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Schulz MA, Bzdok D, Haufe S, Haynes JD, Ritter K. Performance reserves in brain-imaging-based phenotype prediction. Cell Rep 2024; 43:113597. [PMID: 38159275 PMCID: PMC11215805 DOI: 10.1016/j.celrep.2023.113597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 07/03/2023] [Accepted: 12/04/2023] [Indexed: 01/03/2024] Open
Abstract
This study examines the impact of sample size on predicting cognitive and mental health phenotypes from brain imaging via machine learning. Our analysis shows a 3- to 9-fold improvement in prediction performance when sample size increases from 1,000 to 1 M participants. However, despite this increase, the data suggest that prediction accuracy remains worryingly low and far from fully exploiting the predictive potential of brain imaging data. Additionally, we find that integrating multiple imaging modalities boosts prediction accuracy, often equivalent to doubling the sample size. Interestingly, the most informative imaging modality often varied with increasing sample size, emphasizing the need to consider multiple modalities. Despite significant performance reserves for phenotype prediction, achieving substantial improvements may necessitate prohibitively large sample sizes, thus casting doubt on the practical or clinical utility of machine learning in some areas of neuroimaging.
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Affiliation(s)
- Marc-Andre Schulz
- Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Psychotherapy, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany.
| | - Danilo Bzdok
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, QC, Canada; Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, QC, Canada; Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Stefan Haufe
- Bernstein Center for Computational Neuroscience, Berlin, Germany; Technische Universität Berlin, Berlin, Germany; Physikalisch-Technische Bundesanstalt, Berlin, Germany; Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Neurology, Berlin Center for Advanced Neuroimaging, Berlin, Germany
| | - John-Dylan Haynes
- Bernstein Center for Computational Neuroscience, Berlin, Germany; Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Neurology, Berlin Center for Advanced Neuroimaging, Berlin, Germany
| | - Kerstin Ritter
- Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Psychotherapy, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
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2
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Peng J, Wang W, Song Q, Hou J, Jin H, Qin X, Yuan Z, Wei Y, Shu Z. 18F-FDG-PET Radiomics Based on White Matter Predicts The Progression of Mild Cognitive Impairment to Alzheimer Disease: A Machine Learning Study. Acad Radiol 2023; 30:1874-1884. [PMID: 36587998 DOI: 10.1016/j.acra.2022.12.033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 12/11/2022] [Accepted: 12/18/2022] [Indexed: 12/31/2022]
Abstract
RATIONALE AND OBJECTIVES To build a model using white-matter radiomics features on positron-emission tomography (PET) and machine learning methods to predict progression from mild cognitive impairment (MCI) to Alzheimer disease (AD). MATERIALS AND METHODS We analyzed the data of 341 MCI patients from the Alzheimer's Disease Neuroimaging Initiative, of whom 102 progressed to AD during an 8-year follow-up. The patients were divided into the training (238 patients) and test groups (103 patients). PET-based radiomics features were extracted from the white matter in the training group, and dimensionally reduced to construct a psychoradiomics signature (PS), which was combined with multimodal data using machine learning methods to construct an integrated model. Model performance was evaluated using receiver operating characteristic curves in the test group. RESULTS Clinical Dementia Rating (CDR) scores, Alzheimer's Disease Assessment Scale (ADAS) scores, and PS independently predicted MCI progression to AD on multivariate logistic regression. The areas under the curve (AUCs) of the CDR, ADAS and PS in the training and test groups were 0.683, 0.755, 0.747 and 0.737, 0.743, 0.719 respectively, and were combined using a support vector machine to construct an integrated model. The AUC of the integrated model in the training and test groups was 0.868 and 0.865, respectively (sensitivity, 0.873 and 0.839, respectively; specificity, 0.784 and 0.806, respectively). The AUCs of the integrated model significantly differed from those of other predictors in both groups (p < 0.05, Delong test). CONCLUSION Our psych radiomics signature based on white-matter PET data predicted MCI progression to AD. The integrated model built using multimodal data and machine learning identified MCI patients at a high risk of progression to AD.
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Affiliation(s)
- Jiaxuan Peng
- Jinzhou medical university, Jinzhou, Liaoning Province, China
| | - Wei Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical and Pharmaceutical College, Chongqin, China
| | - Qiaowei Song
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jie Hou
- Jinzhou medical university, Jinzhou, Liaoning Province, China
| | - Hui Jin
- Bengbu medical college, Bengbu, China
| | - Xue Qin
- Bengbu medical college, Bengbu, China
| | - Zhongyu Yuan
- Jinzhou medical university, Jinzhou, Liaoning Province, China
| | - Yuguo Wei
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Zhenyu Shu
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
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3
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Yao Q, Jiang K, Lin F, Zhu T, Khan NH, Jiang E. Pathophysiological Association of Alzheimer's Disease and Hypertension: A Clinical Concern for Elderly Population. Clin Interv Aging 2023; 18:713-728. [PMID: 37181536 PMCID: PMC10167960 DOI: 10.2147/cia.s400527] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 04/22/2023] [Indexed: 05/16/2023] Open
Abstract
Alzheimer's disease (AD), the most common cause of dementia and the fifth leading cause of death in the adult population has a complex pathophysiological link with hypertension (HTN). A growing volume of published literature on a parallel elevation of blood pressure (BP), amyloid plaques, and neurofibrillary tangles formation in post-middle of human brain cells has developed new, widely accepting foundations on this association. In particular, HTN in elderly life mediates cerebral blood flow dysfunction, neuronal dysfunction, and significant decline in cognitive impairment, primarily in the late-life populace, governing the onset of AD. Thus, HTN is an established risk factor for AD. Considering the impact of AD, 1.89 million deaths annually, and the failure of palliative therapies to cure AD, the scientific research community is looking to adopt integrated approaches to target early modified risk factors like HTN to reduce AD burden. The current review highlights the significance and impact of HTN-based prevention in lowering the AD burden in the elderly by providing a comprehensive overview of the physiological relationship between AD and HTN with an in-detail explanation of the role and applications of pathological biomarkers in this clinical association. The review will gain worth in presenting new insights and providing inclusive discussion on the correlation between HTN and cognitive impairment. It will increase across a wider scientific audience to expand understanding of this pathophysiological association.
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Affiliation(s)
- Qianqian Yao
- Institute of Nursing and Health, Henan University, Kaifeng, People’s Republic of China
| | - Kexin Jiang
- Institute of Nursing and Health, Henan University, Kaifeng, People’s Republic of China
| | - Fei Lin
- School of Medicine, Shangqiu Institute of Technology, Shangqiu, People’s Republic of China
| | - Tao Zhu
- Department of Geriatrics, Kaifeng Traditional Chinese Medicine Hospital, Kaifeng, People’s Republic of China
| | - Nazeer Hussain Khan
- Institute of Nursing and Health, Henan University, Kaifeng, People’s Republic of China
- Henan International Joint Laboratory for Nuclear Protein Regulation, Henan University, Kaifeng, People’s Republic of China
| | - Enshe Jiang
- Institute of Nursing and Health, Henan University, Kaifeng, People’s Republic of China
- Henan International Joint Laboratory for Nuclear Protein Regulation, Henan University, Kaifeng, People’s Republic of China
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4
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Abd Algani YM, Vidhya S, Ghai B, Acharjee PB, Kathiravan MN, Dwivedi VK. Innovative Method for Alzheimer Disease Prediction using GP-ELM-RNN. 2023 2ND INTERNATIONAL CONFERENCE ON APPLIED ARTIFICIAL INTELLIGENCE AND COMPUTING (ICAAIC) 2023. [DOI: 10.1109/icaaic56838.2023.10140571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
| | - S. Vidhya
- RMK College of Engineering and Technology,S&H(Mathematics),Chennai,Tamilnadu,India
| | - Bhupaesh Ghai
- CCSIT Teerthankar Mahaveer University,Moradabad,Uttar Pradesh,India
| | | | | | - Vijay Kumar Dwivedi
- Vishwavidyalaya Engineering College,Department of Mathematics,Surguja,Chhattisgarh,India
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Hollenbenders Y, Pobiruchin M, Reichenbach A. Two Routes to Alzheimer's Disease Based on Differential Structural Changes in Key Brain Regions. J Alzheimers Dis 2023; 92:1399-1412. [PMID: 36911937 DOI: 10.3233/jad-221061] [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: 03/09/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disorder with homogenous disease patterns. Neuropathological changes precede symptoms by up to two decades making neuroimaging biomarkers a prime candidate for early diagnosis, prognosis, and patient stratification. OBJECTIVE The goal of the study was to discern intermediate AD stages and their precursors based on neuroanatomical features for stratifying patients on their progression through different stages. METHODS Data include grey matter features from 14 brain regions extracted from longitudinal structural MRI and cognitive data obtained from 1,017 healthy controls and AD patients of ADNI. AD progression was modeled with a Hidden Markov Model, whose hidden states signify disease stages derived from the neuroanatomical data. To tie the progression in brain atrophy to a behavioral marker, we analyzed the ADAS-cog sub-scores in the stages. RESULTS The optimal model consists of eight states with differentiable neuroanatomical features, forming two routes crossing once at a very early point and merging at the final state. The cortical route is characterized by early and sustained atrophy in cortical regions. The limbic route is characterized by early decrease in limbic regions. Cognitive differences between the two routes are most noticeable in the memory domain with subjects from the limbic route experiencing stronger memory impairments. CONCLUSION Our findings corroborate that more than one pattern of grey matter deterioration with several discernable stages can be identified in the progression of AD. These neuroanatomical subtypes are behaviorally meaningful and provide a door into early diagnosis of AD and prognosis of the disease's progression.
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Affiliation(s)
- Yasmin Hollenbenders
- Medical Faculty Heidelberg, Heidelberg University, Germany.,Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,Center for Machine Learning, Heilbronn University of Applied Sciences, Germany
| | - Monika Pobiruchin
- Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,GECKO Institute for Medicine, Informatics and Economics, Heilbronn University of Applied Sciences, Germany
| | - Alexandra Reichenbach
- Medical Faculty Heidelberg, Heidelberg University, Germany.,Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,Center for Machine Learning, Heilbronn University of Applied Sciences, Germany
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Hu J, Wang Y, Guo D, Qu Z, Sui C, He G, Wang S, Chen X, Wang C, Liu X. Diagnostic performance of magnetic resonance imaging-based machine learning in Alzheimer's disease detection: a meta-analysis. Neuroradiology 2023; 65:513-527. [PMID: 36477499 DOI: 10.1007/s00234-022-03098-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022]
Abstract
PURPOSE Advanced machine learning (ML) algorithms can assist rapid medical image recognition and realize automatic, efficient, noninvasive, and convenient diagnosis. We aim to further evaluate the diagnostic performance of ML to distinguish patients with probable Alzheimer's disease (AD) from normal older adults based on structural magnetic resonance imaging (MRI). METHODS The Medline, Embase, and Cochrane Library databases were searched for relevant literature published up until July 2021. We used the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to evaluate all included studies' quality and potential bias. Random-effects models were used to calculate pooled sensitivity and specificity, and the Deeks' test was used to assess publication bias. RESULTS We included 24 models based on different brain features extracted by ML algorithms in 19 papers. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve for ML in detecting AD were 0.85 (95%CI 0.81-0.89), 0.88 (95%CI 0.84-0.91), 7.15 (95%CI 5.40-9.47), 0.17 (95%CI 0.12-0.22), 43.34 (95%CI 26.89-69.84), and 0.93 (95%CI 0.91-0.95). CONCLUSION ML using structural MRI data performed well in diagnosing probable AD patients and normal elderly. However, more high-quality, large-scale prospective studies are needed to further enhance the reliability and generalizability of ML for clinical applications before it can be introduced into clinical practice.
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Affiliation(s)
- Jiayi Hu
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Yashan Wang
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Dingjie Guo
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Zihan Qu
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Chuanying Sui
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Guangliang He
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Song Wang
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Xiaofei Chen
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China
| | - Chunpeng Wang
- School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China.
| | - Xin Liu
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China.
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7
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Raza N, Naseer A, Tamoor M, Zafar K. Alzheimer Disease Classification through Transfer Learning Approach. Diagnostics (Basel) 2023; 13:diagnostics13040801. [PMID: 36832292 PMCID: PMC9955379 DOI: 10.3390/diagnostics13040801] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/13/2023] [Accepted: 01/18/2023] [Indexed: 02/25/2023] Open
Abstract
Alzheimer's disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer's disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, we discuss the segmentation and classification of the Magnetic resonance imaging (MRI) of Alzheimer's disease, through the concept of transfer learning and customizing of the convolutional neural network (CNN) by specifically using images that are segmented by the Gray Matter (GM) of the brain. Instead of training and computing the proposed model accuracy from the start, we used a pre-trained deep learning model as our base model, and, after that, transfer learning was applied. The accuracy of the proposed model was tested over a different number of epochs, 10, 25, and 50. The overall accuracy of the proposed model was 97.84%.
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Affiliation(s)
- Noman Raza
- Department of Computer Science, National University of Computer and Emerging Sciences, Lahore 54770, Pakistan
| | - Asma Naseer
- Department of Computer Science, National University of Computer and Emerging Sciences, Lahore 54770, Pakistan
| | - Maria Tamoor
- Department of Computer Science, Forman Christian College, Lahore 54600, Pakistan
| | - Kashif Zafar
- Department of Computer Science, National University of Computer and Emerging Sciences, Lahore 54770, Pakistan
- Correspondence:
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8
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Garg N, Choudhry MS, Bodade RM. A review on Alzheimer's disease classification from normal controls and mild cognitive impairment using structural MR images. J Neurosci Methods 2023; 384:109745. [PMID: 36395961 DOI: 10.1016/j.jneumeth.2022.109745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 10/04/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder that degrades the memory and cognitive ability in elderly people. The main reason for memory loss and reduction in cognitive ability is the structural changes in the brain that occur due to neuronal loss. These structural changes are most conspicuous in the hippocampus, cortex, and grey matter and can be assessed by using neuroimaging techniques viz. Positron Emission Tomography (PET), structural Magnetic Resonance Imaging (MRI) and functional MRI (fMRI), etc. Out of these neuroimaging techniques, structural MRI has evolved as the best technique as it indicates the best soft tissue contrast and high spatial resolution which is important for AD detection. Currently, the focus of researchers is on predicting the conversion of Mild Cognitive Impairment (MCI) into AD. MCI represents the transition state between expected cognitive changes with normal aging and Alzheimer's disease. Not every MCI patient progresses into Alzheimer's disease. MCI can develop into stable MCI (sMCI, patients are called non-converters) or into progressive MCI (pMCI, patients are diagnosed as MCI converters). This paper discusses the prognosis of MCI to AD conversion and presents a review of structural MRI-based studies for AD detection. AD detection framework includes feature extraction, feature selection, and classification process. This paper reviews the studies for AD detection based on different feature extraction methods and machine learning algorithms for classification. The performance of various feature extraction methods has been compared and it has been observed that the wavelet transform-based feature extraction method would give promising results for AD classification. The present study indicates that researchers are successful in classifying AD from Normal Controls (NrmC) but, it still requires a lot of work to be done for MCI/ NrmC and MCI/AD, which would help in detecting AD at its early stage.
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Affiliation(s)
- Neha Garg
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Mahipal Singh Choudhry
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Rajesh M Bodade
- Military College of Telecommunication Engineering (MCTE), Mhow, Indore 453441, Madhya Pradesh, India.
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9
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Giraldo DL, Smith RE, Struyfs H, Niemantsverdriet E, De Roeck E, Bjerke M, Engelborghs S, Romero E, Sijbers J, Jeurissen B. Investigating Tissue-Specific Abnormalities in Alzheimer's Disease with Multi-Shell Diffusion MRI. J Alzheimers Dis 2022; 90:1771-1791. [PMID: 36336929 PMCID: PMC9789487 DOI: 10.3233/jad-220551] [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] [Indexed: 12/12/2022]
Abstract
BACKGROUND Most studies using diffusion-weighted MRI (DW-MRI) in Alzheimer's disease (AD) have focused their analyses on white matter (WM) microstructural changes using the diffusion (kurtosis) tensor model. Although recent works have addressed some limitations of the tensor model, such as the representation of crossing fibers and partial volume effects with cerebrospinal fluid (CSF), the focus remains in modeling and analyzing the WM. OBJECTIVE In this work, we present a brain analysis approach for DW-MRI that disentangles multiple tissue compartments as well as micro- and macroscopic effects to investigate differences between groups of subjects in the AD continuum and controls. METHODS By means of the multi-tissue constrained spherical deconvolution of multi-shell DW-MRI, underlying brain tissue is modeled with a WM fiber orientation distribution function along with the contributions of gray matter (GM) and CSF to the diffusion signal. From this multi-tissue model, a set of measures capturing tissue diffusivity properties and morphology are extracted. Group differences were interrogated following fixel-, voxel-, and tensor-based morphometry approaches while including strong FWE control across multiple comparisons. RESULTS Abnormalities related to AD stages were detected in WM tracts including the splenium, cingulum, longitudinal fasciculi, and corticospinal tract. Changes in tissue composition were identified, particularly in the medial temporal lobe and superior longitudinal fasciculus. CONCLUSION This analysis framework constitutes a comprehensive approach allowing simultaneous macro and microscopic assessment of WM, GM, and CSF, from a single DW-MRI dataset.
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Affiliation(s)
- Diana L. Giraldo
- Computer Imaging and Medical Applications Laboratory - Cim@Lab, Universidad Nacional de Colombia, Bogotá, Colombia,imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium,μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Robert E. Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia,The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Hanne Struyfs
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Ellen De Roeck
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Maria Bjerke
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium,Laboratory of Neurochemistry, Department of Clinical Chemistry, and Center for Neurosciences (C4N), Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium,Department of Neurology, and Center for Neurosciences (C4N), Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Eduardo Romero
- Computer Imaging and Medical Applications Laboratory - Cim@Lab, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Jan Sijbers
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium,μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium,μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium,Lab for Equilibrium Investigations and Aerospace, Department of Physics, University of Antwerp, Antwerp, Belgium,Correspondence to: Ben Jeurissen, PhD, imec - Vision Lab, Department of Physics, University of Antwerp (CDE), Universiteitsplein 1, Building N, 2610 Antwerp, Belgium. Tel.: +32 3 265 24 77; E-mail:
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10
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Durieux J, Rombouts SARB, de Vos F, Koini M, Wilderjans TF. Clusterwise Independent Component Analysis (C-ICA): Using fMRI resting state networks to cluster subjects and find neurofunctional subtypes. J Neurosci Methods 2022; 382:109718. [PMID: 36209940 DOI: 10.1016/j.jneumeth.2022.109718] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 08/18/2022] [Accepted: 09/29/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND FMRI resting state networks (RSNs) are used to characterize brain disorders. They also show extensive heterogeneity across patients. Identifying systematic differences between RSNs in patients, i.e. discovering neurofunctional subtypes, may further increase our understanding of disease heterogeneity. Currently, no methodology is available to estimate neurofunctional subtypes and their associated RSNs simultaneously. NEW METHOD We present an unsupervised learning method for fMRI data, called Clusterwise Independent Component Analysis (C-ICA). This enables the clustering of patients into neurofunctional subtypes based on differences in shared ICA-derived RSNs. The parameters are estimated simultaneously, which leads to an improved estimation of subtypes and their associated RSNs. RESULTS In five simulation studies, the C-ICA model is successfully validated using both artificially and realistically simulated data (N = 30-40). The successful performance of the C-ICA model is also illustrated on an empirical data set consisting of Alzheimer's disease patients and elderly control subjects (N = 250). C-ICA is able to uncover a meaningful clustering that partially matches (balanced accuracy = .72) the diagnostic labels and identifies differences in RSNs between the Alzheimer and control cluster. COMPARISON WITH OTHER METHODS Both in the simulation study and the empirical application, C-ICA yields better results compared to competing clustering methods (i.e., a two step clustering procedure based on single subject ICA's and a Group ICA plus dual regression variant thereof) that do not simultaneously estimate a clustering and associated RSNs. Indeed, the overall mean adjusted Rand Index, a measure for cluster recovery, equals 0.65 for C-ICA and ranges from 0.27 to 0.46 for competing methods. CONCLUSIONS The successful performance of C-ICA indicates that it is a promising method to extract neurofunctional subtypes from multi-subject resting state-fMRI data. This method can be applied on fMRI scans of patient groups to study (neurofunctional) subtypes, which may eventually further increase understanding of disease heterogeneity.
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Affiliation(s)
- Jeffrey Durieux
- Methodology and Statistics Unit, Institute of Psychology, Faculty of Social and Behavioural Sciences, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition (LIBC), Leiden, The Netherlands; Econometric Institute, Erasmus University Rotterdam, The Netherlands.
| | - Serge A R B Rombouts
- Methodology and Statistics Unit, Institute of Psychology, Faculty of Social and Behavioural Sciences, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition (LIBC), Leiden, The Netherlands; Department of Radiology, Leiden University Medical Center, The Netherlands
| | - Frank de Vos
- Methodology and Statistics Unit, Institute of Psychology, Faculty of Social and Behavioural Sciences, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition (LIBC), Leiden, The Netherlands; Department of Radiology, Leiden University Medical Center, The Netherlands
| | - Marisa Koini
- Department of Neurology, Medical University of Graz, Austria
| | - Tom F Wilderjans
- Methodology and Statistics Unit, Institute of Psychology, Faculty of Social and Behavioural Sciences, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition (LIBC), Leiden, The Netherlands; Research Group of Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium; Department of Clinical Psychology, Vrije Universiteit Amsterdam, Netherlands
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11
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Yu J, Kong Z, Zhan L, Shen L, He L. Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis. COMPUTER SCIENCE & INFORMATION TECHNOLOGY 2022; 12:123-134. [PMID: 36880061 PMCID: PMC9985071 DOI: 10.5121/csit.2022.121812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multi-modality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM-MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our proposed method against the state-of-the-art for the disease diagnosis and the identification of disease-specific regions and modality-related differences. The code for this work is publicly available at https://github.com/junfish/BIOS22.
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Affiliation(s)
- Jun Yu
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania, USA
| | - Zhaoming Kong
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania, USA
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Lifang He
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania, USA
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12
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Biondo F, Jewell A, Pritchard M, Aarsland D, Steves CJ, Mueller C, Cole JH. Brain-age is associated with progression to dementia in memory clinic patients. Neuroimage Clin 2022; 36:103175. [PMID: 36087560 PMCID: PMC9467894 DOI: 10.1016/j.nicl.2022.103175] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 07/30/2022] [Accepted: 08/27/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Biomarkers for the early detection of dementia risk hold promise for better disease monitoring and targeted interventions. However, most biomarker studies, particularly in neuroimaging, have analysed artificially 'clean' research groups, free from comorbidities, erroneous referrals, contraindications and from a narrow sociodemographic pool. Such biases mean that neuroimaging samples are often unrepresentative of the target population for dementia risk (e.g., people referred to a memory clinic), limiting the generalisation of these studies to real-world clinical settings. To facilitate better translation from research to the clinic, datasets that are more representative of dementia patient groups are warranted. METHODS We analysed T1-weighted MRI scans from a real-world setting of patients referred to UK memory clinic services (n = 1140; 60.2 % female and mean [SD] age of 70.0[10.8] years) to derive 'brain-age'. Brain-age is an index of age-related brain health based on quantitative analysis of structural neuroimaging, largely reflecting brain atrophy. Brain-predicted age difference (brain-PAD) was calculated as brain-age minus chronological age. We determined which patients went on to develop dementia between three months and 7.8 years after neuroimaging assessment (n = 476) using linkage to electronic health records. RESULTS Survival analysis, using Cox regression, indicated a 3 % increased risk of dementia per brain-PAD year (hazard ratio [95 % CI] = 1.03 [1.02,1.04], p < 0.0001), adjusted for baseline age, age2, sex, Mini Mental State Examination (MMSE) score and normalised brain volume. In sensitivity analyses, brain-PAD remained significant when time-to-dementia was at least 3 years (hazard ratio [95 % CI] = 1.06 [1.02, 1.09], p = 0.0006), or when baseline MMSE score ≥ 27 (hazard ratio [95 % CI] = 1.03 [1.01, 1.05], p = 0.0006). CONCLUSIONS Memory clinic patients with older-appearing brains are more likely to receive a subsequent dementia diagnosis. Potentially, brain-age could aid decision-making during initial memory clinic assessment to improve early detection of dementia. Even when neuroimaging assessment was more than 3 years prior to diagnosis and when cognitive functioning was not clearly impaired, brain-age still proved informative. These real-world results support the use of quantitative neuroimaging biomarkers like brain-age in memory clinics.
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Affiliation(s)
- Francesca Biondo
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, WC1V 6LJ, UK.
| | | | | | - Dag Aarsland
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK; Centre for Age-Related Research, Stavanger University Hospital, Stavanger, Norway
| | - Claire J Steves
- Department of Ageing and Health, Guy's and St Thomas' NHS Foundation Trust, SE1 7EH, UK; Department of Twin Research and Genetic Epidemiology, King's College London, SE1 7EH, UK
| | - Christoph Mueller
- South London and Maudsley NHS Foundation Trust, UK; Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK
| | - James H Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, WC1V 6LJ, UK; Dementia Research Centre, Institute of Neurology, University College London, WC1N 3AR, UK.
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Faldu KG, Shah JS. Alzheimer's disease: a scoping review of biomarker research and development for effective disease diagnosis. Expert Rev Mol Diagn 2022; 22:681-703. [PMID: 35855631 DOI: 10.1080/14737159.2022.2104639] [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: 11/04/2022]
Abstract
INTRODUCTION Alzheimer's disease (AD) is regarded as the foremost reason for neurodegeneration that prominently affects the geriatric population. Characterized by extracellular accumulation of amyloid-beta (Aβ), intracellular aggregation of hyperphosphorylated tau (p-tau), and neuronal degeneration that causes impairment of memory and cognition. Amyloid/tau/neurodegeneration (ATN) classification is utilized for research purposes and involves amyloid, tau, and neuronal injury staging through MRI, PET scanning, and CSF protein concentration estimations. CSF sampling is invasive, and MRI and PET scanning requires sophisticated radiological facilities which limit its widespread diagnostic use. ATN classification lacks effectiveness in preclinical AD. AREAS COVERED This publication intends to collate and review the existing biomarker profile and the current research and development of a new arsenal of biomarkers for AD pathology from different biological samples, microRNA (miRNA), proteomics, metabolomics, artificial intelligence, and machine learning for AD screening, diagnosis, prognosis, and monitoring of AD treatments. EXPERT OPINION It is an accepted observation that AD-related pathological changes occur over a long period of time before the first symptoms are observed providing ample opportunity for detection of biological alterations in various biological samples that can aid in early diagnosis and modify treatment outcomes.
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Affiliation(s)
- Khushboo Govind Faldu
- Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat, India
| | - Jigna Samir Shah
- Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat, India
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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: 19] [Impact Index Per Article: 9.5] [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.
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Chyzhyk D, Varoquaux G, Milham M, Thirion B. How to remove or control confounds in predictive models, with applications to brain biomarkers. Gigascience 2022; 11:giac014. [PMID: 35277962 PMCID: PMC8917515 DOI: 10.1093/gigascience/giac014] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 09/19/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND With increasing data sizes and more easily available computational methods, neurosciences rely more and more on predictive modeling with machine learning, e.g., to extract disease biomarkers. Yet, a successful prediction may capture a confounding effect correlated with the outcome instead of brain features specific to the outcome of interest. For instance, because patients tend to move more in the scanner than controls, imaging biomarkers of a disease condition may mostly reflect head motion, leading to inefficient use of resources and wrong interpretation of the biomarkers. RESULTS Here we study how to adapt statistical methods that control for confounds to predictive modeling settings. We review how to train predictors that are not driven by such spurious effects. We also show how to measure the unbiased predictive accuracy of these biomarkers, based on a confounded dataset. For this purpose, cross-validation must be modified to account for the nuisance effect. To guide understanding and practical recommendations, we apply various strategies to assess predictive models in the presence of confounds on simulated data and population brain imaging settings. Theoretical and empirical studies show that deconfounding should not be applied to the train and test data jointly: modeling the effect of confounds, on the training data only, should instead be decoupled from removing confounds. CONCLUSIONS Cross-validation that isolates nuisance effects gives an additional piece of information: confound-free prediction accuracy.
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Affiliation(s)
- Darya Chyzhyk
- Parietal project-team, INRIA Saclay-île de France, France
- CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Gaël Varoquaux
- Parietal project-team, INRIA Saclay-île de France, France
- CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France
| | - Michael Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
| | - Bertrand Thirion
- Parietal project-team, INRIA Saclay-île de France, France
- CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France
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A Discussion of Machine Learning Approaches for Clinical Prediction Modeling. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:65-73. [PMID: 34862529 DOI: 10.1007/978-3-030-85292-4_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
While machine learning has occupied a niche in clinical medicine for decades, continued method development and increased accessibility of medical data have led to broad diversification of approaches. These range from humble regression-based models to more complex artificial neural networks; yet, despite heterogeneity in foundational principles and architecture, the spectrum of machine learning approaches to clinical prediction modeling have invariably led to the development of algorithms advancing our ability to provide optimal care for our patients. In this chapter, we briefly review early machine learning approaches in medicine before delving into common approaches being applied for clinical prediction modeling today. For each, we offer a brief introduction into theory and application with accompanying examples from the medical literature. In doing so, we present a summarized image of the current state of machine learning and some of its many forms in medical predictive modeling.
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Neuroanatomical associations of depression, anxiety and apathy neuropsychiatric symptoms in patients with Alzheimer's disease. Acta Neurol Belg 2021; 121:1469-1480. [PMID: 32319015 DOI: 10.1007/s13760-020-01349-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 03/24/2020] [Indexed: 12/21/2022]
Abstract
Depression, anxiety and apathy are 'common neuropsychiatric symptoms (NPS) in Alzheimer's disease (AD). We aimed to find regional gray matter (GM) volume difference of these symptoms, in AD patients compared to AD control, and investigate possible associations of GM atrophy with cognitive covariant. Study subjects were retrieved from the Alzheimer's Disease Neuroimaging Initiative database. Thirty-five participants are AD control, 27 AD patients with anxiety, 19 with depression and 24 with apathy, ages ≥ 55.1 years. Recruited subjects had an assessment of their clinical and structural MRI data. GM differences and clinical data were analyzed using voxel-based morphometry and ANOVA with Scheffe post hoc test, respectively. We found significant GM volumes differences in the left insula, left parahippocampal, posterior cingulate and the bilateral putamen in the anxiety group. The results also revealed that the right parahippocampal, Brodmann area 38 and the middle frontal gyrus were significant in patients with depression. Significant results were with a p < 0.05, corrected with AlphaSim program for multiple comparisons. The left insula had a strong negative association with Clinical Dementia Rate Sum of Boxes and Alzheimer's Disease Assessment Scale-cognitive subscale-13 items in anxiety and apathy groups. The difference in GM density in the left insula and hippocampus plays a crucial role in depression, anxiety and apathy NPS and outline precise approaches to test these symptoms.
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Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models. Neurobiol Dis 2021; 161:105558. [PMID: 34767943 PMCID: PMC10112395 DOI: 10.1016/j.nbd.2021.105558] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.
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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.
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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
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Lu P, Colliot O. Multilevel Survival Modeling with Structured Penalties for Disease Prediction from Imaging Genetics data. IEEE J Biomed Health Inform 2021; 26:798-808. [PMID: 34329174 DOI: 10.1109/jbhi.2021.3100918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper introduces a framework for disease prediction from multimodal genetic and imaging data. We propose a multilevel survival model which allows predicting the time of occurrence of a future disease state in patients initially exhibiting mild symptoms. This new multilevel setting allows modeling the interactions between genetic and imaging variables. This is in contrast with classical additive models which treat all modalities in the same manner and can result in undesirable elimination of specific modalities when their contributions are unbalanced. Moreover, the use of a survival model allows overcoming the limitations of previous approaches based on classification which consider a fixed time frame. Furthermore, we introduce specific penalties taking into account the structure of the different types of data, such as a group lasso penalty over the genetic modality and a L2-penalty over the imaging modality. Finally, we propose a fast optimization algorithm, based on a proximal gradient method. The approach was applied to the prediction of Alzheimer's disease (AD) among patients with mild cognitive impairment (MCI) based on genetic (single nucleotide polymorphisms - SNP) and imaging (anatomical MRI measures) data from the ADNI database. The experiments demonstrate the effectiveness of the method for predicting the time of conversion to AD. It revealed how genetic variants and brain imaging alterations interact in the prediction of future disease status. The approach is generic and could potentially be useful for the prediction of other diseases.
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Wong R, Luo Y, Mok VCT, Shi L. Advances in computerized MRI‐based biomarkers in Alzheimer’s disease. BRAIN SCIENCE ADVANCES 2021. [DOI: 10.26599/bsa.2021.9050005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The use of neuroimaging examinations is crucial in Alzheimer’s disease (AD), in both research and clinical settings. Over the years, magnetic resonance imaging (MRI)–based computer‐aided diagnosis has been shown to be helpful for early screening and predicting cognitive decline. Meanwhile, an increasing number of studies have adopted machine learning for the classification of AD, with promising results. In this review article, we focus on computerized MRI‐based biomarkers of AD by reviewing representative studies that used computerized techniques to identify AD patients and predict cognitive progression. We categorized these studies based on the following applications: (1) identifying AD from normal control; (2) identifying AD from other dementia types, including vascular dementia, dementia with Lewy bodies, and frontotemporal dementia; and (3) predicting conversion from NC to mild cognitive impairment (MCI) and from MCI to AD. This systematic review could act as a state‐of‐the‐art overview of this emerging field as well as a basis for designing future studies.
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Affiliation(s)
- Raymond Wong
- BrainNow Research Institute, Shenzhen 518081, Guangdong, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen 518081, Guangdong, China
| | - Vincent Chung-tong Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong 999077, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen 518081, Guangdong, China
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong 999077, China
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22
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Wang SH, Zhou Q, Yang M, Zhang YD. ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation. Front Aging Neurosci 2021; 13:687456. [PMID: 34220487 PMCID: PMC8250430 DOI: 10.3389/fnagi.2021.687456] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/18/2021] [Indexed: 11/23/2022] Open
Abstract
Aim: Alzheimer's disease is a neurodegenerative disease that causes 60-70% of all cases of dementia. This study is to provide a novel method that can identify AD more accurately. Methods: We first propose a VGG-inspired network (VIN) as the backbone network and investigate the use of attention mechanisms. We proposed an Alzheimer's Disease VGG-Inspired Attention Network (ADVIAN), where we integrate convolutional block attention modules on a VIN backbone. Also, 18-way data augmentation is proposed to avoid overfitting. Ten runs of 10-fold cross-validation are carried out to report the unbiased performance. Results: The sensitivity and specificity reach 97.65 ± 1.36 and 97.86 ± 1.55, respectively. Its precision and accuracy are 97.87 ± 1.53 and 97.76 ± 1.13, respectively. The F1 score, MCC, and FMI are obtained as 97.75 ± 1.13, 95.53 ± 2.27, and 97.76 ± 1.13, respectively. The AUC is 0.9852. Conclusion: The proposed ADVIAN gives better results than 11 state-of-the-art methods. Besides, experimental results demonstrate the effectiveness of 18-way data augmentation.
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Affiliation(s)
- Shui-Hua Wang
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
- School of Mathematics and Actuarial Science, University of Leicester, Leicester, United Kingdom
| | - Qinghua Zhou
- School of Informatics, University of Leicester, Leicester, United Kingdom
| | - Ming Yang
- Department of Radiology, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Yu-Dong Zhang
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
- School of Informatics, University of Leicester, Leicester, United Kingdom
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Fletcher E, Gavett B, Crane P, Soldan A, Hohman T, Farias S, Widaman K, Groot C, Renteria MA, Zahodne L, DeCarli C, Mungas D. A robust brain signature region approach for episodic memory performance in older adults. Brain 2021; 144:1089-1102. [PMID: 33895818 PMCID: PMC8105039 DOI: 10.1093/brain/awab007] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 10/11/2020] [Accepted: 10/30/2020] [Indexed: 01/26/2023] Open
Abstract
The brain signature concept aims to characterize brain regions most strongly associated with an outcome of interest. Brain signatures derive their power from data-driven searches that select features based solely on performance metrics of prediction or classification. This approach has important potential to delineate biologically relevant brain substrates for prediction or classification of future trajectories. Recent work has used exploratory voxel-wise or atlas-based searches, with some using machine learning techniques to define salient features. These have shown undoubted usefulness, but two issues remain. The preponderance of recent work has been aimed at categorical rather than continuous outcomes, and it is rare for non-atlas reliant voxel-based signatures to be reported that would be useful for modelling and hypothesis testing. We describe a cross-validated signature region model for structural brain components associated with baseline and longitudinal episodic memory across cognitively heterogeneous populations including normal, mild impairment and dementia. We used three non-overlapping cohorts of older participants: from the UC Davis Aging and Diversity cohort (n = 255; mean age 75.3 ± 7.1 years; 128 cognitively normal, 97 mild cognitive impairment, 30 demented and seven unclassified); from Alzheimer's Disease Neuroimaging Initiative (ADNI) 1 (n = 379; mean age 75.1 ± 7.2; 82 cognitively normal, 176 mild cognitive impairment, 121 Alzheimer's dementia); and from ADNI2/GO (n = 680; mean age 72.5 ± 7.1; 220 cognitively normal, 381 mild cognitive impairment and 79 Alzheimer's dementia). We used voxel-wise regression analysis, correcting for multiple comparisons, to generate an array of regional masks corresponding to different association strength levels of cortical grey matter with baseline memory and brain atrophy with memory change. Cognitive measures were episodic memory using Spanish and English Neuropsychological Assessment Scales instruments for UC Davis and ADNI-Mem for ADNI 1 and ADNI2/GO. Performance metric was the adjusted R2 coefficient of determination of each model explaining outcomes in two cohorts other than where it was computed. We compared within-cohort performances of signature models against each other and against other recent signature models of episodic memory. Findings were: (i) two independently generated signature region of interest models performed similarly in a third separate cohort; (ii) a signature region of interest generated in one imaging cohort replicated its performance level when explaining cognitive outcomes in each of other, separate cohorts; and (iii) this approach better explained baseline and longitudinal memory than other recent theory-driven and data-driven models. This suggests our approach can generate signatures that may be easily and robustly applied for modelling and hypothesis testing in mixed cognition cohorts.
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Affiliation(s)
- Evan Fletcher
- Department of Neurology, UC Davis School of Medicine, Sacramento, CA, USA
| | - Brandon Gavett
- School of Psychological Science, University of Western Australia, Perth, Australia
| | - Paul Crane
- University of Washington, Seattle, WA, USA
| | - Anja Soldan
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Timothy Hohman
- Department of Neurology, Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sarah Farias
- Department of Neurology, UC Davis School of Medicine, Sacramento, CA, USA
| | - Keith Widaman
- Graduate School of Education, UC Riverside, Riverside, CA, USA
| | - Colin Groot
- Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, The Netherlands
| | | | - Laura Zahodne
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Charles DeCarli
- Department of Neurology, UC Davis School of Medicine, Sacramento, CA, USA
| | - Dan Mungas
- Department of Neurology, UC Davis School of Medicine, Sacramento, CA, USA
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Liu J, Li M, Luo Y, Yang S, Li W, Bi Y. Alzheimer's disease detection using depthwise separable convolutional neural networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106032. [PMID: 33713959 DOI: 10.1016/j.cmpb.2021.106032] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Accepted: 02/25/2021] [Indexed: 05/02/2023]
Abstract
To diagnose Alzheimer's disease (AD), neuroimaging methods such as magnetic resonance imaging have been employed. Recent progress in computer vision with deep learning (DL) has further inspired research focused on machine learning algorithms. However, a few limitations of these algorithms, such as the requirement for large number of training images and the necessity for powerful computers, still hinder the extensive usage of AD diagnosis based on machine learning. In addition, large number of training parameters and heavy computation make the DL systems difficult in integrating with mobile embedded devices, for example the mobile phones. For AD detection using DL, most of the current research solely focused on improving the classification performance, while few studies have been done to obtain a more compact model with less complexity and relatively high recognition accuracy. In order to solve this problem and improve the efficiency of the DL algorithm, a deep separable convolutional neural network model is proposed for AD classification in this paper. The depthwise separable convolution (DSC) is used in this work to replace the conventional convolution. Compared to the traditional neural networks, the parameters and computing cost of the proposed neural network are found greatly reduced. The parameters and computational costs of the proposed neural network are found to be significantly reduced compared with conventional neural networks. With its low power consumption, the proposed model is particularly suitable for embedding mobile devices. Experimental findings show that the DSC algorithm, based on the OASIS magnetic resonance imaging dataset, is very successful for AD detection. Moreover, transfer learning is employed in this work to improve model performance. Two trained models with complex networks, namely AlexNet and GoogLeNet, are used for transfer learning, with average classification rates of 91.40%, 93.02% and a less power consumption.
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Affiliation(s)
- Junxiu Liu
- School of Electronic Engineering, Guangxi Normal University, Guilin, 541004, China
| | - Mingxing Li
- School of Electronic Engineering, Guangxi Normal University, Guilin, 541004, China
| | - Yuling Luo
- School of Electronic Engineering, Guangxi Normal University, Guilin, 541004, China.
| | - Su Yang
- School of Computing and Engineering, University of West London, London, United Kingdom
| | - Wei Li
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Yifei Bi
- College of Foreign Languages, University of Shanghai for Science and Technology, Shanghai, China
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Tăuţan AM, Ionescu B, Santarnecchi E. Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques. Artif Intell Med 2021; 117:102081. [PMID: 34127244 DOI: 10.1016/j.artmed.2021.102081] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/21/2021] [Accepted: 04/26/2021] [Indexed: 10/21/2022]
Abstract
Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.
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Affiliation(s)
- Alexandra-Maria Tăuţan
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Bogdan Ionescu
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Harvard Medical School, 330 Brookline Avenue, Boston, United States.
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26
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Li J, Antonecchia E, Camerlenghi M, Chiaravalloti A, Chu Q, Costanzo AD, Li Z, Wan L, Zhang X, D'Ascenzo N, Schillaci O, Xie Q. Correlation of [ 18F]florbetaben textural features and age of onset of Alzheimer's disease: a principal components analysis approach. EJNMMI Res 2021; 11:40. [PMID: 33881633 PMCID: PMC8060386 DOI: 10.1186/s13550-021-00774-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 03/15/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND When Alzheimer's disease (AD) is occurring at an early onset before 65 years old, its clinical course is generally more aggressive than in the case of a late onset. We aim at identifying [[Formula: see text]F]florbetaben PET biomarkers sensitive to differences between early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD). We conducted [[Formula: see text]F]florbetaben PET/CT scans of 43 newly diagnosed AD subjects. We calculated 93 textural parameters for each of the 83 Hammers areas. We identified 41 independent principal components for each brain region, and we studied their Spearman correlation with the age of AD onset, by taking into account multiple comparison corrections. Finally, we calculated the probability that EOAD and LOAD patients have different amyloid-[Formula: see text] ([Formula: see text]) deposition by comparing the mean and the variance of the significant principal components obtained in the two groups with a 2-tailed Student's t-test. RESULTS We found that four principal components exhibit a significant correlation at a 95% confidence level with the age of onset in the left lateral part of the anterior temporal lobe, the right anterior orbital gyrus of the frontal lobe, the right lateral orbital gyrus of the frontal lobe and the left anterior part of the superior temporal gyrus. The data are consistent with the hypothesis that EOAD patients have a significantly different [[Formula: see text]F]florbetaben uptake than LOAD patients in those four brain regions. CONCLUSIONS Early-onset AD implies a very irregular pattern of [Formula: see text] deposition. The authors suggest that the identified textural features can be used as quantitative biomarkers for the diagnosis and characterization of EOAD patients.
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Affiliation(s)
- Jing Li
- Department of Biomedical Engineering, Huazhong University of Science and Technology, Luoyu Road, Wuhan, 430074, China
| | - Emanuele Antonecchia
- Department of Biomedical Engineering, Huazhong University of Science and Technology, Luoyu Road, Wuhan, 430074, China.,Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo NEUROMED I.R.C.C.S, Via Dell'Elettronica, 83008, Pozzilli, Italy
| | - Marco Camerlenghi
- NIM Competence Center for Digital Healthcare GmbH, Potsdamerplatz, 10, 10785, Berlin, Germany
| | - Agostino Chiaravalloti
- Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo NEUROMED I.R.C.C.S, Via Dell'Elettronica, 83008, Pozzilli, Italy. .,Department of Biomedicine and Prevention, University of Tor Vergata, 86100, Rome, Italy.
| | - Qian Chu
- Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road, Wuhan, 430030, China.,Department of Oncology, Tongji Hospital, Jiefang Avenue, Wuhan, 430030, China
| | - Alfonso Di Costanzo
- Universita degli Studi del Molise, Via Francesco de Sanctis, 1, 10115, Campobasso, Italy
| | - Zhen Li
- Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road, Wuhan, 430030, China.,Department of Radiology, Tongji Hospital, Jiefang Avenue, Wuhan, 430030, China
| | - Lin Wan
- Department of Software Engineering, Huazhong University of Science and Technology, Luoyu Road, Wuhan, 430074, China
| | - Xiangsong Zhang
- The First Affiliated Hospital, Sun Yat-sen University, Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Nicola D'Ascenzo
- Department of Biomedical Engineering, Huazhong University of Science and Technology, Luoyu Road, Wuhan, 430074, China. .,Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo NEUROMED I.R.C.C.S, Via Dell'Elettronica, 83008, Pozzilli, Italy.
| | - Orazio Schillaci
- Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo NEUROMED I.R.C.C.S, Via Dell'Elettronica, 83008, Pozzilli, Italy.,Department of Biomedicine and Prevention, University of Tor Vergata, 86100, Rome, Italy
| | - Qingguo Xie
- Department of Biomedical Engineering, Huazhong University of Science and Technology, Luoyu Road, Wuhan, 430074, China. .,Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo NEUROMED I.R.C.C.S, Via Dell'Elettronica, 83008, Pozzilli, Italy.
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Zhao X, Ang CKE, Acharya UR, Cheong KH. Application of Artificial Intelligence techniques for the detection of Alzheimer’s disease using structural MRI images. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Ouyang T, Pedrycz W, Reyes-Galaviz OF, Pizzi NJ. Granular Description of Data Structures: A Two-Phase Design. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:1902-1912. [PMID: 30605118 DOI: 10.1109/tcyb.2018.2887115] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The study is concerned with a description of large numeric data with the aid of building a limited collection of representative information granules with the objective of capturing the structure of the original data. The proposed development scheme consists of two steps. First, a clustering algorithm characterized by high flexibility of coping with the diverse geometry of data structure and efficient computational overhead is invoked. At the second step, a clustering algorithm applied to the clusters already formed during the first phase, yielding a collection of numeric prototypes is involved and the numeric prototypes produced there are then generalized into their granular prototypes. The quality of granular prototypes is quantified while their build-up is supported by the mechanisms of granular computing such as the principle of justifiable granularity. In this paper, the clustering algorithms of DBSCAN and fuzzy C -means were used in successive phases of the processed approach. The experimental studies concerning synthetic data and publicly available data are covered and the performance of the developed approach is assessed along with a comparative analysis.
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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: 10] [Impact Index Per Article: 3.3] [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.
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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
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Kolling ML, Furstenau LB, Sott MK, Rabaioli B, Ulmi PH, Bragazzi NL, Tedesco LPC. Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18063099. [PMID: 33802880 PMCID: PMC8002654 DOI: 10.3390/ijerph18063099] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/12/2021] [Accepted: 03/15/2021] [Indexed: 12/15/2022]
Abstract
In order to identify the strategic topics and the thematic evolution structure of data mining applied to healthcare, in this paper, a bibliometric performance and network analysis (BPNA) was conducted. For this purpose, 6138 articles were sourced from the Web of Science covering the period from 1995 to July 2020 and the SciMAT software was used. Our results present a strategic diagram composed of 19 themes, of which the 8 motor themes ('NEURAL-NETWORKS', 'CANCER', 'ELETRONIC-HEALTH-RECORDS', 'DIABETES-MELLITUS', 'ALZHEIMER'S-DISEASE', 'BREAST-CANCER', 'DEPRESSION', and 'RANDOM-FOREST') are depicted in a thematic network. An in-depth analysis was carried out in order to find hidden patterns and to provide a general perspective of the field. The thematic network structure is arranged thusly that its subjects are organized into two different areas, (i) practices and techniques related to data mining in healthcare, and (ii) health concepts and disease supported by data mining, embodying, respectively, the hotspots related to the data mining and medical scopes, hence demonstrating the field's evolution over time. Such results make it possible to form the basis for future research and facilitate decision-making by researchers and practitioners, institutions, and governments interested in data mining in healthcare.
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Affiliation(s)
- Maikel Luis Kolling
- Graduate Program of Industrial Systems and Processes, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil; (M.L.K.); (M.K.S.)
| | - Leonardo B. Furstenau
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre 90035-190, Brazil;
| | - Michele Kremer Sott
- Graduate Program of Industrial Systems and Processes, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil; (M.L.K.); (M.K.S.)
| | - Bruna Rabaioli
- Department of Medicine, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil;
| | - Pedro Henrique Ulmi
- Department of Computer Science, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil;
| | - Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
- Correspondence: (N.L.B.); (L.P.C.T.)
| | - Leonel Pablo Carvalho Tedesco
- Graduate Program of Industrial Systems and Processes, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil; (M.L.K.); (M.K.S.)
- Department of Computer Science, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil;
- Correspondence: (N.L.B.); (L.P.C.T.)
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31
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Rangaprakash D, Odemuyiwa T, Narayana Dutt D, Deshpande G. Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment. Brain Inform 2020; 7:19. [PMID: 33242116 PMCID: PMC7691406 DOI: 10.1186/s40708-020-00120-2] [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: 10/16/2020] [Accepted: 10/29/2020] [Indexed: 11/29/2022] Open
Abstract
Various machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of training data to generate a predictive model. Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose utility for high-dimensional neuroimaging data has not been previously evaluated. Its advantages include insensitivity to outliers and ability to work with unlabeled data. Unlike the popular k-means clustering, the number of clusters need not be specified. In this study, we compare the performance of two popular density-based clustering methods, DBSCAN and OPTICS, in accurately identifying individuals with three stages of cognitive impairment, including Alzheimer’s disease. We used static and dynamic functional connectivity features for clustering, which captures the strength and temporal variation of brain connectivity respectively. To assess the robustness of clustering to noise/outliers, we propose a novel method called recursive-clustering using additive-noise (R-CLAN). Results demonstrated that both clustering algorithms were effective, although OPTICS with dynamic connectivity features outperformed in terms of cluster purity (95.46%) and robustness to noise/outliers. This study demonstrates that density-based clustering can accurately and robustly identify diagnostic classes in an unsupervised way using brain connectivity.
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Affiliation(s)
- D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA.,Division of Health Sciences and Technology, Harvard University and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Toluwanimi Odemuyiwa
- Division of Engineering Science, Faculty of Applied Science & Engineering, University of Toronto, Toronto, ON, Canada
| | - D Narayana Dutt
- Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore, India
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA. .,Department of Psychological Sciences, Auburn University, Auburn, AL, USA. .,Alabama Advanced Imaging Consortium, University of Alabama Birmingham, Alabama, USA. .,Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA. .,Center for Neuroscience, Auburn University, Auburn, AL, USA. .,School of Psychology, Capital Normal University, Beijing, China. .,Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China. .,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India.
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Fang C, Li C, Forouzannezhad P, Cabrerizo M, Curiel RE, Loewenstein D, Duara R, Adjouadi M. Gaussian discriminative component analysis for early detection of Alzheimer's disease: A supervised dimensionality reduction algorithm. J Neurosci Methods 2020; 344:108856. [PMID: 32663548 PMCID: PMC11167623 DOI: 10.1016/j.jneumeth.2020.108856] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 07/06/2020] [Accepted: 07/07/2020] [Indexed: 01/09/2023]
Abstract
BACKGROUND Using multiple modalities of biomarkers, several machine leaning-based approaches have been proposed to characterize patterns of structural, functional and metabolic differences discernible from multimodal neuroimaging data for Alzheimer's disease (AD). Current investigations report several studies using binary classification often augmented with local feature selection methods, while fewer other studies address the challenging problem of multiclass classification. NEW METHOD To assess the merits of each of these research directions, this study introduces a supervised Gaussian discriminative component analysis (GDCA) algorithm, which can effectively delineate subtle changes of early mild cognitive impairment (EMCI) group in relation to the cognitively normal control (CN) group. Using 251 CN, 297 EMCI, 196 late MCI (LMCI), and 162 AD subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and considering both structural and functional (metabolic) information from magnetic resonance imaging (MRI) and positron emission tomography (PET) modalities as input, the proposed method conducts a dimensionality reduction algorithm taking into consideration the interclass information to define an optimal eigenspace that maximizes the discriminability of selected eigenvectors. RESULTS The proposed algorithm achieves an accuracy of 79.25 % for delineating EMCI from CN using 38.97 % of Gaussian discriminative components (i.e., dimensionality reduction). Moreover, for detecting the different stages of AD, a multiclass classification experiment attained an overall accuracy of 67.69 %, and more notably, discriminates MCI and AD groups from the CN group with an accuracy of 75.28 % using 48.90 % of the Gaussian discriminative components. COMPARISON WITH EXISTING METHOD(S) The classification results of the proposed GDCA method outperform the more recently published state-of-the-art methods in AD-related multiclass classification tasks, and seems to be the most stable and reliable in terms of relating the most relevant features to the optimal classification performance. CONCLUSION The proposed GDCA model with its high prospects for multiclass classification has a high potential for deployment as a computer aided clinical diagnosis system for AD.
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Affiliation(s)
- Chen Fang
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA.
| | - Chunfei Li
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Parisa Forouzannezhad
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Mercedes Cabrerizo
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Rosie E Curiel
- Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA; 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA
| | - David Loewenstein
- Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA; 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA
| | - Ranjan Duara
- 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA; Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - Malek Adjouadi
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA; 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA.
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Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10155135] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.
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Yang BH, Chen JC, Chou WH, Huang WS, Fuh JL, Liu R, Wu CH. Classification of Alzheimer’s Disease from 18F-FDG and 11C-PiB PET Imaging Biomarkers Using Support Vector Machine. J Med Biol Eng 2020. [DOI: 10.1007/s40846-020-00548-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Leandrou S, Lamnisos D, Mamais I, Kyriacou PA, Pattichis CS. Assessment of Alzheimer's Disease Based on Texture Analysis of the Entorhinal Cortex. Front Aging Neurosci 2020; 12:176. [PMID: 32714177 PMCID: PMC7351503 DOI: 10.3389/fnagi.2020.00176] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 05/20/2020] [Indexed: 01/09/2023] Open
Abstract
Alzheimer’s disease (AD) brain magnetic resonance imaging (MRI) biomarkers based on larger-scale tissue neurodegeneration changes, such as atrophy, are currently widely used. Texture analysis evaluates the statistical properties of the tissue image quantitatively; therefore, it could detect smaller-scale changes of neurodegeneration. Entorhinal cortex is the first region affected, and no study has investigated texture analysis on this region before. This study aims to differentiate AD patients from Normal Control (NC) and Mild Cognitive Impairment (MCI) subjects using entorhinal cortex texture features. Furthermore, it was evaluated whether texture has association to MCI beyond that of volume, to evaluate if atrophy development may precede. Texture features were extracted from 194 NC, 200 MCI, 84 MCI who converted to AD (MCIc), and 130 AD subjects. Receiving operating characteristic curves determined the performance of the various features in discriminating the groups, and a predictive model was used to predict conversion of MCIc subjects to AD. An area under the curve (AUC) of 0.872, 0.710, 0.730, and 0.764 was seen between NC vs. AD, NC vs. MCI, MCI vs. MCIc, and MCI vs. AD subjects, respectively. Including entorhinal cortex volume improved the AUCs to 0.914, 0.740, 0.756, and 0.780, respectively. For the disease prediction, binary logistic regression was applied on five randomly selected test groups and achieved on average AUC’s of 0.760 and 0.764 on the training and validation cohorts, respectively. Entorhinal cortex texture features were significantly different between the four groups and in many cases provided better results compared to other methods such as volumetry.
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Affiliation(s)
- Stephanos Leandrou
- School of Science, European University Cyprus, Nicosia, Cyprus.,School of Mathematical Sciences, Computer Science and Engineering, City, University of London, London, United Kingdom
| | | | - Ioannis Mamais
- School of Science, European University Cyprus, Nicosia, Cyprus
| | - Panicos A Kyriacou
- School of Mathematical Sciences, Computer Science and Engineering, City, University of London, London, United Kingdom
| | - Constantinos S Pattichis
- Department of Computer Science, University of Cyprus, Nicosia, Cyprus.,Research Centre on Interactive Media, Smart Systems and Emerging Technologies (RISE CoE), Nicosia, Cyprus
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36
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Automated detection of Alzheimer's disease using bi-directional empirical model decomposition. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2020.03.014] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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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]
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A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10051894] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.
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Subramanian S, Rajamanickam K, Prakash JS, Ramachandran M. Study on structural atrophy changes and functional connectivity measures in Alzheimer's disease. J Med Imaging (Bellingham) 2020; 7:016002. [PMID: 32118092 DOI: 10.1117/1.jmi.7.1.016002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 02/03/2020] [Indexed: 11/14/2022] Open
Abstract
Alzheimer's disease (AD) is characterized by the progressive accumulation of neurofibrillary tangles associated with amyloid plaques. We used 80 resting-state functional magnetic resonance imaging and 80 T 1 images acquired using MP-RAGE (magnetization-prepared rapid acquisition gradient echo) from Alzheimer's Disease Neuroimaging Initiative data to detect atrophy changes and functional connectivity patterns of the default mode networks (DMNs). The study subjects were classified into four groups (each with n = 20 ) based on their Mini-Mental State Examination (MMSE) score as follows: cognitively normal (CN), early mild cognitive impairment, late mild cognitive impairment, and AD. The resting-state functional connectivity of the DMN was examined between the groups using the CONN functional connectivity toolbox. Loss of gray matter in AD was observed. Atrophy measured by the volume of selected subcortical regions, using the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library's Integrated Registration and Segmentation Tool (FIRST), revealed significant volume loss in AD when compared to CN ( p < 0.05 ). DMNs were selected to assess functional connectivity. The negative connectivity of DMN increased in AD group compared to controls. Graph theory parameters, such as global and local efficiency, betweenness centrality, average path length, and cluster coefficient, were computed. Relatively higher correlation between MMSE and functional metrics ( r = 0.364 , p = 0.001 ) was observed as compared to atrophy measures ( r = 0.303 , p = 0.006 ). In addition, the receiver operating characteristic analysis showed large area under the curve ( A Z ) for functional parameters ( A Z > 0.9 ), compared to morphometric changes ( A Z < 0.8 ). In summary, it is observed that the functional connectivity measures may serve a better predictor in comparison to structural atrophy changes. We postulate that functional connectivity measures have the potential to evolve as a marker for the early detection of AD.
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Affiliation(s)
- Saraswathi Subramanian
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
| | - Karunanithi Rajamanickam
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
| | - Joy Sebastian Prakash
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
| | - Murugesan Ramachandran
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
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Forouzannezhad P, Abbaspour A, Li C, Fang C, Williams U, Cabrerizo M, Barreto A, Andrian J, Rishe N, Curiel RE, Loewenstein D, Duara R, Adjouadi M. A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging. J Neurosci Methods 2020; 333:108544. [PMID: 31838182 PMCID: PMC11163390 DOI: 10.1016/j.jneumeth.2019.108544] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Revised: 11/26/2019] [Accepted: 12/05/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Diagnosis of early mild cognitive impairment (EMCI) as a prodromal stage of Alzheimer's disease (AD) with its delineation from the cognitively normal (CN) group remains a challenging but essential step for the planning of early treatment. Although several studies have focused on the MCI diagnosis, this study introduces the early stage of MCI to assess more thoroughly the earliest signs of disease manifestation and progression. NEW METHOD We used random forest feature selection model with a Gaussian-based algorithm to perform method evaluation. This integrated method serves to define multivariate normal distributions in order to classify different stages of AD, with the focus placed on detecting EMCI subjects in the most challenging classification of CN vs. EMCI. RESULTS Using 896 participants classified into the four categories of CN, EMCI, late mild cognitive impairment (LMCI) and AD, the results show that the EMCI group can be delineated from the CN group with a relatively high accuracy of 78.8% and sensitivity of 81.3%. COMPARISON WITH EXISTING METHOD(S) The feature selection model and classifier are compared with some other prominent algorithms. Although higher accuracy has been achieved using the Gaussian process (GP) model (78.8%) over the SVM classifier (75.6%) for CN vs. EMCI classification, with 0.05 being the cutoff for significance, and based on student's t-test, it was determined that the differences for accuracy, sensitivity, specificity between the GP method and support vector machine (SVM) are not statistically significant. CONCLUSION Addressing the challenging classification of CN vs. EMCI provides useful information to help clinicians and researchers determine essential measures that can help in the early detection of AD.
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Affiliation(s)
- Parisa Forouzannezhad
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Alireza Abbaspour
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Chunfei Li
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Chen Fang
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Ulyana Williams
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Mercedes Cabrerizo
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Armando Barreto
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Jean Andrian
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Naphtali Rishe
- School of Computing and Information Sciences, Florida International University, Miami, FL, USA
| | - Rosie E Curiel
- Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami School of Medicine, Miami, FL, USA; 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA
| | - David Loewenstein
- Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami School of Medicine, Miami, FL, USA; 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA; Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - Ranjan Duara
- 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA; Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - Malek Adjouadi
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA; 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA
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Franzmeier N, Koutsouleris N, Benzinger T, Goate A, Karch CM, Fagan AM, McDade E, Duering M, Dichgans M, Levin J, Gordon BA, Lim YY, Masters CL, Rossor M, Fox NC, O'Connor A, Chhatwal J, Salloway S, Danek A, Hassenstab J, Schofield PR, Morris JC, Bateman RJ, Ewers M. Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning. Alzheimers Dement 2020; 16:501-511. [PMID: 32043733 PMCID: PMC7222030 DOI: 10.1002/alz.12032] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 10/21/2019] [Accepted: 11/25/2019] [Indexed: 11/22/2022]
Abstract
Introduction: Developing cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer’s disease (AD) is a critical yet unmet clinical challenge. Methods: We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid-PET and fluorodeoxyglucose positron-emission tomography (FDG-PET) to predict rates of cognitive decline. Prediction models were trained in autosomal-dominant Alzheimer’s disease (ADAD, n = 121) and subsequently cross-validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model-based risk enrichment was estimated. Results: A model combining all biomarker modalities and established in ADAD predicted the 4-year rate of decline in global cognition (R2 = 24%) and memory (R2 =25%) in sporadic AD. Model-based risk-enrichment reduced the sample size required for detecting simulated intervention effects by 50%–75%. Discussion: Our independently validated machine-learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD.
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Affiliation(s)
- Nicolai Franzmeier
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Tammie Benzinger
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.,Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Alison Goate
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Ronald M. Loeb Center for Alzheimer's Disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Celeste M Karch
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.,Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Anne M Fagan
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.,Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Eric McDade
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Marco Duering
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany.,Munich Cluster for Systems Neurology, Munich, Germany.,German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Johannes Levin
- Munich Cluster for Systems Neurology, Munich, Germany.,German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Brian A Gordon
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.,Mallinckrodt Institute of Radiology, Washington University, St. Louis, Missouri, USA.,Department of Psychological and Brain Sciences, Washington University, St. Louis, Missouri, USA
| | - Yen Ying Lim
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Colin L Masters
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Martin Rossor
- Dementia Research Centre, University College London, Queen Square, London, UK
| | - Nick C Fox
- Dementia Research Centre, University College London, Queen Square, London, UK
| | - Antoinette O'Connor
- Dementia Research Centre, University College London, Queen Square, London, UK
| | - Jasmeer Chhatwal
- Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Stephen Salloway
- Department of Neurology, Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Adrian Danek
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Jason Hassenstab
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Psychological and Brain Sciences, Washington University, St. Louis, Missouri, USA
| | - Peter R Schofield
- Neuroscience Research Australia, Randwick, New South Wales, Australia.,School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - John C Morris
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Randall J Bateman
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
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- ADNI Consortium members are listed in the appendix
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- DIAN Consortium members are listed in the appendix
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
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Mehmood A, Maqsood M, Bashir M, Shuyuan Y. A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease. Brain Sci 2020; 10:E84. [PMID: 32033462 PMCID: PMC7071616 DOI: 10.3390/brainsci10020084] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 01/29/2020] [Accepted: 02/01/2020] [Indexed: 01/25/2023] Open
Abstract
Alzheimer's disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer's disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.
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Grants
- P01 AG003991 NIA NIH HHS
- P01 AG026276 NIA NIH HHS
- *Nos.61771380, 61906145, U1730109, 91438103, 61771376, 61703328, 91438201, U1701267, 61703328), National Natural Science Foundation of China
- Nos.16JK1823, 2017JM6086, 2019JQ-663 The Science Basis Research Program in Shaanxi Province of China
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Affiliation(s)
- Atif Mehmood
- School of Artificial Intelligence, Xidian University, No. 2 South Taibai Road, Xian 710071, China;
| | - Muazzam Maqsood
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan;
| | - Muzaffar Bashir
- Department of Physics, University of the Punjab, Lahore 54590, Pakistan
| | - Yang Shuyuan
- School of Artificial Intelligence, Xidian University, No. 2 South Taibai Road, Xian 710071, China;
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43
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Accurate BAPL Score Classification of Brain PET Images Based on Convolutional Neural Networks with a Joint Discriminative Loss Function †. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10030965] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Alzheimer’s disease (AD) is an irreversible progressive cerebral disease with most of its symptoms appearing after 60 years of age. Alzheimer’s disease has been largely attributed to accumulation of amyloid beta (Aβ), but a complete cure has remained elusive. 18F-Florbetaben amyloid positron emission tomography (PET) has been shown as a more powerful tool for understanding AD-related brain changes than magnetic resonance imaging and computed tomography. In this paper, we propose an accurate classification method for scoring brain amyloid plaque load (BAPL) based on deep convolutional neural networks. A joint discriminative loss function was formulated by adding a discriminative intra-loss function to the conventional (cross-entropy) loss function. The performance of the proposed joint loss function was compared with that of the conventional loss function in three state-of-the-art deep neural network architectures. The intra-loss function significantly improved the BAPL classification performance. In addition, we showed that the mix-up data augmentation method, originally proposed for natural image classification, was also useful for medical image classification.
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44
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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.
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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
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Feng Q, Song Q, Wang M, Pang P, Liao Z, Jiang H, Shen D, Ding Z. Hippocampus Radiomic Biomarkers for the Diagnosis of Amnestic Mild Cognitive Impairment: A Machine Learning Method. Front Aging Neurosci 2019; 11:323. [PMID: 31824302 PMCID: PMC6881244 DOI: 10.3389/fnagi.2019.00323] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 11/06/2019] [Indexed: 12/11/2022] Open
Abstract
Background: Recent evidence suggests the presence of hippocampal neuroanatomical abnormalities in subjects of amnestic mild cognitive impairment (aMCI). Our study aimed to identify the radiomic biomarkers of the hippocampus for building the classification models in aMCI diagnosis. Methods: For this target, we recruited 42 subjects with aMCI and 44 normal controls (NC). The right and left hippocampi were segmented for each subject using an efficient learning-based method. Then, the radiomic analysis was applied to calculate and select the radiomic features. Finally, two logistic regression models were built based on the selected features obtained from the right and left hippocampi. Results: There were 385 features derived after calculation, and four features remained after feature selection from each group of data. The area under the receiver operating characteristic (ROC) curve, specificity, sensitivity, positive predictive value, negative predictive value, precision, recall, and F-score of the classification evaluation index of the right hippocampus logistic regression model were 0.76, 0.71, 0.69, 0.69, 0.71, 0.69, 0.69, and 0.69, and those of the left hippocampus model were 0.79, 0.71, 0.54, 0.64, 0.63, 0.64, 0.54, and 0.58, respectively. Conclusion: Results demonstrate the potential hippocampal radiomic biomarkers are valid for the aMCI diagnosis. The MRI-based radiomic analysis, with further improvement and validation, can be used to identify patients with aMCI and guide the individual treatment.
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Affiliation(s)
- Qi Feng
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qiaowei Song
- Department of Radiology, Zhejiang Provincial People's Hospital/People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Mei Wang
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - PeiPei Pang
- GE Healthcare Life Sciences, Hangzhou, China
| | - Zhengluan Liao
- Department of Psychiatry, Zhejiang Provincial People's Hospital/People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Hongyang Jiang
- Department of Radiology, Zhejiang Provincial People's Hospital/People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Pervolaraki E, Hall SP, Foresteire D, Saito T, Saido TC, Whittington MA, Lever C, Dachtler J. Insoluble Aβ overexpression in an App knock-in mouse model alters microstructure and gamma oscillations in the prefrontal cortex, affecting anxiety-related behaviours. Dis Model Mech 2019; 12:dmm040550. [PMID: 31439589 PMCID: PMC6765200 DOI: 10.1242/dmm.040550] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 08/15/2019] [Indexed: 12/31/2022] Open
Abstract
We studied a new amyloid-beta precursor protein (App) knock-in mouse model of Alzheimer's disease (AppNL-G-F ), containing the Swedish KM670/671NL mutation, the Iberian I716F mutation and the Artic E693G mutation, which generates elevated levels of amyloid beta (Aβ)40 and Aβ42 without the confounds associated with APP overexpression. This enabled us to assess changes in anxiety-related and social behaviours, and neural alterations potentially underlying such changes, driven specifically by Aβ accumulation. AppNL-G-F knock-in mice exhibited subtle deficits in tasks assessing social olfaction, but not in social motivation tasks. In anxiety-assessing tasks, AppNL-G-F knock-in mice exhibited: (1) increased thigmotaxis in the open field (OF), yet; (2) reduced closed-arm, and increased open-arm, time in the elevated plus maze (EPM). Their ostensibly anxiogenic OF profile, yet ostensibly anxiolytic EPM profile, could hint at altered cortical mechanisms affecting decision-making (e.g. 'disinhibition'), rather than simple core deficits in emotional motivation. Consistent with this possibility, alterations in microstructure, glutamatergic-dependent gamma oscillations and glutamatergic gene expression were all observed in the prefrontal cortex, but not the amygdala, of AppNL-G-F knock-in mice. Thus, insoluble Aβ overexpression drives prefrontal cortical alterations, potentially underlying changes in social and anxiety-related behavioural tasks.This article has an associated First Person interview with the first author of the paper.
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Affiliation(s)
| | - Stephen P Hall
- Hull York Medical School, University of York, Heslington, YO10 5DD, UK
| | - Denise Foresteire
- Department of Psychology, Durham University, South Road, Durham, DH1 3LE, UK
| | - Takashi Saito
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Wako-shi, Saitama, Japan
| | - Takaomi C Saido
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Wako-shi, Saitama, Japan
| | | | - Colin Lever
- Department of Psychology, Durham University, South Road, Durham, DH1 3LE, UK
| | - James Dachtler
- Department of Psychology, Durham University, South Road, Durham, DH1 3LE, UK
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47
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Differentiating Between Healthy Control Participants and Those with Mild Cognitive Impairment Using Volumetric MRI Data. J Int Neuropsychol Soc 2019; 25:800-810. [PMID: 31130145 PMCID: PMC6995275 DOI: 10.1017/s135561771900047x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
OBJECTIVE To determine whether volumetric measures of the hippocampus, entorhinal cortex, and other cortical measures can differentiate between cognitively normal individuals and subjects with mild cognitive impairment (MCI). METHOD Magnetic resonance imaging (MRI) data from 46 cognitively normal subjects and 50 subjects with MCI as part of the Boston University Alzheimer's Disease Center research registry and the Alzheimer's Disease Neuroimaging Initiative were used in this cross-sectional study. Cortical, subcortical, and hippocampal subfield volumes were generated from each subject's MRI data using FreeSurfer v6.0. Nominal logistic regression models containing these variables were used to identify subjects as control or MCI. RESULTS A model containing regions of interest (superior temporal cortex, caudal anterior cingulate, pars opercularis, subiculum, precentral cortex, caudal middle frontal cortex, rostral middle frontal cortex, pars orbitalis, middle temporal cortex, insula, banks of the superior temporal sulcus, parasubiculum, paracentral lobule) fit the data best (R2 = .7310, whole model test chi-square = 97.16, p < .0001). CONCLUSIONS MRI data correctly classified most subjects using measures of selected medial temporal lobe structures in combination with those from other cortical areas, yielding an overall classification accuracy of 93.75%. These findings support the notion that, while volumes of medial temporal lobe regions differ between cognitively normal and MCI subjects, differences that can be used to distinguish between these two populations are present elsewhere in the brain.
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Zhao L, Luo Y, Lew D, Liu W, Au L, Mok V, Shi L. Risk estimation before progression to mild cognitive impairment and Alzheimer's disease: an AD resemblance atrophy index. Aging (Albany NY) 2019; 11:6217-6236. [PMID: 31467257 PMCID: PMC6738429 DOI: 10.18632/aging.102184] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 08/10/2019] [Indexed: 01/18/2023]
Abstract
To realize an individual-level risk evaluation of progression of early Alzheimer’s disease (AD), we applied an AD resemblance atrophy index (AD-RAI) to differentiate the subjects at risk of progression from normal subjects (NC) to mild cognitive impairment (MCI) and from MCI to AD. We included 183 subjects with a two-year follow-up: 50 NC stable (NCs), 23 NC-to-MCI converters (NCc), 50 MCI stable (MCIs), 35 MCI-to-AD converters (MCIc), 25 AD stable (ADs). ANCOVA analyses were used to identify baseline brain atrophy in converters compared with non-converters. To explore the relative merits of AD-RAI over individual regional volumetric measures in prediction of disease progression, we searched for the optimal cutoff for each measure in logistic regressions and plotted the longitudinal trajectories of these brain volumetric measures in converters and non-converters. Baseline AD-RAI performed the best in differentiating NCc from NCs (odds ratio 26.35, AUC 0.740) and MCIc from MCIs (odds ratio 8.91, AUC 0.771). The AD-RAI presented greater increase in the second year for NCc vs. NCs but not for MCIc vs. MCIs. Baseline AD-RAIs were also associated with CSF-based and PET-based AD biomarkers. These results showed the potential of AD-RAI in early risk estimation before progression to MCI/AD at an individual-level.
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Affiliation(s)
- Lei Zhao
- BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - Darson Lew
- BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - Wenyan Liu
- BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - Lisa Au
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, China.,Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, China
| | - Vincent Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, China.,Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, China.,Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, China.,Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Shatin, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen, Guangdong Province, China.,Department of Medicine and Therapeutics, The Chinese University of Hong Kong, China.,Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, China
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- Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database . As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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Zhang Y, Wang S, Sui Y, Yang M, Liu B, Cheng H, Sun J, Jia W, Phillips P, Gorriz JM. Multivariate Approach for Alzheimer's Disease Detection Using Stationary Wavelet Entropy and Predator-Prey Particle Swarm Optimization. J Alzheimers Dis 2019; 65:855-869. [PMID: 28731432 DOI: 10.3233/jad-170069] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The number of patients with Alzheimer's disease is increasing rapidly every year. Scholars often use computer vision and machine learning methods to develop an automatic diagnosis system. OBJECTIVE In this study, we developed a novel machine learning system that can make diagnoses automatically from brain magnetic resonance images. METHODS First, the brain imaging was processed, including skull stripping and spatial normalization. Second, one axial slice was selected from the volumetric image, and stationary wavelet entropy (SWE) was done to extract the texture features. Third, a single-hidden-layer neural network was used as the classifier. Finally, a predator-prey particle swarm optimization was proposed to train the weights and biases of the classifier. RESULTS Our method used 4-level decomposition and yielded 13 SWE features. The classification yielded an overall accuracy of 92.73±1.03%, a sensitivity of 92.69±1.29%, and a specificity of 92.78±1.51%. The area under the curve is 0.95±0.02. Additionally, this method only cost 0.88 s to identify a subject in online stage, after its volumetric image is preprocessed. CONCLUSION In terms of classification performance, our method performs better than 10 state-of-the-art approaches and the performance of human observers. Therefore, this proposed method is effective in the detection of Alzheimer's disease.
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Affiliation(s)
- Yudong Zhang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, P. R. China.,School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, P. R. China
| | - Shuihua Wang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, P. R. China.,School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, P. R. China
| | - Yuxiu Sui
- Department of Psychiatry, Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing, P. R.China
| | - Ming Yang
- Department of Radiology, Children's Hospital of Nanjing Medical University, Nanjing, P. R. China
| | - Bin Liu
- Department of Radiology, Zhong-Da Hospital of Southeast University, Nanjing, P. R. China
| | - Hong Cheng
- Department of Neurology, First Affiliated Hospital of Nanjing Medical University, Nanjing, P. R. China
| | - Junding Sun
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, P. R. China
| | - Wenjuan Jia
- School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, P. R. China
| | - Preetha Phillips
- West Virginia School of Osteopathic Medicine, Lewisburg, WV, USA
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
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50
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Petrone PM, Casamitjana A, Falcon C, Artigues M, Operto G, Cacciaglia R, Molinuevo JL, Vilaplana V, Gispert JD. Prediction of amyloid pathology in cognitively unimpaired individuals using voxel-wise analysis of longitudinal structural brain MRI. ALZHEIMERS RESEARCH & THERAPY 2019; 11:72. [PMID: 31421683 PMCID: PMC6698344 DOI: 10.1186/s13195-019-0526-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 07/23/2019] [Indexed: 01/01/2023]
Abstract
Background Magnetic resonance imaging (MRI) has unveiled specific alterations at different stages of Alzheimer’s disease (AD) pathophysiologic continuum constituting what has been established as “AD signature”. To what extent MRI can detect amyloid-related cerebral changes from structural MRI in cognitively unimpaired individuals is still an area open for exploration. Method Longitudinal 3D-T1 MRI scans were acquired from a subset of the ADNI cohort comprising 403 subjects: 79 controls (Ctrls), 50 preclinical AD (PreAD), and 274 MCI and dementia due to AD (MCI/AD). Amyloid CSF was used as gold-standard measure with established cutoffs (< 192 pg/mL) to establish diagnostic categories. Cognitively unimpaired individuals were defined as Ctrls if were amyloid negative and PreAD otherwise. The MCI/AD group was amyloid positive. Only subjects with the same diagnostic category at baseline and follow-up visits were considered for the study. Longitudinal morphometric analysis was performed using SPM12 to calculate Jacobian determinant maps. Statistical analysis was carried out on these Jacobian maps to identify structural changes that were significantly different between diagnostic categories. A machine learning classifier was applied on Jacobian determinant maps to predict the presence of abnormal amyloid levels in cognitively unimpaired individuals. The performance of this classifier was evaluated using receiver operating characteristic curve analysis and as a function of the follow-up time between MRI scans. We applied a cost function to assess the benefit of using this classifier in the triaging of individuals in a clinical trial-recruitment setting. Results The optimal follow-up time for classification of Ctrls vs PreAD was Δt > 2.5 years, and hence, only subjects within this temporal span are used for evaluation (15 Ctrls, 10 PreAD). The longitudinal voxel-based classifier achieved an AUC = 0.87 (95%CI 0.72–0.97). The brain regions that showed the highest discriminative power to detect amyloid abnormalities were the medial, inferior, and lateral temporal lobes; precuneus; caudate heads; basal forebrain; and lateral ventricles. Conclusions Our work supports that machine learning applied to longitudinal brain volumetric changes can be used to predict, with high precision, the presence of amyloid abnormalities in cognitively unimpaired subjects. Used as a triaging method to identify a fixed number of amyloid-positive individuals, this longitudinal voxel-wise classifier is expected to avoid 55% of unnecessary CSF and/or PET scans and reduce economic cost by 40%. Electronic supplementary material The online version of this article (10.1186/s13195-019-0526-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Paula M Petrone
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, C/ Wellington 30, 08005, Barcelona, Spain
| | - Adrià Casamitjana
- Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, C/ Jordi Girona 1-3, edifici D5 Campus Nord UPC, 08034, Barcelona, Spain
| | - Carles Falcon
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, C/ Wellington 30, 08005, Barcelona, Spain.,Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, 28029, Spain
| | - Miquel Artigues
- Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, C/ Jordi Girona 1-3, edifici D5 Campus Nord UPC, 08034, Barcelona, Spain
| | - Grégory Operto
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, C/ Wellington 30, 08005, Barcelona, Spain
| | - Raffaele Cacciaglia
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, C/ Wellington 30, 08005, Barcelona, Spain
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, C/ Wellington 30, 08005, Barcelona, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Verónica Vilaplana
- Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, C/ Jordi Girona 1-3, edifici D5 Campus Nord UPC, 08034, Barcelona, Spain.
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, C/ Wellington 30, 08005, Barcelona, Spain. .,CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain. .,Universitat Pompeu Fabra, Barcelona, Spain.
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