurrent Neural Network machine learning architecture was developed to predict the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores post-drug initiation. The drug that resulted in the smallest decline in cognitive scores between prescription and the next visit was selected as the treatment of choice. Change of cognitive scores up to 2 years after treatment initiation was compared for model evaluation. RESULTS Overall, 1343 patients with MMSE scores were identified for internal validation and 285 [21.22%] took the drug recommended. After 2 years, the reduction of mean [standard deviation] MMSE score in this group was significantly smaller than the remaining 1058 [78.78%] patients (0.60 [0.26] vs 2.80 [0.28]; P = 0.02). In the external validation cohort (N = 1772), 222 [12.53%] patients took the drug recommended and reported a smaller MMSE reduction compared to the 1550 [87.47%] patients who did not (1.01 [0.49] vs 4.23 [0.60]; P = 0.01). A similar performance gap was seen when testing the model on patients prescribed with AChEIs only. CONCLUSIONS It was possible to identify the most effective drug for the real-world treatment of cognitive impairment in dementia at an individual patient level. Routine care patients whose prescribed medications were the best fit according to the model had better cognitive performance after 2 years.
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Affiliation(s)
- Qiang Liu
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK.
| | - Nemanja Vaci
- Department of Psychology, University of Sheffield, Sheffield, UK
| | - Ivan Koychev
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Andrey Kormilitzin
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
- Institute of Mathematics, University of Oxford, Oxford, UK
| | - Zhenpeng Li
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Alejo Nevado-Holgado
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
- Big Data Institute, University of Oxford, Oxford, UK
- Akrivia Health, Oxford, UK
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He H, Ding S, Jiang C, Wang Y, Luo Q, Wang Y. Information Flow Pattern in Early Mild Cognitive Impairment Patients. Front Neurol 2021; 12:706631. [PMID: 34858306 PMCID: PMC8631864 DOI: 10.3389/fneur.2021.706631] [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: 05/07/2021] [Accepted: 10/11/2021] [Indexed: 12/05/2022] Open
Abstract
Purpose: To investigate the brain information flow pattern in patients with early mild cognitive impairment (EMCI) and explore its potential ability of differentiation and prediction for EMCI. Methods: In this study, 49 patients with EMCI and 40 age- and sex-matched healthy controls (HCs) with available resting-state functional MRI images and neurological measures [including the neuropsychological evaluation and cerebrospinal fluid (CSF) biomarkers] were included from the Alzheimer's Disease Neuroimaging Initiative. Functional MRI measures including preferred information flow direction between brain regions and preferred information flow index of each brain region parcellated by the Atlas of Intrinsic Connectivity of Homotopic Areas (AICHA) were calculated by using non-parametric multiplicative regression-Granger causality analysis (NPMR-GCA). Edge- and node-wise Student's t-test was conducted for between-group comparison. Support vector classification was performed to differentiate EMCI from HC. The least absolute shrinkage and selection operator (lasso) regression were used to evaluate the predictive ability of information flow measures for the neurological state. Results: Compared to HC, disturbed preferred information flow directions between brain regions involving default mode network (DMN), executive control network (ECN), somatomotor network (SMN), and visual network (VN) were observed in patients with EMCI. An altered preferred information flow index in several brain regions (including the thalamus, posterior cingulate, and precentral gyrus) was also observed. Classification accuracy of 80% for differentiating patients with EMCI from HC was achieved by using the preferred information flow directions. The preferred information flow directions have a good ability to predict memory and executive function, level of amyloid β, tau protein, and phosphorylated tau protein with the high Pearson's correlation coefficients (r > 0.7) between predictive and actual neurological measures. Conclusion: Patients with EMCI were presented with a disturbed brain information flow pattern, which could help clinicians to identify patients with EMCI and assess their neurological state.
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Affiliation(s)
- Haijuan He
- Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Xinjiang, China
| | - Shuang Ding
- Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Xinjiang, China
| | - Chunhui Jiang
- Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Xinjiang, China
| | - Yuanyuan Wang
- Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Xinjiang, China
| | - Qiaoya Luo
- Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Xinjiang, China
| | - Yunling Wang
- Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Xinjiang, China
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Abuhmed T, El-Sappagh S, Alonso JM. Robust hybrid deep learning models for Alzheimer’s progression detection. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106688] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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13
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Guan Y, Cheng CH, Chen W, Zhang Y, Koo S, Krengel M, Janulewicz P, Toomey R, Yang E, Bhadelia R, Steele L, Kim JH, Sullivan K, Koo BB. Neuroimaging Markers for Studying Gulf-War Illness: Single-Subject Level Analytical Method Based on Machine Learning. Brain Sci 2020; 10:brainsci10110884. [PMID: 33233672 PMCID: PMC7699718 DOI: 10.3390/brainsci10110884] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 11/11/2020] [Accepted: 11/17/2020] [Indexed: 12/17/2022] Open
Abstract
Gulf War illness (GWI) refers to the multitude of chronic health symptoms, spanning from fatigue, musculoskeletal pain, and neurological complaints to respiratory, gastrointestinal, and dermatologic symptoms experienced by about 250,000 GW veterans who served in the 1991 Gulf War (GW). Longitudinal studies showed that the severity of these symptoms often remain unchanged even years after the GW, and these veterans with GWI continue to have poorer general health and increased chronic medical conditions than their non-deployed counterparts. For better management and treatment of this condition, there is an urgent need for developing objective biomarkers that can help with simple and accurate diagnosis of GWI. In this study, we applied multiple neuroimaging techniques, including T1-weighted magnetic resonance imaging (T1W-MRI), diffusion tensor imaging (DTI), and novel neurite density imaging (NDI) to perform both a group-level statistical comparison and a single-subject level machine learning (ML) analysis to identify diagnostic imaging features of GWI. Our results supported NDI as the most sensitive in defining GWI characteristics. In particular, our classifier trained with white matter NDI features achieved an accuracy of 90% and F-score of 0.941 for classifying GWI cases from controls after the cross-validation. These results are consistent with our previous study which suggests that NDI measures are sensitive to the microstructural and macrostructural changes in the brain of veterans with GWI, which can be valuable for designing better diagnosis method and treatment efficacy studies.
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Affiliation(s)
- Yi Guan
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
| | - Chia-Hsin Cheng
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
| | - Weifan Chen
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
| | - Yingqi Zhang
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
| | - Sophia Koo
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
| | - Maxine Krengel
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
| | | | - Rosemary Toomey
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
| | - Ehwa Yang
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (E.Y.); (J.-H.K.)
| | - Rafeeque Bhadelia
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, USA;
| | - Lea Steele
- Neuropsychiatry Division, Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Jae-Hun Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (E.Y.); (J.-H.K.)
| | - Kimberly Sullivan
- School of Public Health, Boston University, Boston, MA 02118, USA;
- Correspondence: (K.S.); (B.-B.K.)
| | - Bang-Bon Koo
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
- Correspondence: (K.S.); (B.-B.K.)
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14
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Meena MK, Kumar D, Jayaraj A, Kumar A, Kumari K, Katata-Seru LM, Bahadur I, Kumar V, Sherawat A, Singh P. Designed thiazolidines: an arsenal for the inhibition of nsP3 of CHIKV using molecular docking and MD simulations. J Biomol Struct Dyn 2020; 40:1607-1616. [PMID: 33073705 DOI: 10.1080/07391102.2020.1832918] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Mahendra Kumar Meena
- Department of Chemistry, Atma Ram Sanatan Dharma College, University of Delhi, New Delhi, India
- Department of Chemistry, University of Delhi, Delhi, India
- Department of Chemistry, Shivaji College, University of Delhi, New Delhi, India
| | - Durgesh Kumar
- Department of Chemistry, Atma Ram Sanatan Dharma College, University of Delhi, New Delhi, India
- Department of Chemistry, University of Delhi, Delhi, India
- Department of Chemistry, Lady Irwin College, University of Delhi, New Delhi, India
| | | | - Ajay Kumar
- Department of Chemistry, Indian Institute of Technology, New Delhi, India
| | - Kamlesh Kumari
- Department of Zoology, Deen Dayal Upadhyaya College, University of Delhi, New Delhi, India
| | - L. M. Katata-Seru
- Department of Chemistry, Faculty of Natural Sciences, North-West University, Mmabatho, South Africa
| | - Indra Bahadur
- Department of Chemistry, Faculty of Natural and Agricultural Sciences, North-West University, Mmabatho, South Africa
| | - Vinod Kumar
- SCNS, Jawaharlal Nehru University, New Delhi, India
| | - Anjali Sherawat
- Department of Chemistry, Lady Irwin College, University of Delhi, New Delhi, India
| | - Prashant Singh
- Department of Chemistry, Atma Ram Sanatan Dharma College, University of Delhi, New Delhi, India
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15
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Kang L, Jiang J, Huang J, Zhang T. Identifying Early Mild Cognitive Impairment by Multi-Modality MRI-Based Deep Learning. Front Aging Neurosci 2020; 12:206. [PMID: 33101003 PMCID: PMC7498722 DOI: 10.3389/fnagi.2020.00206] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/12/2020] [Indexed: 12/17/2022] Open
Abstract
Mild cognitive impairment (MCI) is a clinical state with a high risk of conversion to Alzheimer's Disease (AD). Since there is no effective treatment for AD, it is extremely important to diagnose MCI as early as possible, as this makes it possible to delay its progression toward AD. However, it's challenging to identify early MCI (EMCI) because there are only mild changes in the brain structures of patients compared with a normal control (NC). To extract remarkable features for these mild changes, in this paper, a multi-modality diagnosis approach based on deep learning is presented. Firstly, we propose to use structure MRI and diffusion tensor imaging (DTI) images as the multi-modality data to identify EMCI. Then, a convolutional neural network based on transfer learning technique is developed to extract features of the multi-modality data, where an L1-norm is introduced to reduce the feature dimensionality and retrieve essential features for the identification. At last, the classifier produces 94.2% accuracy for EMCI vs. NC on an ADNI dataset. Experimental results show that multi-modality data can provide more useful information to distinguish EMCI from NC compared with single modality data, and the proposed method can improve classification performance, which is beneficial to early intervention of AD. In addition, it is found that DTI image can act as an important biomarker for EMCI from the point of view of a clinical diagnosis.
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Affiliation(s)
- Li Kang
- College of Information Engineering, Shenzhen University, Shenzhen, China
| | - Jingwan Jiang
- College of Information Engineering, Shenzhen University, Shenzhen, China
| | - Jianjun Huang
- College of Information Engineering, Shenzhen University, Shenzhen, China
| | - Tijiang Zhang
- Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
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16
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Jiang J, Kang L, Huang J, Zhang T. Deep learning based mild cognitive impairment diagnosis using structure MR images. Neurosci Lett 2020; 730:134971. [PMID: 32380147 DOI: 10.1016/j.neulet.2020.134971] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 03/24/2020] [Accepted: 04/06/2020] [Indexed: 11/28/2022]
Abstract
Mild cognitive impairment (MCI) is an early sign of Alzheimer's disease (AD) which is the fourth leading disease mostly found in the aged population. Early intervention of MCI will possibly delay the progress towards AD, and this makes it very important to diagnose early MCI (EMCI). However, it is very difficult since the subtle difference between EMCI and cognitively normal control (NC). For improving classification performance, this paper presents a deep learning based diagnosis approach using structure MRI images for exploiting deeply embedded diagnosis features; then a feature selection strategy is performed to eliminate redundant features. A Support Vector Machine (SVM) is further employed to distinguish EMCI from NC. Experiments were performed on the publicly available ADNI dataset with a total of 120 subjects. The classification results demonstrate the superior performance of the proposed method with accuracy of 89.4% for EMCI versus NC.
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Affiliation(s)
- Jingwan Jiang
- College of Information Engineering, Shenzhen University, Shenzhen 518060, China
| | - Li Kang
- College of Information Engineering, Shenzhen University, Shenzhen 518060, China.
| | - Jianjun Huang
- College of Information Engineering, Shenzhen University, Shenzhen 518060, China
| | - Tijiang Zhang
- College of Information Engineering, Shenzhen University, Shenzhen 518060, China
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