1
|
Alarjani M, Almarri B. Multivariate pattern analysis of medical imaging-based Alzheimer's disease. Front Med (Lausanne) 2024; 11:1412592. [PMID: 39099597 PMCID: PMC11294205 DOI: 10.3389/fmed.2024.1412592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 06/06/2024] [Indexed: 08/06/2024] Open
Abstract
Alzheimer's disease (AD) is a devastating brain disorder that steadily worsens over time. It is marked by a relentless decline in memory and cognitive abilities. As the disease progresses, it leads to a significant loss of mental function. Early detection of AD is essential to starting treatments that can mitigate the progression of this disease and enhance patients' quality of life. This study aims to observe AD's brain functional connectivity pattern to extract essential patterns through multivariate pattern analysis (MVPA) and analyze activity patterns across multiple brain voxels. The optimized feature extraction techniques are used to obtain the important features for performing the training on the models using several hybrid machine learning classifiers for performing binary classification and multi-class classification. The proposed approach using hybrid machine learning classification has been applied to two public datasets named the Open Access Series of Imaging Studies (OASIS) and the AD Neuroimaging Initiative (ADNI). The results are evaluated using performance metrics, and comparisons have been made to differentiate between different stages of AD using visualization tools.
Collapse
Affiliation(s)
| | - Badar Almarri
- Department of Computer Science, College of Computer Sciences and Information Technology, King Faisal University, Al-Hofuf, Saudi Arabia
| |
Collapse
|
2
|
Bolla G, Berente DB, Andrássy A, Zsuffa JA, Hidasi Z, Csibri E, Csukly G, Kamondi A, Kiss M, Horvath AA. Comparison of the diagnostic accuracy of resting-state fMRI driven machine learning algorithms in the detection of mild cognitive impairment. Sci Rep 2023; 13:22285. [PMID: 38097674 PMCID: PMC10721802 DOI: 10.1038/s41598-023-49461-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 12/08/2023] [Indexed: 12/17/2023] Open
Abstract
Mild cognitive impairment (MCI) is a potential therapeutic window in the prevention of dementia; however, automated detection of early cognitive deterioration is an unresolved issue. The aim of our study was to compare various classification approaches to differentiate MCI patients from healthy controls, based on rs-fMRI data, using machine learning (ML) algorithms. Own dataset (from two centers) and ADNI database were used during the analysis. Three fMRI parameters were applied in five feature selection algorithms: local correlation, intrinsic connectivity, and fractional amplitude of low frequency fluctuations. Support vector machine (SVM) and random forest (RF) methods were applied for classification. We achieved a relatively wide range of 78-87% accuracy for the various feature selection methods with SVM combining the three rs-fMRI parameters. In the ADNI datasets case we can also see even 90% accuracy scores. RF provided a more harmonized result among the feature selection algorithms in both datasets with 80-84% accuracy for our local and 74-82% for the ADNI database. Despite some lower performance metrics of some algorithms, most of the results were positive and could be seen in two unrelated datasets which increase the validity of our methods. Our results highlight the potential of ML-based fMRI applications for automated diagnostic techniques to recognize MCI patients.
Collapse
Affiliation(s)
- Gergo Bolla
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- School of PhD Studies, Semmelweis University, Budapest, Hungary
| | - Dalida Borbala Berente
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- School of PhD Studies, Semmelweis University, Budapest, Hungary
| | - Anita Andrássy
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
| | - Janos Andras Zsuffa
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- Department of Family Medicine, Semmelweis University, Budapest, Hungary
| | - Zoltan Hidasi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Eva Csibri
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Gabor Csukly
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Anita Kamondi
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Mate Kiss
- Siemens Healthcare, Budapest, Hungary
| | - Andras Attila Horvath
- Department of Anatomy Histology and Embryology, Semmelweis University, Budapest, Hungary.
| |
Collapse
|
3
|
Borchert RJ, Azevedo T, Badhwar A, Bernal J, Betts M, Bruffaerts R, Burkhart MC, Dewachter I, Gellersen HM, Low A, Lourida I, Machado L, Madan CR, Malpetti M, Mejia J, Michopoulou S, Muñoz-Neira C, Pepys J, Peres M, Phillips V, Ramanan S, Tamburin S, Tantiangco HM, Thakur L, Tomassini A, Vipin A, Tang E, Newby D, Ranson JM, Llewellyn DJ, Veldsman M, Rittman T. Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review. Alzheimers Dement 2023; 19:5885-5904. [PMID: 37563912 DOI: 10.1002/alz.13412] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 05/18/2023] [Accepted: 06/02/2023] [Indexed: 08/12/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.
Collapse
Affiliation(s)
- Robin J Borchert
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Tiago Azevedo
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - AmanPreet Badhwar
- Department of Pharmacology and Physiology, University of Montreal, Montreal, Canada
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Jose Bernal
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Matthew Betts
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences, University of Magdeburg, Magdeburg, Germany
| | - Rose Bruffaerts
- Computational Neurology, Experimental Neurobiology Unit, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | | | - Ilse Dewachter
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Audrey Low
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - Luiza Machado
- Department of Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Maura Malpetti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Jhony Mejia
- Department of Biomedical Engineering, Universidad de Los Andes, Bogotá, Colombia
| | - Sofia Michopoulou
- Imaging Physics, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Carlos Muñoz-Neira
- Research into Memory, Brain sciences and dementia Group (ReMemBr Group), Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Artificial Intelligence & Computational Neuroscience Group (AICN Group), Sheffield Institute for Translational Neuroscience (SITraN), Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Jack Pepys
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Marion Peres
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Siddharth Ramanan
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | - Lokendra Thakur
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, UK
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Alessandro Tomassini
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | | | - Eugene Tang
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Danielle Newby
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- Alan Turing Institute, London, UK
| | - Michele Veldsman
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| |
Collapse
|
4
|
Jiang Y, Zhang Y, Nie L, Liu H, Zheng J. Identification and effective connections of core networks in patients with temporal lobe epilepsy and cognitive impairment: Granger causality analysis and multivariate pattern analysis. Int J Neurosci 2023; 133:935-946. [PMID: 34923894 DOI: 10.1080/00207454.2021.2017926] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 11/25/2021] [Accepted: 12/03/2021] [Indexed: 10/19/2022]
Abstract
OBJECTIVE This study aimed to explore effective connectivity (EC) of the core networks in cognition impairment associated with temporal lobe epilepsy (CI-TLE) by applying resting state and Granger causality analysis (REST-GCA). The specific brain regions that played a critical role in classification were assessed using multivariate pattern analysis (MVPA). METHODS Thirty-two patients with CI-TLE and 29 healthy controls who were matched based on age and gender underwent functional magnetic resonance imaging (fMRI). RESULTS REST-GCA revealed that patients with CI-TLE displayed decreased GC values in the following brain areas: from the posterior cingulate cortex (PCC) to the left fusiform gyrus (lFFG) and the right parahippocampal gyrus (rPPG); from the right dorsal prefrontal cortex (rDPFC) to the left superior parietal lobule (lSPL); from the left amygdala (lAG) to the PCC. Inhibitory EC was observed from the rDPFC to the PCC compared to HCs. The GC values increased from the right dorsal prefrontal cingulate cortex (rdACC) to the PCC and from the right dorsal forebrain insula (rDAI) to the right middle temporal gyrus (rMTG) in the CI-TLE patients. MVPA showed that the classification yielded an accuracy of 81.91% (78.12%, specificity =85.71%). CONCLUSION Our observations indicated that the abnormal EC between the frontal and parietal regions might be associated with the pathophysiological mechanism of CI-TLE. These results also indicated that EC might be play a role as a potential discriminative pattern to detect CI-TLE in patients.
Collapse
Affiliation(s)
- Yanchun Jiang
- The Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yanbo Zhang
- The Department of Neurology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Liluo Nie
- The Department of neurology, Hengyang Central Hospital, Hengyang, China
| | - Huihua Liu
- The Department of Neurology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Jinou Zheng
- The Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| |
Collapse
|
5
|
Park HJ, Lee JY, Yang JJ, Kim HJ, Kim YS, Kim JY, Choi YY. Prediction of Amyloid β-Positivity with both MRI Parameters and Cognitive Function Using Machine Learning. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:638-652. [PMID: 37325007 PMCID: PMC10265247 DOI: 10.3348/jksr.2022.0084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 09/05/2022] [Accepted: 10/02/2022] [Indexed: 06/17/2023]
Abstract
Purpose To investigate the MRI markers for the prediction of amyloid β (Aβ)-positivity in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to evaluate the differences in MRI markers between Aβ-positive (Aβ [+]) and -negative groups using the machine learning (ML) method. Materials and Methods This study included 139 patients with MCI and AD who underwent amyloid PET-CT and brain MRI. Patients were divided into Aβ (+) (n = 84) and Aβ-negative (n = 55) groups. Visual analysis was performed with the Fazekas scale of white matter hyperintensity (WMH) and cerebral microbleeds (CMB) scores. The WMH volume and regional brain volume were quantitatively measured. The multivariable logistic regression and ML using support vector machine, and logistic regression were used to identify the best MRI predictors of Aβ-positivity. Results The Fazekas scale of WMH (p = 0.02) and CMB scores (p = 0.04) were higher in Aβ (+). The volumes of hippocampus, entorhinal cortex, and precuneus were smaller in Aβ (+) (p < 0.05). The third ventricle volume was larger in Aβ (+) (p = 0.002). The logistic regression of ML showed a good accuracy (81.1%) with mini-mental state examination (MMSE) and regional brain volumes. Conclusion The application of ML using the MMSE, third ventricle, and hippocampal volume is helpful in predicting Aβ-positivity with a good accuracy.
Collapse
|
6
|
Wang J, Lu S, Wang SH, Zhang YD. A review on extreme learning machine. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:41611-41660. [DOI: 10.1007/s11042-021-11007-7] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 02/26/2021] [Accepted: 05/05/2021] [Indexed: 08/30/2023]
Abstract
AbstractExtreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.
Collapse
|
7
|
Merone M, D'Addario SL, Mirino P, Bertino F, Guariglia C, Ventura R, Capirchio A, Baldassarre G, Silvetti M, Caligiore D. A multi-expert ensemble system for predicting Alzheimer transition using clinical features. Brain Inform 2022; 9:20. [PMID: 36056985 PMCID: PMC9440971 DOI: 10.1186/s40708-022-00168-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/16/2022] [Indexed: 11/18/2022] Open
Abstract
Alzheimer’s disease (AD) diagnosis often requires invasive examinations (e.g., liquor analyses), expensive tools (e.g., brain imaging) and highly specialized personnel. The diagnosis commonly is established when the disorder has already caused severe brain damage, and the clinical signs begin to be apparent. Instead, accessible and low-cost approaches for early identification of subjects at high risk for developing AD years before they show overt symptoms are fundamental to provide a critical time window for more effective clinical management, treatment, and care planning. This article proposes an ensemble-based machine learning algorithm for predicting AD development within 9 years from first overt signs and using just five clinical features that are easily detectable with neuropsychological tests. The validation of the system involved both healthy individuals and mild cognitive impairment (MCI) patients drawn from the ADNI open dataset, at variance with previous studies that considered only MCI. The system shows higher levels of balanced accuracy, negative predictive value, and specificity than other similar solutions. These results represent a further important step to build a preventive fast-screening machine-learning-based tool to be used as a part of routine healthcare screenings.
Collapse
Affiliation(s)
- Mario Merone
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy
| | - Sebastian Luca D'Addario
- Department of Psychology, Sapienza University, Piazzale Aldo Moro 5, 00185, Rome, Italy.,Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy.,IRCCS Fondazione Santa Lucia, Via Ardeatina, 306 and Via Del Fosso di Fiorano, 64, 00143, Rome, Italy
| | - Pierandrea Mirino
- Department of Psychology, Sapienza University, Piazzale Aldo Moro 5, 00185, Rome, Italy.,Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy.,AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, 00199, Rome, Italy
| | - Francesca Bertino
- Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy
| | - Cecilia Guariglia
- Department of Psychology, Sapienza University, Piazzale Aldo Moro 5, 00185, Rome, Italy.,IRCCS Fondazione Santa Lucia, Via Ardeatina, 306 and Via Del Fosso di Fiorano, 64, 00143, Rome, Italy
| | - Rossella Ventura
- Department of Psychology, Sapienza University, Piazzale Aldo Moro 5, 00185, Rome, Italy.,IRCCS Fondazione Santa Lucia, Via Ardeatina, 306 and Via Del Fosso di Fiorano, 64, 00143, Rome, Italy
| | - Adriano Capirchio
- AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, 00199, Rome, Italy
| | - Gianluca Baldassarre
- AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, 00199, Rome, Italy.,Laboratory of Embodied Natural and Artificial Intelligence, Institute of Cognitive Sciences and Technologies, National Research Council (LENAI-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy
| | - Massimo Silvetti
- Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy
| | - Daniele Caligiore
- Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy. .,AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, 00199, Rome, Italy.
| |
Collapse
|
8
|
Nguyen D, Nguyen H, Ong H, Le H, Ha H, Duc NT, Ngo HT. Ensemble learning using traditional machine learning and deep neural network for diagnosis of Alzheimer's disease. IBRO Neurosci Rep 2022; 13:255-263. [PMID: 36590098 PMCID: PMC9795286 DOI: 10.1016/j.ibneur.2022.08.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/07/2022] [Accepted: 08/31/2022] [Indexed: 01/04/2023] Open
Abstract
In recent years, Alzheimer's disease (AD) diagnosis using neuroimaging and deep learning has drawn great research attention. However, due to the scarcity of training neuroimaging data, many deep learning models have suffered from severe overfitting. In this study, we propose an ensemble learning framework that combines deep learning and machine learning. The deep learning model was based on a 3D-ResNet to exploit 3D structural features of neuroimaging data. Meanwhile, Extreme Gradient Boosting (XGBoost) machine learning was applied on a voxel-wise basis to draw the most significant voxel groups out of the image. The 3D-ResNet and XGBoost predictions were combined with patient demographics and cognitive test scores (Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR)) to give a final diagnosis prediction. Our proposed method was trained and validated on brain MRI brain images of the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. During the training phase, multiple data augmentation methods were employed to tackle overfitting. Our test set contained only baseline scans, i.e., the first visit scans since we aimed to investigate the ability of our approach in detecting AD during the first visit of AD patients. Our 5-fold cross-validation implementation achieved an average AUC of 100% during training and 96% during testing. Using the same computer, our method was much faster in scoring a prediction, approximately 10 min, than feature extraction-based machine learning methods, which often take many hours to score a prediction. To make the prediction explainable, we visualized the brain MRI image regions that primarily affected the 3D-ResNet model's prediction via heatmap. Lastly, we observed that proper generation of test sets was critical to avoiding the data leakage issue and ensuring the validity of results.
Collapse
Affiliation(s)
- Dong Nguyen
- School of Biomedical Engineering, International University, Vietnam,Vietnam National University, Ho Chi Minh City, Vietnam
| | - Hoang Nguyen
- School of Biomedical Engineering, International University, Vietnam,Vietnam National University, Ho Chi Minh City, Vietnam
| | - Hong Ong
- School of Biomedical Engineering, International University, Vietnam,Vietnam National University, Ho Chi Minh City, Vietnam
| | - Hoang Le
- Department of Electrical Engineering and Computer Science, York University, Toronto, Ontario, Canada
| | - Huong Ha
- School of Biomedical Engineering, International University, Vietnam,Vietnam National University, Ho Chi Minh City, Vietnam,Corresponding authors at: School of Biomedical Engineering, International University, Vietnam.
| | - Nguyen Thanh Duc
- Department of Biomedical Science and Engineering, Institute of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea,Montreal Neurological Institute, McGill University, Montreal, Canada,Corresponding author at: Department of Biomedical Science and Engineering, Institute of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea.
| | - Hoan Thanh Ngo
- School of Biomedical Engineering, International University, Vietnam,Vietnam National University, Ho Chi Minh City, Vietnam,Corresponding authors at: School of Biomedical Engineering, International University, Vietnam.
| |
Collapse
|
9
|
Hu M, Yu Y, He F, Su Y, Zhang K, Liu X, Liu P, Liu Y, Peng G, Luo B. Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2535954. [PMID: 36035823 PMCID: PMC9417789 DOI: 10.1155/2022/2535954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 06/12/2022] [Accepted: 07/06/2022] [Indexed: 11/22/2022]
Abstract
The combination and integration of multimodal imaging and clinical markers have introduced numerous classifiers to improve diagnostic accuracy in detecting and predicting AD; however, many studies cannot ensure the homogeneity of data sets and consistency of results. In our study, the XGBoost algorithm was used to classify mild cognitive impairment (MCI) and normal control (NC) populations through five rs-fMRI analysis datasets. Shapley Additive exPlanations (SHAP) is used to analyze the interpretability of the model. The highest accuracy for diagnosing MCI was 65.14% (using the mPerAF dataset). The characteristics of the left insula, right middle frontal gyrus, and right cuneus correlated positively with the output value using DC datasets. The characteristics of left cerebellum 6, right inferior frontal gyrus, opercular part, and vermis 6 correlated positively with the output value using fALFF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, left temporal pole, and middle temporal gyrus correlated positively with the output value using mPerAF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, and left hippocampus correlated positively with the output value using PerAF datasets. The characteristics of left cerebellum 9, vermis 9, and right precentral gyrus, right amygdala, and left middle occipital gyrus correlated positively with the output value using Wavelet-ALFF datasets. We found that the XGBoost algorithm constructed from rs-fMRI data is effective for the diagnosis and classification of MCI. The accuracy rates obtained by different rs-fMRI data analysis methods are similar, but the important features are different and involve multiple brain regions, which suggests that MCI may have a negative impact on brain function.
Collapse
Affiliation(s)
- Mengjie Hu
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
- Department of General Practice, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Yang Yu
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Fangping He
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Yujie Su
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Kan Zhang
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Xiaoyan Liu
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Ping Liu
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Ying Liu
- Department of General Practice, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Guoping Peng
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Benyan Luo
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| |
Collapse
|
10
|
Lu J, Zeng W, Zhang L, Shi Y. A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer's Disease Study. Front Aging Neurosci 2022; 14:888575. [PMID: 35693342 PMCID: PMC9177228 DOI: 10.3389/fnagi.2022.888575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 04/25/2022] [Indexed: 12/31/2022] Open
Abstract
The Extreme Learning Machine (ELM) is a simple and efficient Single Hidden Layer Feedforward Neural Network(SLFN) algorithm. In recent years, it has been gradually used in the study of Alzheimer's disease (AD). When using ELM to diagnose AD based on high-dimensional features, there are often some features that have no positive impact on the diagnosis, while others have a significant impact on the diagnosis. In this paper, a novel Key Features Screening Method based on Extreme Learning Machine (KFS-ELM) is proposed. It can screen for key features that are relevant to the classification (diagnosis). It can also assign weights to key features based on their importance. We designed an experiment to screen for key features of AD. A total of 920 key functional connections screened from 4005 functional connections. Their weights were also obtained. The results of the experiment showed that: (1) Using all (4,005) features to diagnose AD, the accuracy is 95.33%. Using 920 key features to diagnose AD, the accuracy is 99.20%. The 3,085 (4,005 - 920) features that were screened out had a negative effect on the diagnosis of AD. This indicates the KFS-ELM is effective in screening key features. (2) The higher the weight of the key features and the smaller their number, the greater their impact on AD diagnosis. This indicates that the KFS-ELM is rational in assigning weights to the key features for their importance. Therefore, KFS-ELM can be used as a tool for studying features and also for improving classification accuracy.
Collapse
Affiliation(s)
- Jia Lu
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Weiming Zeng
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Lu Zhang
- Basic Experiment and Training Center, Shanghai Maritime University, Shanghai, China
| | - Yuhu Shi
- College of Information Engineering Shanghai Maritime University, Shanghai, China
| |
Collapse
|
11
|
Frizzell TO, Glashutter M, Liu CC, Zeng A, Pan D, Hajra SG, D’Arcy RC, Song X. Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review. Ageing Res Rev 2022; 77:101614. [PMID: 35358720 DOI: 10.1016/j.arr.2022.101614] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 03/02/2022] [Accepted: 03/24/2022] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast-growing effort in applying artificial intelligence (AI) to analyze these data. Here, we review and evaluate the AI studies in brain MRI analysis with synthesis. METHODS A systematic review of the literature, spanning the years from 2009 to 2020, was completed using the PubMed database. AI studies using MRI imaging to investigate normal aging, mild cognitive impairment, and AD-dementia were retrieved for review. Bias assessment was completed using the PROBAST criteria. RESULTS 97 relevant studies were included in the review. The studies were typically focused on the classification of AD, MCI, and normal aging (71% of the reported studies) and the prediction of MCI conversion to AD (25%). The best performance was achieved by using the deep learning-based convolution neural network algorithms (weighted average accuracy 89%), in contrast to 76-86% using Logistic Regression, Support Vector Machines, and other AI methods. DISCUSSION The synthesized evidence is paramount to developing sophisticated AI approaches to reliably capture and quantify multiple subtle MRI changes in the whole brain that exemplify the complexity and heterogeneity of AD and brain aging.
Collapse
|
12
|
Tan T, Xu Z, Gao C, Shen T, Li L, Chen Z, Chen L, Xu M, Chen B, Liu J, Zhang Z, Yuan Y. Influence and interaction of resting state functional magnetic resonance and tryptophan hydroxylase-2 methylation on short-term antidepressant drug response. BMC Psychiatry 2022; 22:218. [PMID: 35337298 PMCID: PMC8957120 DOI: 10.1186/s12888-022-03860-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 03/11/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Most antidepressants have been developed on the basis of the monoamine deficiency hypothesis of depression, in which neuronal serotonin (5-HT) plays a key role. 5-HT biosynthesis is regulated by the rate-limiting enzyme tryptophan hydroxylase-2 (TPH2). TPH2 methylation is correlated with antidepressant effects. Resting-state functional MRI (rs-fMRI) is applied for detecting abnormal brain functional activity in patients with different antidepressant effects. We will investigate the effect of the interaction between rs-fMRI and TPH2 DNA methylation on the early antidepressant effects. METHODS A total of 300 patients with major depressive disorder (MDD) and 100 healthy controls (HCs) were enrolled, of which 60 patients with MDD were subjected to rs-fMRI. Antidepressant responses was assessed by a 50% reduction in 17-item Hamilton Rating Scale for Depression (HAMD-17) scores at baseline and after two weeks of medication. The RESTPlus software in MATLAB was used to analyze the rs-fMRI data. The amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), fractional ALFF (fALFF), and functional connectivity (FC) were used, and the above results were used as regions of interest (ROIs) to extract the average value of brain ROIs regions in the RESTPlus software. Generalized linear model analysis was performed to analyze the association between abnormal activity found in rs-fMRI and the effect of TPH2 DNA methylation on antidepressant responses. RESULTS Two hundred ninety-one patients with MDD and 100 HCs were included in the methylation statistical analysis, of which 57 patients were included in the further rs-fMRI analysis (3 patients were excluded due to excessive head movement). 57 patients were divided into the responder group (n = 36) and the non-responder group (n = 21). Rs-fMRI results showed that the ALFF of the left inferior frontal gyrus (IFG) was significantly different between the two groups. The results showed that TPH2-1-43 methylation interacted with ALFF of left IFG to affect the antidepressant responses (p = 0.041, false discovery rate (FDR) corrected p = 0.149). CONCLUSIONS Our study demonstrated that the differences in the ALFF of left IFG between the two groups and its association with TPH2 methylation affect short-term antidepressant drug responses.
Collapse
Affiliation(s)
- Tingting Tan
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.263826.b0000 0004 1761 0489Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Zhi Xu
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009, People's Republic of China. .,Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China.
| | - Chenjie Gao
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.263826.b0000 0004 1761 0489Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Tian Shen
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.89957.3a0000 0000 9255 8984Department of Psychiatric Rehabilitation, Wuxi Mental Health Center, Nanjing Medical University, WuXi, 214123 People’s Republic of China
| | - Lei Li
- grid.263826.b0000 0004 1761 0489School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Zimu Chen
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.263826.b0000 0004 1761 0489Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Lei Chen
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,Department of Psychology and Psychiatry, School of Medicine, Jinling Hospital, Nanjing University, Nanjing, 210018 People’s Republic of China
| | - Min Xu
- grid.263826.b0000 0004 1761 0489Department of Anatomy, Medical School, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Bingwei Chen
- grid.263826.b0000 0004 1761 0489Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Jiacheng Liu
- grid.452290.80000 0004 1760 6316Department of Nuclear Medicine, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Zhijun Zhang
- grid.452290.80000 0004 1760 6316Department of Neurology, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Yonggui Yuan
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.263826.b0000 0004 1761 0489Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
| |
Collapse
|
13
|
Li R, Wang X, Lawler K, Garg S, Bai Q, Alty J. Applications of Artificial Intelligence to aid detection of dementia: a scoping review on current capabilities and future directions. J Biomed Inform 2022; 127:104030. [DOI: 10.1016/j.jbi.2022.104030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/21/2022] [Accepted: 02/12/2022] [Indexed: 12/17/2022]
|
14
|
The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence. Biomedicines 2022; 10:biomedicines10020315. [PMID: 35203524 PMCID: PMC8869403 DOI: 10.3390/biomedicines10020315] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 02/05/2023] Open
Abstract
Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.
Collapse
|
15
|
Kim K, Duc NT, Choi M, Lee B. EEG microstate features for schizophrenia classification. PLoS One 2021; 16:e0251842. [PMID: 33989352 PMCID: PMC8121321 DOI: 10.1371/journal.pone.0251842] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 05/04/2021] [Indexed: 12/11/2022] Open
Abstract
Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. However, previous studies have only reported differences in each microstate feature and have not determined whether microstate features are suitable for schizophrenia classification. Therefore, it is necessary to validate microstate features for schizophrenia classification. Nineteen microstate features, including duration, occurrence, and coverage as well as thirty-one conventional EEG features, including statistical, frequency, and temporal characteristics were obtained from resting-state EEG recordings of 14 patients diagnosed with schizophrenia and from 14 healthy (control) subjects. Machine-learning based multivariate analysis was used to evaluate classification performance. EEG recordings of patients and controls showed different microstate features. More importantly, when differentiating among patients and controls, EEG microstate features outperformed conventional EEG ones. The performance of the microstate features exceeded that of conventional EEG, even after optimization using recursive feature elimination. EEG microstate features applied with conventional EEG features also showed better classification performance than conventional EEG features alone. The current study is the first to validate the use of microstate features to discriminate schizophrenia, suggesting that EEG microstate features are useful for schizophrenia classification.
Collapse
Affiliation(s)
- Kyungwon Kim
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Cheomdan-gwagiro, Gwangju, South Korea
- Department of Psychiatry and Biomedical Research Institute, Pusan National University Hospital, Busan, South Korea
| | - Nguyen Thanh Duc
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Cheomdan-gwagiro, Gwangju, South Korea
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada
- McConnel Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montreal, Canada
| | - Min Choi
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Cheomdan-gwagiro, Gwangju, South Korea
| | - Boreom Lee
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Cheomdan-gwagiro, Gwangju, South Korea
| |
Collapse
|
16
|
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]
|
17
|
Noroozi A, Rezghi M. A Tensor-Based Framework for rs-fMRI Classification and Functional Connectivity Construction. Front Neuroinform 2020; 14:581897. [PMID: 33328948 PMCID: PMC7734298 DOI: 10.3389/fninf.2020.581897] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 08/14/2020] [Indexed: 11/13/2022] Open
Abstract
Recently, machine learning methods have gained lots of attention from researchers seeking to analyze brain images such as Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) to obtain a deeper understanding of the brain and such related diseases, for example, Alzheimer's disease. Finding the common patterns caused by a brain disorder through analysis of the functional connectivity (FC) network along with discriminating brain diseases from normal controls have long been the two principal goals in studying rs-fMRI data. The majority of FC extraction methods calculate the FC matrix for each subject and then use simple techniques to combine them and obtain a general FC matrix. In addition, the state-of-the-art classification techniques for finding subjects with brain disorders also rely on calculating an FC for each subject, vectorizing, and feeding them to the classifier. Considering these problems and based on multi-dimensional nature of the data, we have come up with a novel tensor framework in which a general FC matrix is obtained without the need to construct an FC matrix for each sample. This framework also allows us to reduce the dimensionality and create a novel discriminant function that rather than using FCs works directly with each sample, avoids vectorization in any step, and uses the test data in the training process without forcing any prior knowledge of its label into the classifier. Extensive experiments using the ADNI dataset demonstrate that our proposed framework effectively boosts the fMRI classification performance and reveals novel connectivity patterns in Alzheimer's disease at its early stages.
Collapse
Affiliation(s)
| | - Mansoor Rezghi
- Department of Computer Science, Tarbiat Modares University, Tehran, Iran
| |
Collapse
|
18
|
3D-Deep Learning Based Automatic Diagnosis of Alzheimer's Disease with Joint MMSE Prediction Using Resting-State fMRI. Neuroinformatics 2020; 18:71-86. [PMID: 31093956 DOI: 10.1007/s12021-019-09419-w] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer's disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans of 331 participants, we obtained functional 3-dimensional (3-D) independent component spatial maps for use as features in classification and regression tasks. A 3-D convolutional neural network (CNN) architecture was developed for the classification task. MMSE scores were predicted using: linear least square regression (LLSR), support vector regression, bagging-based ensemble regression, and tree regression with group independent component analysis (gICA) features. To improve MMSE regression performance, we applied feature optimization methods including least absolute shrinkage and selection operator and support vector machine-based recursive feature elimination (SVM-RFE). The mean balanced test accuracy was 85.27% for the classification of AD versus healthy controls. The medial visual, default mode, dorsal attention, executive, and auditory related networks were mainly associated with AD. The maximum clinical MMSE score prediction accuracy with the LLSR method applied on gICA combined with SVM-RFE features had the lowest root mean square error (3.27 ± 0.58) and the highest R2 value (0.63 ± 0.02). Classification of AD and healthy controls can be successfully achieved using only rs-fMRI and MMSE scores can be accurately predicted using functional independent component features. In the absence of trained clinicians, AD disease status and clinical MMSE scores can be jointly predicted using 3-D deep learning and regression learning approaches with rs-fMRI data.
Collapse
|
19
|
Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020; 4:041503. [PMID: 33094213 PMCID: PMC7556883 DOI: 10.1063/5.0011697] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 09/09/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.
Collapse
Affiliation(s)
- Alice Segato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| |
Collapse
|
20
|
Khatri U, Kwon GR. An Efficient Combination among sMRI, CSF, Cognitive Score, and APOE ε4 Biomarkers for Classification of AD and MCI Using Extreme Learning Machine. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8015156. [PMID: 32565773 PMCID: PMC7292973 DOI: 10.1155/2020/8015156] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Revised: 01/13/2020] [Accepted: 02/17/2020] [Indexed: 11/17/2022]
Abstract
Alzheimer's disease (AD) is the most common cause of dementia and a progressive neurodegenerative condition, characterized by a decline in cognitive function. Symptoms usually appear gradually and worsen over time, becoming severe enough to interfere with individual daily tasks. Thus, the accurate diagnosis of both AD and the prodromal stage (i.e., mild cognitive impairment (MCI)) is crucial for timely treatment. As AD is inherently dynamic, the relationship between AD indicators is unclear and varies over time. To address this issue, we first aimed at investigating differences in atrophic patterns between individuals with AD and MCI and healthy controls (HCs). Then we utilized multiple biomarkers, along with filter- and wrapper-based feature selection and an extreme learning machine- (ELM-) based approach, with 10-fold cross-validation for classification. Increasing efforts are focusing on the use of multiple biomarkers, which can be useful for the diagnosis of AD and MCI. However, optimum combinations have yet to be identified and most multimodal analyses use only volumetric measures obtained from magnetic resonance imaging (MRI). Anatomical structural MRI (sMRI) measures have also so far mostly been used separately. The full possibilities of using anatomical MRI for AD detection have thus yet to be explored. In this study, three measures (cortical thickness, surface area, and gray matter volume), obtained from sMRI through preprocessing for brain atrophy measurements; cerebrospinal fluid (CSF), for quantification of specific proteins; cognitive score, as a measure of cognitive performance; and APOE ε4 allele status were utilized. Our results show that a combination of specific biomarkers performs well, with accuracies of 97.31% for classifying AD vs. HC, 91.72% for MCI vs. HC, 87.91% for MCI vs. AD, and 83.38% for MCIs vs. MCIc, respectively, when evaluated using the proposed algorithm. Meanwhile, the areas under the curve (AUC) from the receiver operating characteristic (ROC) curves combining multiple biomarkers provided better classification performance. The proposed features combination and selection algorithm effectively classified AD and MCI, and MCIs vs. MCIc, the most challenging classification task, and therefore could increase the accuracy of AD classification in clinical practice. Furthermore, we compared the performance of the proposed method with SVM classifiers, using a cross-validation method with Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets.
Collapse
Affiliation(s)
- Uttam Khatri
- Department. of Information and Communication Engineering, Chosun University, 309 Pilmun-Daero, Dong-Gu, Gwangju 61452, Republic of Korea
| | - Goo-Rak Kwon
- Department. of Information and Communication Engineering, Chosun University, 309 Pilmun-Daero, Dong-Gu, Gwangju 61452, Republic of Korea
| |
Collapse
|
21
|
Duc NT, Ryu S, Choi M, Iqbal Qureshi MN, Lee B. Mild Cognitive Impairment Diagnosis Using Extreme Learning Machine Combined With Multivoxel Pattern Analysis on Multi-Biomarker Resting-State FMRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:882-885. [PMID: 31946035 DOI: 10.1109/embc.2019.8857623] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper proposed a classification framework that integrates hybrid multivoxel pattern analyses (MVPA) and extreme learning machine (ELM) for automated Mild Cognitive Impairment (MCI) diagnosis applied on concatenations of multi-biomarker resting-state fMRI. Given three-dimensional (3D) regional coherences and functional connectivity patterns measured during resting state, we performed 3D univariate t-tests to obtain initial univariate features which show the significant changes. To enhance discriminative patterns, we employed multivariate feature reductions using recursive feature elimination in combination with univariate t-test. The maximal amount of information changes were achieved by concatenations of multiple functional metrics. The classifications were performed by an ELM, and its efficiency was compared to SVMs. This study reported mean accuracies using 10-fold cross-validation, followed by permutation tests to assess the statistical significance of discriminative results. In diagnosis of MCI, the proposed method achieved a maximal accuracy of 97.86% (p<; 0.001) in ADNI2 cohort and thus has potentials to assist the clinicians in MCI diagnosis.
Collapse
|