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Shaban M, Amara AW. Resting-state electroencephalography based deep-learning for the detection of Parkinson's disease. PLoS One 2022; 17:e0263159. [PMID: 35202420 PMCID: PMC8870584 DOI: 10.1371/journal.pone.0263159] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 01/12/2022] [Indexed: 02/02/2023] Open
Abstract
Parkinson's disease (PD) is one of the most serious and challenging neurodegenerative disorders to diagnose. Clinical diagnosis on observing motor symptoms is the gold standard, yet by this point nerve cells are degenerated resulting in a lower efficacy of therapeutic treatments. In this study, we introduce a deep-learning approach based on a recently-proposed 20-Layer Convolutional Neural Network (CNN) applied on the visual realization of the Wavelet domain of a resting-state EEG. The proposed approach was able to efficiently and accurately detect PD as well as distinguish subjects with PD on medications from subjects who are off medication. The gradient-weighted class activation mapping (Grad-CAM) was used to visualize the features based on which the approach provided the predictions. A significantly high accuracy, sensitivity, specificity, AUC, and Weighted Kappa Score up to 99.9% were achieved and the visualization of the regions in the Wavelet images that contributed to the deep-learning approach decisions was provided. The proposed framework can then serve as an effective computer-aided diagnostic tool that will support physicians and scientists in further understanding the nature of PD and providing an objective and confident opinion regarding the clinical diagnosis of the disease.
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Affiliation(s)
- Mohamed Shaban
- Electrical and Computer Engineering, University of South Alabama, Mobile, AL, United States of America
| | - Amy W. Amara
- Neurology, University of Alabama at Birmingham, Birmingham, AL, United States of America
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2
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Zhu H, Qiu J, Sun X, Yang X, Zhang B, Tan Y. Intelligent Algorithm-Based Quantitative Electroencephalography in Evaluating Cerebral Small Vessel Disease Complicated by Cognitive Impairment. Comput Math Methods Med 2022; 2022:9398551. [PMID: 35132334 PMCID: PMC8817878 DOI: 10.1155/2022/9398551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 12/18/2021] [Accepted: 01/03/2022] [Indexed: 11/26/2022]
Abstract
To analyze the application value of artificial intelligence model based on Visual Geometry Group- (VGG-) 16 combined with quantitative electroencephalography (QEEG) in cerebral small vessel disease (CSVD) with cognitive impairment, 72 patients with CSVD complicated by cognitive impairment were selected as the research subjects. As per Diagnostic and Statistical Manual (5th Edition), they were divided into the vascular dementia (VD) group of 34 cases and vascular cognitive impairment with no dementia (VCIND) group of 38 cases. The two groups were analyzed for the clinical information, neuropsychological test results, and monitoring results of QEEG based on intelligent algorithms for more than 2 hours. The accuracy rate of VGG was 84.27% and Kappa value was 0.7, while that of modified VGG (nVGG) was 88.76% and Kappa value was 0.78. The improved VGG algorithm obviously had higher accuracy. The test results found that the QEEG identified 8 normal, 19 mild, 10 moderate, and 0 severe cases in the VCIND group, while in the VD group, the corresponding numbers were 4, 13, 11, and 7; in the VCIND group, 7 cases had the normal QEEG, 11 cases had background changes, 9 cases had abnormal waves, and 11 cases had in both background changes and abnormal waves, and in the VD group, the corresponding numbers were 5, 2, 5, and 22, respectively; in the VCIND group, QEEG of 18 patients had no abnormal waves, QEEG of 11 patients had a few abnormal waves, and QEEG of 9 patients had many abnormal waves, and QEEG of 0 people had a large number of abnormal waves, and in the VD group, the corresponding numbers were 7, 6, 12, and 9. The above data were statistically different between the two groups (P < 0.05). Hence, QEEG based on intelligent algorithms can make a good assessment of CSVD with cognitive impairment, which had good clinical application value.
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Affiliation(s)
- Hengya Zhu
- Department of Neurology, Huzhou Center Hospital, Affiliated Center Hospital of Huzhou University, No. 1558 Sanhuan North Road, Huzhou, 313000 Zhejiang, China
| | - Jingjing Qiu
- Department of Neurology, Huzhou Center Hospital, Affiliated Center Hospital of Huzhou University, No. 1558 Sanhuan North Road, Huzhou, 313000 Zhejiang, China
| | - Xiaoyan Sun
- Department of Neurology, Huzhou Center Hospital, Affiliated Center Hospital of Huzhou University, No. 1558 Sanhuan North Road, Huzhou, 313000 Zhejiang, China
| | - Xiangyan Yang
- Department of Neurology, Huzhou Center Hospital, Affiliated Center Hospital of Huzhou University, No. 1558 Sanhuan North Road, Huzhou, 313000 Zhejiang, China
| | - Bin Zhang
- Department of Neurology, Huzhou Center Hospital, Affiliated Center Hospital of Huzhou University, No. 1558 Sanhuan North Road, Huzhou, 313000 Zhejiang, China
| | - Ying Tan
- Department of Neurology, Huzhou Center Hospital, Affiliated Center Hospital of Huzhou University, No. 1558 Sanhuan North Road, Huzhou, 313000 Zhejiang, China
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Natu M, Bachute M, Gite S, Kotecha K, Vidyarthi A. Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches. Comput Math Methods Med 2022; 2022:7751263. [PMID: 35096136 PMCID: PMC8794701 DOI: 10.1155/2022/7751263] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 12/28/2021] [Indexed: 12/12/2022]
Abstract
Epileptic seizures occur due to brain abnormalities that can indirectly affect patient's health. It occurs abruptly without any symptoms and thus increases the mortality rate of humans. Almost 1% of world's population suffers from epileptic seizures. Prediction of seizures before the beginning of onset is beneficial for preventing seizures by medication. Nowadays, modern computational tools, machine learning, and deep learning methods have been used to predict seizures using EEG. However, EEG signals may get corrupted with background noise, and artifacts such as eye blinks and physical movements of muscles may lead to "pops" in the signal, resulting in electrical interference, which is cumbersome to detect through visual inspection for longer duration recordings. These limitations in automatic detection of interictal spikes and epileptic seizures are preferred, which is an essential tool for examining and scrutinizing the EEG recording more precisely. These restrictions bring our attention to present a review of automated schemes that will help neurologists categorize epileptic and nonepileptic signals. While preparing this review paper, it is observed that feature selection and classification are the main challenges in epilepsy prediction algorithms. This paper presents various techniques depending on various features and classifiers over the last few years. The methods presented will give a detailed understanding and ideas about seizure prediction and future research directions.
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Affiliation(s)
- Milind Natu
- Department of Electronics and Telecommunication, Symbiosis Institute of Technology, Symbiosis International (Deemed University), SIU, Lavale, Pune, Maharashtra, India
| | - Mrinal Bachute
- Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), SIU, Lavale, Pune, Maharashtra, India
| | - Shilpa Gite
- Computer Science and Information Technology Department, Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India
- Symbiosis Centre of Applied AI (SCAAI), Symbiosis International (Deemed) University, Pune 412115, India
| | - Ketan Kotecha
- Computer Science and Information Technology Department, Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India
- Symbiosis Centre of Applied AI (SCAAI), Symbiosis International (Deemed) University, Pune 412115, India
| | - Ankit Vidyarthi
- Department of CSE&IT, Jaypee Institute of Information Technology Noida, India
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Kirschen MP, LaRovere K, Balakrishnan B, Erklauer J, Francoeur C, Ganesan SL, Jayakar A, Lovett M, Luchette M, Press CA, Wolf M, Ferrazzano P, Wainwright MS, Appavu B. A Survey of Neuromonitoring Practices in North American Pediatric Intensive Care Units. Pediatr Neurol 2022; 126:125-130. [PMID: 34864306 PMCID: PMC9135309 DOI: 10.1016/j.pediatrneurol.2021.11.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 11/06/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Neuromonitoring is the use of continuous measures of brain physiology to detect clinically important events in real-time. Neuromonitoring devices can be invasive or non-invasive and are typically used on patients with acute brain injury or at high risk for brain injury. The goal of this study was to characterize neuromonitoring infrastructure and practices in North American pediatric intensive care units (PICUs). METHODS An electronic, web-based survey was distributed to 70 North American institutions participating in the Pediatric Neurocritical Care Research Group. Questions related to the clinical use of neuromonitoring devices, integrative multimodality neuromonitoring capabilities, and neuromonitoring infrastructure were included. Survey results were presented using descriptive statistics. RESULTS The survey was completed by faculty at 74% (52 of 70) of institutions. All 52 institutions measure intracranial pressure and have electroencephalography capability, whereas 87% (45 of 52) use near-infrared spectroscopy and 40% (21/52) use transcranial Doppler. Individual patient monitoring decisions were driven by institutional protocols and collaboration between critical care, neurology, and neurosurgery attendings. Reported device utilization varied by brain injury etiology. Only 15% (eight of 52) of institutions utilized a multimodality neuromonitoring platform to integrate and synchronize data from multiple devices. A database of neuromonitoring patients was maintained at 35% (18 of 52) of institutions. Funding for neuromonitoring programs was variable with contributions from hospitals (19%, 10 of 52), private donations (12%, six of 52), and research funds (12%, six of 52), although 73% (40 of 52) have no dedicated funds. CONCLUSIONS Neuromonitoring indications, devices, and infrastructure vary by institution in North American pediatric critical care units. Noninvasive modalities were utilized more liberally, although not uniformly, than invasive monitoring. Further studies are needed to standardize the acquisition, interpretation, and reporting of clinical neuromonitoring data, and to determine whether neuromonitoring systems impact neurological outcomes.
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Affiliation(s)
- Matthew P Kirschen
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Kerri LaRovere
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Binod Balakrishnan
- Division of Pediatric Critical Care Medicine, Children's Wisconsin, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jennifer Erklauer
- Departments of Critical Care Medicine and Neurology, Texas Children's Hospital, Houston, Texas
| | - Conall Francoeur
- Department of Pediatrics, CHU de Québec - Université Laval Research Center, Quebec City, Quebec, Canada
| | - Saptharishi Lalgudi Ganesan
- Department of Paediatrics, Children's Hospital of Western Ontario, Schulich School of Medicine & Dentistry at the Western University, London, Ontario, Canada
| | - Anuj Jayakar
- Department of Neurology, Nicklaus Children's Hospital, Miami, Florida
| | - Marlina Lovett
- Division of Critical Care Medicine, Department of Pediatrics, Nationwide Children's Hospital, The Ohio State University College of Medicine, Columbus, Ohio
| | - Matthew Luchette
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Craig A Press
- Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado
| | - Michael Wolf
- Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Peter Ferrazzano
- Division of Critical Care Medicine, Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Mark S Wainwright
- Division of Pediatric Neurology, University of Washington School of Medicine, Seattle, Washington
| | - Brian Appavu
- Department of Neurosciences, Barrow Neurological Institute at Phoenix Children's Hospital, University of Arizona College of Medicine - Phoenix, Phoenix, UK
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Zhang J, Dai W. Research on Night Light Comfort of Pedestrian Space in Urban Park. Comput Math Methods Med 2021; 2021:3130747. [PMID: 34970329 PMCID: PMC8714376 DOI: 10.1155/2021/3130747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/23/2021] [Accepted: 11/25/2021] [Indexed: 11/26/2022]
Abstract
The outdoor light environment significantly affects aspects of public psychological and physiological health. This study conducted experiments to quantify the effects of the light environment on visitor light comfort in urban park pedestrian space. Nine sets of lighting conditions with different average horizontal illuminance (2 lx, 6 lx, 10 lx) and colour temperatures (5600 K, 4300 K, 3000 K) were established virtual reality scenarios. Subjective light comfort was evaluated, and electroencephalogram (EEG) was measured on 18 subjects to comprehensively study the effects of different light environments on human light comfort. The results of the comprehensive evaluation showed that colour temperature had a very significant impact on subjective light comfort, with warm light being generally more favourable than cool light in enhancing human subjective light comfort. The results of the EEG analysis show that the average horizontal illuminance is an important factor in the level of physiological fatigue, and that physiological fatigue can be maintained in a superior state at an appropriate level of illuminance. Based on the results of both subjective and objective factors, a comprehensive analysis was carried out to propose a range of average horizontal illuminance (4.08 lx, 6.99 lx) and a range of colour temperature (3126 K, 4498 K) for the comprehensive light comfort zone in urban park pedestrian space.
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Affiliation(s)
- Jun Zhang
- School of Landscape Architecture, Northeast Forestry University, Harbin, 150040 Heilongjiang, China
| | - Wenhan Dai
- School of Landscape Architecture, Northeast Forestry University, Harbin, 150040 Heilongjiang, China
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Hansen BC, Greene MR, Field DJ. Dynamic Electrode-to-Image (DETI) mapping reveals the human brain's spatiotemporal code of visual information. PLoS Comput Biol 2021; 17:e1009456. [PMID: 34570753 PMCID: PMC8496831 DOI: 10.1371/journal.pcbi.1009456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 10/07/2021] [Accepted: 09/16/2021] [Indexed: 11/18/2022] Open
Abstract
A number of neuroimaging techniques have been employed to understand how visual information is transformed along the visual pathway. Although each technique has spatial and temporal limitations, they can each provide important insights into the visual code. While the BOLD signal of fMRI can be quite informative, the visual code is not static and this can be obscured by fMRI’s poor temporal resolution. In this study, we leveraged the high temporal resolution of EEG to develop an encoding technique based on the distribution of responses generated by a population of real-world scenes. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. Our analyses of the mapping results revealed that scenes undergo a series of nonuniform transformations that prioritize different spatial frequencies at different regions of scenes over time. This mapping technique offers a potential avenue for future studies to explore how dynamic feedforward and recurrent processes inform and refine high-level representations of our visual world. The visual information that we sample from our environment undergoes a series of neural modifications, with each modification state (or visual code) consisting of a unique distribution of responses across neurons along the visual pathway. However, current noninvasive neuroimaging techniques provide an account of that code that is coarse with respect to time or space. Here, we present dynamic electrode-to-image (DETI) mapping, an analysis technique that capitalizes on the high temporal resolution of EEG to map neural signals to each pixel within a given image to reveal location-specific modifications of the visual code. The DETI technique reveals maps of features that are associated with the neural signal at each pixel and at each time point. DETI mapping shows that real-world scenes undergo a series of nonuniform modifications over both space and time. Specifically, we find that the visual code varies in a location-specific manner, likely reflecting that neural processing prioritizes different features at different image locations over time. DETI mapping therefore offers a potential avenue for future studies to explore how each modification state informs and refines the conceptual meaning of our visual world.
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Affiliation(s)
- Bruce C. Hansen
- Colgate University, Department of Psychological & Brain Sciences, Neuroscience Program, Hamilton New York, United States of America
- * E-mail:
| | - Michelle R. Greene
- Bates College, Neuroscience Program, Lewiston, Maine, United States of America
| | - David J. Field
- Cornell University, Department of Psychology, Ithaca, New York, United States of America
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Fahimi F, Dosen S, Ang KK, Mrachacz-Kersting N, Guan C. Generative Adversarial Networks-Based Data Augmentation for Brain-Computer Interface. IEEE Trans Neural Netw Learn Syst 2021; 32:4039-4051. [PMID: 32841127 DOI: 10.1109/tnnls.2020.3016666] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The performance of a classifier in a brain-computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled environment. However, users' attention may be diverted in real-life BCI applications and this may decrease the performance of the classifier. To improve the robustness of the classifier, additional data can be acquired in such conditions, but it is not practical to record electroencephalogram (EEG) data over several long calibration sessions. A potentially time- and cost-efficient solution is artificial data generation. Hence, in this study, we proposed a framework based on the deep convolutional generative adversarial networks (DCGANs) for generating artificial EEG to augment the training set in order to improve the performance of a BCI classifier. To make a comparative investigation, we designed a motor task experiment with diverted and focused attention conditions. We used an end-to-end deep convolutional neural network for classification between movement intention and rest using the data from 14 subjects. The results from the leave-one subject-out (LOO) classification yielded baseline accuracies of 73.04% for diverted attention and 80.09% for focused attention without data augmentation. Using the proposed DCGANs-based framework for augmentation, the results yielded a significant improvement of 7.32% for diverted attention ( ) and 5.45% for focused attention ( ). In addition, we implemented the method on the data set IVa from BCI competition III to distinguish different motor imagery tasks. The proposed method increased the accuracy by 3.57% ( ). This study shows that using GANs for EEG augmentation can significantly improve BCI performance, especially in real-life applications, whereby users' attention may be diverted.
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Zuk NJ, Murphy JW, Reilly RB, Lalor EC. Envelope reconstruction of speech and music highlights stronger tracking of speech at low frequencies. PLoS Comput Biol 2021; 17:e1009358. [PMID: 34534211 PMCID: PMC8480853 DOI: 10.1371/journal.pcbi.1009358] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 09/29/2021] [Accepted: 08/18/2021] [Indexed: 11/19/2022] Open
Abstract
The human brain tracks amplitude fluctuations of both speech and music, which reflects acoustic processing in addition to the encoding of higher-order features and one's cognitive state. Comparing neural tracking of speech and music envelopes can elucidate stimulus-general mechanisms, but direct comparisons are confounded by differences in their envelope spectra. Here, we use a novel method of frequency-constrained reconstruction of stimulus envelopes using EEG recorded during passive listening. We expected to see music reconstruction match speech in a narrow range of frequencies, but instead we found that speech was reconstructed better than music for all frequencies we examined. Additionally, models trained on all stimulus types performed as well or better than the stimulus-specific models at higher modulation frequencies, suggesting a common neural mechanism for tracking speech and music. However, speech envelope tracking at low frequencies, below 1 Hz, was associated with increased weighting over parietal channels, which was not present for the other stimuli. Our results highlight the importance of low-frequency speech tracking and suggest an origin from speech-specific processing in the brain.
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Affiliation(s)
- Nathaniel J. Zuk
- Department of Electronic & Electrical Engineering, Trinity College, The University of Dublin, Dublin, Ireland
- Department of Mechanical, Manufacturing & Biomedical Engineering, Trinity College, The University of Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College, The University of Dublin, Dublin, Ireland
- Department of Biomedical Engineering, University of Rochester, Rochester, New York, United States of America
- Del Monte Institute of Neuroscience, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Jeremy W. Murphy
- Department of Electronic & Electrical Engineering, Trinity College, The University of Dublin, Dublin, Ireland
| | - Richard B. Reilly
- Department of Mechanical, Manufacturing & Biomedical Engineering, Trinity College, The University of Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College, The University of Dublin, Dublin, Ireland
- Trinity Centre for Biomedical Engineering, Trinity College, The University of Dublin, Dublin, Ireland
| | - Edmund C. Lalor
- Department of Electronic & Electrical Engineering, Trinity College, The University of Dublin, Dublin, Ireland
- Department of Biomedical Engineering, University of Rochester, Rochester, New York, United States of America
- Del Monte Institute of Neuroscience, University of Rochester Medical Center, Rochester, New York, United States of America
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Salim AA, Ali SH, Hussain AM, Ibrahim WN. Electroencephalographic evidence of gray matter lesions among multiple sclerosis patients: A case-control study. Medicine (Baltimore) 2021; 100:e27001. [PMID: 34414988 PMCID: PMC8376360 DOI: 10.1097/md.0000000000027001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 07/30/2021] [Indexed: 01/04/2023] Open
Abstract
This study aimed to investigate evidence of gray matter brain lesions in multiple sclerosis (MS) patients by evaluating the resting state alpha rhythm of brain electrical activity.The study included 50 patients diagnosed with MS recruited from the MS clinic with 50 age and gender-matched control participants. The study investigated parameters of posterior dominant rhythm (PDR) in the electroencephalography (EEG) recordings including wave frequency and amplitude. Functional disability among the patients was evaluated according to the expanded disability status scale. Univariate statistical analysis was completed using one-way analysis of variance and t test with a P value of less than .05 to indicate statistical significance.Patients with MS had significantly lower PDR frequency and amplitude values compared to the controls (P value < .01) and 34% of the MS patients had a PDR frequency of less than 8.5 Hz. The PDR frequency was negatively associated with the level of functional disability among the patients (P value <.001) and 4% of the patients had abnormal epileptiform discharges.Background slowing of resting alpha rhythms and epileptiform discharges are suggestive of gray matter degeneration and may help in the prediction and follow-up of cortical damage and functional disabilities among MS patients. Therefore, electroencephalography monitoring of the PDR spectrum may serve as an alternative or complementary tool with other imaging techniques to detect and monitor cerebral cortical lesions.
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Affiliation(s)
| | - Safaa Hussain Ali
- Department of Physiology, College of Medicine, University of Al-Mustansiriyah, Baghdad, Iraq
| | | | - Wisam Nabeel Ibrahim
- Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
- Biomedical and Pharmaceutical Research Unit, QU Health, Qatar University, Doha, Qatar
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Tait L, Lopes MA, Stothart G, Baker J, Kazanina N, Zhang J, Goodfellow M. A large-scale brain network mechanism for increased seizure propensity in Alzheimer's disease. PLoS Comput Biol 2021; 17:e1009252. [PMID: 34379638 PMCID: PMC8382184 DOI: 10.1371/journal.pcbi.1009252] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 08/23/2021] [Accepted: 07/06/2021] [Indexed: 11/19/2022] Open
Abstract
People with Alzheimer’s disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies. People with Alzheimer’s disease (AD) are more likely to develop seizures than cognitively healthy people. In this study, we aimed to understand whether whole-brain network structure is related to this increased seizure likelihood. We used electroencephalography (EEG) to estimate brain networks from people with AD and healthy controls. We subsequently inserted these networks into a model brain and simulated disease progression by increasing the excitability of brain tissue. We found the simulated AD brains were more likely to develop seizures than the simulated control brains. No participants had seizures when we collected data, so our results suggest an increased probability of developing seizures at a future time for AD participants. Therefore functional brain network structure may play a role in increased seizure likelihood in AD. We also used the model to examine which brain regions were most important for generating seizures, and found that the seizure-generating regions corresponded to those typically affected in early AD. Our results also provide a potential explanation for why people with AD are more likely to have generalized seizures (i.e. seizures involving the whole brain, as opposed to ‘focal’ seizures which only involve certain areas) than the general population with epilepsy.
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Affiliation(s)
- Luke Tait
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- * E-mail:
| | - Marinho A. Lopes
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - George Stothart
- Department of Psychology, University of Bath, Bath, United Kingdom
| | - John Baker
- Dementia Research Centre, Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Nina Kazanina
- School of Psychological Science, University of Bristol, Bristol, United Kingdom
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
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11
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Jakovljević T, Janković MM, Savić AM, Soldatović I, Mačužić I, Jakulin TJ, Papa G, Ković V. The effect of colour on reading performance in children, measured by a sensor hub: From the perspective of gender. PLoS One 2021; 16:e0252622. [PMID: 34125863 PMCID: PMC8202909 DOI: 10.1371/journal.pone.0252622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 05/19/2021] [Indexed: 11/18/2022] Open
Abstract
In recent decades reported findings regarding gender differences in reading achievement, cognitive abilities and maturation process in boys and girls are conflicting. As reading is one of the most important processes in the maturation of an individual, the aim of the study was to better understand gender differences between primary school students. The study evaluates differences in Heart Rate Variability (HRV), Electroencephalography (EEG), Electrodermal Activities (EDA) and eye movement of participants during the reading task. Taking into account that colour may affect reading skills, in that it affects the emotional and physiological state of the body, the research attempts to provide a better understanding of gender differences in reading through examining the effect of colour, as applied to reading content. The physiological responses of 50 children (25 boys and 25 girls) to 12 different background and overlay colours of reading content were measured and summarised during the reading process. Our findings show that boys have shorter reading duration scores and a longer Saccade Count, Saccade Duration Total, and Saccade Duration Average when reading on a coloured background, especially purple, which could be caused by their motivation and by the type of reading task. Also, the boys had higher values for the Delta band and the Whole Range of EEG measurements in comparison to the girls when reading on coloured backgrounds, which could reflect the faster maturation of the girls. Regarding EDA measurements we did not find systematic differences between groups either on white or on coloured/overlay background. We found the most significant differences arose in the HRV parameters, namely (SDNN (ms), STD HR (beats/min), RMSSD (ms), NN50 (beats), pNN50 (%), CVRR) when children read the text on coloured/overlay backgrounds, where the girls showed systematically higher values on HRV measurements in comparison to the boys, mostly with yellow, red, and orange overlay colours.
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Affiliation(s)
- Tamara Jakovljević
- Sensor Technologies, Jožef Stefan International Postgraduate School, Ljubljana, Slovenia
| | - Milica M. Janković
- School of Electrical Engineering, University of Belgrade, Belgrade, Serbia
| | - Andrej M. Savić
- School of Electrical Engineering, University of Belgrade, Belgrade, Serbia
| | - Ivan Soldatović
- Institute of Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Ivan Mačužić
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
| | | | | | - Vanja Ković
- Laboratory for Neurocognition and Applied Cognition, Faculty of Philosophy, University of Belgrade, Belgrade, Serbia
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12
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Xu HA, Modirshanechi A, Lehmann MP, Gerstner W, Herzog MH. Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making. PLoS Comput Biol 2021; 17:e1009070. [PMID: 34081705 PMCID: PMC8205159 DOI: 10.1371/journal.pcbi.1009070] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 06/15/2021] [Accepted: 05/12/2021] [Indexed: 11/19/2022] Open
Abstract
Classic reinforcement learning (RL) theories cannot explain human behavior in the absence of external reward or when the environment changes. Here, we employ a deep sequential decision-making paradigm with sparse reward and abrupt environmental changes. To explain the behavior of human participants in these environments, we show that RL theories need to include surprise and novelty, each with a distinct role. While novelty drives exploration before the first encounter of a reward, surprise increases the rate of learning of a world-model as well as of model-free action-values. Even though the world-model is available for model-based RL, we find that human decisions are dominated by model-free action choices. The world-model is only marginally used for planning, but it is important to detect surprising events. Our theory predicts human action choices with high probability and allows us to dissociate surprise, novelty, and reward in EEG signals.
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Affiliation(s)
- He A. Xu
- Laboratory of Psychophysics, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Alireza Modirshanechi
- Brain-Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Marco P. Lehmann
- Brain-Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Wulfram Gerstner
- Brain-Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Michael H. Herzog
- Laboratory of Psychophysics, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Brain-Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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13
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Abstract
Statistical power is key for robust, replicable science. Here, we systematically explored how numbers of trials and subjects affect statistical power in MEG sensor-level data. More specifically, we simulated "experiments" using the MEG resting-state dataset of the Human Connectome Project (HCP). We divided the data in two conditions, injected a dipolar source at a known anatomical location in the "signal condition", but not in the "noise condition", and detected significant differences at sensor level with classical paired t-tests across subjects, using amplitude, squared amplitude, and global field power (GFP) measures. Group-level detectability of these simulated effects varied drastically with anatomical origin. We thus examined in detail which spatial properties of the sources affected detectability, looking specifically at the distance from closest sensor and orientation of the source, and at the variability of these parameters across subjects. In line with previous single-subject studies, we found that the most detectable effects originate from source locations that are closest to the sensors and oriented tangentially with respect to the head surface. In addition, cross-subject variability in orientation also affected group-level detectability, boosting detection in regions where this variability was small and hindering detection in regions where it was large. Incidentally, we observed a considerable covariation of source position, orientation, and their cross-subject variability in individual brain anatomical space, making it difficult to assess the impact of each of these variables independently of one another. We thus also performed simulations where we controlled spatial properties independently of individual anatomy. These additional simulations confirmed the strong impact of distance and orientation and further showed that orientation variability across subjects affects detectability, whereas position variability does not. Importantly, our study indicates that strict unequivocal recommendations as to the ideal number of trials and subjects for any experiment cannot be realistically provided for neurophysiological studies and should be adapted according to the brain regions under study.
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Affiliation(s)
- Maximilien Chaumon
- Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Centre MEG-EEG, Centre de NeuroImagerie Recherche (CENIR), 47 Boulevard de l'hôpital, 75013 Paris, France.
| | - Aina Puce
- Department of Psychological & Brain Sciences, Programs in Neuroscience, Cognitive Science, Indiana University, 1101 East 10th St, Bloomington, IN 47405, United States
| | - Nathalie George
- Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Centre MEG-EEG, Centre de NeuroImagerie Recherche (CENIR), 47 Boulevard de l'hôpital, 75013 Paris, France
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14
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Amini M, Pedram MM, Moradi A, Ouchani M. Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal. Comput Math Methods Med 2021; 2021:5511922. [PMID: 33981355 PMCID: PMC8088352 DOI: 10.1155/2021/5511922] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 03/26/2021] [Accepted: 04/07/2021] [Indexed: 12/22/2022]
Abstract
Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer's disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: k-nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer's disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural network approach, the accurate meaning of accuracy is 82.3%. 85% of mild cognitive impairment cases are accurately detected in-depth, but 89.1% of the Alzheimer's disease and 75% of the healthy population are correctly diagnosed. The presented convolutional neural network outperforms other approaches because performance and the k-nearest neighbors' approach is the next target. The linear discriminant analysis and support vector machine were at the low area under the curve values.
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Affiliation(s)
- Morteza Amini
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Shahid Beheshti University, Tehran, Iran
| | - Mir Mohsen Pedram
- Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Tehran, Iran
| | - AliReza Moradi
- Department of Clinical Psychology, Faculty of Psychology and Educational Science, Kharazmi University, Tehran, Iran
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - Mahshad Ouchani
- Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran
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15
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Zorick T, Gaines KD, Berenji GR, Mandelkern MA, Smith J. Information Transfer and Multifractal Analysis of EEG in Mild Blast-Induced TBI. Comput Math Methods Med 2021; 2021:6638724. [PMID: 33927783 PMCID: PMC8051525 DOI: 10.1155/2021/6638724] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 02/25/2021] [Accepted: 03/17/2021] [Indexed: 11/18/2022]
Abstract
Mild, blast-induced traumatic brain injury (mbTBI) is a common combat brain injury characterized by typically normal neuroimaging findings, with unpredictable future cognitive recovery. Traditional methods of electroencephalography (EEG) analysis (e.g., spectral analysis) have not been successful in detecting the degree of cognitive and functional impairment in mbTBI. We therefore collected resting state EEG (5 minutes, 64 leads) from twelve patients with a history of mbTBI, along with repeat neuropsychological testing (D-KEFS Tower test) to compare two new methods for analyzing EEG (multifractal detrended fluctuation analysis (MF-DFA) and information transfer modeling (ITM)) with spectral analysis. For MF-DFA, we extracted relevant parameters from the resultant multifractal spectrum from all leads and compared with traditional power by frequency band for spectral analysis. For ITM, because the number of parameters from each lead far exceeded the number of subjects, we utilized a reduced set of 10 leads which were compared with spectral analysis. We utilized separate 30 second EEG segments for training and testing statistical models based upon regression tree analysis. ITM and MF-DFA models both generally had improved accuracy at correlating with relevant measures of cognitive performance as compared to spectral analytic models ITM and MF-DFA both merit additional research as analytic tools for EEG and cognition in TBI.
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Affiliation(s)
- Todd Zorick
- Department of Psychiatry, Harbor-UCLA Medical Center and UCLA Geffen School of Medicine, USA
| | | | - Gholam R. Berenji
- Greater Los Angeles VA Department of Nuclear Imaging, University of California, Irvine, USA
| | - Mark A. Mandelkern
- Greater Los Angeles VA Department of Nuclear Imaging, University of California, Irvine, USA
- Department of Physics, University of California, Irvine, USA
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16
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Chiu YE, Shmuylovich L, Kiguradze T, Anderson K, Sibbald C, Tollefson M, Kunzler E, Tom WL, Bond K, Ahmad RC, Garcia-Romero MT, Irfan M, Kollman K, Hunt R, Stein SL, Arkin L, Wong V, Pope E, Jacobe H, Brandling-Bennett HA, Cordoro KM, Bercovitch L, Rangel SM, Liu X, Szabo A, Paller AS. Body site distribution of pediatric-onset morphea and association with extracutaneous manifestations. J Am Acad Dermatol 2021; 85:38-45. [PMID: 33689776 DOI: 10.1016/j.jaad.2021.03.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 02/25/2021] [Accepted: 03/02/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND The distribution of pediatric-onset morphea and site-based likelihood for extracutaneous complications has not been well characterized. OBJECTIVE To characterize the lesional distribution of pediatric-onset morphea and to determine the sites with the highest association of extracutaneous manifestations. METHODS A retrospective cross-sectional study was performed. Using clinical photographs, morphea lesions were mapped onto body diagrams using customized software. RESULTS A total of 823 patients with 2522 lesions were included. Lesions were more frequent on the superior (vs inferior) anterior aspect of the head and extensor (vs flexor) extremities. Linear morphea lesions were more likely on the head and neck, whereas plaque and generalized morphea lesions were more likely on the trunk. Musculoskeletal complications were more likely with lesions on the extensor (vs flexor) extremity (odds ratio [OR], 2.0; 95% confidence interval [CI], 1.2-3.4), whereas neurologic manifestations were more likely with lesions on the anterior (vs posterior) (OR, 2.8; 95% CI, 1.7-4.6) and superior (vs inferior) aspect of the head (OR, 2.3; 95% CI, 1.6-3.4). LIMITATIONS Retrospective nature and the inclusion of only patients with clinical photographs. CONCLUSION The distribution of pediatric-onset morphea is not random and varies with body site and within individual body sites. The risk stratification of extracutaneous manifestations by body site may inform decisions about screening for extracutaneous manifestations, although prospective studies are needed.
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Affiliation(s)
- Yvonne E Chiu
- Departments of Dermatology and Pediatrics, Section of Pediatric Dermatology, Medical College of Wisconsin, Milwaukee, Wisconsin.
| | - Leonid Shmuylovich
- Division of Dermatology, Washington University School of Medicine, St. Louis, Missouri
| | - Tina Kiguradze
- Departments of Dermatology and Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | | | - Cathryn Sibbald
- The Hospital for Sick Children and University of Toronto, Toronto, Canada
| | - Megha Tollefson
- Department of Dermatology, Mayo Clinic, Rochester, Minnesota
| | - Elaine Kunzler
- Department of Dermatology, University of Texas Southwestern, Dallas, Texas
| | - Wynnis L Tom
- Departments of Dermatology and Pediatrics, University of California San Diego and Rady Children's Hospital, San Diego, California
| | - Kelsie Bond
- Department of Pediatrics, Division of Dermatology, Seattle Children's Hospital and University of Washington, San Francisco, California
| | - Regina-Celeste Ahmad
- Department of Dermatology, Section of Pediatric Dermatology, University of California San Francisco, San Francisco, California
| | | | - Mahwish Irfan
- Department of Dermatology, Nationwide Children's Hospital, Columbus, Ohio
| | - Kaitlyn Kollman
- Departments of Dermatology and Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Raegan Hunt
- Departments of Dermatology and Pediatrics, Texas Children's Hospital, Baylor College of Medicine, Houston, Texas
| | - Sarah L Stein
- Departments of Medicine and Pediatrics, Section of Dermatology, University of Chicago, Chicago, Illinois
| | - Lisa Arkin
- Departments of Dermatology and Pediatrics, University of Wisconsin, Madison, Wisconsin
| | - Vivian Wong
- Department of Dermatology, Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - Elena Pope
- The Hospital for Sick Children and University of Toronto, Toronto, Canada
| | - Heidi Jacobe
- Department of Dermatology, University of Texas Southwestern, Dallas, Texas
| | - Heather A Brandling-Bennett
- Department of Pediatrics, Division of Dermatology, Seattle Children's Hospital and University of Washington, San Francisco, California
| | - Kelly M Cordoro
- Department of Dermatology, Section of Pediatric Dermatology, University of California San Francisco, San Francisco, California
| | - Lionel Bercovitch
- Department of Dermatology, Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - Stephanie M Rangel
- Departments of Dermatology and Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Xuerong Liu
- Division of Biostatistics, Institute for Health & Equity, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Aniko Szabo
- Division of Biostatistics, Institute for Health & Equity, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Amy S Paller
- Departments of Dermatology and Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
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17
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Gijsen S, Grundei M, Lange RT, Ostwald D, Blankenburg F. Neural surprise in somatosensory Bayesian learning. PLoS Comput Biol 2021; 17:e1008068. [PMID: 33529181 PMCID: PMC7880500 DOI: 10.1371/journal.pcbi.1008068] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 02/12/2021] [Accepted: 12/18/2020] [Indexed: 02/08/2023] Open
Abstract
Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here, we describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures. Specifically, we recorded EEG data from 40 participants subjected to a somatosensory roving-stimulus paradigm and performed single-trial modeling across peri-stimulus time in both sensor and source space. Our Bayesian model selection procedure indicates that evoked potentials are best described by a non-hierarchical learning model that tracks transitions between observations using leaky integration. From around 70ms post-stimulus onset, secondary somatosensory cortices are found to represent confidence-corrected surprise as a measure of model inadequacy. Indications of Bayesian surprise encoding, reflecting model updating, are found in primary somatosensory cortex from around 140ms. This dissociation is compatible with the idea that early surprise signals may control subsequent model update rates. In sum, our findings support the hypothesis that early somatosensory processing reflects Bayesian perceptual learning and contribute to an understanding of its underlying mechanisms. Our environment features statistical regularities, such as a drop of rain predicting imminent rainfall. Despite the importance for behavior and survival, much remains unknown about how these dependencies are learned, particularly for somatosensation. As surprise signalling about novel observations indicates a mismatch between one’s beliefs and the world, it has been hypothesized that surprise computation plays an important role in perceptual learning. By analyzing EEG data from human participants receiving sequences of tactile stimulation, we compare different formulations of surprise and investigate the employed underlying learning model. Our results indicate that the brain estimates transitions between observations. Furthermore, we identified different signatures of surprise computation and thereby provide a dissociation of the neural correlates of belief inadequacy and belief updating. Specifically, early surprise responses from around 70ms were found to signal the need for changes to the model, with encoding of its subsequent updating occurring from around 140ms. These results provide insights into how somatosensory surprise signals may contribute to the learning of environmental statistics.
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Affiliation(s)
- Sam Gijsen
- Neurocomputation and Neuroimaging Unit, Freie Universität Berlin, Germany
- Humboldt-Universität zu Berlin, Faculty of Philosophy, Berlin School of Mind and Brain, Berlin, Germany
- * E-mail: (SG); (MG)
| | - Miro Grundei
- Neurocomputation and Neuroimaging Unit, Freie Universität Berlin, Germany
- Humboldt-Universität zu Berlin, Faculty of Philosophy, Berlin School of Mind and Brain, Berlin, Germany
- * E-mail: (SG); (MG)
| | - Robert T. Lange
- Berlin Institute of Technology, Berlin, Germany
- Einstein Center for Neurosciences, Berlin, Germany
| | - Dirk Ostwald
- Computational Cognitive Neuroscience, Freie Universität Berlin, Germany
| | - Felix Blankenburg
- Neurocomputation and Neuroimaging Unit, Freie Universität Berlin, Germany
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18
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Abstract
Measurements on physical systems result from the systems' activity being converted into sensor measurements by a forward model. In a number of cases, inversion of the forward model is extremely sensitive to perturbations such as sensor noise or numerical errors in the forward model. Regularization is then required, which introduces bias in the reconstruction of the systems' activity. One domain in which this is particularly problematic is the reconstruction of interactions in spatially-extended complex systems such as the human brain. Brain interactions can be reconstructed from non-invasive measurements such as electroencephalography (EEG) or magnetoencephalography (MEG), whose forward models are linear and instantaneous, but have large null-spaces and high condition numbers. This leads to incomplete unmixing of the forward models and hence to spurious interactions. This motivated the development of interaction measures that are exclusively sensitive to lagged, i.e. delayed interactions. The drawback of such measures is that they only detect interactions that have sufficiently large lags and this introduces bias in reconstructed brain networks. We introduce three estimators for linear interactions in spatially-extended systems that are uniformly sensitive to all lags. We derive some basic properties of and relationships between the estimators and evaluate their performance using numerical simulations from a simple benchmark model.
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Affiliation(s)
- Rikkert Hindriks
- Department of Mathematics, VU University Amsterdam, Amsterdam, The Netherlands
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Radomski TR, Feldman R, Huang Y, Sileanu FE, Thorpe CT, Thorpe JM, Fine MJ, Gellad WF. Evaluation of Low-Value Diagnostic Testing for 4 Common Conditions in the Veterans Health Administration. JAMA Netw Open 2020; 3:e2016445. [PMID: 32960278 PMCID: PMC7509631 DOI: 10.1001/jamanetworkopen.2020.16445] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
IMPORTANCE Low-value care is associated with harm among patients and with wasteful health care spending but has not been well characterized in the Veterans Health Administration. OBJECTIVES To characterize the frequency of and variation in low-value diagnostic testing for 4 common conditions at Veterans Affairs medical centers (VAMCs) and to examine the correlation between receipt of low-value testing for each condition. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study used Veterans Health Administration data from 127 VAMCs from fiscal years 2014 to 2015. Data were analyzed from April 2018 to March 2020. EXPOSURES Continuous enrollment in Veterans Health Administration during fiscal year 2015. MAIN OUTCOMES AND MEASURES Receipt of low-value testing for low back pain, headache, syncope, and sinusitis. For each condition, sensitive and specific criteria were used to evaluate the overall frequency and range of low-value testing, adjusting for sociodemographic and VAMC characteristics. VAMC-level variation was calculated using median adjusted odds ratios. The Pearson correlation coefficient was used to evaluate the degree of correlation between low-value testing for each condition at the VAMC level. RESULTS Among 1 022 987 veterans, the mean (SD) age was 60 (16) years, 1 008 336 (92.4%) were male, and 761 485 (69.8%) were non-Hispanic White. A total of 343 024 veterans (31.4%) were diagnosed with low back pain, 79 176 (7.3%) with headache, 23 776 (2.2%) with syncope, and 52 889 (4.8%) with sinusitis. With the sensitive criteria, overall and VAMC-level low-value testing frequency varied substantially across conditions: 4.6% (range, 2.7%-10.1%) for sinusitis, 12.8% (range, 8.6%-22.6%) for headache, 18.2% (range, 10.9%-24.6%) for low back pain, and 20.1% (range, 16.3%-27.7%) for syncope. With the specific criteria, the overall frequency of low-value testing across VAMCs was 2.4% (range, 1.3%-5.1%) for sinusitis, 8.6% (range, 6.2%-14.6%) for headache, 5.6% (range, 3.6%-7.7%) for low back pain, and 13.3% (range, 11.3%-16.8%) for syncope. The median adjusted odds ratio ranged from 1.21 for low back pain to 1.40 for sinusitis. At the VAMC level, low-value testing was most strongly correlated for syncope and headache (ρ = 0.56; P < .001) and low back pain and headache (ρ = 0.48; P < .001). CONCLUSIONS AND RELEVANCE In this cohort study, low-value diagnostic testing was common, varied substantially across VAMCs, and was correlated between veterans' receipt of different low-value tests at the VAMC level. The findings suggest a need to address low-value diagnostic testing, even in integrated health systems, with robust utilization management practices.
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Affiliation(s)
- Thomas R. Radomski
- Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
| | - Robert Feldman
- Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
| | - Yan Huang
- Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- UPMC Center for High-Value Health Care, UPMC Insurance Services Division Steel Tower, Pittsburgh, Pennsylvania
| | - Florentina E. Sileanu
- Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
| | - Carolyn T. Thorpe
- Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
- Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill
| | - Joshua M. Thorpe
- Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
- Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill
| | - Michael J. Fine
- Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
| | - Walid F. Gellad
- Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
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20
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Iešmantas T, Alzbutas R. Convolutional neural network for detection and classification of seizures in clinical data. Med Biol Eng Comput 2020; 58:1919-1932. [PMID: 32533511 DOI: 10.1007/s11517-020-02208-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 05/31/2020] [Indexed: 12/13/2022]
Abstract
Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific. Epilepsy patients suffer from severe detrimental effects like physical injury or depression due to unpredictable seizures. However, even in hospitals due to the high rate of false positives, the seizure alert systems are of poor help for patients as tools of seizure detection are mostly trained on unrealistically clean data, containing little noise and obtained under controlled laboratory conditions, where patient groups are homogeneous, e.g. in terms of age or type of seizures. In this study authors present the approach for detection and classification of a seizure using clinical data of electroencephalograms and a convolutional neural network trained on features of brain synchronisation and power spectrum. Various deep learning methods were applied, and the network was trained on a very heterogeneous clinical electroencephalogram dataset. In total, eight different types of seizures were considered, and the patients were of various ages, health conditions and they were observed under clinical conditions. Despite this, the classifier presented in this paper achieved sensitivity and specificity equal to 0.68 and 0.67, accordingly, which is a significant improvement as compared to the known results for clinical data. Graphical abstract.
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Affiliation(s)
- Tomas Iešmantas
- Department of Mathematics and Natural Sciences, Kaunas University of Technology, 44249, Kaunas, Lithuania.
| | - Robertas Alzbutas
- Department of Mathematics and Natural Sciences, Kaunas University of Technology, 44249, Kaunas, Lithuania
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Sethi NK. EEG during the COVID-19 pandemic: What remains the same and what is different. Clin Neurophysiol 2020; 131:1462. [PMID: 32388156 PMCID: PMC7182743 DOI: 10.1016/j.clinph.2020.04.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 04/20/2020] [Accepted: 04/21/2020] [Indexed: 11/17/2022]
Affiliation(s)
- Nitin K Sethi
- Department of Neurology, New York-Presbyterian Hospital, Weill Cornell Medical Center, New York, NY, USA.
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22
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Shi W, He L, Lv B, Li L, Wu T. Evaluating the Acute Effect of Stereoscopic Recovery by Dichoptic Stimulation Using Electroencephalogram. Comput Math Methods Med 2020; 2020:9497369. [PMID: 32351615 PMCID: PMC7174909 DOI: 10.1155/2020/9497369] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 03/19/2020] [Accepted: 03/23/2020] [Indexed: 11/17/2022]
Abstract
Amblyopia is a common developmental disorder in adolescents and children. Stereoscopic loss is a symptom of amblyopia that can seriously affect the quality of patient's life. Recent studies have shown that the push-pull perceptual learning protocol had a positive effect on stereoscopic recovery. In this study, we developed a stereoscopic training method using a polarized visualization system according to the push-pull protocol. Dichoptic stimulation for 36 anisometropic and amblyopic subjects and 33 children with normal visual acuity (VA) has been conducted. Electroencephalogram (EEG) was used to evaluate the neurophysiological changes before, during, and after stimulation. For the anisometropic and amblyopic subjects, the statistical analysis demonstrated significant differences (p < 0.01) in the beta rhythm at the middle temporal and occipital lobes, while the EEG from the normal VA subjects indicated no significant changes when comparing the results before and after training. We concluded that the dichoptic training in our study can activate the middle temporal visual area and visual cortex. The EEG changes can be used to evaluate the training effects. This study also found that the beta band EEG acquired during visual stimulation at the dorsal visual stream can be potentially used for predicting acute training effect. The results facilitated the optimization of the individual training plan.
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Affiliation(s)
- Wei Shi
- Department of Ophthalmology, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Luyang He
- China Academy of Information and Communications Technology, Beijing, China
| | - Bin Lv
- China Academy of Information and Communications Technology, Beijing, China
| | - Li Li
- Department of Ophthalmology, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Tongning Wu
- China Academy of Information and Communications Technology, Beijing, China
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Jiang B, Petkova E, Tarpey T, Ogden RT. A Bayesian approach to joint modeling of matrix-valued imaging data and treatment outcome with applications to depression studies. Biometrics 2020; 76:87-97. [PMID: 31529701 PMCID: PMC7067625 DOI: 10.1111/biom.13151] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 08/28/2019] [Indexed: 11/28/2022]
Abstract
In this paper, we propose a unified Bayesian joint modeling framework for studying association between a binary treatment outcome and a baseline matrix-valued predictor. Specifically, a joint modeling approach relating an outcome to a matrix-valued predictor through a probabilistic formulation of multilinear principal component analysis is developed. This framework establishes a theoretical relationship between the outcome and the matrix-valued predictor, although the predictor is not explicitly expressed in the model. Simulation studies are provided showing that the proposed method is superior or competitive to other methods, such as a two-stage approach and a classical principal component regression in terms of both prediction accuracy and estimation of association; its advantage is most notable when the sample size is small and the dimensionality in the imaging covariate is large. Finally, our proposed joint modeling approach is shown to be a very promising tool in an application exploring the association between baseline electroencephalography data and a favorable response to treatment in a depression treatment study by achieving a substantial improvement in prediction accuracy in comparison to competing methods.
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Affiliation(s)
- Bei Jiang
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Eva Petkova
- Department of Population Health, New York University, New York, NY 10016, USA
- Department of Child and Adolescent Psychiatry, New York University, New York, NY 10016, USA
| | - Thaddeus Tarpey
- Department of Population Health, New York University, New York, NY 10016, USA
| | - R. Todd Ogden
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
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Dominguez MC, O'Keeffe C, O'Rourke E, Feerick N, Reilly RB. Cortical Theta Activity and Postural Control in Non-Visual and High Cognitive Load Tasks: Impact for Clinical Studies. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2019:1539-1542. [PMID: 31946187 DOI: 10.1109/embc.2019.8857663] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Due to the major role of balance in our everyday lives and the unsatisfying understanding of the role of neural mechanism on balance control, the focus of this study was to explore the role of the cerebral cortex and its effects on stability. We investigated the effects of non-visual and cognitive tasks on balance performance and cortical theta response in a small, convenient sample. The cognitive tasks were N-back and Sustained Attention Response Task (SART). Cortical theta activity showed strong correlations with balance performance metrics. Particularly, central regions showed an increase in theta power in more cognitively challenging tasks but not statistically significant. Parietal theta power had a statistically significant increase in tasks that led to a heavier reliance on proprioception and vestibular information. This study shows the efficacy of EEG recording during balance control tasks. Future studies on neurodegenerative diseases that affect neuromotor control could investigate these outcomes.
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Kinoshita T, Fujiwara K, Kano M, Ogawa K, Sumi Y, Matsuo M, Kadotani H. Sleep Spindle Detection Using RUSBoost and Synchrosqueezed Wavelet Transform. IEEE Trans Neural Syst Rehabil Eng 2020; 28:390-398. [PMID: 31944960 DOI: 10.1109/tnsre.2020.2964597] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Sleep spindles are important electroencephalographic (EEG) waveforms in sleep medicine; however, it is burdensome even for experts to detect spindles, so automatic spindle detection methodologies have been investigated. Conventional methods utilize waveforms template matching or machine learning for detecting spindles. In the former approach, it is necessary to tune thresholds for individual adaptation, while the latter approach has the problem of imbalanced data because the amount of sleep spindles is small compared with the entire EEG data. The present work proposes a sleep spindle detection method that combines wavelet synchrosqueezed transform (SST) and random under-sampling boosting (RUSBoost). SST is a time-frequency analysis method suitable for extracting features of spindle waveforms. RUSBoost is a framework for coping with the imbalanced data problem. The proposed SST-RUS can deal with the imbalanced data in spindle detection and does not require threshold tuning because RUSBoost uses majority voting of weak classifiers for discrimination. The performance of SST-RUS was validated using an open-access database called the Montreal archives of sleep studies cohort 1 (MASS-C1), which showed an F-measure of 0.70 with a sensitivity of 76.9% and a positive predictive value of 61.2%. The proposed method can reduce the burden of PSG scoring.
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Zhdanov A, Atluri S, Wong W, Vaghei Y, Daskalakis ZJ, Blumberger DM, Frey BN, Giacobbe P, Lam RW, Milev R, Mueller DJ, Turecki G, Parikh SV, Rotzinger S, Soares CN, Brenner CA, Vila-Rodriguez F, McAndrews MP, Kleffner K, Alonso-Prieto E, Arnott SR, Foster JA, Strother SC, Uher R, Kennedy SH, Farzan F. Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression. JAMA Netw Open 2020; 3:e1918377. [PMID: 31899530 PMCID: PMC6991244 DOI: 10.1001/jamanetworkopen.2019.18377] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
IMPORTANCE Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient's response to treatment could significantly reduce the burden of depression. OBJECTIVE To estimate how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic (EEG) data on patients with depression. DESIGN, SETTING, AND PARTICIPANTS This prognostic study used a support vector machine classifier to predict treatment outcome using data from the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study. The CAN-BIND-1 study comprised 180 patients (aged 18-60 years) diagnosed with major depressive disorder who had completed 8 weeks of treatment. Of this group, 122 patients had EEG data recorded before the treatment; 115 also had EEG data recorded after the first 2 weeks of treatment. INTERVENTIONS All participants completed 8 weeks of open-label escitalopram (10-20 mg) treatment. MAIN OUTCOMES AND MEASURES The ability of EEG data to predict treatment outcome, measured as accuracy, specificity, and sensitivity of the classifier at baseline and after the first 2 weeks of treatment. The treatment outcome was defined in terms of change in symptom severity, measured by the Montgomery-Åsberg Depression Rating Scale, before and after 8 weeks of treatment. A patient was designated as a responder if the Montgomery-Åsberg Depression Rating Scale score decreased by at least 50% during the 8 weeks and as a nonresponder if the score decrease was less than 50%. RESULTS Of the 122 participants who completed a baseline EEG recording (mean [SD] age, 36.3 [12.7] years; 76 [62.3%] female), the classifier was able to identify responders with an estimated accuracy of 79.2% (sensitivity, 67.3%; specificity, 91.0%) when using only the baseline EEG data. For a subset of 115 participants who had additional EEG data recorded after the first 2 weeks of treatment, use of these data increased the accuracy to 82.4% (sensitivity, 79.2%; specificity, 85.5%). CONCLUSIONS AND RELEVANCE These findings demonstrate the potential utility of EEG as a treatment planning tool for escitalopram therapy. Further development of the classification tools presented in this study holds the promise of expediting the search for optimal treatment for each patient.
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Affiliation(s)
- Andrey Zhdanov
- School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, British Columbia, Canada
- Centre for Engineering-Led Brain Research, Simon Fraser University, Surrey, British Columbia, Canada
| | - Sravya Atluri
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Institute of Biomaterial and Biomedical Engineering, Toronto, Ontario, Canada
| | - Willy Wong
- The Edward S. Rogers Sr Department of Electrical & Computer Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Yasaman Vaghei
- School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, British Columbia, Canada
- Centre for Engineering-Led Brain Research, Simon Fraser University, Surrey, British Columbia, Canada
| | - Zafiris J. Daskalakis
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Daniel M. Blumberger
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Benicio N. Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
- Mood Disorders Program and Women’s Health Concerns Clinic, St Joseph’s Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Peter Giacobbe
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Raymond W. Lam
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen’s University, Providence Care Hospital, Kingston, Ontario, Canada
| | - Daniel J. Mueller
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Gustavo Turecki
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | | | - Susan Rotzinger
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
| | - Claudio N. Soares
- Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
- Department of Psychiatry, Queen’s University, Kingston, Ontario, Canada
| | | | - Fidel Vila-Rodriguez
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Mary Pat McAndrews
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Killian Kleffner
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Esther Alonso-Prieto
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Stephen R. Arnott
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada
| | - Jane A. Foster
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
- St Michael’s Hospital, Toronto, Ontario, Canada
| | - Stephen C. Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
- Institute of Psychiatry, Psychology & Neuroscience, Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, United Kingdom
| | - Sidney H. Kennedy
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
- St Michael’s Hospital, Toronto, Ontario, Canada
| | - Faranak Farzan
- School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, British Columbia, Canada
- Centre for Engineering-Led Brain Research, Simon Fraser University, Surrey, British Columbia, Canada
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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Chriskos P, Frantzidis CA, Gkivogkli PT, Bamidis PD, Kourtidou-Papadeli C. Automatic Sleep Staging Employing Convolutional Neural Networks and Cortical Connectivity Images. IEEE Trans Neural Netw Learn Syst 2020; 31:113-123. [PMID: 30892246 DOI: 10.1109/tnnls.2019.2899781] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Understanding of the neuroscientific sleep mechanisms is associated with mental/cognitive and physical well-being and pathological conditions. A prerequisite for further analysis is the identification of the sleep macroarchitecture through manual sleep staging. Several computer-based approaches have been proposed to extract time and/or frequency-domain features with accuracy ranging from 80% to 95% compared with the golden standard of manual staging. However, their acceptability by the medical community is still suboptimal. Recently, utilizing deep learning methodologies increased the research interest in computer-assisted recognition of sleep stages. Aiming to enhance the arsenal of automatic sleep staging, we propose a novel classification framework based on convolutional neural networks. These receive as input synchronizations features derived from cortical interactions within various electroencephalographic rhythms (delta, theta, alpha, and beta) for specific cortical regions which are critical for the sleep deepening. These functional connectivity metrics are then processed as multidimensional images. We also propose to augment the small portion of sleep onset (N1 stage) through the Synthetic Minority Oversampling Technique in order to deal with the great difference in its duration when compared with the remaining sleep stages. Our results (99.85%) indicate the flexibility of deep learning techniques to learn sleep-related neurophysiological patterns.
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Scheffler AW, Telesca D, Sugar CA, Jeste S, Dickinson A, DiStefano C, Şentürk D. Covariate-adjusted region-referenced generalized functional linear model for EEG data. Stat Med 2019; 38:5587-5602. [PMID: 31659786 PMCID: PMC6891124 DOI: 10.1002/sim.8384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 08/05/2019] [Accepted: 08/28/2019] [Indexed: 11/07/2022]
Abstract
Electroencephalography (EEG) studies produce region-referenced functional data in the form of EEG signals recorded across electrodes on the scalp. It is of clinical interest to relate the highly structured EEG data to scalar outcomes such as diagnostic status. In our motivating study, resting-state EEG is collected on both typically developing (TD) children and children with autism spectrum disorder (ASD) aged 2 to 12 years old. The peak alpha frequency (PAF), defined as the location of a prominent peak in the alpha frequency band of the spectral density, is an important biomarker linked to neurodevelopment and is known to shift from lower to higher frequencies as children age. To retain the most amount of information from the data, we consider the oscillations in the spectral density within the alpha band, rather than just the peak location, as a functional predictor of diagnostic status (TD vs ASD), adjusted for chronological age. A covariate-adjusted region-referenced generalized functional linear model is proposed for modeling scalar outcomes from region-referenced functional predictors, which utilizes a tensor basis formed from one-dimensional discrete and continuous bases to estimate functional effects across a discrete regional domain while simultaneously adjusting for additional nonfunctional covariates, such as age. The proposed methodology provides novel insights into differences in neural development of TD and ASD children. The efficacy of the proposed methodology is investigated through extensive simulation studies.
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Affiliation(s)
- Aaron W. Scheffler
- Department of Biostatistics, University of California, Los Angeles, CA, U.S.A
| | - Donatello Telesca
- Department of Biostatistics, University of California, Los Angeles, CA, U.S.A
| | - Catherine A. Sugar
- Department of Biostatistics, University of California, Los Angeles, CA, U.S.A
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, U.S.A
| | - Shafali Jeste
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, U.S.A
| | - Abigail Dickinson
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, U.S.A
| | - Charlotte DiStefano
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, U.S.A
| | - Damla Şentürk
- Department of Biostatistics, University of California, Los Angeles, CA, U.S.A
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Kawe TNJ, Shadli SM, McNaughton N. Higuchi's fractal dimension, but not frontal or posterior alpha asymmetry, predicts PID-5 anxiousness more than depressivity. Sci Rep 2019; 9:19666. [PMID: 31873184 PMCID: PMC6928148 DOI: 10.1038/s41598-019-56229-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 12/06/2019] [Indexed: 12/28/2022] Open
Abstract
Depression is a major cause of health disability. EEG measures may provide one or more economical biomarkers for the diagnosis of depression. Here we compared frontal alpha asymmetry (FAA), posterior alpha asymmetry (PAA), and Higuchi's fractal dimension (HFD) for their capacity to predict PID-5 depressivity and for the specificity of these predictions relative to PID-5 anxiousness. University students provided 8 or 10 minutes of resting EEG and PID-5 depressivity and PID-5 anxiousness questionnaire scores. FAA and PAA had no significant correlations with the measures at any electrode pair. There were distinct frontal and posterior factors underlying HFD that correlated significantly with anxiousness and with each other. Posterior HFD also correlated significantly with depressivity, though this was weaker than the correlation with anxiousness. The portion of depressivity variance accounted for by posterior HFD was not unique but shared with anxiousness. Inclusion of anxiety disorder patients into the sample rendered the frontal factor somewhat more predictive than the posterior one but generally strengthened the prior conclusions. Contrary to our predictions, none of our measures specifically predicted depressivity. Previous reports of links with depression may involve confounds with concurrent anxiety. Indeed, HFD may be a better measure of anxiety than depression; and its previous linkage to depression may be due to a confound between the two, given the high incidence of depression in cases of severe anxiety.
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Affiliation(s)
- Tame N J Kawe
- Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Shabah M Shadli
- Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Neil McNaughton
- Department of Psychology, University of Otago, Dunedin, New Zealand.
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Zhang Y, Zhang M, Fang Q. Scoping Review of EEG Studies in Construction Safety. Int J Environ Res Public Health 2019; 16:ijerph16214146. [PMID: 31661845 PMCID: PMC6862257 DOI: 10.3390/ijerph16214146] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 10/23/2019] [Accepted: 10/24/2019] [Indexed: 11/16/2022]
Abstract
Construction safety is critical in the success of a project. A considerable amount of effort has been placed on research and practice in order to reduce the potential risks on the construction site. Recent application of electroencephalogram (EEG) to construction research enables researchers to gain insight into construction workers’ physical and mental status during construction tasks. By summarizing existing studies that involve EEG and construction safety, the literature review aims to provide practical suggestions for future research and on-site safety management. The literature search and inclusion process included eleven eligible studies. Comprehensive analysis was conducted based on primary and secondary measures. The primary measures considered the frequency bands of EEG and the channels for detecting electrical activity of the brain. The secondary measures that were involved with physical and mental status with respect to EEG signal variations as a result of task, working hour, and work conditions. Although the field of study that combines EEG measures with construction tasks is still emerging, it is worth continuous attention in the future, as relevant findings would be of great value to the safety management and risk control in the construction industry.
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Affiliation(s)
- Yamei Zhang
- School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, China.
| | - Mingyi Zhang
- School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, China.
| | - Qun Fang
- Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA.
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Hadriche A, Jmail N, Blanc JL, Pezard L. Using centrality measures to extract core pattern of brain dynamics during the resting state. Comput Methods Programs Biomed 2019; 179:104985. [PMID: 31443863 DOI: 10.1016/j.cmpb.2019.104985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Revised: 07/10/2019] [Accepted: 07/13/2019] [Indexed: 06/10/2023]
Abstract
The patterns of brain dynamics were studied during resting state on a macroscopic scale for control subjects and multiple sclerosis patients. Macroscopic brain dynamics is defined after successive coarse-grainings and selection of significant patterns and transitions based on Markov representation of brain activity. The resulting networks show that control dynamics is merely organized according to a single principal pattern whereas patients dynamics depict more variable patterns. Centrality measures are used to extract core dynamical pattern in brain dynamics and classification technique allow to define MS dynamics with relevant error rate.
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Affiliation(s)
- Abir Hadriche
- Université de Sfax, ENIS, REGIM Lab, Sfax, Tunisie; Université de Gabes, ISIMG, Gabes, Tunisie; Université de Sfax, Centre de Recherche Numérique de Sfax, Sfax, Tunisie.
| | - Nawel Jmail
- Université de Sfax, Centre de Recherche Numérique de Sfax, Sfax, Tunisie; Université de Sfax, MIRACL, Sfax, Tunisie.
| | - Jean-Luc Blanc
- Aix-Marseille Université, CNRS, LNSC UMR 7260, 3 Place Victor Hugo, Marseille 13003, France.
| | - Laurent Pezard
- Aix-Marseille Université, CNRS, LNSC UMR 7260, 3 Place Victor Hugo, Marseille 13003, France.
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Zhang D, Yao L, Chen K, Wang S, Haghighi PD, Sullivan C. A Graph-Based Hierarchical Attention Model for Movement Intention Detection from EEG Signals. IEEE Trans Neural Syst Rehabil Eng 2019; 27:2247-2253. [PMID: 31562095 DOI: 10.1109/tnsre.2019.2943362] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
An EEG-based Brain-Computer Interface (BCI) is a system that enables a user to communicate with and intuitively control external devices solely using the user's intentions. Current EEG-based BCI research usually involves a subject-specific adaptation step before a BCI system is ready to be employed by a new user. However, the subject-independent scenario, in which a well-trained model can be directly applied to new users without pre-calibration, is particularly desirable yet rarely explored. Considering this critical gap, our focus in this paper is the subject-independent scenario of EEG-based human intention recognition. We present a G raph-based H ierarchical A ttention M odel (G-HAM) that utilizes the graph structure to represent the spatial information of EEG sensors and the hierarchical attention mechanism to focus on both the most discriminative temporal periods and EEG nodes. Extensive experiments on a large EEG dataset containing 105 subjects indicate that our model is capable of exploiting the underlying invariant EEG patterns across different subjects and generalizing the patterns to new subjects with better performance than a series of state-of-the-art and baseline approaches.
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Abstract
Complex network theory has been successful at unveiling the topology of the brain and showing alterations to the network structure due to brain disease, cognitive function and behavior. Functional connectivity networks (FCNs) represent different brain regions as the nodes and the connectivity between them as the edges of a graph. Graph theoretic measures provide a way to extract features from these networks enabling subsequent characterization and discrimination of networks across conditions. However, these measures are constrained mostly to binary networks and highly dependent on the network size. In this paper, we propose a novel graph-to-signal transform that overcomes these shortcomings to extract features from functional connectivity networks. The proposed transformation is based on classical multidimensional scaling (CMDS) theory and transforms a graph into signals such that the Euclidean distance between the nodes of the network is preserved. In this paper, we propose to use the resistance distance matrix for transforming weighted functional connectivity networks into signals. Our results illustrate how well-known network structures transform into distinct signals using the proposed graph-to-signal transformation. We then compute well-known signal features on the extracted graph signals to discriminate between FCNs constructed across different experimental conditions. Based on our results, the signals obtained from the graph-to-signal transformation allow for the characterization of functional connectivity networks, and the corresponding features are more discriminative compared to graph theoretic measures.
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Affiliation(s)
- Tamanna Tabassum Khan Munia
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, United States of America
| | - Selin Aviyente
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, United States of America
- * E-mail:
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Zhang Y, Yin E, Li F, Zhang Y, Tanaka T, Zhao Q, Cui Y, Xu P, Yao D, Guo D. Two-Stage Frequency Recognition Method Based on Correlated Component Analysis for SSVEP-Based BCI. IEEE Trans Neural Syst Rehabil Eng 2019; 26:1314-1323. [PMID: 29985141 DOI: 10.1109/tnsre.2018.2848222] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A canonical correlation analysis (CCA) is a state-of-the-art method for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. Various extended methods have been developed, and among such methods, a combination method of CCA and individual-template-based CCA has achieved the best performance. However, the CCA requires the canonical vectors to be orthogonal, which may not be a reasonable assumption for the EEG analysis. In this paper, we propose using the correlated component analysis (CORRCA) rather than CCA to implement frequency recognition. CORRCA can relax the constraint of canonical vectors in CCA and generate the same projection vector for two multichannel EEG signals. Furthermore, we propose a two-stage method based on the basic CORRCA method (termed TSCORRCA). Evaluated on a benchmark data set of 35 subjects, the experimental results demonstrate that CORRCA significantly outperformed CCA, and TSCORRCA obtained the best performance among the compared methods. This paper demonstrates that CORRCA-based methods have a great potential for implementing high-performance SSVEP-based BCI systems.
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Edelman BJ, Meng J, Gulachek N, Cline CC, He B. Exploring Cognitive Flexibility With a Noninvasive BCI Using Simultaneous Steady-State Visual Evoked Potentials and Sensorimotor Rhythms. IEEE Trans Neural Syst Rehabil Eng 2019; 26:936-947. [PMID: 29752228 DOI: 10.1109/tnsre.2018.2817924] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
EEG-based brain-computer interface (BCI) technology creates non-biological pathways for conveying a user's mental intent solely through noninvasively measured neural signals. While optimizing the performance of a single task has long been the focus of BCI research, in order to translate this technology into everyday life, realistic situations, in which multiple tasks are performed simultaneously, must be investigated. In this paper, we explore the concept of cognitive flexibility, or multitasking, within the BCI framework by utilizing a 2-D cursor control task, using sensorimotor rhythms (SMRs), and a four-target visual attention task, using steady-state visual evoked potentials (SSVEPs), both individually and simultaneously. We found no significant difference between the accuracy of the tasks when executing them alone (SMR-57.9% ± 15.4% and SSVEP-59.0% ± 14.2%) and simultaneously (SMR-54.9% ± 17.2% and SSVEP-57.5% ± 15.4%). These modest decreases in performance were supported by similar, non-significant changes in the electrophysiology of the SSVEP and SMR signals. In this sense, we report that multiple BCI tasks can be performed simultaneously without a significant deterioration in performance; this finding will help drive these systems toward realistic daily use in which a user's cognition will need to be involved in multiple tasks at once.
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Mamashli F, Hämäläinen M, Ahveninen J, Kenet T, Khan S. Permutation Statistics for Connectivity Analysis between Regions of Interest in EEG and MEG Data. Sci Rep 2019; 9:7942. [PMID: 31138854 PMCID: PMC6538606 DOI: 10.1038/s41598-019-44403-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 05/15/2019] [Indexed: 01/01/2023] Open
Abstract
Connectivity estimates based on electroencephalography (EEG) and magnetoencephalography (MEG) are unique in their ability to provide neurophysiologically meaningful spectral and temporal information non-invasively. This multi-dimensional aspect of the MEG/EEG based connectivity increases the challenges of the analysis and interpretation of the data. Many MEG/EEG studies address this complexity by using a hypothesis-driven approach, which focuses on particular regions of interest (ROI). However, if an effect is distributed unevenly over a large ROI and variable across subjects, it may not be detectable using conventional methods. Here, we propose a novel approach, which enhances the statistical power for weak and spatially discontinuous effects. This results in the ability to identify statistically significant connectivity patterns with spectral, temporal, and spatial specificity while correcting for multiple comparisons using nonparametric permutation methods. We call this new approach the Permutation Statistics for Connectivity Analysis between ROI (PeSCAR). We demonstrate the processing steps with simulated and real human data. The open-source Matlab code implementing PeSCAR are provided online.
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Affiliation(s)
- Fahimeh Mamashli
- Department of Neurology, MGH, Harvard Medical School, Boston, MA, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Boston, MA, USA.
- Department of Radiology, MGH, Harvard Medical School, Boston, MA, USA.
| | - Matti Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Boston, MA, USA
- Department of Radiology, MGH, Harvard Medical School, Boston, MA, USA
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Boston, MA, USA
- Department of Radiology, MGH, Harvard Medical School, Boston, MA, USA
| | - Tal Kenet
- Department of Neurology, MGH, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Boston, MA, USA
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Boston, MA, USA.
- Department of Radiology, MGH, Harvard Medical School, Boston, MA, USA.
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
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Schaworonkow N, Nikulin VV. Spatial neuronal synchronization and the waveform of oscillations: Implications for EEG and MEG. PLoS Comput Biol 2019; 15:e1007055. [PMID: 31086368 PMCID: PMC6534335 DOI: 10.1371/journal.pcbi.1007055] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 05/24/2019] [Accepted: 04/26/2019] [Indexed: 11/24/2022] Open
Abstract
Neuronal oscillations are ubiquitous in the human brain and are implicated in virtually all brain functions. Although they can be described by a prominent peak in the power spectrum, their waveform is not necessarily sinusoidal and shows rather complex morphology. Both frequency and temporal descriptions of such non-sinusoidal neuronal oscillations can be utilized. However, in non-invasive EEG/MEG recordings the waveform of oscillations often takes a sinusoidal shape which in turn leads to a rather oversimplified view on oscillatory processes. In this study, we show in simulations how spatial synchronization can mask non-sinusoidal features of the underlying rhythmic neuronal processes. Consequently, the degree of non-sinusoidality can serve as a measure of spatial synchronization. To confirm this empirically, we show that a mixture of EEG components is indeed associated with more sinusoidal oscillations compared to the waveform of oscillations in each constituent component. Using simulations, we also show that the spatial mixing of the non-sinusoidal neuronal signals strongly affects the amplitude ratio of the spectral harmonics constituting the waveform. Finally, our simulations show how spatial mixing can affect the strength and even the direction of the amplitude coupling between constituent neuronal harmonics at different frequencies. Validating these simulations, we also demonstrate these effects in real EEG recordings. Our findings have far reaching implications for the neurophysiological interpretation of spectral profiles, cross-frequency interactions, as well as for the unequivocal determination of oscillatory phase.
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Affiliation(s)
- Natalie Schaworonkow
- Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe University, Frankfurt am Main, Germany
- Department of Neurology & Stroke, and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Vadim V. Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russian Federation
- Neurophysics Group, Department of Neurology, Charité-University Medicine Berlin – Campus Benjamin Franklin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
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Chambon S, Galtier MN, Arnal PJ, Wainrib G, Gramfort A. A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series. IEEE Trans Neural Syst Rehabil Eng 2019; 26:758-769. [PMID: 29641380 DOI: 10.1109/tnsre.2018.2813138] [Citation(s) in RCA: 182] [Impact Index Per Article: 36.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of the signal of a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEGs), electrooculograms (EOGs), electrocardiograms, and electromyograms (EMGs). We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting handcrafted features, that exploits all multivariate and multimodal polysomnography (PSG) signals (EEG, EMG, and EOG), and that can exploit the temporal context of each 30-s window of data. For each modality, the first layer learns linear spatial filters that exploit the array of sensors to increase the signal-to-noise ratio, and the last layer feeds the learnt representation to a softmax classifier. Our model is compared to alternative automatic approaches based on convolutional networks or decisions trees. Results obtained on 61 publicly available PSG records with up to 20 EEG channels demonstrate that our network architecture yields the state-of-the-art performance. Our study reveals a number of insights on the spatiotemporal distribution of the signal of interest: a good tradeoff for optimal classification performance measured with balanced accuracy is to use 6 EEG with 2 EOG (left and right) and 3 EMG chin channels. Also exploiting 1 min of data before and after each data segment offers the strongest improvement when a limited number of channels are available. As sleep experts, our system exploits the multivariate and multimodal nature of PSG signals in order to deliver the state-of-the-art classification performance with a small computational cost.
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Dalla Porta L, Copelli M. Modeling neuronal avalanches and long-range temporal correlations at the emergence of collective oscillations: Continuously varying exponents mimic M/EEG results. PLoS Comput Biol 2019; 15:e1006924. [PMID: 30951525 PMCID: PMC6469813 DOI: 10.1371/journal.pcbi.1006924] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Revised: 04/17/2019] [Accepted: 03/06/2019] [Indexed: 01/17/2023] Open
Abstract
We revisit the CROS ("CRitical OScillations") model which was recently proposed as an attempt to reproduce both scale-invariant neuronal avalanches and long-range temporal correlations. With excitatory and inhibitory stochastic neurons locally connected in a two-dimensional disordered network, the model exhibits a transition where alpha-band oscillations emerge. Precisely at the transition, the fluctuations of the network activity have nontrivial detrended fluctuation analysis (DFA) exponents, and avalanches (defined as supra-threshold activity) have power law distributions of size and duration. We show that, differently from previous results, the exponents governing the distributions of avalanche size and duration are not necessarily those of the mean-field directed percolation universality class (3/2 and 2, respectively). Instead, in a narrow region of parameter space, avalanche exponents obtained via a maximum-likelihood estimator vary continuously and follow a linear relation, in good agreement with results obtained from M/EEG data. In that region, moreover, the values of avalanche and DFA exponents display a spread with positive correlations, reproducing human MEG results.
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Affiliation(s)
- Leonardo Dalla Porta
- Departamento de Física, Universidade Federal de Pernambuco (UFPE), Recife, PE, Brazil
| | - Mauro Copelli
- Departamento de Física, Universidade Federal de Pernambuco (UFPE), Recife, PE, Brazil
- * E-mail:
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Abstract
OBJECTIVE the recent emergence and success of electroencephalography (EEG) in low-cost portable devices, has opened the door to a new generation of applications processing a small number of EEG channels for health monitoring and brain-computer interfacing. These recordings are, however, contaminated by many sources of noise degrading the signals of interest, thus compromising the interpretation of the underlying brain state. In this paper, we propose a new data-driven algorithm to effectively remove ocular and muscular artifacts from single-channel EEG: the surrogate-based artifact removal (SuBAR). METHODS by means of the time-frequency analysis of surrogate data, our approach is able to identify and filter automatically ocular and muscular artifacts embedded in single-channel EEG. RESULTS in a comparative study using artificially contaminated EEG signals, the efficacy of the algorithm in terms of noise removal and signal distortion was superior to other traditionally-employed single-channel EEG denoizing techniques: wavelet thresholding and the canonical correlation analysis combined with an advanced version of the empirical mode decomposition. Even in the presence of mild and severe artifacts, our artifact removal method provides a relative error 4 to 5 times lower than traditional techniques. SIGNIFICANCE in view of these results, the SuBAR method is a promising solution for mobile environments, such as ambulatory healthcare systems, sleep stage scoring, or anesthesia monitoring, where very few EEG channels or even a single channel is available.
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Heimann K, Uithol S, Calbi M, Umiltà MA, Guerra M, Fingerhut J, Gallese V. Embodying the camera: An EEG study on the effect of camera movements on film spectators´ sensorimotor cortex activation. PLoS One 2019; 14:e0211026. [PMID: 30865624 PMCID: PMC6415856 DOI: 10.1371/journal.pone.0211026] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 01/07/2019] [Indexed: 12/05/2022] Open
Abstract
One key feature of film consists in its power to bodily engage the viewer. Previous research has suggested lens and camera movements to be among the most effective stylistic devices involved in such engagement. In an EEG experiment we assessed the role of such movements in modulating specific spectators´ neural and experiential responses, likely reflecting such engagement. We produced short video clips of an empty room with a still, a zooming and a moving camera (steadicam) that might simulate the movement of an observer in different ways. We found an event related desynchronization of the beta components of the rolandic mu rhythm that was stronger for the clips produced with steadicam than for those produced with a still or zooming camera. No equivalent modulation in the attention related occipital areas was found, thus confirming the sensorimotor nature of spectators´ neural responses to the film clips. The present study provides the first empirical evidence that filmic means such as camera movements alone can modulate spectators’ bodily engagement with film.
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Affiliation(s)
- Katrin Heimann
- Department of Medicine and Surgery, Unit of Neuroscience, University of Parma, Parma, Italy
- * E-mail:
| | - Sebo Uithol
- Department of Medicine and Surgery, Unit of Neuroscience, University of Parma, Parma, Italy
| | - Marta Calbi
- Department of Medicine and Surgery, Unit of Neuroscience, University of Parma, Parma, Italy
| | | | - Michele Guerra
- Department of Humanities, Social Sciences, and Cultural Industries, University of Parma, Parma, Italy
| | - Joerg Fingerhut
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Vittorio Gallese
- Department of Medicine and Surgery, Unit of Neuroscience, University of Parma, Parma, Italy
- Institute of Philosophy, School of Advanced Study, University of London, London, United Kingdom
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Abstract
Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time. In this paper, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet (source code is available at http://github.com/pquochuy/SeqSleepNet). At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention-based recurrent layer designed for short-term sequential modeling. At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modeling of sequential epochs. The classification is then carried out on the output vectors at every time step of the top recurrent layer to produce the sequence of output labels. Despite being hierarchical, we present a strategy to train the network in an end-to-end fashion. We show that the proposed network outperforms the state-of-the-art approaches, achieving an overall accuracy, macro F1-score, and Cohen's kappa of 87.1%, 83.3%, and 0.815 on a publicly available dataset with 200 subjects.
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Affiliation(s)
- Huy Phan
- School of Computing, University of Kent, Chatham Maritime, Kent ME4 4AG, United Kingdom and the Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7DQ, United Kingdom
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Pizzo F, Roehri N, Medina Villalon S, Trébuchon A, Chen S, Lagarde S, Carron R, Gavaret M, Giusiano B, McGonigal A, Bartolomei F, Badier JM, Bénar CG. Deep brain activities can be detected with magnetoencephalography. Nat Commun 2019; 10:971. [PMID: 30814498 PMCID: PMC6393515 DOI: 10.1038/s41467-019-08665-5] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 01/12/2019] [Indexed: 12/22/2022] Open
Abstract
The hippocampus and amygdala are key brain structures of the medial temporal lobe, involved in cognitive and emotional processes as well as pathological states such as epilepsy. Despite their importance, it is still unclear whether their neural activity can be recorded non-invasively. Here, using simultaneous intracerebral and magnetoencephalography (MEG) recordings in patients with focal drug-resistant epilepsy, we demonstrate a direct contribution of amygdala and hippocampal activity to surface MEG recordings. In particular, a method of blind source separation, independent component analysis, enabled activity arising from large neocortical networks to be disentangled from that of deeper structures, whose amplitude at the surface was small but significant. This finding is highly relevant for our understanding of hippocampal and amygdala brain activity as it implies that their activity could potentially be measured non-invasively.
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Affiliation(s)
- F Pizzo
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, 13005, France.
- APHM, Timone Hospital, Epileptology and cerebral rhythmology, Marseille, 13005, France.
| | - N Roehri
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, 13005, France
| | - S Medina Villalon
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, 13005, France
- APHM, Timone Hospital, Epileptology and cerebral rhythmology, Marseille, 13005, France
| | - A Trébuchon
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, 13005, France
- APHM, Timone Hospital, Epileptology and cerebral rhythmology, Marseille, 13005, France
| | - S Chen
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, 13005, France
| | - S Lagarde
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, 13005, France
- APHM, Timone Hospital, Epileptology and cerebral rhythmology, Marseille, 13005, France
| | - R Carron
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, 13005, France
- APHM, Timone Hospital, Functional and Stereotactic Neurosurgery, Marseille, 13005, France
| | - M Gavaret
- INSERM UMR894, Paris Descartes university, GHU Paris Psychiatrie Neurosciences, 75013, Paris, France
| | - B Giusiano
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, 13005, France
| | - A McGonigal
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, 13005, France
- APHM, Timone Hospital, Epileptology and cerebral rhythmology, Marseille, 13005, France
| | - F Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, 13005, France
- APHM, Timone Hospital, Epileptology and cerebral rhythmology, Marseille, 13005, France
| | - J M Badier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, 13005, France
| | - C G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, 13005, France.
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Meinel A, Kolkhorst H, Tangermann M. Mining Within-Trial Oscillatory Brain Dynamics to Address the Variability of Optimized Spatial Filters. IEEE Trans Neural Syst Rehabil Eng 2019; 27:378-388. [PMID: 30703030 DOI: 10.1109/tnsre.2019.2894914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Data-driven spatial filtering algorithms optimize scores, such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for the filter estimation, however, disregard within-trial temporal dynamics and are extremely sensitive to changes in training data and involved hyperparameters. This leads to highly variable solutions and impedes the selection of a suitable candidate for, e.g., neurotechnological applications. Fostering component introspection, we propose to embrace this variability by condensing the functional signatures of a large set of oscillatory components into homogeneous clusters, each representing specific within-trial envelope dynamics. The proposed method is exemplified by and evaluated on a complex hand force task with a rich within-trial structure. Based on electroencephalography data of 18 healthy subjects, we found that the components' distinct temporal envelope dynamics are highly subject-specific. On average, we obtained seven clusters per subject, which were strictly confined regarding their underlying frequency bands. As the analysis method is not limited to a specific spatial filtering algorithm, it could be utilized for a wide range of neurotechnological applications, e.g., to select and monitor functionally relevant features for brain-computer interface protocols in stroke rehabilitation.
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Lee JY, Choi SH, Park KS, Choi YB, Jung HK, Lee D, Jang JH, Moon JY, Kang DH. Comparison of complex regional pain syndrome and fibromyalgia: Differences in beta and gamma bands on quantitative electroencephalography. Medicine (Baltimore) 2019; 98:e14452. [PMID: 30762759 PMCID: PMC6407989 DOI: 10.1097/md.0000000000014452] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Complex regional pain syndrome (CRPS) and fibromyalgia (FM) share many features. Both can cause severe pain and are considered to have a mechanism of action, including dysfunction of the sympathetic nervous system. However, they have clinical differences in pain range and degree. The present study aimed to find neurophysiologic differences between CRPS and FM using quantitative electroencephalography (QEEG). Thirty-eight patients with CRPS and 33 patients with FM were included in the analysis. Resting-state QEEG data were grouped into frontal, central, and posterior regions to analyze for regional differences. General linear models were utilized to test for group differences in absolute and relative powers. As a result, the CRPS group relative to FM group showed lower total absolute powers in the beta band (F = 5.159, P < .05), high beta (F = 14.120, P < .05), and gamma band (F = 15.034, P < .05). There were no significant differences between 2 groups in the delta, theta, and alpha bands. The present findings show that the CRPS and FM groups differ mainly in the high frequency, which may reflect their distinct pathophysiology and symptomatology. Our study suggests that the QEEG differences can be clinically useful in assessing brain function in patients with CRPS and FM.
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Affiliation(s)
- Jae-Yeon Lee
- Department of Psychiatry, Seoul National University Hospital
| | - Soo-Hee Choi
- Department of Psychiatry, Seoul National University Hospital
- Department of Psychiatry and Institute of Human Behavioral Medicine in SNU-MRC
| | - Ki-Soon Park
- Department of Medicine, Seoul National University College of Medicine
| | - Yoo Bin Choi
- Department of Psychiatry, Seoul National University Hospital
| | - Hee Kyung Jung
- Department of Psychiatry, Seoul National University Hospital
| | - Dasom Lee
- Department of Psychiatry, Seoul National University Hospital
| | - Joon Hwan Jang
- Department of Psychiatry, Seoul National University Hospital
- Department of Medicine, Seoul National University College of Medicine
| | - Jee Youn Moon
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Do-Hyung Kang
- Department of Psychiatry, Seoul National University Hospital
- Department of Psychiatry and Institute of Human Behavioral Medicine in SNU-MRC
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De-Arteaga M, Chen J, Huggins P, Elmer J, Clermont G, Dubrawski A. Predicting neurological recovery with Canonical Autocorrelation Embeddings. PLoS One 2019; 14:e0210966. [PMID: 30689648 PMCID: PMC6349311 DOI: 10.1371/journal.pone.0210966] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 01/03/2019] [Indexed: 11/18/2022] Open
Abstract
Early prediction of the potential for neurological recovery after resuscitation from cardiac arrest is difficult but important. Currently, no clinical finding or combination of findings are sufficient to accurately predict or preclude favorable recovery of comatose patients in the first 24 to 48 hours after resuscitation. Thus, life-sustaining therapy is often continued for several days in patients whose irrecoverable injury is not yet recognized. Conversely, early withdrawal of life-sustaining therapy increases mortality among patients who otherwise might have gone on to recover. In this work, we present Canonical Autocorrelation Analysis (CAA) and Canonical Autocorrelation Embeddings (CAE), novel methods suitable for identifying complex patterns in high-resolution multivariate data often collected in highly monitored clinical environments such as intensive care units. CAE embeds sets of datapoints onto a space that characterizes their latent correlation structures and allows direct comparison of these structures through the use of a distance metric. The methodology may be particularly suitable when the unit of analysis is not just an individual datapoint but a dataset, as for instance in patients for whom physiological measures are recorded over time, and where changes of correlation patterns in these datasets are informative for the task at hand. We present a proof of concept to illustrate the potential utility of CAE by applying it to characterize electroencephalographic recordings from 80 comatose survivors of cardiac arrest, aiming to identify patients who will survive to hospital discharge with favorable functional recovery. Our results show that with very low probability of making a Type 1 error, we are able to identify 32.5% of patients who are likely to have a good neurological outcome, some of whom have otherwise unfavorable clinical characteristics. Importantly, some of these had 5% predicted chance of favorable recovery based on initial illness severity measures alone. Providing this information to support clinical decision-making could motivate the continuation of life-sustaining therapies for these patients.
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Affiliation(s)
- Maria De-Arteaga
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, United States of America
- Heinz College, Carnegie Mellon University, Pittsburgh, United States of America
- Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States of America
| | - Jieshi Chen
- Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States of America
| | - Peter Huggins
- Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States of America
| | - Jonathan Elmer
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, United States of America
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, United States of America
| | - Gilles Clermont
- CRISMA laboratory, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, United States of America
| | - Artur Dubrawski
- Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States of America
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Lei B, Liu X, Liang S, Hang W, Wang Q, Choi KS, Qin J. Walking Imagery Evaluation in Brain Computer Interfaces via a Multi-View Multi-Level Deep Polynomial Network. IEEE Trans Neural Syst Rehabil Eng 2019; 27:497-506. [PMID: 30703032 DOI: 10.1109/tnsre.2019.2895064] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Brain-computer interfaces based on motor imagery (MI) have been widely used to support the rehabilitation of motor functions of the upper limbs rather than lower limbs. This is probably because it is more difficult to detect the brain activities of lower limb MI. In order to reliably detect the brain activities of lower limbs to restore or improve the walking ability of the disabled, we propose a new paradigm of walking imagery (WI) in a virtual environment (VE), in order to elicit the reliable brain activities and achieve a significant training effect. First, we extract and fuse both the spatial and time-frequency features as a multi-view feature to represent the patterns in the brain activity. Second, we design a multi-view multi-level deep polynomial network (MMDPN) to explore the complementarity among the features so as to improve the detection of walking from an idle state. Our extensive experimental results show that the VE-based paradigm significantly performs better than the traditional text-based paradigm. In addition, the VE-based paradigm can effectively help users to modulate the brain activities and improve the quality of electroencephalography signals. We also observe that the MMDPN outperforms other deep learning methods in terms of classification performance.
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Barthelemy Q, Mayaud L, Ojeda D, Congedo M. The Riemannian Potato Field: A Tool for Online Signal Quality Index of EEG. IEEE Trans Neural Syst Rehabil Eng 2019; 27:244-255. [PMID: 30668501 DOI: 10.1109/tnsre.2019.2893113] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Electroencephalographic (EEG) recordings are contaminated by instrumental, environmental, and biological artifacts, resulting in low signal-to-noise ratio. Artifact detection is a critical task for real-time applications where the signal is used to give a continuous feedback to the user. In these applications, it is therefore necessary to estimate online a signal quality index (SQI) in order to stop the feedback when the signal quality is unacceptable. In this paper, we introduce the Riemannian potato field (RPF) algorithm as such SQI. It is a generalization and extensionof theRiemannian potato, a previouslypublished real-time artifact detection algorithm, whose performance is degraded as the number of channels increases. The RPF overcomes this limitation by combining the outputs of several smaller potatoes into a unique SQI resulting in a higher sensitivity and specificity, regardless of the number of electrodes. We demonstrate these results on a clinical dataset totalizing more than 2200 h of EEG recorded at home, that is, in a non-controlled environment.
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Brown T, McConnell M, Rupp G, Meghdadi A, Richard C, Schmitt R, Gaffney G, Milavetz G, Berka C. Correlation of EEG biomarkers of cannabis with measured driving impairment. Traffic Inj Prev 2019; 20:S148-S151. [PMID: 31674856 PMCID: PMC8733968 DOI: 10.1080/15389588.2019.1662256] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Objective: The objective of this study was to use electroencephalogram (EEG) biomarkers derived from a short, easily administered neurocognitive testbed to determine acute cannabis intoxication and its effect on driving performance in a driving simulator.Methods: The data analyzed were from a study examining the relationship between psychomotor task performance, EEG data, and driving performance in a simulator. EEG data were collected using a STAT® X-24 EEG Wireless Sensor Headset, which was worn during the psychomotor and driving tasks. Driving data were collected for segments of consistent driving environments, including urban driving, urban curves, interstate, interstate curves, dark rural, and rural straightaways. Dependent measures included measures of lateral and longitudinal vehicle control.Results: There was a significant relationship between impaired driving performance as indicated by increased standard deviation of lane position and EEG power in slow theta band (3-5 Hz) in parietal and occipital areas.Conclusions: These results, combined with our prior reported results, suggest that EEG and electrocardiogram (ECG) acquired concurrent with neuropsychological tests hold potential to provide a highly sensitive, specific, and dose-dependent profile of cannabis intoxication and level of impairment.
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Affiliation(s)
- Timothy Brown
- National Advanced Driving Simulator, The University of Iowa, Iowa City, Iowa
| | | | - Greg Rupp
- Advanced Brain Monitoring, Inc., Carlsbad, California
| | - Amir Meghdadi
- Advanced Brain Monitoring, Inc., Carlsbad, California
| | | | - Rose Schmitt
- National Advanced Driving Simulator, The University of Iowa, Iowa City, Iowa
| | - Gary Gaffney
- Carver College of Medicine, The University of Iowa, Iowa City, Iowa
| | - Gary Milavetz
- College of Pharmacy, The University of Iowa, Iowa City, Iowa
| | - Chris Berka
- Advanced Brain Monitoring, Inc., Carlsbad, California
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Wen D, Jia P, Hsu SH, Zhou Y, Lan X, Cui D, Li G, Yin S, Wang L. Estimating coupling strength between multivariate neural series with multivariate permutation conditional mutual information. Neural Netw 2018; 110:159-169. [PMID: 30562649 DOI: 10.1016/j.neunet.2018.11.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 10/05/2018] [Accepted: 11/20/2018] [Indexed: 02/03/2023]
Abstract
Recently, coupling between groups of neurons or different brain regions has been widely studied to provide insights into underlying mechanisms of brain functions. To comprehensively understand the effect of such coupling, it is necessary to accurately extract the coupling strength information among multivariate neural signals from the whole brain. This study proposed a new method named multivariate permutation conditional mutual information (MPCMI) to quantitatively estimate the coupling strength of multivariate neural signals (MNS). The performance of the MPCMI method was validated on the simulated MNS generated by multi-channel neural mass model (MNMM). The coupling strength feature of simulated MNS extracted by MPCMI showed better performance compared with standard methods, such as permutation conditional mutual information (PCMI), multivariate Granger causality (MVGC), and Granger causality analysis (GCA). Furthermore, the MPCMI was applied to estimate the coupling strengths of two-channel resting-state electroencephalographic (rsEEG) signals from different brain regions of 19 patients with amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) and 20 normal control (NC) with T2DM in Alpha1 and Alpha2 frequency bands. Empirical results showed that the MPCMI could effectively extract the coupling strength features that were significantly different between the aMCI and the NC. Hence, the proposed MPCMI method could be an effective estimate of coupling strengths of MNS, and might be a viable biomarker for clinical applications.
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Affiliation(s)
- Dong Wen
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Software Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China.
| | - Peilei Jia
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Software Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China
| | - Sheng-Hsiou Hsu
- Swartz Center for Computational Neuroscience, University of California San Diego, La Jolla, CA, 92093, United States
| | - Yanhong Zhou
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China.
| | - Xifa Lan
- Department of Neurology, First Hospital of Qinhuangdao, Qinhuangdao 066000, China
| | - Dong Cui
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao 066004, China
| | - Guolin Li
- School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China
| | - Shimin Yin
- Department of Neurology, The Rocket Force General Hospital of Chinese People's Liberation Army, Beijing 100088, China
| | - Lei Wang
- Department of Neurology, The Rocket Force General Hospital of Chinese People's Liberation Army, Beijing 100088, China
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