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Zheng D, Ruan Y, Cao X, Guo W, Zhang X, Qi W, Yuan Q, Liang X, Zhang D, Huang Q, Xue C. Directed Functional Connectivity Changes of Triple Networks for Stable and Progressive Mild Cognitive Impairment. Neuroscience 2024; 545:47-58. [PMID: 38490330 DOI: 10.1016/j.neuroscience.2024.03.003] [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: 11/08/2023] [Revised: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 03/17/2024]
Abstract
Mild cognitive impairment includes two distinct subtypes, namely progressive mild cognitive impairment and stable mild cognitive impairment. While alterations in extensive functional connectivity have been observed in both subtypes, limited attention has been given to directed functional connectivity. A triple network, composed of the central executive network, default mode network, and salience network, is considered to be the core cognitive network. We evaluated the alterations in directed functional connectivity within and between the triple network in progressive and stable mild cognitive impairment groups and investigated its role in predicting disease conversion. Resting-state functional magnetic resonance imaging was used to analyze directed functional connectivity within the triple networks. A correlation analysis was performed to investigate potential associations between altered directed functional connectivity within the triple networks and the neurocognitive performance of the participants. Our study revealed significant differences in directed functional connectivity within and between the triple network in the progressive and stable mild cognitive impairment groups. Altered directed functional connectivity within the triple network was involved in episodic memory and executive function. Thus, the directed functional connectivity of the triple network may be used as an imaging marker of mild cognitive impairment.
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Affiliation(s)
- Darui Zheng
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Yiming Ruan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xuan Cao
- Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, USA
| | - Wenxuan Guo
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xulian Zhang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Wenzhang Qi
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Qianqian Yuan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xuhong Liang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Da Zhang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Qingling Huang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
| | - Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
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2
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Kollas N, Gewehr S, Kioutsioukis I. Empirical dynamic modelling and enhanced causal analysis of short-length Culex abundance timeseries with vector correlation metrics. Sci Rep 2024; 14:3597. [PMID: 38351267 PMCID: PMC10864305 DOI: 10.1038/s41598-024-54054-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/08/2024] [Indexed: 02/16/2024] Open
Abstract
Employing Empirical Dynamic Modelling we investigate whether model free methods could be applied in the study of Culex mosquitoes in Northern Greece. Applying Simplex Projection and S-Map algorithms on yearly timeseries of maximum abundances from 2011 to 2020 we successfully predict the decreasing trend in the maximum number of mosquitoes which was observed in the rural area of Thessaloniki during 2021. Leveraging the use of vector correlation metrics we were able to deduce the main environmental factors driving mosquito abundance such as temperature, rain and wind during 2012 and study the causal interaction between neighbouring populations in the industrial area of Thessaloniki between 2019 and 2020. In all three cases a chaotic and non-linear behaviour of the underlying system was observed. Given the health risk associated with the presence of mosquitoes as vectors of viral diseases these results hint to the usefulness of EDM methods in entomological studies as guides for the construction of more accurate and realistic mechanistic models which are indispensable to public health authorities for the design of targeted control strategies and health prevention measures.
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Affiliation(s)
- Nikos Kollas
- Department of Physics, University of Patras, 26504, Patras, Greece
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3
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Alves CL, Toutain TGLDO, de Carvalho Aguiar P, Pineda AM, Roster K, Thielemann C, Porto JAM, Rodrigues FA. Diagnosis of autism spectrum disorder based on functional brain networks and machine learning. Sci Rep 2023; 13:8072. [PMID: 37202411 DOI: 10.1038/s41598-023-34650-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/04/2023] [Indexed: 05/20/2023] Open
Abstract
Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets.
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Affiliation(s)
- Caroline L Alves
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil.
- BioMEMS Lab, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany.
| | | | - Patricia de Carvalho Aguiar
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Department of Neurology and Neurosurgery, Federal University of São Paulo, São Paulo, Brazil
| | - Aruane M Pineda
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
| | - Kirstin Roster
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
| | | | | | - Francisco A Rodrigues
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
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4
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Faldu KG, Shah JS. Alzheimer's disease: a scoping review of biomarker research and development for effective disease diagnosis. Expert Rev Mol Diagn 2022; 22:681-703. [PMID: 35855631 DOI: 10.1080/14737159.2022.2104639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Alzheimer's disease (AD) is regarded as the foremost reason for neurodegeneration that prominently affects the geriatric population. Characterized by extracellular accumulation of amyloid-beta (Aβ), intracellular aggregation of hyperphosphorylated tau (p-tau), and neuronal degeneration that causes impairment of memory and cognition. Amyloid/tau/neurodegeneration (ATN) classification is utilized for research purposes and involves amyloid, tau, and neuronal injury staging through MRI, PET scanning, and CSF protein concentration estimations. CSF sampling is invasive, and MRI and PET scanning requires sophisticated radiological facilities which limit its widespread diagnostic use. ATN classification lacks effectiveness in preclinical AD. AREAS COVERED This publication intends to collate and review the existing biomarker profile and the current research and development of a new arsenal of biomarkers for AD pathology from different biological samples, microRNA (miRNA), proteomics, metabolomics, artificial intelligence, and machine learning for AD screening, diagnosis, prognosis, and monitoring of AD treatments. EXPERT OPINION It is an accepted observation that AD-related pathological changes occur over a long period of time before the first symptoms are observed providing ample opportunity for detection of biological alterations in various biological samples that can aid in early diagnosis and modify treatment outcomes.
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Affiliation(s)
- Khushboo Govind Faldu
- Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat, India
| | - Jigna Samir Shah
- Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat, India
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5
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Tautan AM, Casula E, Borghi I, Maiella M, Bonni S, Minei M, Assogna M, Ionescu B, Koch G, Santarnecchi E. Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:394-397. [PMID: 36086206 DOI: 10.1109/embc48229.2022.9870920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Transcranial magnetic stimulation co-registered with electroencephalographic (TMS-EEG) has previously proven a helpful tool in the study of Alzheimer's disease (AD). In this work, we investigate the use of TMS-evoked EEG responses to classify AD patients from healthy controls (HC). By using a dataset containing 17AD and 17HC, we extract various time domain features from individual TMS responses and average them over a low, medium and high density EEG electrode set. Within a leave-one-subject-out validation scenario, the best classification performance for AD vs. HC was obtained using a high-density electrode with a Random Forest classifier. The accuracy, sensitivity and specificity were of 92.7%, 96.58% and 88.82% respectively. Clinical relevance- TMS-EEG responses were successfully used to identify Alzheimer's disease patients from healthy controls.
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REACTIVITY OF POSTERIOR CORTICAL ELECTROENCEPHALOGRAPHIC ALPHA RHYTHMS DURING EYES OPENING IN COGNITIVELY INTACT OLDER ADULTS AND PATIENTS WITH DEMENTIA DUE TO ALZHEIMER'S AND LEWY BODY DISEASES. Neurobiol Aging 2022; 115:88-108. [DOI: 10.1016/j.neurobiolaging.2022.04.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 03/17/2022] [Accepted: 04/02/2022] [Indexed: 12/19/2022]
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Jiang Y, Jessee W, Hoyng S, Borhani S, Liu Z, Zhao X, Price LK, High W, Suhl J, Cerel-Suhl S. Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work? Front Aging Neurosci 2022; 14:780817. [PMID: 35418848 PMCID: PMC8995767 DOI: 10.3389/fnagi.2022.780817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 02/08/2022] [Indexed: 09/19/2023] Open
Abstract
Growing evidence supports the idea that the ultimate biofeedback is to reward sensory pleasure (e.g., enhanced visual clarity) in real-time to neural circuits that are associated with a desired performance, such as excellent memory retrieval. Neurofeedback is biofeedback that uses real-time sensory reward to brain activity associated with a certain performance (e.g., accurate and fast recall). Working memory is a key component of human intelligence. The challenges are in our current limited understanding of neurocognitive dysfunctions as well as in technical difficulties for closed-loop feedback in true real-time. Here we review recent advancements of real time neurofeedback to improve memory training in healthy young and older adults. With new advancements in neuromarkers of specific neurophysiological functions, neurofeedback training should be better targeted beyond a single frequency approach to include frequency interactions and event-related potentials. Our review confirms the positive trend that neurofeedback training mostly works to improve memory and cognition to some extent in most studies. Yet, the training typically takes multiple weeks with 2-3 sessions per week. We review various neurofeedback reward strategies and outcome measures. A well-known issue in such training is that some people simply do not respond to neurofeedback. Thus, we also review the literature of individual differences in psychological factors e.g., placebo effects and so-called "BCI illiteracy" (Brain Computer Interface illiteracy). We recommend the use of Neural modulation sensitivity or BCI insensitivity in the neurofeedback literature. Future directions include much needed research in mild cognitive impairment, in non-Alzheimer's dementia populations, and neurofeedback using EEG features during resting and sleep for memory enhancement and as sensitive outcome measures.
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Affiliation(s)
- Yang Jiang
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
- College of Medicine, University of Kentucky, Lexington, KY, United States
| | - William Jessee
- College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Stevie Hoyng
- College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Soheil Borhani
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Ziming Liu
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Lacey K. Price
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
| | - Walter High
- New Mexico Veteran Affairs Medical Center, Albuquerque, NM, United States
| | - Jeremiah Suhl
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
| | - Sylvia Cerel-Suhl
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
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Wen D, Li R, Jiang M, Li J, Liu Y, Dong X, Saripan MI, Song H, Han W, Zhou Y. Multi-dimensional conditional mutual information with application on the EEG signal analysis for spatial cognitive ability evaluation. Neural Netw 2021; 148:23-36. [PMID: 35051867 DOI: 10.1016/j.neunet.2021.12.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 12/16/2021] [Accepted: 12/17/2021] [Indexed: 10/19/2022]
Abstract
This study aims to explore an effective method to evaluate spatial cognitive ability, which can effectively extract and classify the feature of EEG signals collected from subjects participating in the virtual reality (VR) environment; and evaluate the training effect objectively and quantitatively to ensure the objectivity and accuracy of spatial cognition evaluation, according to the classification results. Therefore, a multi-dimensional conditional mutual information (MCMI) method is proposed, which could calculate the coupling strength of two channels considering the influence of other channels. The coupled characteristics of the multi-frequency combination were transformed into multi-spectral images, and the image data were classified employing the convolutional neural networks (CNN) model. The experimental results showed that the multi-spectral image transform features based on MCMI are better in classification than other methods, and among the classification results of six band combinations, the best classification accuracy of Beta1-Beta2-Gamma combination is 98.3%. The MCMI characteristics on the Beta1-Beta2-Gamma band combination can be a biological marker for the evaluation of spatial cognition. The proposed feature extraction method based on MCMI provides a new perspective for spatial cognitive ability assessment and analysis.
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Affiliation(s)
- Dong Wen
- Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
| | - Rou Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Mengmeng Jiang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Jingjing Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Yijun Liu
- School of Science, Yanshan University, Qinhuangdao, China
| | - Xianling Dong
- Department of Biomedical Engineering, Chengde Medical University, Chengde, China
| | - M Iqbal Saripan
- Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia
| | - Haiqing Song
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Wei Han
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Yanhong Zhou
- School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China.
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9
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Jiang Y, Li J, Schmitt FA, Jicha GA, Munro NB, Zhao X, Smith CD, Kryscio RJ, Abner EL. Memory-Related Frontal Brainwaves Predict Transition to Mild Cognitive Impairment in Healthy Older Individuals Five Years Before Diagnosis. J Alzheimers Dis 2021; 79:531-541. [PMID: 33337367 PMCID: PMC7902960 DOI: 10.3233/jad-200931] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background: Early prognosis of high-risk older adults for amnestic mild cognitive impairment (aMCI), using noninvasive and sensitive neuromarkers, is key for early prevention of Alzheimer’s disease. We have developed individualized measures in electrophysiological brain signals during working memory that distinguish patients with aMCI from age-matched cognitively intact older individuals. Objective: Here we test longitudinally the prognosis of the baseline neuromarkers for aMCI risk. We hypothesized that the older individuals diagnosed with incident aMCI already have aMCI-like brain signatures years before diagnosis. Methods: Electroencephalogram (EEG) and memory performance were recorded during a working memory task at baseline. The individualized baseline neuromarkers, annual cognitive status, and longitudinal changes in memory recall scores up to 10 years were analyzed. Results: Seven of the 19 cognitively normal older adults were diagnosed with incident aMCI for a median 5.2 years later. The seven converters’ frontal brainwaves were statistically identical to those patients with diagnosed aMCI (n = 14) at baseline. Importantly, the converters’ baseline memory-related brainwaves (reduced mean frontal responses to memory targets) were significantly different from those who remained normal. Furthermore, differentiation pattern of left frontal memory-related responses (targets versus nontargets) was associated with an increased risk hazard of aMCI (HR = 1.47, 95% CI 1.03, 2.08). Conclusion: The memory-related neuromarkers detect MCI-like brain signatures about five years before diagnosis. The individualized frontal neuromarkers index increased MCI risk at baseline. These noninvasive neuromarkers during our Bluegrass memory task have great potential to be used repeatedly for individualized prognosis of MCI risk and progression before clinical diagnosis.
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Affiliation(s)
- Yang Jiang
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, USA.,Alzheimer's Disease Center, Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Juan Li
- Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Science, Beijing, China
| | - Frederick A Schmitt
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, USA.,Alzheimer's Disease Center, Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Gregory A Jicha
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, USA.,Alzheimer's Disease Center, Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA
| | | | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN, USA
| | - Charles D Smith
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, USA.,Alzheimer's Disease Center, Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Richard J Kryscio
- Department of Statistics, College of Art and Sciences, University of Kentucky, Lexington, KY, USA.,Alzheimer's Disease Center, Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Erin L Abner
- Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY, USA.,Alzheimer's Disease Center, Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA
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10
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Sabbaghi N, Sheikhani A, Noroozian M, Sabbaghi N. Interval-based features of auditory ERPs for diagnosis of early Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12191. [PMID: 34027021 PMCID: PMC8129855 DOI: 10.1002/dad2.12191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 03/26/2021] [Accepted: 03/30/2021] [Indexed: 11/17/2022]
Abstract
INTRODUCTION It has been demonstrated that event-related potentials (ERPs) mirror the neurodegenerative process of Alzheimer's disease (AD) and may therefore qualify as diagnostic markers. The aim of this study was to explore the potential of interval-based features as possible ERP biomarkers for early detection of AD patients. METHODS The current results are based on 7-channel ERP recordings of 95 healthy controls (HCs) and 75 subjects with mild AD acquired during a three-stimulus auditory oddball task. To evaluate interval-based features as diagnostic biomarkers in AD, two classifiers were applied to the selected features to distinguish AD and healthy control ERPs: RBFNN (radial basis function neural network) and MLP (multilayer perceptron). RESULTS Using extracted features and a radial basis function neural network, a high overall diagnostic accuracy of 98.3% was achieved. DISCUSSION Our findings demonstrate the great promise for scalp ERP and interval-based features as non-invasive, objective, and low-cost biomarkers for early AD detection.
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Affiliation(s)
- Neda Sabbaghi
- Department of Biomedical EngineeringScience and Research BranchIslamic Azad UniversityTehranIran
| | - Ali Sheikhani
- Department of Biomedical EngineeringScience and Research BranchIslamic Azad UniversityTehranIran
| | - Maryam Noroozian
- Department of PsychiatrySchool of MedicineTehran University of Medical SciencesTehranIran
| | - Navide Sabbaghi
- Department of Biomedical EngineeringScience and Research BranchIslamic Azad UniversityTehranIran
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11
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Tăuţan AM, Ionescu B, Santarnecchi E. Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques. Artif Intell Med 2021; 117:102081. [PMID: 34127244 DOI: 10.1016/j.artmed.2021.102081] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/21/2021] [Accepted: 04/26/2021] [Indexed: 10/21/2022]
Abstract
Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.
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Affiliation(s)
- Alexandra-Maria Tăuţan
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Bogdan Ionescu
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Harvard Medical School, 330 Brookline Avenue, Boston, United States.
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12
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Babiloni C, Arakaki X, Azami H, Bennys K, Blinowska K, Bonanni L, Bujan A, Carrillo MC, Cichocki A, de Frutos-Lucas J, Del Percio C, Dubois B, Edelmayer R, Egan G, Epelbaum S, Escudero J, Evans A, Farina F, Fargo K, Fernández A, Ferri R, Frisoni G, Hampel H, Harrington MG, Jelic V, Jeong J, Jiang Y, Kaminski M, Kavcic V, Kilborn K, Kumar S, Lam A, Lim L, Lizio R, Lopez D, Lopez S, Lucey B, Maestú F, McGeown WJ, McKeith I, Moretti DV, Nobili F, Noce G, Olichney J, Onofrj M, Osorio R, Parra-Rodriguez M, Rajji T, Ritter P, Soricelli A, Stocchi F, Tarnanas I, Taylor JP, Teipel S, Tucci F, Valdes-Sosa M, Valdes-Sosa P, Weiergräber M, Yener G, Guntekin B. Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel. Alzheimers Dement 2021; 17:1528-1553. [PMID: 33860614 DOI: 10.1002/alz.12311] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 12/28/2020] [Accepted: 01/01/2021] [Indexed: 12/25/2022]
Abstract
The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy.,San Raffaele of Cassino, Cassino (FR), Italy
| | | | - Hamed Azami
- Department of Neurology and Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Karim Bennys
- Centre Mémoire de Ressources et de Recherche (CMRR), Centre Hospitalier, Universitaire de Montpellier, Montpellier, France
| | - Katarzyna Blinowska
- Institute of Biocybernetics, Warsaw, Poland.,Faculty of Physics University of Warsaw and Nalecz, Warsaw, Poland
| | - Laura Bonanni
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University "G. D'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Ana Bujan
- Psychological Neuroscience Lab, School of Psychology, University of Minho, Minho, Portugal
| | - Maria C Carrillo
- Division of Medical & Scientific Relations, Alzheimer's Association, Chicago, Illinois, USA
| | - Andrzej Cichocki
- Skolkowo Institute of Science and Technology (SKOLTECH), Moscow, Russia.,Systems Research Institute PAS, Warsaw, Poland.,Nicolaus Copernicus University (UMK), Torun, Poland
| | - Jaisalmer de Frutos-Lucas
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | - Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Bruno Dubois
- Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Institute of Memory and Alzheimer's Disease (IM2A), Paris, France.,ICM, INSERM U1127, CNRS UMR 7225, Sorbonne Université, Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Rebecca Edelmayer
- Division of Medical & Scientific Relations, Alzheimer's Association, Chicago, Illinois, USA
| | - Gary Egan
- Foundation Director of the Monash Biomedical Imaging (MBI) Research Facilities, Monash University, Clayton, Australia
| | - Stephane Epelbaum
- Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Institute of Memory and Alzheimer's Disease (IM2A), Paris, France.,ICM, INSERM U1127, CNRS UMR 7225, Sorbonne Université, Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Javier Escudero
- School of Engineering, Institute for Digital Communications, The University of Edinburgh, Edinburgh, UK
| | - Alan Evans
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Francesca Farina
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Keith Fargo
- Division of Medical & Scientific Relations, Alzheimer's Association, Chicago, Illinois, USA
| | - Alberto Fernández
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Giovanni Frisoni
- IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Harald Hampel
- GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Sorbonne University, Paris, France
| | | | - Vesna Jelic
- Division of Clinical Geriatrics, NVS Department, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Jaeseung Jeong
- Department of Bio and Brain Engineering/Program of Brain and Cognitive Engineering Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Yang Jiang
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Maciej Kaminski
- Faculty of Physics University of Warsaw and Nalecz, Warsaw, Poland
| | - Voyko Kavcic
- Institute of Gerontology, Wayne State University, Detroit, Michigan, USA
| | - Kerry Kilborn
- School of Psychology, University of Glasgow, Glasgow, UK
| | - Sanjeev Kumar
- Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Alice Lam
- MGH Epilepsy Service, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lew Lim
- Vielight Inc., Toronto, Ontario, Canada
| | | | - David Lopez
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Brendan Lucey
- Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | - William J McGeown
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, UK
| | - Ian McKeith
- Newcastle upon Tyne, Translational and Clinical Research Institute, Newcastle University, UK
| | | | - Flavio Nobili
- Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy.,Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - John Olichney
- UC Davis Department of Neurology and Center for Mind and Brain, Davis, California, USA
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University "G. D'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Ricardo Osorio
- Center for Brain Health, Department of Psychiatry, NYU Langone Medical Center, New York, New York, USA
| | | | - Tarek Rajji
- Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Petra Ritter
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Andrea Soricelli
- IRCCS SDN, Napoli, Italy.,Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | | | - Ioannis Tarnanas
- Global Brain Health Institute, University of California San Francisco, San Francisco, USA.,Global Brain Health Institute, Trinity College Dublin, Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - John Paul Taylor
- Newcastle upon Tyne, Translational and Clinical Research Institute, Newcastle University, UK
| | - Stefan Teipel
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany.,German Center for Neurodegenerative Diseases (DZNE) - Rostock/Greifswald, Rostock, Germany
| | - Federico Tucci
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | | | - Pedro Valdes-Sosa
- Cuban Neuroscience Center, Havana, Cuba.,Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Marco Weiergräber
- Experimental Neuropsychopharmacology, BfArM), Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, Bonn, Germany
| | - Gorsev Yener
- Departments of Neurosciences and Department of Neurology, Dokuz Eylül University Medical School, Izmir, Turkey
| | - Bahar Guntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey.,REMER, Clinical Electrophysiology, Neuroimaging and Neuromodulation Lab, Istanbul Medipol University, Istanbul, Turkey
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13
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Oltu B, Akşahin MF, Kibaroğlu S. A novel electroencephalography based approach for Alzheimer’s disease and mild cognitive impairment detection. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102223] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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14
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Santos Toural JE, Montoya Pedrón A, Marañón Reyes EJ. A new method for classification of subjects with major cognitive disorder, Alzheimer type, based on electroencephalographic biomarkers. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
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15
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Santos Toural JE, Montoya Pedrón A, Marañón Reyes EJ. Classification among healthy, mild cognitive impairment and Alzheimer’s disease subjects based on wavelet entropy and relative beta and theta power. Pattern Anal Appl 2020. [DOI: 10.1007/s10044-020-00910-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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Zhang H, Silva FHS, Ohata EF, Medeiros AG, Rebouças Filho PP. Bi-Dimensional Approach Based on Transfer Learning for Alcoholism Pre-disposition Classification via EEG Signals. Front Hum Neurosci 2020; 14:365. [PMID: 33061900 PMCID: PMC7530264 DOI: 10.3389/fnhum.2020.00365] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 08/10/2020] [Indexed: 01/16/2023] Open
Abstract
Recent statistics have shown that the main difficulty in detecting alcoholism is the unreliability of the information presented by patients with alcoholism; this factor confusing the early diagnosis and it can reduce the effectiveness of treatment. However, electroencephalogram (EEG) exams can provide more reliable data for analysis of this behavior. This paper proposes a new approach for the automatic diagnosis of patients with alcoholism and introduces an analysis of the EEG signals from a two-dimensional perspective according to changes in the neural activity, highlighting the influence of high and low-frequency signals. This approach uses a two-dimensional feature extraction method, as well as the application of recent Computer Vision (CV) techniques, such as Transfer Learning with Convolutional Neural Networks (CNN). The methodology to evaluate our proposal used 21 combinations of the traditional classification methods and 84 combinations of recent CNN architectures used as feature extractors combined with the following classical classifiers: Gaussian Naive Bayes, K-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM). CNN MobileNet combined with SVM achieved the best results in Accuracy (95.33%), Precision (95.68%), F1-Score (95.24%), and Recall (95.00%). This combination outperformed the traditional methods by up to 8%. Thus, this approach is applicable as a classification stage for computer-aided diagnoses, useful for the triage of patients, and clinical support for the early diagnosis of this disease.
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Affiliation(s)
- Hongyi Zhang
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China
| | - Francisco H S Silva
- Laboratório de Processamento de Imagens, Sinais e Computação Aplicada, Instituto Federal do Ceará, Fortaleza, Brazil
| | - Elene F Ohata
- Laboratório de Processamento de Imagens, Sinais e Computação Aplicada, Instituto Federal do Ceará, Fortaleza, Brazil.,Programa de Pós-Graduação em Engenharia de Teleinformática, Universidade Federal do Ceará, Fortaleza, Brazil
| | - Aldisio G Medeiros
- Laboratório de Processamento de Imagens, Sinais e Computação Aplicada, Instituto Federal do Ceará, Fortaleza, Brazil.,Programa de Pós-Graduação em Engenharia de Teleinformática, Universidade Federal do Ceará, Fortaleza, Brazil
| | - Pedro P Rebouças Filho
- Laboratório de Processamento de Imagens, Sinais e Computação Aplicada, Instituto Federal do Ceará, Fortaleza, Brazil.,Programa de Pós-Graduação em Engenharia de Teleinformática, Universidade Federal do Ceará, Fortaleza, Brazil.,Programa de Pós-Graduação em Ciência da Computação, Instituto Federal do Ceará, Fortaleza, Brazil
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17
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Yu H, Zhu L, Cai L, Wang J, Liu J, Wang R, Zhang Z. Identification of Alzheimer's EEG With a WVG Network-Based Fuzzy Learning Approach. Front Neurosci 2020; 14:641. [PMID: 32848530 PMCID: PMC7396629 DOI: 10.3389/fnins.2020.00641] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 05/25/2020] [Indexed: 12/15/2022] Open
Abstract
A novel analytical framework combined fuzzy learning and complex network approaches is proposed for the identification of Alzheimer's disease (AD) with multichannel scalp-recorded electroencephalograph (EEG) signals. Weighted visibility graph (WVG) algorithm is first applied to transform each channel EEG into network and its topological parameters were further extracted. Statistical analysis indicates that AD and normal subjects show significant difference in the structure of WVG network and thus can be used to identify Alzheimer's disease. Taking network parameters as input features, a Takagi-Sugeno-Kang (TSK) fuzzy model is established to identify AD's EEG signal. Three feature sets-single parameter from multi-networks, multi-parameters from single network, and multi-parameters from multi-networks-are considered as input vectors. The number and order of input features in each set is optimized with various feature selection methods. Classification results demonstrate the ability of network-based TSK fuzzy classifiers and the feasibility of three input feature sets. The highest accuracy that can be achieved is 95.28% for single parameter from four networks, 93.41% for three parameters from single network. In particular, multi-parameters from the multi-networks set obtained the best result. The highest accuracy, 97.12%, is achieved with five features selected from four networks. The combination of network and fuzzy learning can highly improve the efficiency of AD's EEG identification.
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Affiliation(s)
- Haitao Yu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Lin Zhu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Lihui Cai
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jing Liu
- Department of Neurology, Tangshan Gongren Hospital, Tangshan, China
| | - Ruofan Wang
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Zhiyong Zhang
- Department of Pathology, Tangshan Gongren Hospital, Tangshan, China
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18
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Ospina-Forero L, Castañeda G, Guerrero OA. Estimating networks of sustainable development goals. INFORMATION & MANAGEMENT 2020. [DOI: 10.1016/j.im.2020.103342] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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19
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Giron-Nava A, Munch SB, Johnson AF, Deyle E, James CC, Saberski E, Pao GM, Aburto-Oropeza O, Sugihara G. Circularity in fisheries data weakens real world prediction. Sci Rep 2020; 10:6977. [PMID: 32332835 PMCID: PMC7181812 DOI: 10.1038/s41598-020-63773-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 12/19/2019] [Indexed: 11/30/2022] Open
Abstract
The systematic substitution of direct observational data with synthesized data derived from models during the stock assessment process has emerged as a low-cost alternative to direct data collection efforts. What is not widely appreciated, however, is how the use of such synthesized data can overestimate predictive skill when forecasting recruitment is part of the assessment process. Using a global database of stock assessments, we show that Standard Fisheries Models (SFMs) can successfully predict synthesized data based on presumed stock-recruitment relationships, however, they are generally less skillful at predicting observational data that are either raw or minimally filtered (denoised without using explicit stock-recruitment models). Additionally, we find that an equation-free approach that does not presume a specific stock-recruitment relationship is better than SFMs at predicting synthesized data, and moreover it can also predict observational recruitment data very well. Thus, while synthesized datasets are cheaper in the short term, they carry costs that can limit their utility in predicting real world recruitment.
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Affiliation(s)
- Alfredo Giron-Nava
- Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Dr, CA, La Jolla, 92093, USA.,National Center for Ecological Analysis and Synthesis, University of California Santa Barbara, 735 State St #300, Santa Barbara, CA, 93101, USA.,Future Earth, School of Global Environmental Sustainability, Colorado State University, 108 Johnson Dr, Fort Collins, 80523, CO, USA
| | - Stephan B Munch
- Fisheries Ecology Division. Southwest Fisheries Science Center. National Oceanographic and Atmospheric Administration, 110 Shaffer Rd, Santa Cruz, CA, 95060, USA.,Department of Ecology and Evolutionary Biology, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA, 95064, USA
| | | | - Ethan Deyle
- Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Dr, CA, La Jolla, 92093, USA
| | - Chase C James
- Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Dr, CA, La Jolla, 92093, USA
| | - Erik Saberski
- Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Dr, CA, La Jolla, 92093, USA
| | - Gerald M Pao
- Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Dr, CA, La Jolla, 92093, USA.,Salk Institute of Biological Sciences, 10010 North Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Octavio Aburto-Oropeza
- Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Dr, CA, La Jolla, 92093, USA
| | - George Sugihara
- Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Dr, CA, La Jolla, 92093, USA.
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20
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Cai C, Huang C, Yang C, Lu H, Hong X, Ren F, Hong D, Ng E. Altered Patterns of Functional Connectivity and Causal Connectivity in Salience Subnetwork of Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment. Front Neurosci 2020; 14:288. [PMID: 32390791 PMCID: PMC7189119 DOI: 10.3389/fnins.2020.00288] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 03/12/2020] [Indexed: 12/22/2022] Open
Abstract
The subjective cognitive decline (SCD) may last for decades prior to the onset of dementia and has been proposed as a risk population for development to amnestic mild cognitive impairment (aMCI) and Alzheimer disease (AD). Disruptions of functional connectivity and causal connectivity (CC) in the salience network (SN) are generally perceived as prominent hallmarks of the preclinical AD. Nevertheless, the alterations in anterior SN (aSN), and posterior SN (pSN) remain unclear. Here, we hypothesized that both the functional connectivity (FC) and CC of the SN subnetworks, comprising aSN and pSN, were distinct disruptive in the SCD and aMCI. We utilized resting-state functional magnetic resonance imaging to investigate the altered FC and CC of the SN subnetworks in 28 healthy controls, 23 SCD subjects, and 29 aMCI subjects. In terms of altered patterns of FC in SN subnetworks, aSN connected to the whole brain was significantly increased in the left orbital superior frontal gyrus, left insula lobule, right caudate lobule, and left rolandic operculum gyrus (ROG), whereas decreased FC was found in the left cerebellum superior lobule and left middle temporal gyrus when compared with the HC group. Notably, no prominent statistical differences were obtained in pSN. For altered patterns of CC in SN subnetworks, compared to the HC group, the aberrant connections in aMCI group were separately involved in the right cerebellum inferior lobule (CIL), right supplementary motor area (SMA), and left ROG, whereas the SCD group exhibited more regions of aberrant connection, comprising the right superior parietal lobule, right CIL, left inferior parietal lobule, left post-central gyrus (PG), and right angular gyrus. Especially, SCD group showed increased CC in the right CIL and left PG, whereas the aMCI group showed decreased CC in the left pre-cuneus, corpus callosum, and right SMA when compared to the SCD group. Collectively, our results suggest that analyzing the altered FC and CC observed in SN subnetworks, served as impressible neuroimaging biomarkers, may supply novel insights for designing preclinical interventions in the preclinical stages of AD.
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Affiliation(s)
- Chunting Cai
- School of Informatics, Xiamen University, Xiamen, China
| | - Chenxi Huang
- School of Informatics, Xiamen University, Xiamen, China
| | - Chenhui Yang
- School of Informatics, Xiamen University, Xiamen, China
| | - Haijie Lu
- Department of Radiation Oncology, Zhongshan Hospital of Xiamen University, Xiamen, China
| | - Xin Hong
- School of Informatics, Xiamen University, Xiamen, China.,College of Computer Science and Technology, Huaqiao University, Xiamen, China
| | - Fujia Ren
- School of Informatics, Xiamen University, Xiamen, China
| | - Dan Hong
- School of Informatics, Xiamen University, Xiamen, China
| | - Eyk Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
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21
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Farina FR, Emek-Savaş DD, Rueda-Delgado L, Boyle R, Kiiski H, Yener G, Whelan R. A comparison of resting state EEG and structural MRI for classifying Alzheimer's disease and mild cognitive impairment. Neuroimage 2020; 215:116795. [PMID: 32278090 DOI: 10.1016/j.neuroimage.2020.116795] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 03/27/2020] [Accepted: 03/28/2020] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's disease (AD) is the leading cause of dementia, accounting for 70% of cases worldwide. By 2050, dementia prevalence will have tripled, with most new cases occurring in low- and middle-income countries. Mild cognitive impairment (MCI) is a stage between healthy aging and dementia, marked by cognitive deficits that do not impair daily living. People with MCI are at increased risk of dementia, with an average progression rate of 39% within 5 years. There is urgent need for low-cost, accessible and objective methods to facilitate early dementia detection. Electroencephalography (EEG) has potential to address this need due to its low cost and portability. Here, we collected resting state EEG, structural MRI (sMRI) and rich neuropsychological data from older adults (55+ years) with AD, amnestic MCI (aMCI) and healthy controls (~60 per group). We evaluated a range of candidate EEG markers (i.e., frequency band power and functional connectivity) for AD and aMCI classification and compared their performance with sMRI. We also tested a combined EEG and cognitive classification model (using Mini-Mental State Examination; MMSE). sMRI outperformed resting state EEG at classifying AD (AUCs = 1.00 vs 0.76, respectively). However, both EEG and sMRI were only moderately good at distinguishing aMCI from healthy aging (AUCs = 0.67-0.73), and neither method achieved sensitivity above 70%. The addition of EEG to MMSE scores had no added benefit relative to MMSE scores alone. This is the first direct comparison of EEG and sMRI for classification of AD and aMCI.
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Affiliation(s)
- F R Farina
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.
| | - D D Emek-Savaş
- Department of Psychology, Faculty of Letters, Dokuz Eylul University, Izmir, 35160, Turkey; Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, 35340, Turkey; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland
| | - L Rueda-Delgado
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - R Boyle
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - H Kiiski
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - G Yener
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, 35340, Turkey; Department of Neurology, Dokuz Eylul University Medical School, Izmir, 35340, Turkey
| | - R Whelan
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland.
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22
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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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23
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Laneri K, Cabella B, Prado PI, Mendes Coutinho R, Kraenkel RA. Climate drivers of malaria at its southern fringe in the Americas. PLoS One 2019; 14:e0219249. [PMID: 31291316 PMCID: PMC6619762 DOI: 10.1371/journal.pone.0219249] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 06/19/2019] [Indexed: 01/01/2023] Open
Abstract
In this work we analyze potential environmental drivers of malaria cases in Northwestern Argentina. We inspect causal links between malaria and climatic variables by means of the convergent cross mapping technique, which provides a causality criterion from the theory of dynamic systems. Analysis is based on 12 years of weekly malaria P. vivax cases in Tartagal, Salta, Argentina-at the southern fringe of malaria incidence in the Americas-together with humidity and temperature time-series spanning the same period. Our results show that there are causal links between malaria cases and both maximum temperature, with a delay of five weeks, and minimum temperature, with delays of zero and twenty two weeks. Humidity is also a driver of malaria cases, with thirteen weeks delay between cause and effect. Furthermore we also determined the sign and strength of the effects. Temperature has always a positive non-linear effect on cases, with maximum temperature effects more pronounced above 25°C and minimum above 17°C, while effects of humidity are more intricate: maximum humidity above 85% has a negative effect, whereas minimum humidity has a positive effect on cases. These results might be signaling processes operating at short (below 5 weeks) and long (over 12 weeks) time delays, corresponding to effects related to parasite cycle and mosquito population dynamics respectively. The non-linearities found for the strength of the effect of temperature on malaria cases make warmer areas more prone to higher increases in the disease incidence. Moreover, our results indicate that an increase of extreme weather events could enhance the risks of malaria spreading and re-emergence beyond the current distribution. Both situations, warmer climate and increase of extreme events, will be remarkably increased by the end of the century in this hot spot of climate change.
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Affiliation(s)
- Karina Laneri
- Grupo de Física Estadística e Interdisciplinaria, CONICET, Centro Atómico Bariloche, Bariloche, Río Negro, Argentina
- * E-mail:
| | - Brenno Cabella
- Instituto de Física Teórica, Universidade Estadual Paulista - UNESP, São Paulo, SP, Brazil
| | - Paulo Inácio Prado
- LAGE do Departamento de Ecologia, Instituto de Biociências da Universidade de São Paulo, São Paulo, SP, Brazil
| | - Renato Mendes Coutinho
- Centro de Matemática, Computação e Cognição (CMCC), Universidade Federal do ABC, Santo André, SP, Brazil
| | - Roberto André Kraenkel
- Instituto de Física Teórica, Universidade Estadual Paulista - UNESP, São Paulo, SP, Brazil
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24
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Hejazi M, Motie Nasrabadi A. Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods. Cogn Neurodyn 2019; 13:461-473. [PMID: 31565091 DOI: 10.1007/s11571-019-09534-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 02/07/2019] [Accepted: 04/25/2019] [Indexed: 01/09/2023] Open
Abstract
Epilepsy is a chronic disorder, which causes strange perceptions, muscle spasms, sometimes seizures, and loss of awareness, associated with abnormal neuronal activity in the brain. The goal of this study is to investigate how effective connectivity (EC) changes effect on unexpected seizures prediction, as this will authorize the patients to play it safe and avoid risk. We approve the hypothesis that EC variables near seizure change significantly so seizure can be predicted in accordance with this variation. We introduce two time-variant coefficients based on standard deviation of EC on Freiburg EEG dataset by using directed transfer function and Granger causality methods and compare index changes over the course of time in five different frequency bands. Comparison of the multivariate and bivariate analysis of factors is implemented in this investigation. The performance based on the suggested methods shows the seizure occurrence period is approximately 50 min that is expected onset stated in, the maximum value of sensitivity approaching ~ 80%, and 0.33 FP/h is the false prediction rate. The findings revealed that greater accuracy and sensitivity are obtained by the designed system in comparison with the results of other works in the same condition. Even though these results still are not sufficient for clinical applications. Based on the conclusions, it can generally be observed that the greater results by DTF method are in the gamma and beta frequency bands.
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Affiliation(s)
- Mona Hejazi
- 1Department of Biomedical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
| | - Ali Motie Nasrabadi
- 2Department of Biomedical Engineering, Faculty of Biomedical Engineering, Shahed University, Tehran, Iran
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25
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Extracting Interactions between Flying Bat Pairs Using Model-Free Methods. ENTROPY 2019; 21:e21010042. [PMID: 33266758 PMCID: PMC7514148 DOI: 10.3390/e21010042] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 11/15/2018] [Accepted: 01/03/2019] [Indexed: 12/03/2022]
Abstract
Social animals exhibit collective behavior whereby they negotiate to reach an agreement, such as the coordination of group motion. Bats are unique among most social animals, since they use active sensory echolocation by emitting ultrasonic waves and sensing echoes to navigate. Bats’ use of active sensing may result in acoustic interference from peers, driving different behavior when they fly together rather than alone. The present study explores quantitative methods that can be used to understand whether bats flying in pairs move independently of each other or interact. The study used field data from bats in flight and is based on the assumption that interactions between two bats are evidenced in their flight patterns. To quantify pairwise interaction, we defined the strength of coupling using model-free methods from dynamical systems and information theory. We used a control condition to eliminate similarities in flight path due to environmental geometry. Our research question is whether these data-driven methods identify directed coupling between bats from their flight paths and, if so, whether the results are consistent between methods. Results demonstrate evidence of information exchange between flying bat pairs, and, in particular, we find significant evidence of rear-to-front coupling in bats’ turning behavior when they fly in the absence of obstacles.
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Fonseca A, Kerick S, King JT, Lin CT, Jung TP. Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data. Front Hum Neurosci 2018; 12:418. [PMID: 30483080 PMCID: PMC6240698 DOI: 10.3389/fnhum.2018.00418] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 09/27/2018] [Indexed: 11/13/2022] Open
Abstract
The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.
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Affiliation(s)
- André Fonseca
- Center of Mathematics, Computation and Cognition, Federal University of ABC, São Paulo, Brazil.,Swartz Center for Computational Neuroscience, University of California, San Diego, La Jolla, CA, United States
| | - Scott Kerick
- US Army Research Laboratory, Aberdeen, MD, United States
| | - Jung-Tai King
- Brain Research Center, National Chiao Tung University, Hsinchu, Taiwan
| | - Chin-Teng Lin
- Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia
| | - Tzyy-Ping Jung
- Swartz Center for Computational Neuroscience, University of California, San Diego, La Jolla, CA, United States
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Cassani R, Estarellas M, San-Martin R, Fraga FJ, Falk TH. Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment. DISEASE MARKERS 2018; 2018:5174815. [PMID: 30405860 PMCID: PMC6200063 DOI: 10.1155/2018/5174815] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 07/12/2018] [Accepted: 07/29/2018] [Indexed: 12/17/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.
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Affiliation(s)
- Raymundo Cassani
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
| | - Mar Estarellas
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
- Department of Bioengineering, Imperial College London, London, UK
| | - Rodrigo San-Martin
- Center for Mathematics, Computation and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Francisco J. Fraga
- Engineering, Modeling and Applied Social Sciences Center, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Tiago H. Falk
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
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Roy S, Jantzen B. Detecting causality using symmetry transformations. CHAOS (WOODBURY, N.Y.) 2018; 28:075305. [PMID: 30070527 DOI: 10.1063/1.5018101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Detecting causality between variables in a time series is a challenge, particularly when the relationship is nonlinear and the dataset is noisy. Here, we present a novel tool for detecting causality that leverages the properties of symmetry transformations. The aim is to develop an algorithm with the potential to detect both unidirectional and bidirectional coupling for nonlinear systems in the presence of significant sampling noise. Most of the existing tools for detecting causality can make determinations of directionality, but those determinations are relatively fragile in the presence of noise. The novel algorithm developed in the present study is robust and very conservative in that it reliably detects causal structure with a very low rate of error even in the presence of high sampling noise. We demonstrate the performance of our algorithm and compare it with two popular model-free methods, namely transfer entropy and convergent cross map. This first implementation of the method of symmetry transformations is limited in that it applies only to first-order autonomous systems.
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29
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Distance-Based Lempel–Ziv Complexity for the Analysis of Electroencephalograms in Patients with Alzheimer’s Disease. ENTROPY 2017. [DOI: 10.3390/e19030129] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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30
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Jiang Y, Abiri R, Zhao X. Tuning Up the Old Brain with New Tricks: Attention Training via Neurofeedback. Front Aging Neurosci 2017; 9:52. [PMID: 28348527 PMCID: PMC5346575 DOI: 10.3389/fnagi.2017.00052] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 02/22/2017] [Indexed: 12/03/2022] Open
Abstract
Neurofeedback (NF) is a form of biofeedback that uses real-time (RT) modulation of brain activity to enhance brain function and behavioral performance. Recent advances in Brain-Computer Interfaces (BCI) and cognitive training (CT) have provided new tools and evidence that NF improves cognitive functions, such as attention and working memory (WM), beyond what is provided by traditional CT. More published studies have demonstrated the efficacy of NF, particularly for treating attention deficit hyperactivity disorder (ADHD) in children. In contrast, there have been fewer studies done in older adults with or without cognitive impairment, with some notable exceptions. The focus of this review is to summarize current success in RT NF training of older brains aiming to match those of younger brains during attention/WM tasks. We also outline potential future advances in RT brainwave-based NF for improving attention training in older populations. The rapid growth in wireless recording of brain activity, machine learning classification and brain network analysis provides new tools for combating cognitive decline and brain aging in older adults. We optimistically conclude that NF, combined with new neuro-markers (event-related potentials and connectivity) and traditional features, promises to provide new hope for brain and CT in the growing older population.
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Affiliation(s)
- Yang Jiang
- Aging Brain and Cognition Laboratory, Department of Behavioral Science, College of Medicine, University of KentuckyLexington, KY, USA; Sanders-Brown Center on Aging, College of Medicine, University of KentuckyLexington, KY, USA
| | - Reza Abiri
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee Knoxville, TN, USA
| | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of TennesseeKnoxville, TN, USA; Institute for Medical Engineering and Science, Massachusetts Institute of TechnologyCambridge, MA, USA
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31
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How an epileptic EEG segment, used as reference, can influence a cross-correlation classifier? APPL INTELL 2017. [DOI: 10.1007/s10489-016-0891-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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32
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Ye H, Sugihara G. Information leverage in interconnected ecosystems: Overcoming the curse of dimensionality. Science 2017; 353:922-5. [PMID: 27563095 DOI: 10.1126/science.aag0863] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Accepted: 08/01/2016] [Indexed: 11/02/2022]
Abstract
In ecological analysis, complexity has been regarded as an obstacle to overcome. Here we present a straightforward approach for addressing complexity in dynamic interconnected systems. We show that complexity, in the form of multiple interacting components, can actually be an asset for studying natural systems from temporal data. The central idea is that multidimensional time series enable system dynamics to be reconstructed from multiple viewpoints, and these viewpoints can be combined into a single model. We show how our approach, multiview embedding (MVE), can improve forecasts for simulated ecosystems and a mesocosm experiment. By leveraging complexity, MVE is particularly effective for overcoming the limitations of short and noisy time series and should be highly relevant for many areas of science.
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Affiliation(s)
- Hao Ye
- Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive 0202, La Jolla, CA 92093-0202, USA
| | - George Sugihara
- Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive 0202, La Jolla, CA 92093-0202, USA.
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Nasrolahzadeh M, Mohammadpoori Z, Haddadnia J. Analysis of mean square error surface and its corresponding contour plots of spontaneous speech signals in Alzheimer's disease with adaptive wiener filter. COMPUTERS IN HUMAN BEHAVIOR 2016. [DOI: 10.1016/j.chb.2016.03.031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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34
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Kashefpoor M, Rabbani H, Barekatain M. Automatic Diagnosis of Mild Cognitive Impairment Using Electroencephalogram Spectral Features. JOURNAL OF MEDICAL SIGNALS AND SENSORS 2016; 6:25-32. [PMID: 27014609 PMCID: PMC4786960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Abstract
Alzheimer's disease (AD) is one of the most expensive and fatal diseases in the elderly population. Up to now, no cure have been found for AD, so early stage diagnosis is the only way to control it. Mild cognitive impairment (MCI) usually is the early stage of AD which is defined as decreasing in mental abilities such a cognition, memory, and speech not too severe to interfere daily activities. MCI diagnosis is rather hard and usually assumed as normal consequences of aging. This study proposes an accurate, mobile, and nonexpensive diagnostic approach based on electroencephalogram (EEG) signal. EEG signals were recorded using 19 electrodes positioned according to the 10-20 International system at resting eyes closed state from 16 normal and 11 MCI participants. Nineteen Spectral features are computed for each channel and examined using a correlation based algorithm to select the best discriminative features. Selected features are classified using a combination of neurofuzzy system and k-nearest neighbor classifier. Final results reach 88.89%, 100%, and 83.33% for accuracy, sensitivity, and specificity, respectively, which shows the potential of proposed method to be used as an MCI diagnostic tool, especially for screening a large population.
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Affiliation(s)
- Masoud Kashefpoor
- Department of Biomedical Engineering, Faculty of Advanced Medical Technologies, Isfahan University of Medical Sciences, Isfahan, Iran,Student Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hossein Rabbani
- Department of Biomedical Engineering, Faculty of Advanced Medical Technologies, Isfahan University of Medical Sciences, Isfahan, Iran,Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran,Address for correspondence: Department of Advanced Medical Technologies, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. E-mail:
| | - Majid Barekatain
- Psychosomatic Research Center, Department of Psychiatry, Medical School, Isfahan University of Medical Sciences, Isfahan, Iran
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McBride J, Zhao X, Munro N, Jicha G, Smith C, Jiang Y. Discrimination of mild cognitive impairment and Alzheimer's disease using transfer entropy measures of scalp EEG. JOURNAL OF HEALTHCARE ENGINEERING 2015; 6:55-70. [PMID: 25708377 DOI: 10.1260/2040-2295.6.1.55] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia including Alzheimer's disease (AD). This study investigates the potential of measures of transfer entropy in scalp EEG for effectively discriminating between normal aging, MCI, and AD participants. Resting EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls, 16 MCI, and 17 early AD-are examined. The mean temporal delays corresponding to peaks in inter-regional transfer entropy are computed and used as features to discriminate between the three groups of participants. Three-way classification schemes based on binary support vector machine models demonstrate overall discrimination accuracies of 91.7- 93.8%, depending on the protocol condition. These results demonstrate the potential for EEG transfer entropy measures as biomarkers in identifying early MCI and AD. Moreover, the analyses based on short data segments (two minutes) render the method practical for a primary care setting.
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Affiliation(s)
- Joseph McBride
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN, USA
| | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN, USA
| | - Nancy Munro
- Oak Ridge Nation Laboratory, Oak Ridge, TN, USA
| | - Gregory Jicha
- Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA Department of Neurology, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Charles Smith
- Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA Department of Neurology, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Yang Jiang
- Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA Department of Behavioral Science, University of Kentucky College of Medicine, Lexington, KY, USA
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