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Yang GZ, Gao QC, Li WR, Cai HY, Zhao HM, Wang JJ, Zhao XR, Wang JX, Wu MN, Zhang J, Hölscher C, Qi JS, Wang ZJ. (D-Ser2) oxyntomodulin recovers hippocampal synaptic structure and theta rhythm in Alzheimer's disease transgenic mice. Neural Regen Res 2022; 17:2072-2078. [PMID: 35142699 PMCID: PMC8848598 DOI: 10.4103/1673-5374.335168] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
In our previous studies, we have shown that (D-Ser2) oxyntomodulin (Oxm), a glucagon-like peptide 1 (GLP-1) receptor (GLP1R)/glucagon receptor (GCGR) dual agonist peptide, protects hippocampal neurons against Aβ1–42 -induced cytotoxicity, and stabilizes the calcium homeostasis and mitochondrial membrane potential of hippocampal neurons. Additionally, we have demonstrated that (D-Ser2) Oxm improves cognitive decline and reduces the deposition of amyloid-beta in Alzheimer's disease model mice. However, the protective mechanism remains unclear. In this study, we showed that 2 weeks of intraperitoneal administration of (D-Ser2) Oxm ameliorated the working memory and fear memory impairments of 9-month-old 3×Tg Alzheimer's disease model mice. In addition, electrophysiological data recorded by a wireless multichannel neural recording system implanted in the hippocampal CA1 region showed that (D-Ser2) Oxm increased the power of the theta rhythm. In addition, (D-Ser2) Oxm treatment greatly increased the expression level of synaptic-associated proteins SYP and PSD-95 and increased the number of dendritic spines in 3×Tg Alzheimer's disease model mice. These findings suggest that (D-Ser2) Oxm improves the cognitive function of Alzheimer's disease transgenic mice by recovering hippocampal synaptic function and theta rhythm.
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Affiliation(s)
- Guang-Zhao Yang
- Department of Cardiovascular Medicine, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China
| | - Qi-Chao Gao
- Department of Physiology, Shanxi Medical University; Key Laboratory of Cellular Physiology, Ministry of Education; Key Laboratory of Cellular Physiology in Shanxi Province, Taiyuan, Shanxi Province, China
| | - Wei-Ran Li
- Department of Physiology, Shanxi Medical University; Key Laboratory of Cellular Physiology, Ministry of Education; Key Laboratory of Cellular Physiology in Shanxi Province, Taiyuan, Shanxi Province, China
| | - Hong-Yan Cai
- Department of Microbiology and Immunology, Shanxi Medical University, Taiyuan, Shanxi Province, China
| | - Hui-Min Zhao
- Department of Physiology, Shanxi Medical University; Key Laboratory of Cellular Physiology, Ministry of Education; Key Laboratory of Cellular Physiology in Shanxi Province, Taiyuan, Shanxi Province, China
| | - Jian-Ji Wang
- Department of Physiology, Shanxi Medical University; Key Laboratory of Cellular Physiology, Ministry of Education; Key Laboratory of Cellular Physiology in Shanxi Province, Taiyuan, Shanxi Province, China
| | - Xin-Rui Zhao
- Department of Physiology, Shanxi Medical University; Key Laboratory of Cellular Physiology, Ministry of Education; Key Laboratory of Cellular Physiology in Shanxi Province, Taiyuan, Shanxi Province, China
| | - Jia-Xin Wang
- Department of Physiology, Shanxi Medical University; Key Laboratory of Cellular Physiology, Ministry of Education; Key Laboratory of Cellular Physiology in Shanxi Province, Taiyuan, Shanxi Province, China
| | - Mei-Na Wu
- Department of Physiology, Shanxi Medical University; Key Laboratory of Cellular Physiology, Ministry of Education; Key Laboratory of Cellular Physiology in Shanxi Province, Taiyuan, Shanxi Province, China
| | - Jun Zhang
- Functional Laboratory Center, Shanxi Medical University, Taiyuan, Shanxi Province, China
| | - Christian Hölscher
- Research and Experimental Center, Henan University of Chinese Medicine, Zhengzhou, Henan Province, China
| | - Jin-Shun Qi
- Department of Physiology, Shanxi Medical University; Key Laboratory of Cellular Physiology, Ministry of Education; Key Laboratory of Cellular Physiology in Shanxi Province, Taiyuan, Shanxi Province, China
| | - Zhao-Jun Wang
- Department of Physiology, Shanxi Medical University; Key Laboratory of Cellular Physiology, Ministry of Education; Key Laboratory of Cellular Physiology in Shanxi Province, Taiyuan, Shanxi Province, China
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2
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Singh NM, Harrod JB, Subramanian S, Robinson M, Chang K, Cetin-Karayumak S, Dalca AV, Eickhoff S, Fox M, Franke L, Golland P, Haehn D, Iglesias JE, O’Donnell LJ, Ou Y, Rathi Y, Siddiqi SH, Sun H, Westover MB, Whitfield-Gabrieli S, Gollub RL. How Machine Learning is Powering Neuroimaging to Improve Brain Health. Neuroinformatics 2022; 20:943-964. [PMID: 35347570 PMCID: PMC9515245 DOI: 10.1007/s12021-022-09572-9] [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] [Subscribe] [Scholar Register] [Accepted: 02/07/2022] [Indexed: 12/31/2022]
Abstract
This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.
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Affiliation(s)
- Nalini M. Singh
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Jordan B. Harrod
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Sandya Subramanian
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Mitchell Robinson
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Ken Chang
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115 USA
| | | | - Simon Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany ,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7) Research Centre Jülich, Jülich, Germany
| | - Michael Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women’s Hospital and Harvard Medical School, 02115 Boston, USA
| | - Loraine Franke
- University of Massachusetts Boston, Boston, MA 02125 USA
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Daniel Haehn
- University of Massachusetts Boston, Boston, MA 02125 USA
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, University College London, London, UK ,Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, 02114 USA ,Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Lauren J. O’Donnell
- Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, MA 02115 Boston, USA
| | - Yangming Ou
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115 USA
| | - Shan H. Siddiqi
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115 USA
| | - Haoqi Sun
- Department of Neurology and McCance Center for Brain Health / Harvard Medical School, Massachusetts General Hospital, Boston, 02114 USA
| | - M. Brandon Westover
- Department of Neurology and McCance Center for Brain Health / Harvard Medical School, Massachusetts General Hospital, Boston, 02114 USA
| | | | - Randy L. Gollub
- Department of Psychiatry and Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114 USA
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Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test. COMPUTERS 2020. [DOI: 10.3390/computers9030067] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual’s heartbeat. Similarly, by using the EEG sensor, one could analyze the individual’s brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly’s experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, linear heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions.
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Sun J, Wang B, Niu Y, Tan Y, Fan C, Zhang N, Xue J, Wei J, Xiang J. Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer's Disease: A Review. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E239. [PMID: 33286013 PMCID: PMC7516672 DOI: 10.3390/e22020239] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 02/15/2020] [Accepted: 02/17/2020] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease (AD) is a degenerative brain disease with a high and irreversible incidence. In recent years, because brain signals have complex nonlinear dynamics, there has been growing interest in studying complex changes in the time series of brain signals in patients with AD. We reviewed studies of complexity analyses of single-channel time series from electroencephalogram (EEG), magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI) in AD and determined future research directions. A systematic literature search for 2000-2019 was performed in the Web of Science and PubMed databases, resulting in 126 identified studies. Compared to healthy individuals, the signals from AD patients have less complexity and more predictable oscillations, which are found mainly in the left parietal, occipital, right frontal, and temporal regions. This complexity is considered a potential biomarker for accurately responding to the functional lesion in AD. The current review helps to reveal the patterns of dysfunction in the brains of patients with AD and to investigate whether signal complexity can be used as a biomarker to accurately respond to the functional lesion in AD. We proposed further studies in the signal complexities of AD patients, including investigating the reliability of complexity algorithms and the spatial patterns of signal complexity. In conclusion, the current review helps to better understand the complexity of abnormalities in the AD brain and provide useful information for AD diagnosis.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.S.); (B.W.); (Y.N.); (Y.T.); (C.F.); (N.Z.); (J.X.); (J.W.)
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5
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Supervised dictionary learning of EEG signals for mild cognitive impairment diagnosis. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101559] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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6
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Automated Multiclass Classification of Spontaneous EEG Activity in Alzheimer's Disease and Mild Cognitive Impairment. ENTROPY 2018; 20:e20010035. [PMID: 33265122 PMCID: PMC7512207 DOI: 10.3390/e20010035] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 01/04/2018] [Accepted: 01/05/2018] [Indexed: 12/24/2022]
Abstract
The discrimination of early Alzheimer’s disease (AD) and its prodromal form (i.e., mild cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the treatment is more effective in the first stages of the dementia. The aim of our study is to evaluate the usefulness of a methodology based on electroencephalography (EEG) to detect AD and MCI. EEG rhythms were recorded from 37 AD patients, 37 MCI subjects and 37 HC subjects. Artifact-free trials were analyzed by means of several spectral and nonlinear features: relative power in the conventional frequency bands, median frequency, individual alpha frequency, spectral entropy, Lempel–Ziv complexity, central tendency measure, sample entropy, fuzzy entropy, and auto-mutual information. Relevance and redundancy analyses were also conducted through the fast correlation-based filter (FCBF) to derive an optimal set of them. The selected features were used to train three different models aimed at classifying the trials: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and multi-layer perceptron artificial neural network (MLP). Afterwards, each subject was automatically allocated in a particular group by applying a trial-based majority vote procedure. After feature extraction, the FCBF method selected the optimal set of features: individual alpha frequency, relative power at delta frequency band, and sample entropy. Using the aforementioned set of features, MLP showed the highest diagnostic performance in determining whether a subject is not healthy (sensitivity of 82.35% and positive predictive value of 84.85% for HC vs. all classification task) and whether a subject does not suffer from AD (specificity of 79.41% and negative predictive value of 84.38% for AD vs. all comparison). Our findings suggest that our methodology can help physicians to discriminate AD, MCI and HC.
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7
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Cuzzolin F, Sapienza M, Esser P, Saha S, Franssen MM, Collett J, Dawes H. Metric learning for Parkinsonian identification from IMU gait measurements. Gait Posture 2017; 54:127-132. [PMID: 28288333 DOI: 10.1016/j.gaitpost.2017.02.012] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 01/24/2017] [Accepted: 02/16/2017] [Indexed: 02/02/2023]
Abstract
Diagnosis of people with mild Parkinson's symptoms is difficult. Nevertheless, variations in gait pattern can be utilised to this purpose, when measured via Inertial Measurement Units (IMUs). Human gait, however, possesses a high degree of variability across individuals, and is subject to numerous nuisance factors. Therefore, off-the-shelf Machine Learning techniques may fail to classify it with the accuracy required in clinical trials. In this paper we propose a novel framework in which IMU gait measurement sequences sampled during a 10m walk are first encoded as hidden Markov models (HMMs) to extract their dynamics and provide a fixed-length representation. Given sufficient training samples, the distance between HMMs which optimises classification performance is learned and employed in a classical Nearest Neighbour classifier. Our tests demonstrate how this technique achieves accuracy of 85.51% over a 156 people with Parkinson's with a representative range of severity and 424 typically developed adults, which is the top performance achieved so far over a cohort of such size, based on single measurement outcomes. The method displays the potential for further improvement and a wider application to distinguish other conditions.
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Affiliation(s)
- Fabio Cuzzolin
- Artificial Intelligence and Vision Group, Department of Computing and Communication Technologies, Oxford Brookes University, Wheatley Campus, Oxford OX33 1HX, UK.
| | - Michael Sapienza
- Torr Vision Group, Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
| | - Patrick Esser
- Movement Science Group, Oxford Institute of Nursing, Midwifery, and Allied Health Research, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, UK.
| | - Suman Saha
- Artificial Intelligence and Vision Group, Department of Computing and Communication Technologies, Oxford Brookes University, Wheatley Campus, Oxford OX33 1HX, UK.
| | - Miss Marloes Franssen
- Movement Science Group, Oxford Institute of Nursing, Midwifery, and Allied Health Research, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, UK.
| | - Johnny Collett
- Movement Science Group, Oxford Institute of Nursing, Midwifery, and Allied Health Research, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, UK.
| | - Helen Dawes
- Movement Science Group, Oxford Institute of Nursing, Midwifery, and Allied Health Research, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, UK.
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8
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Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer's disease. Cogn Neurodyn 2016; 11:217-231. [PMID: 28559952 DOI: 10.1007/s11571-016-9418-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Revised: 10/13/2016] [Accepted: 11/09/2016] [Indexed: 10/20/2022] Open
Abstract
The complexity change of brain activity in Alzheimer's disease (AD) is an interesting topic for clinical purpose. To investigate the dynamical complexity of brain activity in AD, a multivariate multi-scale weighted permutation entropy (MMSWPE) method is proposed to measure the complexity of electroencephalograph (EEG) obtained in AD patients. MMSWPE combines the weighted permutation entropy and the multivariate multi-scale method. It is able to quantify not only the characteristics of different brain regions and multiple time scales but also the amplitude information contained in the multichannel EEG signals simultaneously. The effectiveness of the proposed method is verified by both the simulated chaotic signals and EEG recordings of AD patients. The simulation results from the Lorenz system indicate that MMSWPE has the ability to distinguish the multivariate signals with different complexity. In addition, the EEG analysis results show that in contrast with the normal group, the significantly decreased complexity of AD patients is distributed in the temporal and occipitoparietal regions for the theta and the alpha bands, and also distributed from the right frontal to the left occipitoparietal region for the theta, the alpha and the beta bands at each time scale, which may be attributed to the brain dysfunction. Therefore, it suggests that the MMSWPE method may be a promising method to reveal dynamic changes in AD.
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9
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Karamzadeh N, Amyot F, Kenney K, Anderson A, Chowdhry F, Dashtestani H, Wassermann EM, Chernomordik V, Boccara C, Wegman E, Diaz-Arrastia R, Gandjbakhche AH. A machine learning approach to identify functional biomarkers in human prefrontal cortex for individuals with traumatic brain injury using functional near-infrared spectroscopy. Brain Behav 2016; 6:e00541. [PMID: 27843695 PMCID: PMC5102640 DOI: 10.1002/brb3.541] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2016] [Revised: 05/12/2016] [Accepted: 06/23/2016] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND We have explored the potential prefrontal hemodynamic biomarkers to characterize subjects with Traumatic Brain Injury (TBI) by employing the multivariate machine learning approach and introducing a novel task-related hemodynamic response detection followed by a heuristic search for optimum set of hemodynamic features. To achieve this goal, the hemodynamic response from a group of 31 healthy controls and 30 chronic TBI subjects were recorded as they performed a complexity task. METHODS To determine the optimum hemodynamic features, we considered 11 features and their combinations in characterizing TBI subjects. We investigated the significance of the features by utilizing a machine learning classification algorithm to score all the possible combinations of features according to their predictive power. RESULTS AND CONCLUSIONS The identified optimum feature elements resulted in classification accuracy, sensitivity, and specificity of 85%, 85%, and 84%, respectively. Classification improvement was achieved for TBI subject classification through feature combination. It signified the major advantage of the multivariate analysis over the commonly used univariate analysis suggesting that the features that are individually irrelevant in characterizing the data may become relevant when used in combination. We also conducted a spatio-temporal classification to identify regions within the prefrontal cortex (PFC) that contribute in distinguishing between TBI and healthy subjects. As expected, Brodmann areas (BA) 10 within the PFC were isolated as the region that healthy subjects (unlike subjects with TBI), showed major hemodynamic activity in response to the High Complexity task. Overall, our results indicate that identified temporal and spatio-temporal features from PFC's hemodynamic activity are promising biomarkers in classifying subjects with TBI.
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Affiliation(s)
- Nader Karamzadeh
- Department of Computational and Data Sciences George Mason University Fairfax VA USA; National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA
| | - Franck Amyot
- Department of Neurology Center for Neuroscience and Regenerative Medicine Uniformed Services Bethesda MD USA
| | - Kimbra Kenney
- Department of Neurology Center for Neuroscience and Regenerative Medicine Uniformed Services Bethesda MD USA
| | - Afrouz Anderson
- National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA
| | - Fatima Chowdhry
- National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA
| | - Hadis Dashtestani
- National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA
| | - Eric M Wassermann
- National Institute of Mental Health National Institutes of Healthy Bethesda MD USA
| | - Victor Chernomordik
- National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA
| | | | - Edward Wegman
- Department of Computational and Data Sciences George Mason University Fairfax VA USA
| | - Ramon Diaz-Arrastia
- Department of Neurology Center for Neuroscience and Regenerative Medicine Uniformed Services Bethesda MD USA
| | - Amir H Gandjbakhche
- National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA
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10
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Weighted-Permutation Entropy Analysis of Resting State EEG from Diabetics with Amnestic Mild Cognitive Impairment. ENTROPY 2016. [DOI: 10.3390/e18080307] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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11
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Houmani N, Dreyfus G, Vialatte FB. Epoch-based Entropy for Early Screening of Alzheimer’s Disease. Int J Neural Syst 2015; 25:1550032. [DOI: 10.1142/s012906571550032x] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, we introduce a novel entropy measure, termed epoch-based entropy. This measure quantifies disorder of EEG signals both at the time level and spatial level, using local density estimation by a Hidden Markov Model on inter-channel stationary epochs. The investigation is led on a multi-centric EEG database recorded from patients at an early stage of Alzheimer’s disease (AD) and age-matched healthy subjects. We investigate the classification performances of this method, its robustness to noise, and its sensitivity to sampling frequency and to variations of hyperparameters. The measure is compared to two alternative complexity measures, Shannon’s entropy and correlation dimension. The classification accuracies for the discrimination of AD patients from healthy subjects were estimated using a linear classifier designed on a development dataset, and subsequently tested on an independent test set. Epoch-based entropy reached a classification accuracy of 83% on the test dataset (specificity = 83.3%, sensitivity = 82.3%), outperforming the two other complexity measures. Furthermore, it was shown to be more stable to hyperparameter variations, and less sensitive to noise and sampling frequency disturbances than the other two complexity measures.
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Affiliation(s)
- N. Houmani
- ESPCI ParisTech, PSL Research University, 10 rue Vauquelin, 75005 Paris, France
- SIGMA (SIGnal processing and MAchine learning) Laboratory, 10 rue Vauquelin, 75231 Paris Cedex 05, France
| | - G. Dreyfus
- ESPCI ParisTech, PSL Research University, 10 rue Vauquelin, 75005 Paris, France
- SIGMA (SIGnal processing and MAchine learning) Laboratory, 10 rue Vauquelin, 75231 Paris Cedex 05, France
| | - F. B. Vialatte
- ESPCI ParisTech, PSL Research University, 10 rue Vauquelin, 75005 Paris, France
- Brain Plasticity Laboratory, CNRS UMR 8249, 10 rue Vauquelin, 75231 Paris Cedex 05, France
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12
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Deng B, Liang L, Li S, Wang R, Yu H, Wang J, Wei X. Complexity extraction of electroencephalograms in Alzheimer's disease with weighted-permutation entropy. CHAOS (WOODBURY, N.Y.) 2015; 25:043105. [PMID: 25933653 DOI: 10.1063/1.4917013] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In this paper, weighted-permutation entropy (WPE) is applied to investigating the complexity abnormalities of Alzheimer's disease (AD) by analyzing 16-channel electroencephalograph (EEG) signals from 14 severe AD patients and 14 age-matched normal subjects. The WPE values are estimated in the delta, the theta, the alpha, and the beta sub-bands for each channel with an overlapped sliding window. WPE is modified from the permutation entropy (PE), which has been recently suggested as a measurement to extract the complexity of the EEG signals. The advantage of WPE over PE is verified by both the model simulated and the experimental EEG signals. Although the results show that both the average PE and WPE of AD patients are decreased in contrast with the normal group in these four sub-bands, especially in the theta band, WPE can exhibit a better performance in distinguishing the AD patients from the normal controls by the more significant differences in the four sub-bands, which may be attributed to the brain dysfunction. Thus, it suggests that WPE may become a probable useful tool to detect brain dysfunction in AD and it seems to be promising to disclose the abnormalities of brain activity for other neural disease.
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Affiliation(s)
- Bin Deng
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Li Liang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Shunan Li
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Ruofan Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Haitao Yu
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Jiang Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Xile Wei
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
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Gallego-Jutglà E, Solé-Casals J, Vialatte FB, Elgendi M, Cichocki A, Dauwels J. A hybrid feature selection approach for the early diagnosis of Alzheimer’s disease. J Neural Eng 2015; 12:016018. [DOI: 10.1088/1741-2560/12/1/016018] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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14
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Ben Ali J, Abid S, William Jervis B, Fnaiech F, Bigan C, Besleaga M. Identification of early-stage Alzheimer׳s disease using SFAM neural network. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2014.06.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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15
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Gallego-Jutglà E, Elgendi M, Vialatte F, Solé-Casals J, Cichocki A, Latchoumane C, Jeong J, Dauwels J. Diagnosis of Alzheimer's disease from EEG by means of synchrony measures in optimized frequency bands. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:4266-70. [PMID: 23366870 DOI: 10.1109/embc.2012.6346909] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Several clinical studies have reported that EEG synchrony is affected by Alzheimer's disease (AD). In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD using EEG signals. In this paper, multiple synchrony measures are assessed through statistical tests (Mann-Whitney U test), including correlation, phase synchrony and Granger causality measures. Moreover, linear discriminant analysis (LDA) is conducted with those synchrony measures as features. For the data set at hand, the frequency range (5-6 Hz) yields the best accuracy for diagnosing AD, which lies within the classical theta band (4-8 Hz). The corresponding classification error is 4.88% for directed transfer function (DTF) Granger causality measure. Interestingly, results show that EEG of AD patients is more synchronous than in healthy subjects within the optimized range 5-6 Hz, which is in sharp contrast with the loss of synchrony in AD EEG reported in many earlier studies. This new finding may provide new insights about the neurophysiology of AD. Additional testing on larger AD datasets is required to verify the effectiveness of the proposed approach.
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Elgendi M, Vialatte F, Cichocki A, Latchoumane C, Jeong J, Dauwels J. Optimization of EEG frequency bands for improved diagnosis of Alzheimer disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:6087-91. [PMID: 22255728 DOI: 10.1109/iembs.2011.6091504] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Many clinical studies have shown that electroencephalograms (EEG) of Alzheimer patients (AD) often have an abnormal power spectrum. In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD from EEG signals. Relative power in different EEG frequency bands is used as features to distinguish between AD patients and healthy control subjects. Many different frequency bands between 4 and 30 Hz are systematically tested, besides the traditional frequency bands, e.g., theta band (4-8 Hz). The discriminative power of the resulting spectral features is assessed through statistical tests (Mann-Whitney U test). Moreover, linear discriminant analysis is conducted with those spectral features. The optimized frequency ranges (4-7 Hz, 8-15 Hz, 19-24 Hz) yield substantially better classification performance than the traditional frequency bands (4-8 Hz, 8-12 Hz, 12-30 Hz); the frequency band 4-7 Hz is the optimal frequency range for detecting AD, which is similar to the classical theta band. The frequency bands were also optimized as features through leave-one-out crossvalidation, resulting in error-free classification. The optimized frequency bands may improve existing EEG based diagnostic tools for AD. Additional testing on larger AD datasets is required to verify the effectiveness of the proposed approach.
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Affiliation(s)
- Mohamed Elgendi
- Institute for Media Innovation and the School of Electrical & Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
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Multiway array decomposition analysis of EEGs in Alzheimer's disease. J Neurosci Methods 2012; 207:41-50. [PMID: 22480988 DOI: 10.1016/j.jneumeth.2012.03.005] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2011] [Revised: 03/14/2012] [Accepted: 03/19/2012] [Indexed: 11/22/2022]
Abstract
Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer's disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied two state of the art multiway array decomposition (MAD) methods to extract unique features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE), and singular value decomposition (SVD) coupled to tensor unfolding. We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.
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Vialatte FB, Dauwels J, Maurice M, Musha T, Cichocki A. Improving the specificity of EEG for diagnosing Alzheimer's disease. Int J Alzheimers Dis 2011; 2011:259069. [PMID: 21660242 PMCID: PMC3109519 DOI: 10.4061/2011/259069] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2011] [Revised: 03/18/2011] [Accepted: 03/28/2011] [Indexed: 12/04/2022] Open
Abstract
Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ (3.5–7.5 Hz), α1 (7.5–9.5 Hz),
α2 (9.5–12.5 Hz), and β
(12.5–25 Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the θ
range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.
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Escudero J, Hornero R, Abásolo D, Fernández A. Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer's disease. Med Eng Phys 2009; 31:872-9. [PMID: 19482539 DOI: 10.1016/j.medengphy.2009.04.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2008] [Revised: 12/12/2008] [Accepted: 04/22/2009] [Indexed: 10/20/2022]
Abstract
This work studied whether a blind source separation (BSS) and component selection procedure could increase the differences between Alzheimer's disease (AD) patients and control subjects' spectral and non-linear features of magnetoencephalogram (MEG) recordings. MEGs were acquired with a 148-channel whole-head magnetometer from 62 subjects (36 AD patients and 26 controls), who were divided randomly into training and test sets. MEGs were decomposed using the algorithm for multiple unknown signals extraction (AMUSE). The extracted AMUSE components were characterised with two spectral--median frequency and spectral entropy (SpecEn)--and two non-linear features: Lempel-Ziv complexity (LZC) and sample entropy (SampEn). One-way analysis of variance with age as a covariate was applied to the training set to decide which components had the most significant differences between groups. Then, partial reconstructions of the MEGs were computed with these significant components. In the test set, the accuracy and area under the ROC curve (AUC) associated with each partial reconstruction of the MEGs were compared with the case where no BSS-preprocessing was applied. This preprocessing increased the AUCs between 0.013 and 0.227, while the accuracy for SpecEn, LZC and SampEn rose between 6.4% and 22.6%, improving the separation between AD patients and control subjects.
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Affiliation(s)
- Javier Escudero
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011 Valladolid, Spain.
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Vialatte FB, Solé-Casals J, Maurice M, Latchoumane C, Hudson N, Wimalaratna S, Jeong J, Cichocki A. Improving the Quality of EEG Data in Patients with Alzheimer’s Disease Using ICA. ADVANCES IN NEURO-INFORMATION PROCESSING 2009. [DOI: 10.1007/978-3-642-03040-6_119] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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