1
|
Amini S, Hao B, Yang J, Karjadi C, Kolachalama VB, Au R, Paschalidis IC. Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models. Alzheimers Dement 2024. [PMID: 38924662 DOI: 10.1002/alz.13886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 03/01/2024] [Accepted: 04/19/2024] [Indexed: 06/28/2024]
Abstract
INTRODUCTION Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. METHODS We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. RESULTS Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years. DISCUSSION The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment. HIGHLIGHTS Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease.
Collapse
Affiliation(s)
- Samad Amini
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Boran Hao
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Jingmei Yang
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Cody Karjadi
- Framingham Heart Study, Boston University, Framingham, Massachusetts, USA
| | - Vijaya B Kolachalama
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
- Faculty of Computing & Data Sciences, Boston University, Boston, Massachusetts, USA
- Department of Computer Science, Boston University, Boston, Massachusetts, USA
| | - Rhoda Au
- Framingham Heart Study, Boston University, Framingham, Massachusetts, USA
- Departments of Anatomy & Neurobiology, Neurology, and Epidemiology, Boston University School of Medicine and School of Public Health, Boston, Massachusetts, USA
| | - Ioannis C Paschalidis
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Faculty of Computing & Data Sciences, Boston University, Boston, Massachusetts, USA
| |
Collapse
|
2
|
Malik I, Iqbal A, Gu YH, Al-antari MA. Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review. Diagnostics (Basel) 2024; 14:1281. [PMID: 38928696 PMCID: PMC11202897 DOI: 10.3390/diagnostics14121281] [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: 05/20/2024] [Revised: 06/10/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
Alzheimer's disease (AD) is a neurological disorder that significantly impairs cognitive function, leading to memory loss and eventually death. AD progresses through three stages: early stage, mild cognitive impairment (MCI) (middle stage), and dementia. Early diagnosis of Alzheimer's disease is crucial and can improve survival rates among patients. Traditional methods for diagnosing AD through regular checkups and manual examinations are challenging. Advances in computer-aided diagnosis systems (CADs) have led to the development of various artificial intelligence and deep learning-based methods for rapid AD detection. This survey aims to explore the different modalities, feature extraction methods, datasets, machine learning techniques, and validation methods used in AD detection. We reviewed 116 relevant papers from repositories including Elsevier (45), IEEE (25), Springer (19), Wiley (6), PLOS One (5), MDPI (3), World Scientific (3), Frontiers (3), PeerJ (2), Hindawi (2), IO Press (1), and other multiple sources (2). The review is presented in tables for ease of reference, allowing readers to quickly grasp the key findings of each study. Additionally, this review addresses the challenges in the current literature and emphasizes the importance of interpretability and explainability in understanding deep learning model predictions. The primary goal is to assess existing techniques for AD identification and highlight obstacles to guide future research.
Collapse
Affiliation(s)
- Isra Malik
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 44000, Pakistan
| | - Ahmed Iqbal
- Department of Computer Science, Sir Syed Case Institute of Technology, Islamabad 45230, Pakistan
| | - Yeong Hyeon Gu
- Department of Artificial Intelligence and Data Science, College of AI Convergence, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
| | - Mugahed A. Al-antari
- Department of Artificial Intelligence and Data Science, College of AI Convergence, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
| |
Collapse
|
3
|
Jomeiri A, Navin AH, Shamsi M. Longitudinal MRI analysis using a hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction. Behav Brain Res 2024; 463:114900. [PMID: 38341100 DOI: 10.1016/j.bbr.2024.114900] [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: 07/30/2023] [Revised: 12/16/2023] [Accepted: 02/06/2024] [Indexed: 02/12/2024]
Abstract
Alzheimer's disease is a progressive neurological disorder characterized by brain atrophy and cell death, leading to cognitive decline and impaired functioning. Previous research has primarily focused on using cross-sectional data for Alzheimer's disease identification, but analyzing longitudinal sequential MR images is crucial for improved diagnostic accuracy and understanding disease progression. However, existing deep learning models face challenges in learning spatial and temporal features from such data. To address these challenges, this study presents a novel hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction using longitudinal MRI analysis. The proposed framework combines Convolutional DenseNet for spatial information extraction and joined BiLSTM layers for capturing temporal characteristics and relationships between longitudinal images at different time points. This approach overcomes issues like overfitting, vanishing gradients, and incomplete patient data. We evaluated the model on 684 longitudinal MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including normal controls, individuals with mild cognitive impairment, and Alzheimer's disease patients. The results demonstrate high classification accuracy, with 95.28% for AD/CN, 88.19% for NC/MCI, 83.51% for sMCI/pMCI, and 92.14% for MCI/AD. These findings highlight the substantial improvement in Alzheimer's disease diagnosis achieved through the utilization of longitudinal MRI images. The contributions of this study lie in both the deep learning and medical domains. In the deep learning domain, our hybrid framework effectively learns spatial and temporal features from longitudinal data, addressing the challenges associated with multi-dimensional and sequential time series data. In the medical domain, our study emphasizes the importance of analyzing baseline and longitudinal MR images for accurate diagnosis and understanding disease progression.
Collapse
Affiliation(s)
- Alireza Jomeiri
- Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran
| | - Ahmad Habibizad Navin
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
| | - Mahboubeh Shamsi
- Department of Engineering, Qom University of Technology, Qom, Iran
| |
Collapse
|
4
|
Sekimitsu S, Shweikh Y, Shareef S, Zhao Y, Elze T, Segrè A, Wiggs J, Zebardast N. Association of retinal optical coherence tomography metrics and polygenic risk scores with cognitive function and future cognitive decline. Br J Ophthalmol 2024; 108:599-606. [PMID: 36990674 DOI: 10.1136/bjo-2022-322762] [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: 10/19/2022] [Accepted: 03/15/2023] [Indexed: 03/31/2023]
Abstract
PURPOSE To evaluate the potential of retinal optical coherence tomography (OCT) measurements and polygenic risk scores (PRS) to identify people at risk of cognitive impairment. METHODS Using OCT images from 50 342 UK Biobank participants, we examined associations between retinal layer thickness and genetic risk for neurodegenerative disease and combined these metrics with PRS to predict baseline cognitive function and future cognitive deterioration. Multivariate Cox proportional hazard models were used to predict cognitive performance. P values for retinal thickness analyses are false-discovery-rate-adjusted. RESULTS Higher Alzheimer's disease PRS was associated with a thicker inner nuclear layer (INL), chorio-scleral interface (CSI) and inner plexiform layer (IPL) (all p<0.05). Higher Parkinson's disease PRS was associated with thinner outer plexiform layer (p<0.001). Worse baseline cognitive performance was associated with thinner retinal nerve fibre layer (RNFL) (aOR=1.038, 95% CI (1.029 to 1.047), p<0.001) and photoreceptor (PR) segment (aOR=1.035, 95% CI (1.019 to 1.051), p<0.001), ganglion cell complex (aOR=1.007, 95% CI (1.002 to 1.013), p=0.004) and thicker ganglion cell layer (aOR=0.981, 95% CI (0.967 to 0.995), p=0.009), IPL (aOR=0.976, 95% CI (0.961 to 0.992), p=0.003), INL (aOR=0.923, 95% CI (0.905 to 0.941), p<0.001) and CSI (aOR=0.998, 95% CI (0.997 to 0.999), p<0.001). Worse future cognitive performance was associated with thicker IPL (aOR=0.945, 95% CI (0.915 to 0.999), p=0.045) and CSI (aOR=0.996, 95% CI (0.993 to 0.999) 95% CI, p=0.014). Prediction of cognitive decline was significantly improved with the addition of PRS and retinal measurements. CONCLUSIONS AND RELEVANCE Retinal OCT measurements are significantly associated with genetic risk of neurodegenerative disease and may serve as biomarkers predictive of future cognitive impairment.
Collapse
Affiliation(s)
| | - Yusrah Shweikh
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
- Sussex Eye Hospital, University Hospitals Sussex NHS Foundation Trust, Sussex, UK
| | - Sarah Shareef
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Yan Zhao
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Tobias Elze
- Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Ayellet Segrè
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Janey Wiggs
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Nazlee Zebardast
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| |
Collapse
|
5
|
Ross FC, Mayer DE, Horn J, Cryan JF, Del Rio D, Randolph E, Gill CIR, Gupta A, Ross RP, Stanton C, Mayer EA. Potential of dietary polyphenols for protection from age-related decline and neurodegeneration: a role for gut microbiota? Nutr Neurosci 2024:1-19. [PMID: 38287652 DOI: 10.1080/1028415x.2023.2298098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Many epidemiological studies have shown the beneficial effects of a largely plant-based diet, and the strong association between the consumption of a Mediterranean-type diet with healthy aging including a lower risk of cognitive decline. The Mediterranean diet is characterized by a high intake of olive oil, fruits and vegetables and is rich in dietary fiber and polyphenols - both of which have been postulated to act as important mediators of these benefits. Polyphenols are large molecules produced by plants to protect them from environmental threats and injury. When ingested by humans, as little as 5% of these molecules are absorbed in the small intestine with the majority metabolized by the gut microbiota into absorbable simple phenolic compounds. Flavan-3-ols, a type of flavonoid, contained in grapes, berries, pome fruits, tea, and cocoa have been associated with many beneficial effects on several risk factors for cardiovascular disease, cognitive function and brain regions involved in memory formation. Both preclinical and clinical studies suggest that these brain and heart benefits can be attributed to endothelial vascular effects and anti-inflammatory properties among others. More recently the gut microbiota has emerged as a potential modulator of the aging brain and intriguingly polyphenols have been shown to alter microbiota composition and be metabolized by different microbial species. However, there is a need for well controlled studies in large populations to identify predictors of response, particularly given the vast inter-individual variation of human gut microbiota.
Collapse
Affiliation(s)
- F C Ross
- APC Microbiome Ireland, University College Cork, Co. Cork, Ireland
| | - D E Mayer
- Institute of Human Nutrition, Columbia University, New York, USA
| | - J Horn
- Oppenheimer Centre for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, UCLA, Los Angeles, USA
| | - J F Cryan
- APC Microbiome Ireland, University College Cork, Co. Cork, Ireland
- Department Anatomy & Neuroscience, University College Cork, Co. Cork, Ireland
| | - D Del Rio
- Department of Food and Drugs, University of Parma, Parma, Italy
| | - E Randolph
- Oppenheimer Centre for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, UCLA, Los Angeles, USA
| | - C I R Gill
- Nutrition Innovation Centre for Food and Health, Northern Ireland, UK
| | - A Gupta
- Division of Digestive Diseases, UCLA, Los Angeles, USA
- Goodman Luskin Microbiome Center at UCLA, Los Angeles, CA, USA
| | - R P Ross
- APC Microbiome Ireland, University College Cork, Co. Cork, Ireland
| | - C Stanton
- Teagasc Food Research Centre, Moorepark, Fermoy, Co. Cork, Ireland
| | - E A Mayer
- Oppenheimer Centre for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, UCLA, Los Angeles, USA
- Goodman Luskin Microbiome Center at UCLA, Los Angeles, CA, USA
| |
Collapse
|
6
|
Hao X, Li J, Ma M, Qin J, Zhang D, Liu F. Hypergraph convolutional network for longitudinal data analysis in Alzheimer's disease. Comput Biol Med 2024; 168:107765. [PMID: 38042101 DOI: 10.1016/j.compbiomed.2023.107765] [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: 07/20/2023] [Revised: 11/06/2023] [Accepted: 11/21/2023] [Indexed: 12/04/2023]
Abstract
Alzheimer's disease (AD) is an irreversible and progressive neurodegenerative disease. Longitudinal structural magnetic resonance imaging (sMRI) data have been widely used for tracking AD pathogenesis and diagnosis. However, existing methods tend to treat each time point equally without considering the temporal characteristics of longitudinal data. In this paper, we propose a weighted hypergraph convolution network (WHGCN) to use the internal correlations among different time points and leverage high-order relationships between subjects for AD detection. Specifically, we construct hypergraphs for sMRI data at each time point using the K-nearest neighbor (KNN) method to represent relationships between subjects, and then fuse the hypergraphs according to the importance of the data at each time point to obtain the final hypergraph. Subsequently, we use hypergraph convolution to learn high-order information between subjects while performing feature dimensionality reduction. Finally, we conduct experiments on 518 subjects selected from the Alzheimer's disease neuroimaging initiative (ADNI) database, and the results show that the WHGCN can get higher AD detection performance and has the potential to improve our understanding of the pathogenesis of AD.
Collapse
Affiliation(s)
- Xiaoke Hao
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China.
| | - Jiawang Li
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China
| | - Mingming Ma
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China
| | - Jing Qin
- Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
| | - Feng Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| |
Collapse
|
7
|
De A, Mishra TK, Saraf S, Tripathy B, Reddy SS. A Review on the Use of Modern Computational Methods in Alzheimer's Disease-Detection and Prediction. Curr Alzheimer Res 2024; 20:845-861. [PMID: 38468529 DOI: 10.2174/0115672050301514240307071217] [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: 01/13/2024] [Revised: 02/07/2024] [Accepted: 02/20/2024] [Indexed: 03/13/2024]
Abstract
Discoveries in the field of medical sciences are blooming rapidly at the cost of voluminous efforts. Presently, multidisciplinary research activities have been especially contributing to catering cutting-edge solutions to critical problems in the domain of medical sciences. The modern age computing resources have proved to be a boon in this context. Effortless solutions have become a reality, and thus, the real beneficiary patients are able to enjoy improved lives. One of the most emerging problems in this context is Alzheimer's disease, an incurable neurological disorder. For this, early diagnosis is made possible with benchmark computing tools and schemes. These benchmark schemes are the results of novel research contributions being made intermittently in the timeline. In this review, an attempt is made to explore all such contributions in the past few decades. A systematic review is made by categorizing these contributions into three folds, namely, First, Second, and Third Generations. However, priority is given to the latest ones as a handful of literature reviews are already available for the classical ones. Key contributions are discussed vividly. The objectives set for this review are to bring forth the latest discoveries in computing methodologies, especially those dedicated to the diagnosis of Alzheimer's disease. A detailed timeline of the contributions is also made available. Performance plots for certain key contributions are also presented for better graphical understanding.
Collapse
Affiliation(s)
- Arka De
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Tusar Kanti Mishra
- Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Sameeksha Saraf
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Balakrushna Tripathy
- School of Information Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Shiva Shankar Reddy
- Department of Computer Science and Engineering, Sagi Rama Krishnam Raju Engineering College, Bhimavaram, Andhra Pradesh, India
| |
Collapse
|
8
|
Shim M, Hwang HJ, Lee SH. Toward practical machine-learning-based diagnosis for drug-naïve women with major depressive disorder using EEG channel reduction approach. J Affect Disord 2023; 338:199-206. [PMID: 37302509 DOI: 10.1016/j.jad.2023.06.007] [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: 07/18/2022] [Revised: 03/30/2023] [Accepted: 06/04/2023] [Indexed: 06/13/2023]
Abstract
BACKGROUND A machine-learning-based computer-aided diagnosis (CAD) system can complement the traditional diagnostic error for major depressive disorder (MDD) using trait-like neurophysiological biomarkers. Previous studies have shown that the CAD system has the potential to differentiate between female MDD patients and healthy controls. The aim of this study was to develop a practically useful resting-state electroencephalography (EEG)-based CAD system to assist in the diagnosis of drug-naïve female MDD patients by considering both the drug and gender effects. In addition, the feasibility of the practical use of the resting-state EEG-based CAD system was evaluated using a channel reduction approach. METHODS Eyes-closed, resting-state EEG data were recorded from 49 drug-naïve female MDD patients and 49 sex-matched healthy controls. Six different EEG feature sets were extracted: power spectrum densities (PSDs), phase-locking values (PLVs), and network indices for both sensor- and source-level, and four different EEG channel montages (62, 30, 19, and 10-channels) were designed to investigate the channel reduction effects in terms of classification performance. RESULTS The classification performances for each feature set were evaluated using a support vector machine with leave-one-out cross-validation. The optimum classification performance was achieved when using sensor-level PLVs (accuracy: 83.67 % and area under curve: 0.92). Moreover, the classification performance was maintained until the number of EEG channels was reduced to 19 (over 80 % accuracy). CONCLUSION We demonstrated the promising potential of sensor-level PLVs as diagnostic features when developing a resting-state EEG-based CAD system for the diagnosis of drug-naïve female MDD patients and verified the feasibility of the practical use of the developed resting-state EEG-based CAD system using the channel reduction approach.
Collapse
Affiliation(s)
- Miseon Shim
- Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea; Industry Development Institute, Korea University, Sejong, Republic of Korea
| | - Han-Jeong Hwang
- Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea; Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, Republic of Korea.
| | - Seung-Hwan Lee
- Psychiatry Department, Ilsan Paik Hospital, Inje University, Goyang, Republic of Korea; Clinical Emotion and Cognition Research Laboratory, Goyang, Republic of Korea; BWAVE Inc., Goyang, Republic of Korea.
| |
Collapse
|
9
|
Ahmadzadeh M, Christie GJ, Cosco TD, Arab A, Mansouri M, Wagner KR, DiPaola S, Moreno S. Neuroimaging and machine learning for studying the pathways from mild cognitive impairment to alzheimer's disease: a systematic review. BMC Neurol 2023; 23:309. [PMID: 37608251 PMCID: PMC10463866 DOI: 10.1186/s12883-023-03323-2] [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: 08/03/2022] [Accepted: 07/08/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND This systematic review synthesizes the most recent neuroimaging procedures and machine learning approaches for the prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia. METHODS We systematically searched PubMed, SCOPUS, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review guidelines. RESULTS Our search returned 2572 articles, 56 of which met the criteria for inclusion in the final selection. The multimodality framework and deep learning techniques showed potential for predicting the conversion of MCI to AD dementia. CONCLUSION Findings of this systematic review identified that the possibility of using neuroimaging data processed by advanced learning algorithms is promising for the prediction of AD progression. We also provided a detailed description of the challenges that researchers are faced along with future research directions. The protocol has been registered in the International Prospective Register of Systematic Reviews- CRD42019133402 and published in the Systematic Reviews journal.
Collapse
Affiliation(s)
- Maryam Ahmadzadeh
- School of Interactive Arts and Technology, Simon Fraser University, 250 - 13450 102 Ave, Surrey, BC, Canada
| | - Gregory J Christie
- School of Interactive Arts and Technology, Simon Fraser University, 250 - 13450 102 Ave, Surrey, BC, Canada
| | - Theodore D Cosco
- Gerontology Research Center, Simon Fraser University, Vancouver, BC, Canada
- Oxford Institute of Population Ageing, University of Oxford, Oxford, UK
| | - Ali Arab
- Department of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Mehrdad Mansouri
- Department of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Kevin R Wagner
- Gerontology Research Center, Simon Fraser University, Vancouver, BC, Canada
| | - Steve DiPaola
- School of Interactive Arts and Technology, Simon Fraser University, 250 - 13450 102 Ave, Surrey, BC, Canada.
| | - Sylvain Moreno
- School of Interactive Arts and Technology, Simon Fraser University, 250 - 13450 102 Ave, Surrey, BC, Canada
| |
Collapse
|
10
|
Liu Y, Mazumdar S, Bath PA. An unsupervised learning approach to diagnosing Alzheimer's disease using brain magnetic resonance imaging scans. Int J Med Inform 2023; 173:105027. [PMID: 36921480 DOI: 10.1016/j.ijmedinf.2023.105027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common cause of dementia, characterised by behavioural and cognitive impairment. Due to the lack of effectiveness of manual diagnosis by doctors, machine learning is now being applied to diagnose AD in many recent studies. Most research developing machine learning algorithms to diagnose AD use supervised learning to classify magnetic resonance imaging (MRI) scans. However, supervised learning requires a considerable volume of labelled data and MRI scans are difficult to label. OBJECTIVE This study applied a statistical method and unsupervised learning methods to discriminate between scans from cognitively normal (CN) and people with AD using a limited number of labelled structural MRI scans. METHODS We used two-sample t-tests to detect the AD-relevant regions, and then employed an unsupervised learning neural network to extract features from the regions. Finally, a clustering algorithm was implemented to discriminate between CN and AD data based on the extracted features. The approach was tested on baseline brain structural MRI scans from 429 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI), of which 231 were CN and 198 had AD. RESULTS The abnormal regions around the lower parts of limbic system were indicated as AD-relevant regions based on the two-sample t-test (p < 0.001), and the proposed method yielded an accuracy of 0.84 for discriminating between CN and AD. CONCLUSION The study combined statistical and unsupervised learning methods to identify scans of people with AD. This method can detect AD-relevant regions and could be used to accurately diagnose AD; it does not require large amounts of labelled MRI scans. Our research could help in the automatic diagnosis of AD and provide a basis for diagnosing stable mild cognitive impairment (stable MCI) and progressive mild cognitive impairment (progressive MCI).
Collapse
Affiliation(s)
- Yuyang Liu
- Information School, University of Sheffield, 211 Portobello, Sheffield S1 4DP, UK.
| | - Suvodeep Mazumdar
- Information School, University of Sheffield, 211 Portobello, Sheffield S1 4DP, UK
| | - Peter A Bath
- Information School, University of Sheffield, 211 Portobello, Sheffield S1 4DP, UK
| | -
- Information School, University of Sheffield, 211 Portobello, Sheffield S1 4DP, UK
| |
Collapse
|
11
|
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.
Collapse
|
12
|
Carcagnì P, Leo M, Del Coco M, Distante C, De Salve A. Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI. SENSORS (BASEL, SWITZERLAND) 2023; 23:1694. [PMID: 36772733 PMCID: PMC9919436 DOI: 10.3390/s23031694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/28/2022] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
Alzheimer's disease (AD) is the most common form of dementia. Computer-aided diagnosis (CAD) can help in the early detection of associated cognitive impairment. The aim of this work is to improve the automatic detection of dementia in MRI brain data. For this purpose, we used an established pipeline that includes the registration, slicing, and classification steps. The contribution of this research was to investigate for the first time, to our knowledge, three current and promising deep convolutional models (ResNet, DenseNet, and EfficientNet) and two transformer-based architectures (MAE and DeiT) for mapping input images to clinical diagnosis. To allow a fair comparison, the experiments were performed on two publicly available datasets (ADNI and OASIS) using multiple benchmarks obtained by changing the number of slices per subject extracted from the available 3D voxels. The experiments showed that very deep ResNet and DenseNet models performed better than the shallow ResNet and VGG versions tested in the literature. It was also found that transformer architectures, and DeiT in particular, produced the best classification results and were more robust to the noise added by increasing the number of slices. A significant improvement in accuracy (up to 7%) was achieved compared to the leading state-of-the-art approaches, paving the way for the use of CAD approaches in real-world applications.
Collapse
|
13
|
Abbas Q, Hussain A, Baig AR. CAD-ALZ: A Blockwise Fine-Tuning Strategy on Convolutional Model and Random Forest Classifier for Recognition of Multistage Alzheimer's Disease. Diagnostics (Basel) 2023; 13:167. [PMID: 36611459 PMCID: PMC9818479 DOI: 10.3390/diagnostics13010167] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/24/2022] [Accepted: 12/31/2022] [Indexed: 01/05/2023] Open
Abstract
Mental deterioration or Alzheimer's (ALZ) disease is progressive and causes both physical and mental dependency. There is a need for a computer-aided diagnosis (CAD) system that can help doctors make an immediate decision. (1) Background: Currently, CAD systems are developed based on hand-crafted features, machine learning (ML), and deep learning (DL) techniques. Those CAD systems frequently require domain-expert knowledge and massive datasets to extract deep features or model training, which causes problems with class imbalance and overfitting. Additionally, there are still manual approaches used by radiologists due to the lack of dataset availability and to train the model with cost-effective computation. Existing works rely on performance improvement by neglecting the problems of the limited dataset, high computational complexity, and unavailability of lightweight and efficient feature descriptors. (2) Methods: To address these issues, a new approach, CAD-ALZ, is developed by extracting deep features through a ConvMixer layer with a blockwise fine-tuning strategy on a very small original dataset. At first, we apply the data augmentation method to images to increase the size of datasets. In this study, a blockwise fine-tuning strategy is employed on the ConvMixer model to detect robust features. Afterwards, a random forest (RF) is used to classify ALZ disease stages. (3) Results: The proposed CAD-ALZ model obtained significant results by using six evaluation metrics such as the F1-score, Kappa, accuracy, precision, sensitivity, and specificity. The CAD-ALZ model performed with a sensitivity of 99.69% and an F1-score of 99.61%. (4) Conclusions: The suggested CAD-ALZ approach is a potential technique for clinical use and computational efficiency compared to state-of-the-art approaches. The CAD-ALZ model code is freely available on GitHub for the scientific community.
Collapse
Affiliation(s)
- Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Ayyaz Hussain
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan
| | - Abdul Rauf Baig
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| |
Collapse
|
14
|
Merone M, D'Addario SL, Mirino P, Bertino F, Guariglia C, Ventura R, Capirchio A, Baldassarre G, Silvetti M, Caligiore D. A multi-expert ensemble system for predicting Alzheimer transition using clinical features. Brain Inform 2022; 9:20. [PMID: 36056985 PMCID: PMC9440971 DOI: 10.1186/s40708-022-00168-2] [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: 02/08/2022] [Accepted: 05/16/2022] [Indexed: 11/18/2022] Open
Abstract
Alzheimer’s disease (AD) diagnosis often requires invasive examinations (e.g., liquor analyses), expensive tools (e.g., brain imaging) and highly specialized personnel. The diagnosis commonly is established when the disorder has already caused severe brain damage, and the clinical signs begin to be apparent. Instead, accessible and low-cost approaches for early identification of subjects at high risk for developing AD years before they show overt symptoms are fundamental to provide a critical time window for more effective clinical management, treatment, and care planning. This article proposes an ensemble-based machine learning algorithm for predicting AD development within 9 years from first overt signs and using just five clinical features that are easily detectable with neuropsychological tests. The validation of the system involved both healthy individuals and mild cognitive impairment (MCI) patients drawn from the ADNI open dataset, at variance with previous studies that considered only MCI. The system shows higher levels of balanced accuracy, negative predictive value, and specificity than other similar solutions. These results represent a further important step to build a preventive fast-screening machine-learning-based tool to be used as a part of routine healthcare screenings.
Collapse
Affiliation(s)
- Mario Merone
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy
| | - Sebastian Luca D'Addario
- Department of Psychology, Sapienza University, Piazzale Aldo Moro 5, 00185, Rome, Italy.,Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy.,IRCCS Fondazione Santa Lucia, Via Ardeatina, 306 and Via Del Fosso di Fiorano, 64, 00143, Rome, Italy
| | - Pierandrea Mirino
- Department of Psychology, Sapienza University, Piazzale Aldo Moro 5, 00185, Rome, Italy.,Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy.,AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, 00199, Rome, Italy
| | - Francesca Bertino
- Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy
| | - Cecilia Guariglia
- Department of Psychology, Sapienza University, Piazzale Aldo Moro 5, 00185, Rome, Italy.,IRCCS Fondazione Santa Lucia, Via Ardeatina, 306 and Via Del Fosso di Fiorano, 64, 00143, Rome, Italy
| | - Rossella Ventura
- Department of Psychology, Sapienza University, Piazzale Aldo Moro 5, 00185, Rome, Italy.,IRCCS Fondazione Santa Lucia, Via Ardeatina, 306 and Via Del Fosso di Fiorano, 64, 00143, Rome, Italy
| | - Adriano Capirchio
- AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, 00199, Rome, Italy
| | - Gianluca Baldassarre
- AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, 00199, Rome, Italy.,Laboratory of Embodied Natural and Artificial Intelligence, Institute of Cognitive Sciences and Technologies, National Research Council (LENAI-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy
| | - Massimo Silvetti
- Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy
| | - Daniele Caligiore
- Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy. .,AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, 00199, Rome, Italy.
| |
Collapse
|
15
|
Li Y, Hsu W. A classification for complex imbalanced data in disease screening and early diagnosis. Stat Med 2022; 41:3679-3695. [PMID: 35603639 PMCID: PMC9541048 DOI: 10.1002/sim.9442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 04/11/2022] [Accepted: 05/10/2022] [Indexed: 11/09/2022]
Abstract
Imbalanced classification has drawn considerable attention in the statistics and machine learning literature. Typically, traditional classification methods often perform poorly when a severely skewed class distribution is observed, not to mention under a high-dimensional longitudinal data structure. Given the ubiquity of big data in modern health research, it is expected that imbalanced classification in disease diagnosis may encounter an additional level of difficulty that is imposed by such a complex data structure. In this article, we propose a nonparametric classification approach for imbalanced data in longitudinal and high-dimensional settings. Technically, the functional principal component analysis is first applied for feature extraction under the longitudinal structure. The univariate exponential loss function coupled with group LASSO penalty is then adopted into the classification procedure in high-dimensional settings. Along with a good improvement in imbalanced classification, our approach provides a meaningful feature selection for interpretation while enjoying a remarkably lower computational complexity. The proposed method is illustrated on the real data application of Alzheimer's disease early detection and its empirical performance in finite sample size is extensively evaluated by simulations.
Collapse
Affiliation(s)
- Yiming Li
- Department of StatisticsKansas State UniversityManhattanKansasUSA
| | - Wei‐Wen Hsu
- Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health SciencesUniversity of CincinnatiCincinnatiOhioUSA
| | | |
Collapse
|
16
|
A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer's disease. COMMUNICATIONS MEDICINE 2022; 2:70. [PMID: 35759330 PMCID: PMC9209493 DOI: 10.1038/s43856-022-00133-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 05/24/2022] [Indexed: 01/12/2023] Open
Abstract
Background Alzheimer's disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care. Methods We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called "Alzheimer's Predictive Vector" (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO). Results The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer's related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype. Conclusions This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.
Collapse
|
17
|
A novelty detection approach to effectively predict conversion from mild cognitive impairment to Alzheimer’s disease. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01570-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
AbstractAccurately recognising patients with progressive mild cognitive impairment (pMCI) who will develop Alzheimer’s disease (AD) in subsequent years is very important, as early identification of those patients will enable interventions to potentially reduce the number of those transitioning from MCI to AD. Most studies in this area have concentrated on high-dimensional neuroimaging data with supervised binary/multi-class classification algorithms. However, neuroimaging data is more costly to obtain than non-imaging, and healthcare datasets are normally imbalanced which may reduce classification performance and reliability. To address these challenges, we proposed a new strategy that employs unsupervised novelty detection (ND) techniques to predict pMCI from the AD neuroimaging initiative non-imaging data. ND algorithms, including the k-nearest neighbours (kNN), k-means, Gaussian mixture model (GMM), isolation forest (IF) and extreme learning machine (ELM), were employed and compared with supervised binary support vector machine (SVM) and random forest (RF). We introduced optimisation with nested cross-validation and focused on maximising the adjusted F measure to ensure maximum generalisation of the proposed system by minimising false negative rates. Our extensive experimental results show that ND algorithms (0.727 ± 0.029 kNN, 0.7179 ± 0.0523 GMM, 0.7276 ± 0.0281 ELM) obtained comparable performance to supervised binary SVM (0.7359 ± 0.0451) with 20% stable MCI misclassification tolerance and were significantly better than RF (0.4771 ± 0.0167). Moreover, we found that the non-invasive, readily obtainable, and cost-effective cognitive and functional assessment was the most efficient predictor for predicting the pMCI within 2 years with ND techniques. Importantly, we presented an accessible and cost-effective approach to pMCI prediction, which does not require labelled data.
Collapse
|
18
|
Predictive Scale for Amyloid PET Positivity Based on Clinical and MRI Variables in Patients with Amnestic Mild Cognitive Impairment. J Clin Med 2022; 11:jcm11123433. [PMID: 35743503 PMCID: PMC9224873 DOI: 10.3390/jcm11123433] [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: 04/11/2022] [Revised: 06/11/2022] [Accepted: 06/12/2022] [Indexed: 12/05/2022] Open
Abstract
The presence of amyloid-β (Aβ) deposition is considered important in patients with amnestic mild cognitive impairment (aMCI), since they can progress to Alzheimer’s disease dementia. Amyloid positron emission tomography (PET) has been used for detecting Aβ deposition, but its high cost is a significant barrier for clinical usage. Therefore, we aimed to develop a new predictive scale for amyloid PET positivity using easily accessible tools. Overall, 161 aMCI patients were recruited from six memory clinics and underwent neuropsychological tests, brain magnetic resonance imaging (MRI), apolipoprotein E (APOE) genotype testing, and amyloid PET. Among the potential predictors, verbal and visual memory tests, medial temporal lobe atrophy, APOE genotype, and age showed significant differences between the Aβ-positive and Aβ-negative groups and were combined to make a model for predicting amyloid PET positivity with the area under the curve (AUC) of 0.856. Based on the best model, we developed the new predictive scale comprising integers, which had an optimal cutoff score ≥ 3. The new predictive scale was validated in another cohort of 98 participants and showed a good performance with AUC of 0.835. This new predictive scale with accessible variables may be useful for predicting Aβ positivity in aMCI patients in clinical practice.
Collapse
|
19
|
Neuroimaging Modalities in Alzheimer’s Disease: Diagnosis and Clinical Features. Int J Mol Sci 2022; 23:ijms23116079. [PMID: 35682758 PMCID: PMC9181385 DOI: 10.3390/ijms23116079] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 11/17/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death. AD affects millions of individuals worldwide in the absence of effective treatment options, and its clinical causes are still uncertain. The onset of dementia symptoms indicates severe neurodegeneration has already taken place. Therefore, AD diagnosis at an early stage is essential as it results in more effective therapy to slow its progression. The current clinical diagnosis of AD relies on mental examinations and brain imaging to determine whether patients meet diagnostic criteria, and biomedical research focuses on finding associated biomarkers by using neuroimaging techniques. Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease. This review presents the advantages and limitations of brain imaging modalities for AD diagnosis and discusses their clinical value.
Collapse
|
20
|
Li P, Sofuoglu SE, Aviyente S, Maiti T. Coupled support tensor machine classification for multimodal neuroimaging data. Stat Anal Data Min 2022. [DOI: 10.1002/sam.11587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Peide Li
- Boehringer Ingelheim Pharmaceuticals Duluth Georgia USA
| | | | - Selin Aviyente
- College of Engineering Michigan State University East Lansing Michigan USA
| | - Tapabrata Maiti
- College of Natural Science Michigan State University East Lansing Michigan USA
| |
Collapse
|
21
|
Ye C, Cheng M, Ma L, Zhang T, Sun Z, Yu C, Wang J, Dou Y. Oxytocin Nanogels Inhibit Innate Inflammatory Response for Early Intervention in Alzheimer's Disease. ACS APPLIED MATERIALS & INTERFACES 2022; 14:21822-21835. [PMID: 35510352 DOI: 10.1021/acsami.2c00007] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Prevention of Alzheimer's disease (AD) is a global imperative, but reliable early interventions are currently lacking. Microglia-mediated chronic neuroinflammation is thought to occur in the early stage of AD and plays a critical role in AD pathogenesis. Here, oxytocin (OT)-loaded angiopep-2-modified chitosan nanogels (AOC NGs) were designed for early treatment of AD via inhibiting innate inflammatory response. Through the effective transcytosis of angiopep-2, AOC NGs were driven intravenously to cross the blood-brain barrier, enter the brain, and enrich in brain areas affected by AD. A large amount of OT was then released and specifically bound to the pathological upregulated OT receptor, thus effectively inhibiting microglial activation and reducing inflammatory cytokine levels through blocking the ERK/p38 MAPK and COX-2/iNOS NF-κB signaling pathways. Consecutive weekly intravenous administration of AOC NGs into 12-week-old young APP/PS1 mice, representing the early stage of AD, remarkably slowed the progression of Aβ deposition and neuronal apoptosis in the APP/PS1 mice as they aged and ultimately prevented cognitive impairment and delayed hippocampal atrophy. Together, the findings suggest that AOC NGs, which show good biosafety, can serve as a promising therapeutic candidate to combat neuroinflammation for early prevention of AD.
Collapse
Affiliation(s)
- Caihua Ye
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, P. R. China
| | - Meng Cheng
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, P. R. China
| | - Lin Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, P. R. China
| | - Tianzhu Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, P. R. China
| | - Zuhao Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, P. R. China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, P. R. China
| | - Junping Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, P. R. China
| | - Yan Dou
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, P. R. China
| |
Collapse
|
22
|
Frizzell TO, Glashutter M, Liu CC, Zeng A, Pan D, Hajra SG, D’Arcy RC, Song X. Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review. Ageing Res Rev 2022; 77:101614. [PMID: 35358720 DOI: 10.1016/j.arr.2022.101614] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 03/02/2022] [Accepted: 03/24/2022] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast-growing effort in applying artificial intelligence (AI) to analyze these data. Here, we review and evaluate the AI studies in brain MRI analysis with synthesis. METHODS A systematic review of the literature, spanning the years from 2009 to 2020, was completed using the PubMed database. AI studies using MRI imaging to investigate normal aging, mild cognitive impairment, and AD-dementia were retrieved for review. Bias assessment was completed using the PROBAST criteria. RESULTS 97 relevant studies were included in the review. The studies were typically focused on the classification of AD, MCI, and normal aging (71% of the reported studies) and the prediction of MCI conversion to AD (25%). The best performance was achieved by using the deep learning-based convolution neural network algorithms (weighted average accuracy 89%), in contrast to 76-86% using Logistic Regression, Support Vector Machines, and other AI methods. DISCUSSION The synthesized evidence is paramount to developing sophisticated AI approaches to reliably capture and quantify multiple subtle MRI changes in the whole brain that exemplify the complexity and heterogeneity of AD and brain aging.
Collapse
|
23
|
Shim M, Im CH, Lee SH, Hwang HJ. Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder. Front Neuroinform 2022; 16:811756. [PMID: 35571868 PMCID: PMC9094422 DOI: 10.3389/fninf.2022.811756] [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: 11/09/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Electroencephalography (EEG)-based diagnosis of psychiatric diseases using machine-learning approaches has made possible the objective diagnosis of various psychiatric diseases. The objective of this study was to improve the performance of a resting-state EEG-based computer-aided diagnosis (CAD) system to diagnose post-traumatic stress disorder (PTSD), by optimizing the frequency bands used to extract EEG features. We used eyes-closed resting-state EEG data recorded from 77 PTSD patients and 58 healthy controls (HC). Source-level power spectrum densities (PSDs) of the resting-state EEG data were extracted from 6 frequency bands (delta, theta, alpha, low-beta, high-beta, and gamma), and the PSD features of each frequency band and their combinations were independently used to discriminate PTSD and HC. The classification performance was evaluated using support vector machine with leave-one-out cross validation. The PSD features extracted from slower-frequency bands (delta and theta) showed significantly higher classification performance than those of relatively higher-frequency bands. The best classification performance was achieved when using delta PSD features (86.61%), which was significantly higher than that reported in a recent study by about 13%. The PSD features selected to obtain better classification performances could be explained from a neurophysiological point of view, demonstrating the promising potential to develop a clinically reliable EEG-based CAD system for PTSD diagnosis.
Collapse
Affiliation(s)
- Miseon Shim
- Department of Electronics and Information, Korea University, Sejong, South Korea
- Industry Development Institute, Korea University, Sejong, South Korea
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Seung-Hwan Lee
- Department of Psychiatry, Ilsan Paik Hospital, Inje University, Goyang, South Korea
- Clinical Emotion and Cognition Research Laboratory, Goyang, South Korea
| | - Han-Jeong Hwang
- Department of Electronics and Information, Korea University, Sejong, South Korea
- Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, South Korea
| |
Collapse
|
24
|
Effect of Denoising and Deblurring 18F-Fluorodeoxyglucose Positron Emission Tomography Images on a Deep Learning Model’s Classification Performance for Alzheimer’s Disease. Metabolites 2022; 12:metabo12030231. [PMID: 35323674 PMCID: PMC8954205 DOI: 10.3390/metabo12030231] [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: 02/14/2022] [Revised: 03/02/2022] [Accepted: 03/04/2022] [Indexed: 11/17/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely used to predict AD using a deep learning model. However, the effects of noise and blurring on 18F-FDG PET images were not considered. The performance of a classification model trained using raw, deblurred (by the fast total variation deblurring method), or denoised (by the median modified Wiener filter) 18F-FDG PET images without or with cropping around the limbic system area using a 3D deep convolutional neural network was investigated. The classification model trained using denoised whole-brain 18F-FDG PET images achieved classification performance (0.75/0.65/0.79/0.39 for sensitivity/specificity/F1-score/Matthews correlation coefficient (MCC), respectively) higher than that with raw and deblurred 18F-FDG PET images. The classification model trained using cropped raw 18F-FDG PET images achieved higher performance (0.78/0.63/0.81/0.40 for sensitivity/specificity/F1-score/MCC) than the whole-brain 18F-FDG PET images (0.72/0.32/0.71/0.10 for sensitivity/specificity/F1-score/MCC, respectively). The 18F-FDG PET image deblurring and cropping (0.89/0.67/0.88/0.57 for sensitivity/specificity/F1-score/MCC) procedures were the most helpful for improving performance. For this model, the right middle frontal, middle temporal, insula, and hippocampus areas were the most predictive of AD using the class activation map. Our findings demonstrate that 18F-FDG PET image preprocessing and cropping improves the explainability and potential clinical applicability of deep learning models.
Collapse
|
25
|
Feng J, Zhang SW, Chen L, Zuo C. Detection of Alzheimer’s Disease Using Features of Brain Region-of-Interest-Based Individual Network Constructed with the sMRI Image. Comput Med Imaging Graph 2022; 98:102057. [DOI: 10.1016/j.compmedimag.2022.102057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 02/18/2022] [Accepted: 03/17/2022] [Indexed: 10/18/2022]
|
26
|
Li R, Wang X, Lawler K, Garg S, Bai Q, Alty J. Applications of Artificial Intelligence to aid detection of dementia: a scoping review on current capabilities and future directions. J Biomed Inform 2022; 127:104030. [DOI: 10.1016/j.jbi.2022.104030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/21/2022] [Accepted: 02/12/2022] [Indexed: 12/17/2022]
|
27
|
Massetti N, Russo M, Franciotti R, Nardini D, Mandolini G, Granzotto A, Bomba M, Delli Pizzi S, Mosca A, Scherer R, Onofrj M, Sensi SL. A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum. J Alzheimers Dis 2021; 85:1639-1655. [PMID: 34958014 DOI: 10.3233/jad-210573] [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] [Indexed: 11/15/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD. OBJECTIVE To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion. METHODS We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables. RESULTS The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects. CONCLUSION Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.
Collapse
Affiliation(s)
- Noemi Massetti
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Mirella Russo
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Raffaella Franciotti
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | | | | | - Alberto Granzotto
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy.,Sue and Bill Gross Stem Cell Research Center, University of California - Irvine, Irvine, CA, USA
| | - Manuela Bomba
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Stefano Delli Pizzi
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Alessandra Mosca
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Reinhold Scherer
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
| | - Marco Onofrj
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Stefano L Sensi
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy.,Institute for Mind Impairments and Neurological Disorders - iMIND, University of California - Irvine, Irvine, CA, USA
| | | | | |
Collapse
|
28
|
Das S, Panigrahi P, Chakrabarti S. Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer's Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques. J Alzheimers Dis Rep 2021; 5:771-788. [PMID: 34870103 PMCID: PMC8609489 DOI: 10.3233/adr-210314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/24/2021] [Indexed: 01/25/2023] Open
Abstract
Background: The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease. Objective: To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques. Methods: We have performed automatic detection and segmentation of the CC and calculated its morphological features to feed into a multivariate pattern analysis using support vector machine (SVM) learning techniques. Results: Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and > 80%specificity in detecting mild AD patients. Conclusion: Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.
Collapse
Affiliation(s)
- Subhrangshu Das
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India
| | - Priyanka Panigrahi
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India.,Academy of Scientific and Innovative Research, Ghaziabad, Uttar Pradesh, India
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India.,Academy of Scientific and Innovative Research, Ghaziabad, Uttar Pradesh, India
| |
Collapse
|
29
|
Agarwal D, Marques G, de la Torre-Díez I, Franco Martin MA, García Zapiraín B, Martín Rodríguez F. Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:7259. [PMID: 34770565 PMCID: PMC8587338 DOI: 10.3390/s21217259] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.
Collapse
Affiliation(s)
- Deevyankar Agarwal
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (G.M.); (I.d.l.T.-D.)
| | - Gonçalo Marques
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (G.M.); (I.d.l.T.-D.)
- Polytechnic of Coimbra, ESTGOH, Rua General Santos Costa, 3400-124 Oliveira do Hospital, Portugal
| | - Isabel de la Torre-Díez
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (G.M.); (I.d.l.T.-D.)
| | - Manuel A. Franco Martin
- Psychiatric Department, University Rio Hortega Hospital–Valladolid, 47011 Valladolid, Spain;
| | - Begoña García Zapiraín
- eVIDA Laboratory, University of Deusto, Avenida de las Universidades 24, 48007 Bilbao, Spain;
| | - Francisco Martín Rodríguez
- Advanced Clinical Simulation Center, School of Medicine, University of Valladolid, 47011 Valladolid, Spain;
| |
Collapse
|
30
|
Grueso S, Viejo-Sobera R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review. Alzheimers Res Ther 2021; 13:162. [PMID: 34583745 PMCID: PMC8480074 DOI: 10.1186/s13195-021-00900-w] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 09/12/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia. METHODS We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer's disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. RESULTS Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. CONCLUSIONS Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
Collapse
Affiliation(s)
- Sergio Grueso
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain.
| | - Raquel Viejo-Sobera
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain
| |
Collapse
|
31
|
Sutoko S, Masuda A, Kandori A, Sasaguri H, Saito T, Saido TC, Funane T. Early Identification of Alzheimer's Disease in Mouse Models: Application of Deep Neural Network Algorithm to Cognitive Behavioral Parameters. iScience 2021; 24:102198. [PMID: 33733064 PMCID: PMC7937558 DOI: 10.1016/j.isci.2021.102198] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 01/12/2021] [Accepted: 02/11/2021] [Indexed: 01/15/2023] Open
Abstract
Alzheimer's disease (AD) is a worldwide burden. Diagnosis is complicated by the fact that AD is asymptomatic at an early stage. Studies using AD-modeled animals offer important and useful insights. Here, we classified mice with a high risk of AD at a preclinical stage by using only their behaviors. Wild-type and knock-in AD-modeled (App NL-G-F/NL-G-F ) mice were raised, and their cognitive behaviors were assessed in an automated monitoring system. The classification utilized a machine learning method, i.e., a deep neural network, together with optimized stepwise feature selection and cross-validation. The AD risk could be identified on the basis of compulsive and learning behaviors (89.3% ± 9.8% accuracy) shown by AD-modeled mice in the early age (i.e., 8-12 months old) when the AD symptomatic cognitions were relatively underdeveloped. This finding reveals the advantage of machine learning in unveiling the importance of compulsive and learning behaviors for early AD diagnosis in mice.
Collapse
Affiliation(s)
- Stephanie Sutoko
- Hitachi, Ltd, Research and Development Group, Center for Exploratory Research, Kokubunji, Tokyo 185-8601, Japan
| | - Akira Masuda
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
- Organization for Research Initiatives and Development, Doshisha University, Kyotanabe, Kyoto 610-0394, Japan
| | - Akihiko Kandori
- Hitachi, Ltd, Research and Development Group, Center for Exploratory Research, Kokubunji, Tokyo 185-8601, Japan
| | - Hiroki Sasaguri
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
| | - Takashi Saito
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
- Department of Neurocognitive Science, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi 467-8601, Japan
| | - Takaomi C. Saido
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
| | - Tsukasa Funane
- Hitachi, Ltd, Research and Development Group, Center for Exploratory Research, Kokubunji, Tokyo 185-8601, Japan
| |
Collapse
|
32
|
Li Q, Yang MQ. Comparison of machine learning approaches for enhancing Alzheimer's disease classification. PeerJ 2021; 9:e10549. [PMID: 33665002 PMCID: PMC7916537 DOI: 10.7717/peerj.10549] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 11/20/2020] [Indexed: 11/24/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.
Collapse
Affiliation(s)
- Qi Li
- MidSouth Bioinformatics Center and Bioinformatics Graduate Program, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences, University of Arkansas at Little Rock, Little Rock, AR, United States of America
| | - Mary Qu Yang
- MidSouth Bioinformatics Center and Bioinformatics Graduate Program, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences, University of Arkansas at Little Rock, Little Rock, AR, United States of America
| |
Collapse
|
33
|
Ahmed S, Kim BC, Lee KH, Jung HY. Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging. PLoS One 2020; 15:e0242712. [PMID: 33290403 PMCID: PMC7723284 DOI: 10.1371/journal.pone.0242712] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/07/2020] [Indexed: 11/26/2022] Open
Abstract
Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design.
Collapse
Affiliation(s)
- Samsuddin Ahmed
- Department of Computer Engineering, Chosun University, Gwangju, South Korea
| | - Byeong C. Kim
- Gwangju Alzheimer’s disease and Related Dementias Cohort Research Center, Chosun University, Gwangju, Korea
- Department of Neurology, Chonnam National University Medical School, Gwangju, South Korea
| | - Kun Ho Lee
- Gwangju Alzheimer’s disease and Related Dementias Cohort Research Center, Chosun University, Gwangju, Korea
- Department of Biomedical Science, Chosun University, Gwangju, South Korea
- Korea Brain Research Institute, Daegu, Korea
| | - Ho Yub Jung
- Department of Computer Engineering, Chosun University, Gwangju, South Korea
| | | |
Collapse
|
34
|
Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods. Sci Rep 2020; 10:20630. [PMID: 33244011 PMCID: PMC7692490 DOI: 10.1038/s41598-020-77296-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 11/09/2020] [Indexed: 12/11/2022] Open
Abstract
Alzheimer's Disease is a complex, multifactorial, and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. is called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study, we leverage a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques.
Collapse
|
35
|
Tuan TA, Pham TB, Kim JY, Tavares JMRS. Alzheimer's diagnosis using deep learning in segmenting and classifying 3D brain MR images. Int J Neurosci 2020; 132:689-698. [PMID: 33045895 DOI: 10.1080/00207454.2020.1835900] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
BACKGROUND AND OBJECTIVES Dementia is one of the brain diseases with serious symptoms such as memory loss, and thinking problems. According to the World Alzheimer Report 2016, in the world, there are 47 million people having dementia and it can be 131 million by 2050. There is no standard method to diagnose dementia, and consequently unable to access the treatment effectively. Hence, the computational diagnosis of the disease from brain Magnetic Resonance Image (MRI) scans plays an important role in supporting the early diagnosis. Alzheimer's Disease (AD), a common type of Dementia, includes problems related to disorientation, mood swings, not managing self-care, and behavioral issues. In this article, we present a new computational method to diagnosis Alzheimer's disease from 3D brain MR images. METHODS An efficient approach to diagnosis Alzheimer's disease from brain MRI scans is proposed comprising two phases: I) segmentation and II) classification, both based on deep learning. After the brain tissues are segmented by a model that combines Gaussian Mixture Model (GMM) and Convolutional Neural Network (CNN), a new model combining Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) is used to classify Alzheimer's disease based on the segmented tissues. RESULTS We present two evaluations for segmentation and classification. For comparison, the new method was evaluated using the AD-86 and AD-126 datasets leading to Dice 0.96 for segmentation in both datasets and accuracies 0.88, and 0.80 for classification, respectively. CONCLUSION Deep learning gives prominent results for segmentation and feature extraction in medical image processing. The combination of XGboost and SVM improves the results obtained.
Collapse
Affiliation(s)
- Tran Anh Tuan
- Faculty of Mathematics and Computer Science, University of Science, Vietnam National University, Ho Chi Minh City, Vietnam
| | - The Bao Pham
- Department of Computer Science, Sai Gon University, Ho Chi Minh City, Vietnam
| | - Jin Young Kim
- Department of Electronic and Computer Engineering, Chonnam National University, Gwangju, South Korea
| | - João Manuel R S Tavares
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal
| |
Collapse
|
36
|
Murray AN, Chandler HL, Lancaster TM. Multimodal hippocampal and amygdala subfield volumetry in polygenic risk for Alzheimer's disease. Neurobiol Aging 2020; 98:33-41. [PMID: 33227567 PMCID: PMC7886309 DOI: 10.1016/j.neurobiolaging.2020.08.022] [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: 04/20/2020] [Revised: 07/28/2020] [Accepted: 08/02/2020] [Indexed: 11/29/2022]
Abstract
Preclinical models of Alzheimer's disease (AD) suggest that volumetric reductions in medial temporal lobe (MTL) structures manifest before clinical onset. AD polygenic risk scores (PRSs) are further linked to reduced MTL volumes (the hippocampus/amygdala); however, the relationship between the PRS and specific subregions remains unclear. We determine the relationship between the AD-PRSs and MTL subregions in a large sample of young participants (N = 730, aged 22–35 years) using a multimodal (T1w/T2w) approach. We first demonstrate that the PRSs for the hippocampus/amygdala predict their respective volumes and specific hippocampal subregions (pFDR < 0.05). We further observe negative relationships between the AD-PRSs and whole hippocampal/amygdala volumes. Critically, we demonstrate novel associations between the AD-PRSs and specific hippocampal subfields such as CA1 (β = −0.096, pFDR = 0.045) and the fissure (β = −0.101, pFDR = 0.041). We provide evidence that the AD-PRS is linked to specific MTL subfields decades before AD onset. This may help inform preclinical models of AD risk, providing additional specificity for intervention and further insight into mechanisms by which common AD variants confer susceptibility. Polygenic risk for Alzheimer's disease (AD-PRS) explains significant proportion of AD. AD-PRS also linked to hippocampus and amygdala volume. AD-PRS is negatively associated with specific hippocampal subfields. Polygenic AD models help us understand genetic contributions to medial temporal lobe nuclei.
Collapse
Affiliation(s)
- Amy N Murray
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Hannah L Chandler
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Thomas M Lancaster
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom; Dementia Research Institute at Cardiff University, School of Medicine, Cardiff University, Cardiff, United Kingdom; School of Psychology, Bath University, Bath, United Kingdom.
| |
Collapse
|
37
|
Yang F, Zhang J, Fan L, Liao M, Wang Y, Chen C, Zhai T, Zhang Y, Li L, Su L, Dai Z. White matter structural network disturbances in first-episode, drug-naïve adolescents with generalized anxiety disorder. J Psychiatr Res 2020; 130:394-404. [PMID: 32889357 DOI: 10.1016/j.jpsychires.2020.08.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/12/2020] [Accepted: 08/09/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND Previous studies have suggested that individuals with generalized anxiety disorder (GAD) would show inefficient whole-brain communication and dysconnectivity in the fronto-parietal-subcortical sub-networks in the white matter (WM) structural network. However, these hypotheses have yet to be tested. METHODS Individual WM structural networks were constructed based on diffusion MRI data and deterministic tractography in 34 first-episode, medication-naïve adolescents with GAD and 27 healthy controls (HCs). Graph theory was applied to investigate the topological organization alterations of the structural network. RESULTS GAD patients showed disrupted small-world configurations (i.e., increased path length and decreased clustering coefficient) and hub organization (i.e., less connection strength in the feeder and local connections). A decreased connection strength was found in a GAD-related sub-network (mainly involving the frontal-subcortical circuits), which was able to distinguish GAD patients from HCs with higher accuracy (area under the curve of 0.96, sensitivity of 94%, specificity of 89%) than clinical scores and other topological alternations. LIMITATIONS The current study just compared GAD patients with HCs based on a small sample, leaving whether the alternations found here are specific to GAD still an open question. Future studies are recommended to recruit patients with other anxiety disorders (e.g., social anxiety disorder) and/or comorbid mood disorders to identify the GAD-specific WM alterations using a larger sample. CONCLUSIONS Our findings highlight the disruption of the topological organization of the whole-brain WM structural network (especially the frontal-subcortical circuits) in GAD, and suggest the potential of using structural connectivity of the GAD-related sub-network as a biomarker for GAD patients.
Collapse
Affiliation(s)
- Fan Yang
- Guangdong Mental Health Center, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jinbo Zhang
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Linlin Fan
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States
| | - Mei Liao
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yuyin Wang
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Chang Chen
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Tianyi Zhai
- Department of Psychiatry, Guangzhou Huiai Hospital, Guangzhou, China
| | - Yan Zhang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Lingjiang Li
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Linyan Su
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, China.
| |
Collapse
|
38
|
Sharma S, Dudeja RK, Aujla GS, Bali RS, Kumar N. DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05327-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
AbstractHealthcare 4.0 paradigm aims at realization of data-driven and patient-centric health systems wherein advanced sensors can be deployed to provide personalized assistance. Hence, extreme mentally affected patients from diseases like Alzheimer can be assisted using sophisticated algorithms and enabling technologies. Motivated from this fact, in this paper,
DeTrAs: Deep Learning-based Internet of Health Framework for the Assistance of Alzheimer Patients is proposed. DeTrAs works in three phases: (1) A recurrent neural network-based Alzheimer prediction scheme is proposed which uses sensory movement data, (2) an ensemble approach for abnormality tracking for Alzheimer patients is designed which comprises two parts:
(a) convolutional neural network-based emotion detection scheme and (b) timestamp window-based natural language processing scheme, and (3) an IoT-based assistance mechanism for the Alzheimer patients is also presented. The evaluation of DeTrAs depicts almost 10–20% improvement in terms of accuracy in contrast to the different existing machine learning algorithms.
Collapse
|
39
|
Predicting Mental Decline Rates in Mild Cognitive Impairment From Baseline MRI Volumetric Data. Alzheimer Dis Assoc Disord 2020; 35:1-7. [PMID: 32925201 DOI: 10.1097/wad.0000000000000406] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 08/08/2020] [Indexed: 01/18/2023]
Abstract
PURPOSE In mild cognitive impairment (MCI), identifying individuals at high risk for progressive cognitive deterioration can be useful for prognostication and intervention. This study quantitatively characterizes cognitive decline rates in MCI and tests whether volumetric data from baseline magnetic resonance imaging (MRI) can predict accelerated cognitive decline. METHODS The authors retrospectively examined Alzheimer Disease Neuroimaging Initiative data to obtain serial Mini-Mental Status Exam (MMSE) scores, diagnoses, and the following baseline MRI volumes: total intracranial volume, whole-brain and ventricular volumes, and volumes of the hippocampus, entorhinal cortex, fusiform gyrus, and medial temporal lobe. Subjects with <24 months or <4 measurements of MMSE data were excluded. Predictive modeling of fast cognitive decline (defined as >0.6/year) from baseline volumetric data was performed on subjects with MCI using a single hidden layer neural network. RESULTS Among 698 baseline MCI subjects, the median annual decline in the MMSE score was 1.3 for converters to dementia versus 0.11 for stable MCI (P<0.001). A 0.6/year threshold captured dementia conversion with 82% accuracy (sensitivity 79%, specificity 85%, area under the receiver operating characteristic curve 0.88). Regional volumes on baseline MRI predicted fast cognitive decline with a test accuracy of 71%. DISCUSSION An MMSE score decrease of >0.6/year is associated with MCI-to-dementia conversion and can be predicted from baseline MRI.
Collapse
|
40
|
Multivariate Data Analysis and Machine Learning for Prediction of MCI-to-AD Conversion. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1194:81-103. [PMID: 32468526 DOI: 10.1007/978-3-030-32622-7_8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
There has always been a need for discovering efficient and dependable Alzheimer's disease (AD) diagnostic biomarkers. Like the majority of diseases, the earlier the diagnosis, the most effective the treatment. (Semi)-automated structural magnetic resonance imaging (MRI) processing approaches are very popular in AD research. Mild cognitive impairment (MCI) is considered to be a stage between normal cognitive ageing and dementia. MCI can often be the prodromal stage of AD. Around 10-15% of MCI patients convert to AD per year. In this study, we used three supervised machine learning (ML) techniques to differentiate MCI converters (MCIc) from MCI non-converters (MCInc) and predict their conversion rates from baseline MRI data (cortical thickness (CTH) and hippocampal volume (HCV)). A total of 803 participants from the ADNI cohort were included in this study (188 AD, 107 MCIc, 257 MCInc and 156 healthy controls (HC)). We studied the classification abilities of three different WEKA classifiers (support vector machine (SVM), decision trees (J48) and Naive Bayes (NB)). We built six different classification models, three models based on CTH and three based on HCV (CTH-SVM, CTH-J48, CTH-NB, HCV-SVM, HCV-J48 and HCV-NB). For the classification experiments, we obtained up to 71% sensitivity and up to 56% specificity. The prediction of conversion showed accuracy for up to 84%. The value of certain multivariate models derived from the classification experiments has exhibited robust and effective results in MCIc identification. However, there was a limitation in this study since we could not compare the CTH with the HCV models seeing as the data used originated from different subjects. As future direction, we propose the creation of a model that would combine various features with data originating from the same subjects, thus being a far more reliable and accurate prognostic tool.
Collapse
|
41
|
Gupta Y, Kim JI, Kim BC, Kwon GR. Classification and Graphical Analysis of Alzheimer's Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype. Front Aging Neurosci 2020; 12:238. [PMID: 32848713 PMCID: PMC7406801 DOI: 10.3389/fnagi.2020.00238] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 07/08/2020] [Indexed: 12/26/2022] Open
Abstract
Graphical, voxel, and region-based analysis has become a popular approach to studying neurodegenerative disorders such as Alzheimer's disease (AD) and its prodromal stage [mild cognitive impairment (MCI)]. These methods have been used previously for classification or discrimination of AD in subjects in a prodromal stage called stable MCI (MCIs), which does not convert to AD but remains stable over a period of time, and converting MCI (MCIc), which converts to AD, but the results reported across similar studies are often inconsistent. Furthermore, the classification accuracy for MCIs vs. MCIc is limited. In this study, we propose combining different neuroimaging modalities (sMRI, FDG-PET, AV45-PET, DTI, and rs-fMRI) with the apolipoprotein-E genotype to form a multimodal system for the discrimination of AD, and to increase the classification accuracy. Initially, we used two well-known analyses to extract features from each neuroimage for the discrimination of AD: whole-brain parcelation analysis (or region-based analysis), and voxel-wise analysis (or voxel-based morphometry). We also investigated graphical analysis (nodal and group) for all six binary classification groups (AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs). Data for a total of 129 subjects (33 AD, 30 MCIs, 31 MCIc, and 35 HCs) for each imaging modality were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) homepage. These data also include two APOE genotype data points for the subjects. Moreover, we used the 2-mm AICHA atlas with the NiftyReg registration toolbox to extract 384 brain regions from each PET (FDG and AV45) and sMRI image. For the rs-fMRI images, we used the DPARSF toolbox in MATLAB for the automatic extraction of data and the results for REHO, ALFF, and fALFF. We also used the pyClusterROI script for the automatic parcelation of each rs-fMRI image into 200 brain regions. For the DTI images, we used the FSL (Version 6.0) toolbox for the extraction of fractional anisotropy (FA) images to calculate a tract-based spatial statistic. Moreover, we used the PANDA toolbox to obtain 50 white-matter-region-parcellated FA images on the basis of the 2-mm JHU-ICBM-labeled template atlas. To integrate the different modalities and different complementary information into one form, and to optimize the classifier, we used the multiple kernel learning (MKL) framework. The obtained results indicated that our multimodal approach yields a significant improvement in accuracy over any single modality alone. The areas under the curve obtained by the proposed method were 97.78, 96.94, 95.56, 96.25, 96.67, and 96.59% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs binary classification, respectively. Our proposed multimodal method improved the classification result for MCIs vs. MCIc groups compared with the unimodal classification results. Our study found that the (left/right) precentral region was present in all six binary classification groups (this region can be considered the most significant region). Furthermore, using nodal network topology, we found that FDG, AV45-PET, and rs-fMRI were the most important neuroimages, and showed many affected regions relative to other modalities. We also compared our results with recently published results.
Collapse
Affiliation(s)
- Yubraj Gupta
- Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea
| | - Ji-In Kim
- Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea
| | - Byeong Chae Kim
- Department of Neurology, Chonnam National University Medical School, Gwangju, South Korea
| | - Goo-Rak Kwon
- Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea
| |
Collapse
|
42
|
Candemir S, Nguyen XV, Prevedello LM, Bigelow MT, White RD, Erdal BS. Predicting rate of cognitive decline at baseline using a deep neural network with multidata analysis. J Med Imaging (Bellingham) 2020; 7:044501. [PMID: 32832577 PMCID: PMC7419712 DOI: 10.1117/1.jmi.7.4.044501] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 07/17/2020] [Indexed: 01/18/2023] Open
Abstract
Purpose: Our study investigates whether a machine-learning-based system can predict the rate of cognitive decline in mildly cognitively impaired patients by processing only the clinical and imaging data collected at the initial visit. Approach: We built a predictive model based on a supervised hybrid neural network utilizing a three-dimensional convolutional neural network to perform volume analysis of magnetic resonance imaging (MRI) and integration of nonimaging clinical data at the fully connected layer of the architecture. The experiments are conducted on the Alzheimer's Disease Neuroimaging Initiative dataset. Results: Experimental results confirm that there is a correlation between cognitive decline and the data obtained at the first visit. The system achieved an area under the receiver operator curve of 0.70 for cognitive decline class prediction. Conclusion: To our knowledge, this is the first study that predicts "slowly deteriorating/stable" or "rapidly deteriorating" classes by processing routinely collected baseline clinical and demographic data [baseline MRI, baseline mini-mental state examination (MMSE), scalar volumetric data, age, gender, education, ethnicity, and race]. The training data are built based on MMSE-rate values. Unlike the studies in the literature that focus on predicting mild cognitive impairment (MCI)-to-Alzheimer's disease conversion and disease classification, we approach the problem as an early prediction of cognitive decline rate in MCI patients.
Collapse
Affiliation(s)
- Sema Candemir
- The Ohio State University College of Medicine, Laboratory for Augmented Intelligence in Imaging, Department of Radiology, Columbus, Ohio, United States
| | - Xuan V. Nguyen
- The Ohio State University College of Medicine, Laboratory for Augmented Intelligence in Imaging, Department of Radiology, Columbus, Ohio, United States
| | - Luciano M. Prevedello
- The Ohio State University College of Medicine, Laboratory for Augmented Intelligence in Imaging, Department of Radiology, Columbus, Ohio, United States
| | - Matthew T. Bigelow
- The Ohio State University College of Medicine, Laboratory for Augmented Intelligence in Imaging, Department of Radiology, Columbus, Ohio, United States
| | - Richard D. White
- The Ohio State University College of Medicine, Laboratory for Augmented Intelligence in Imaging, Department of Radiology, Columbus, Ohio, United States
| | - Barbaros S. Erdal
- The Ohio State University College of Medicine, Laboratory for Augmented Intelligence in Imaging, Department of Radiology, Columbus, Ohio, United States
| | | |
Collapse
|
43
|
Early diagnosis of Parkinson’s disease using EEG, machine learning and partial directed coherence. ACTA ACUST UNITED AC 2020. [DOI: 10.1007/s42600-020-00072-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
|
44
|
Castellazzi G, Cuzzoni MG, Cotta Ramusino M, Martinelli D, Denaro F, Ricciardi A, Vitali P, Anzalone N, Bernini S, Palesi F, Sinforiani E, Costa A, Micieli G, D'Angelo E, Magenes G, Gandini Wheeler-Kingshott CAM. A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features. Front Neuroinform 2020; 14:25. [PMID: 32595465 PMCID: PMC7300291 DOI: 10.3389/fninf.2020.00025] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 04/24/2020] [Indexed: 12/28/2022] Open
Abstract
Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD–AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a “mixed VD–AD dementia” (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations.
Collapse
Affiliation(s)
- Gloria Castellazzi
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, London, United Kingdom.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.,Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Matteo Cotta Ramusino
- Laboratory of Neuropsychology and Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Daniele Martinelli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Headache Center, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Antonio Ricciardi
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Paolo Vitali
- Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy.,Radiology Unit, IRCCS Policlinico San Donato, Milan, Italy
| | - Nicoletta Anzalone
- Scientific Institute H.S. Raffaele Vita e Salute University, Milan, Italy
| | - Sara Bernini
- Laboratory of Neuropsychology and Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Elena Sinforiani
- Laboratory of Neuropsychology and Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Alfredo Costa
- Laboratory of Neuropsychology and Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Giuseppe Micieli
- Department of Emergency Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Giovanni Magenes
- Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, London, United Kingdom.,Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| |
Collapse
|
45
|
Farina FR, Emek-Savaş DD, Rueda-Delgado L, Boyle R, Kiiski H, Yener G, Whelan R. A comparison of resting state EEG and structural MRI for classifying Alzheimer's disease and mild cognitive impairment. Neuroimage 2020; 215:116795. [PMID: 32278090 DOI: 10.1016/j.neuroimage.2020.116795] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 03/27/2020] [Accepted: 03/28/2020] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's disease (AD) is the leading cause of dementia, accounting for 70% of cases worldwide. By 2050, dementia prevalence will have tripled, with most new cases occurring in low- and middle-income countries. Mild cognitive impairment (MCI) is a stage between healthy aging and dementia, marked by cognitive deficits that do not impair daily living. People with MCI are at increased risk of dementia, with an average progression rate of 39% within 5 years. There is urgent need for low-cost, accessible and objective methods to facilitate early dementia detection. Electroencephalography (EEG) has potential to address this need due to its low cost and portability. Here, we collected resting state EEG, structural MRI (sMRI) and rich neuropsychological data from older adults (55+ years) with AD, amnestic MCI (aMCI) and healthy controls (~60 per group). We evaluated a range of candidate EEG markers (i.e., frequency band power and functional connectivity) for AD and aMCI classification and compared their performance with sMRI. We also tested a combined EEG and cognitive classification model (using Mini-Mental State Examination; MMSE). sMRI outperformed resting state EEG at classifying AD (AUCs = 1.00 vs 0.76, respectively). However, both EEG and sMRI were only moderately good at distinguishing aMCI from healthy aging (AUCs = 0.67-0.73), and neither method achieved sensitivity above 70%. The addition of EEG to MMSE scores had no added benefit relative to MMSE scores alone. This is the first direct comparison of EEG and sMRI for classification of AD and aMCI.
Collapse
Affiliation(s)
- F R Farina
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.
| | - D D Emek-Savaş
- Department of Psychology, Faculty of Letters, Dokuz Eylul University, Izmir, 35160, Turkey; Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, 35340, Turkey; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland
| | - L Rueda-Delgado
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - R Boyle
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - H Kiiski
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - G Yener
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, 35340, Turkey; Department of Neurology, Dokuz Eylul University Medical School, Izmir, 35340, Turkey
| | - R Whelan
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland.
| |
Collapse
|
46
|
Shahamat H, Saniee Abadeh M. Brain MRI analysis using a deep learning based evolutionary approach. Neural Netw 2020; 126:218-234. [PMID: 32259762 DOI: 10.1016/j.neunet.2020.03.017] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 12/29/2019] [Accepted: 03/16/2020] [Indexed: 12/13/2022]
Abstract
Convolutional neural network (CNN) models have recently demonstrated impressive performance in medical image analysis. However, there is no clear understanding of why they perform so well, or what they have learned. In this paper, a three-dimensional convolutional neural network (3D-CNN) is employed to classify brain MRI scans into two predefined groups. In addition, a genetic algorithm based brain masking (GABM) method is proposed as a visualization technique that provides new insights into the function of the 3D-CNN. The proposed GABM method consists of two main steps. In the first step, a set of brain MRI scans is used to train the 3D-CNN. In the second step, a genetic algorithm (GA) is applied to discover knowledgeable brain regions in the MRI scans. The knowledgeable regions are those areas of the brain which the 3D-CNN has mostly used to extract important and discriminative features from them. For applying GA on the brain MRI scans, a new chromosome encoding approach is proposed. The proposed framework has been evaluated using ADNI (including 140 subjects for Alzheimer's disease classification) and ABIDE (including 1000 subjects for Autism classification) brain MRI datasets. Experimental results show a 5-fold classification accuracy of 0.85 for the ADNI dataset and 0.70 for the ABIDE dataset. The proposed GABM method has extracted 6 to 65 knowledgeable brain regions in ADNI dataset (and 15 to 75 knowledgeable brain regions in ABIDE dataset). These regions are interpreted as the segments of the brain which are mostly used by the 3D-CNN to extract features for brain disease classification. Experimental results show that besides the model interpretability, the proposed GABM method has increased final performance of the classification model in some cases with respect to model parameters.
Collapse
Affiliation(s)
- Hossein Shahamat
- Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Saniee Abadeh
- Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
| |
Collapse
|
47
|
Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, Lin CT. Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING, COMMUNICATIONS, AND APPLICATIONS 2020; 16:1-35. [DOI: 10.1145/3344998] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 07/01/2019] [Indexed: 08/30/2023]
Abstract
Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.
Collapse
Affiliation(s)
- M. Tanveer
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - B. Richhariya
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - R. U. Khan
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - A. H. Rashid
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore 8 School of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, Odisha, India
| | - P. Khanna
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India
| | - M. Prasad
- Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia
| | - C. T. Lin
- Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia
| |
Collapse
|
48
|
Rallabandi VS, Tulpule K, Gattu M. Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer's disease using structural MRI analysis. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100305] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
|
49
|
Computational Approaches Applied in the Field of Neuroscience. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1194:193-201. [DOI: 10.1007/978-3-030-32622-7_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
50
|
Oh K, Chung YC, Kim KW, Kim WS, Oh IS. Classification and Visualization of Alzheimer's Disease using Volumetric Convolutional Neural Network and Transfer Learning. Sci Rep 2019; 9:18150. [PMID: 31796817 PMCID: PMC6890708 DOI: 10.1038/s41598-019-54548-6] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 11/12/2019] [Indexed: 12/21/2022] Open
Abstract
Recently, deep-learning-based approaches have been proposed for the classification of neuroimaging data related to Alzheimer's disease (AD), and significant progress has been made. However, end-to-end learning that is capable of maximizing the impact of deep learning has yet to receive much attention due to the endemic challenge of neuroimaging caused by the scarcity of data. Thus, this study presents an approach meant to encourage the end-to-end learning of a volumetric convolutional neural network (CNN) model for four binary classification tasks (AD vs. normal control (NC), progressive mild cognitive impairment (pMCI) vs. NC, stable mild cognitive impairment (sMCI) vs. NC and pMCI vs. sMCI) based on magnetic resonance imaging (MRI) and visualizes its outcomes in terms of the decision of the CNNs without any human intervention. In the proposed approach, we use convolutional autoencoder (CAE)-based unsupervised learning for the AD vs. NC classification task, and supervised transfer learning is applied to solve the pMCI vs. sMCI classification task. To detect the most important biomarkers related to AD and pMCI, a gradient-based visualization method that approximates the spatial influence of the CNN model's decision was applied. To validate the contributions of this study, we conducted experiments on the ADNI database, and the results demonstrated that the proposed approach achieved the accuracies of 86.60% and 73.95% for the AD and pMCI classification tasks respectively, outperforming other network models. In the visualization results, the temporal and parietal lobes were identified as key regions for classification.
Collapse
Affiliation(s)
- Kanghan Oh
- Jeonbuk National University, Department of Computer Science and Engineering, Jeonju, 54896, Korea
| | - Young-Chul Chung
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, 54907, Korea
- Jeonbuk National University Medical School, Department of Psychiatry, Jeonju, 54907, Korea
| | - Ko Woon Kim
- Jeonbuk National University Medical School, Department of Neurology, Jeonju, 54907, Korea
| | - Woo-Sung Kim
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, 54907, Korea
- Jeonbuk National University Medical School, Department of Psychiatry, Jeonju, 54907, Korea
| | - Il-Seok Oh
- Jeonbuk National University, Department of Computer Science and Engineering, Jeonju, 54896, Korea.
| |
Collapse
|