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Huang HJ, Ferris DP. Non-invasive brain imaging to advance the understanding of human balance. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2023; 28:100505. [PMID: 38250696 PMCID: PMC10795750 DOI: 10.1016/j.cobme.2023.100505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Affiliation(s)
- Helen J. Huang
- Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, USA
- Disability, Aging, and Technology Cluster, University of Central Florida, Orlando, FL, USA
- Biionix (Bionic Materials, Implants & Interfaces) Cluster, University of Central Florida, Orlando, FL, USA
| | - Daniel P. Ferris
- J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA
- Department of Neurology, University of Florida, Gainesville, FL, USA
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2
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Carr SJA, Gershon A, Shafiabadi N, Lhatoo SD, Tatsuoka C, Sahoo SS. An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data. Front Integr Neurosci 2021; 14:491403. [PMID: 33510622 PMCID: PMC7835283 DOI: 10.3389/fnint.2020.491403] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 11/30/2020] [Indexed: 12/22/2022] Open
Abstract
A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures during seizure events using stereotactic electroencephalogram (SEEG) and diffusion weighted imaging data (DWI). We computed structural connectivity measures using electrode locations involved in recording SEEG signal data as reference points to filter fiber tracts. We used a new workflow-based tool to compute functional connectivity measures based on non-linear correlation coefficient, which allows the derivation of directed graph structures to represent coupling between signal data. We applied a hierarchical clustering based network analysis method over the functional connectivity data to characterize the organization of brain network into modules using data from 27 events across 8 seizures in a patient with refractory left insula epilepsy. The visualization of hierarchical clustering values as dendrograms shows the formation of connected clusters first within each insulae followed by merging of clusters across the two insula; however, there are clear differences between the network structures and clusters formed across the 8 seizures of the patient. The analysis of structural connectivity measures showed strong connections between contacts of certain electrodes within the same brain hemisphere with higher prevalence in the perisylvian/opercular areas. The combination of imaging and signal modalities for connectivity analysis provides information about a patient-specific dynamical functional network and examines the underlying structural connections that potentially influences the properties of the epileptic network. We also performed statistical analysis of the absolute changes in correlation values across all 8 seizures during a baseline normative time period and different seizure events, which showed decreased correlation values during seizure onset; however, the changes during ictal phases were varied.
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Affiliation(s)
- Sarah J. A. Carr
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Neuroimaging Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Arthur Gershon
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Nassim Shafiabadi
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Samden D. Lhatoo
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
| | - Curtis Tatsuoka
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Satya S. Sahoo
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
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3
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Martínez-Cancino R, Delorme A, Truong D, Artoni F, Kreutz-Delgado K, Sivagnanam S, Yoshimoto K, Majumdar A, Makeig S. The open EEGLAB portal Interface: High-Performance computing with EEGLAB. Neuroimage 2021; 224:116778. [PMID: 32289453 PMCID: PMC8341158 DOI: 10.1016/j.neuroimage.2020.116778] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 02/22/2020] [Accepted: 03/20/2020] [Indexed: 10/24/2022] Open
Abstract
EEGLAB signal processing environment is currently the leading open-source software for processing electroencephalographic (EEG) data. The Neuroscience Gateway (NSG, nsgportal.org) is a web and API-based portal allowing users to easily run a variety of neuroscience-related software on high-performance computing (HPC) resources in the U.S. XSEDE network. We have reported recently (Delorme et al., 2019) on the Open EEGLAB Portal expansion of the free NSG services to allow the neuroscience community to build and run MATLAB pipelines using the EEGLAB tool environment. We are now releasing an EEGLAB plug-in, nsgportal, that interfaces EEGLAB with NSG directly from within EEGLAB running on MATLAB on any personal lab computer. The plug-in features a flexible MATLAB graphical user interface (GUI) that allows users to easily submit, interact with, and manage NSG jobs, and to retrieve and examine their results. Command line nsgportal tools supporting these GUI functionalities allow EEGLAB users and plug-in tool developers to build largely automated functions and workflows that include optional NSG job submission and processing. Here we present details on nsgportal implementation and documentation, provide user tutorials on example applications, and show sample test results comparing computation times using HPC versus laptop processing.
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Affiliation(s)
- Ramón Martínez-Cancino
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, USA; Department of Electrical and Computer Engineering, Jacobs School of Engineering, University of California San Diego, USA.
| | - Arnaud Delorme
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, USA
| | - Dung Truong
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, USA
| | - Fiorenzo Artoni
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kenneth Kreutz-Delgado
- Department of Electrical and Computer Engineering, Jacobs School of Engineering, University of California San Diego, USA
| | | | - Kenneth Yoshimoto
- San Diego Supercomputer Center, University of California San Diego, USA
| | - Amitava Majumdar
- San Diego Supercomputer Center, University of California San Diego, USA
| | - Scott Makeig
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, USA
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4
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Bigdely-Shamlo N, Touryan J, Ojeda A, Kothe C, Mullen T, Robbins K. Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies. Neuroimage 2020; 207:116361. [DOI: 10.1016/j.neuroimage.2019.116361] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 11/09/2019] [Accepted: 11/13/2019] [Indexed: 10/25/2022] Open
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5
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Wojcik GM, Masiak J, Kawiak A, Kwasniewicz L, Schneider P, Postepski F, Gajos-Balinska A. Analysis of Decision-Making Process Using Methods of Quantitative Electroencephalography and Machine Learning Tools. Front Neuroinform 2019; 13:73. [PMID: 31827431 PMCID: PMC6892351 DOI: 10.3389/fninf.2019.00073] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 11/14/2019] [Indexed: 01/09/2023] Open
Abstract
The electroencephalographic activity of particular brain areas during the decision making process is still little known. This paper presents results of experiments on the group of 30 patients with a wide range of psychiatric disorders and 41 members of the control group. All subjects were performing the Iowa Gambling Task that is often used for decision process investigations. The electroencephalographical activity of participants was recorded using the dense array amplifier. The most frequently active Brodmann Areas were estimated by means of the photogrammetry techniques and source localization algorithms. The analysis was conducted in the full frequency as well as in alpha, beta, gamma, delta, and theta bands. Next the mean electric charge flowing through each of the most frequently active areas and for each frequency band was calculated. The comparison of the results obtained for the subjects and the control groups is presented. The difference in activity of the selected Brodmann Areas can be observed in all variants of the task. The hyperactivity of amygdala is found in both the patients and the control group. It is noted that the somatosensory association cortex, dorsolateral prefrontal cortex, and primary visual cortex play an important role in the decision-making process as well. Some of our results confirm the previous findings in the fMRI experiments. In addition, the results of the electroencephalographic analysis in the broadband as well as in specific frequency bands were used as inputs to several machine learning classifiers built in Azure Machine Learning environment. Comparison of classifiers' efficiency is presented to some extent and finding the most effective classifier may be important for planning research strategy toward finding decision-making biomarkers in cortical activity for both healthy people and those suffering from psychiatric disorders.
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Affiliation(s)
- Grzegorz M Wojcik
- Chair of Neuroinformatics and Biomedical Engineering, Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
| | - Jolanta Masiak
- Neurophysiological Independent Unit of the Department of Psychiatry, Medical University of Lublin, Lublin, Poland
| | - Andrzej Kawiak
- Chair of Neuroinformatics and Biomedical Engineering, Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
| | - Lukasz Kwasniewicz
- Chair of Neuroinformatics and Biomedical Engineering, Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
| | - Piotr Schneider
- Chair of Neuroinformatics and Biomedical Engineering, Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
| | - Filip Postepski
- Chair of Neuroinformatics and Biomedical Engineering, Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
| | - Anna Gajos-Balinska
- Chair of Neuroinformatics and Biomedical Engineering, Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
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6
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Wagner J, Martinez-Cancino R, Delorme A, Makeig S, Solis-Escalante T, Neuper C, Mueller-Putz G. High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task. Sci Data 2019; 6:211. [PMID: 31624252 PMCID: PMC6797727 DOI: 10.1038/s41597-019-0223-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 09/06/2019] [Indexed: 02/07/2023] Open
Abstract
In this report we present a mobile brain/body imaging (MoBI) dataset that allows study of source-resolved cortical dynamics supporting coordinated gait movements in a rhythmic auditory cueing paradigm. Use of an auditory pacing stimulus stream has been recommended to identify deficits and treat gait impairments in neurologic populations. Here, the rhythmic cueing paradigm required healthy young participants to walk on a treadmill (constant speed) while attempting to maintain step synchrony with an auditory pacing stream and to adapt their step length and rate to unanticipated shifts in tempo of the pacing stimuli (e.g., sudden shifts to a faster or slower tempo). High-density electroencephalography (EEG, 108 channels), surface electromyography (EMG, bilateral tibialis anterior), pressure sensors on the heel (to register timing of heel strikes), and goniometers (knee, hip, and ankle joint angles) were concurrently recorded in 20 participants. The data is provided in the Brain Imaging Data Structure (BIDS) format to promote data sharing and reuse, and allow the inclusion of the data into fully automated data analysis workflows.
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Affiliation(s)
- Johanna Wagner
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA.
- Laboratory for Brain Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, Graz, Austria.
| | - Ramon Martinez-Cancino
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA
- Electric and Computer Engineering Department, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Arnaud Delorme
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA
| | - Scott Makeig
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA
| | - Teodoro Solis-Escalante
- Laboratory for Brain Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- Department of Rehabilitation, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Christa Neuper
- Laboratory for Brain Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- Department of Psychology, University of Graz, Graz, Austria
| | - Gernot Mueller-Putz
- Laboratory for Brain Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, Graz, Austria
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7
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Pedroni A, Bahreini A, Langer N. Automagic: Standardized preprocessing of big EEG data. Neuroimage 2019; 200:460-473. [DOI: 10.1016/j.neuroimage.2019.06.046] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 05/25/2019] [Accepted: 06/19/2019] [Indexed: 01/08/2023] Open
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8
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Poldrack RA, Gorgolewski KJ, Varoquaux G. Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging. Annu Rev Biomed Data Sci 2019. [DOI: 10.1146/annurev-biodatasci-072018-021237] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data-sharing resources that have been developed for neuroimaging data, as well as the role of data standards (particularly the brain imaging data structure) in enabling the automated sharing, processing, and reuse of large neuroimaging data sets. We outline how the open source Python language has provided the basis for a data science platform that enables reproducible data analysis and visualization. We also discuss how new advances in software engineering, such as containerization, provide the basis for greater reproducibility in data analysis. The emergence of this new ecosystem provides an example for many areas of science that are currently struggling with reproducibility.
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Affiliation(s)
- Russell A. Poldrack
- Department of Psychology, Stanford University, Stanford, California 94305, USA
| | | | - Gaël Varoquaux
- Parietal Team, Inria and NeuroSpin/CEA (Atomic Energy Commission), 91191 Gif/-sur-Yvette, France
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9
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Bilder RM, Reise SP. Neuropsychological tests of the future: How do we get there from here? Clin Neuropsychol 2019; 33:220-245. [PMID: 30422045 PMCID: PMC6422683 DOI: 10.1080/13854046.2018.1521993] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 09/04/2018] [Accepted: 09/05/2018] [Indexed: 12/23/2022]
Abstract
OBJECTIVE This article reviews current approaches to neuropsychological assessment, identifies opportunities for development of new methods using modern psychometric theory and advances in technology, and suggests a transition path that promotes application of novel methods without sacrificing validity. METHODS Theoretical/state-of-the-art review. CONCLUSIONS Clinical neuropsychological assessment today does not reflect advances in neuroscience, modern psychometrics, or technology. Major opportunities for improving practice include both psychometric and technological strategies. Modern psychometric approaches including item response theory (IRT) enable linking procedures that can place different measures on common scales; adaptive testing algorithms that can dramatically increase efficiency of assessment; examination of differential item functioning (DIF) to detect measures that behave differently in different groups; and person fit statistics to detect aberrant patterns of responding of high value for performance validity testing. Opportunities to introduce novel technologies include computerized adaptive testing, Web-based assessment, healthcare- and bio-informatics strategies, mobile platforms, wearables, and the 'internet-of-things'. To overcome inertia in current practices, new methods must satisfy requirements for back-compatibility with legacy instrumentation, enabling us to leverage the wealth of validity data already accrued for classic procedures. A path to achieve these goals involves creation of a global network to aggregate item-level data into a shared repository that will enable modern psychometric analyses to refine existing methods, and serve as a platform to evolve novel assessment strategies, which over time can revolutionize neuropsychological assessment practices world-wide.
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Affiliation(s)
- Robert M Bilder
- a Departments of Psychiatry & Biobehavioral Science, Jane & Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles , Los Angeles , California , USA
- b Department of Psychiatry & Biobehavioral Science , Los Angeles , California , USA
| | - Steven P Reise
- b Department of Psychiatry & Biobehavioral Science , Los Angeles , California , USA
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10
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Wojcik GM, Masiak J, Kawiak A, Kwasniewicz L, Schneider P, Polak N, Gajos-Balinska A. Mapping the Human Brain in Frequency Band Analysis of Brain Cortex Electroencephalographic Activity for Selected Psychiatric Disorders. Front Neuroinform 2018; 12:73. [PMID: 30405386 PMCID: PMC6207640 DOI: 10.3389/fninf.2018.00073] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 09/28/2018] [Indexed: 01/09/2023] Open
Abstract
There are still no good quantitative methods to be applied in psychiatric diagnosis. The interview is still the main and most important tool in the psychiatrist work. This paper presents the results of electroencephalographic research with the subjects of a group of 30 patients with psychiatric disorders compared to the control group of healthy volunteers. All subjects were solving working memory task. The digit-span working memory task test was chosen as one of the most popular tasks given to subjects with cognitive dysfunctions, especially for the patients with panic disorders, depression (including the depressive phase of bipolar disorder), phobias, and schizophrenia. Having such cohort of patients some results for the subjects with insomnia and Asperger syndrome are also presented. The cortical activity of their brains was registered by the dense array EEG amplifier. Source localization using the photogrammetry station and the sLORETA algorithm was then performed in five EEG frequency bands. The most active Brodmann Areas are indicated. Methodology for mapping the brain and research protocol are presented. The first results indicate that the presented technique can be useful in finding psychiatric disorder neurophysiological biomarkers. The first attempts were made to associate hyperactivity of selected Brodmann Areas with particular disorders.
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Affiliation(s)
- Grzegorz M Wojcik
- Department of Neuroinformatics, Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Jolanta Masiak
- Neurophysiological Independent Unit of the Department of Psychiatry, Medical University of Lublin, Lublin, Poland
| | - Andrzej Kawiak
- Department of Neuroinformatics, Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Lukasz Kwasniewicz
- Department of Neuroinformatics, Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Piotr Schneider
- Department of Neuroinformatics, Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Nikodem Polak
- Department of Neuroinformatics, Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Anna Gajos-Balinska
- Department of Neuroinformatics, Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
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11
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Wojcik GM, Masiak J, Kawiak A, Schneider P, Kwasniewicz L, Polak N, Gajos-Balinska A. New Protocol for Quantitative Analysis of Brain Cortex Electroencephalographic Activity in Patients With Psychiatric Disorders. Front Neuroinform 2018; 12:27. [PMID: 29881339 PMCID: PMC5976787 DOI: 10.3389/fninf.2018.00027] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 05/02/2018] [Indexed: 01/09/2023] Open
Abstract
The interview is still the main and most important tool in psychiatrist's work. The neuroimaging methods such as CT or MRI are widely used in other fields of medicine, for instance neurology. However, psychiatry lacks effective quantitative methods to support of diagnosis. A novel neuroinformatic approach to help clinical patients by means of electroencephalographic technology in order to build foundations for finding neurophysiological biomarkers of psychiatric disorders is proposed. A cohort of 30 right-handed patients (21 males, 9 females) with psychiatric disorders (mainly with panic and anxiety disorder, Asperger syndrome as well as with phobic anxiety disorders, schizophrenia, bipolar affective disorder, obsessive-compulsive disorder, nonorganic hypersomnia, and moderate depressive episode) were examined using the dense array EEG amplifier in the P300 experiment. The results were compared with the control group of 30 healthy, right-handed male volunteers. The quantitative analysis of cortical activity was conducted using the sLORETA source localization algorithm. The most active Brodmann Areas were pointed out and a new quantitative observable of electrical charge flowing through the selected Brodmann Area is proposed. The precise methodology and research protocol for collecting EEG data as well as the roadmap of future investigations in this area are presented. The essential result of this study is the idea proven by the initial results of our experiments that it is possible to determine quantitatively biomarkers of particular psychiatric disorders in order to support the process of diagnosis and hopefully choose most appropriate medical treatment later.
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Affiliation(s)
- Grzegorz M Wojcik
- Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science-Department of Neuroinformatics, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Jolanta Masiak
- Neurophysiological Independent Unit of the Department of Psychiatry, Medical University of Lublin, Lublin, Poland
| | - Andrzej Kawiak
- Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science-Department of Neuroinformatics, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Piotr Schneider
- Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science-Department of Neuroinformatics, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Lukasz Kwasniewicz
- Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science-Department of Neuroinformatics, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Nikodem Polak
- Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science-Department of Neuroinformatics, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Anna Gajos-Balinska
- Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science-Department of Neuroinformatics, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
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12
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Gennaro F, de Bruin ED. Assessing Brain-Muscle Connectivity in Human Locomotion through Mobile Brain/Body Imaging: Opportunities, Pitfalls, and Future Directions. Front Public Health 2018; 6:39. [PMID: 29535995 PMCID: PMC5834479 DOI: 10.3389/fpubh.2018.00039] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Accepted: 02/01/2018] [Indexed: 12/11/2022] Open
Abstract
Assessment of the cortical role during bipedalism has been a methodological challenge. While surface electroencephalography (EEG) is capable of non-invasively measuring cortical activity during human locomotion, it is associated with movement artifacts obscuring cerebral sources of activity. Recently, statistical methods based on blind source separation revealed potential for resolving this issue, by segregating non-cerebral/artifactual from cerebral sources of activity. This step marked a new opportunity for the investigation of the brains' role while moving and was tagged mobile brain/body imaging (MoBI). This methodology involves simultaneous mobile recording of brain activity with several other body behavioral variables (e.g., muscle activity and kinematics), through wireless recording wearable devices/sensors. Notably, several MoBI studies using EEG-EMG approaches recently showed that the brain is functionally connected to the muscles and active throughout the whole gait cycle and, thus, rejecting the long-lasting idea of a solely spinal-driven bipedalism. However, MoBI and brain/muscle connectivity assessments during human locomotion are still in their fledgling state of investigation. Mobile brain/body imaging approaches hint toward promising opportunities; however, there are some remaining pitfalls that need to be resolved before considering their routine clinical use. This article discusses several of these pitfalls and proposes research to address them. Examples relate to the validity, reliability, and reproducibility of this method in ecologically valid scenarios and in different populations. Furthermore, whether brain/muscle connectivity within the MoBI framework represents a potential biomarker in neuromuscular syndromes where gait disturbances are evident (e.g., age-related sarcopenia) remains to be determined.
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Affiliation(s)
- Federico Gennaro
- Institute of Human Movement Sciences and Sport, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Eling D. de Bruin
- Institute of Human Movement Sciences and Sport, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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13
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Robbins K, Su KM, Hairston WD. An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons. Data Brief 2017; 16:227-230. [PMID: 29226211 PMCID: PMC5712810 DOI: 10.1016/j.dib.2017.11.032] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 11/07/2017] [Accepted: 11/08/2017] [Indexed: 11/29/2022] Open
Abstract
This data note describes an 18-subject EEG (electroencephalogram) data collection from an experiment in which subjects performed a standard visual oddball task. Several research projects have used this data to test artifact detection, classification, transfer learning, EEG preprocessing, blink detection, and automated annotation algorithms. We are releasing the data in three formats to enable benchmarking of EEG algorithms in many areas. The data was acquired using a Biosemi Active 2 EEG headset and includes 64 channels of EEG, 4 channels of EOG (electrooculogram), and 2 mastoid reference channels.
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Affiliation(s)
- Kay Robbins
- Department of Computer Science, University of Texas-San Antonio, San Antonio, TX 78249, USA
| | - Kyung-Min Su
- Department of Computer Science, University of Texas-San Antonio, San Antonio, TX 78249, USA
| | - W David Hairston
- Human Research and Engineering Directorate, US Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
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14
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Farzan F, Atluri S, Frehlich M, Dhami P, Kleffner K, Price R, Lam RW, Frey BN, Milev R, Ravindran A, McAndrews MP, Wong W, Blumberger D, Daskalakis ZJ, Vila-Rodriguez F, Alonso E, Brenner CA, Liotti M, Dharsee M, Arnott SR, Evans KR, Rotzinger S, Kennedy SH. Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: insights from the canadian biomarker integration network in depression. Sci Rep 2017; 7:7473. [PMID: 28785082 PMCID: PMC5547036 DOI: 10.1038/s41598-017-07613-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 06/29/2017] [Indexed: 01/13/2023] Open
Abstract
Subsequent to global initiatives in mapping the human brain and investigations of neurobiological markers for brain disorders, the number of multi-site studies involving the collection and sharing of large volumes of brain data, including electroencephalography (EEG), has been increasing. Among the complexities of conducting multi-site studies and increasing the shelf life of biological data beyond the original study are timely standardization and documentation of relevant study parameters. We present the insights gained and guidelines established within the EEG working group of the Canadian Biomarker Integration Network in Depression (CAN-BIND). CAN-BIND is a multi-site, multi-investigator, and multi-project network supported by the Ontario Brain Institute with access to Brain-CODE, an informatics platform that hosts a multitude of biological data across a growing list of brain pathologies. We describe our approaches and insights on documenting and standardizing parameters across the study design, data collection, monitoring, analysis, integration, knowledge-translation, and data archiving phases of CAN-BIND projects. We introduce a custom-built EEG toolbox to track data preprocessing with open-access for the scientific community. We also evaluate the impact of variation in equipment setup on the accuracy of acquired data. Collectively, this work is intended to inspire establishing comprehensive and standardized guidelines for multi-site studies.
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Affiliation(s)
- Faranak Farzan
- Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON, M6J 1A8, Canada. .,Department of Psychiatry, University of Toronto, 250 College Street, 8th floor, Toronto, ON, M5T 1R8, Canada. .,Institute of Medical Science, Faculty of Medicine, University of Toronto, Medical Sciences Building, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada. .,School of Mechatronic Systems Engineering, Simon Fraser University, 250-13450 102 Avenue, Surrey, BC, V3T 0A3, Canada.
| | - Sravya Atluri
- Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON, M6J 1A8, Canada.,Institute of Biomaterial and Biomedical Engineering, Rosebrugh Building, Room 407, 164 College St, Toronto, ON, M5S 3G9, Canada
| | - Matthew Frehlich
- Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON, M6J 1A8, Canada.,The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, 10 King's College Road, Toronto, ON, M5S 3G4, Canada
| | - Prabhjot Dhami
- Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON, M6J 1A8, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Medical Sciences Building, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada
| | - Killian Kleffner
- University of British Columbia and Vancouver Coastal Health Authority, 2255 Wesbrook Mall, Vancouver, BC, V6T 2A1, Canada
| | - Rae Price
- University Health Network, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
| | - Raymond W Lam
- University of British Columbia and Vancouver Coastal Health Authority, 2255 Wesbrook Mall, Vancouver, BC, V6T 2A1, Canada
| | - Benicio N Frey
- McMaster University, and St. Joseph's Healthcare Hamilton, 1280 Main Street West, Hamilton, ON, L8S 4L8, Canada
| | - Roumen Milev
- Queen's University, Providence Care, Mental Health Services, 752 King Street West, Kingston, ON, K7L 4X3, Canada
| | - Arun Ravindran
- Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON, M6J 1A8, Canada.,Department of Psychiatry, University of Toronto, 250 College Street, 8th floor, Toronto, ON, M5T 1R8, Canada
| | - Mary Pat McAndrews
- Department of Psychiatry, University of Toronto, 250 College Street, 8th floor, Toronto, ON, M5T 1R8, Canada.,University Health Network, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
| | - Willy Wong
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, 10 King's College Road, Toronto, ON, M5S 3G4, Canada
| | - Daniel Blumberger
- Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON, M6J 1A8, Canada.,Department of Psychiatry, University of Toronto, 250 College Street, 8th floor, Toronto, ON, M5T 1R8, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Medical Sciences Building, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada
| | - Zafiris J Daskalakis
- Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON, M6J 1A8, Canada.,Department of Psychiatry, University of Toronto, 250 College Street, 8th floor, Toronto, ON, M5T 1R8, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Medical Sciences Building, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada
| | - Fidel Vila-Rodriguez
- University of British Columbia and Vancouver Coastal Health Authority, 2255 Wesbrook Mall, Vancouver, BC, V6T 2A1, Canada
| | - Esther Alonso
- University of British Columbia and Vancouver Coastal Health Authority, 2255 Wesbrook Mall, Vancouver, BC, V6T 2A1, Canada
| | | | - Mario Liotti
- Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada
| | - Moyez Dharsee
- Indoc Research, 258 Adelaide St. East, Suite 200, Toronto, ON, M5A 1N1, Canada
| | - Stephen R Arnott
- Rotman Research Institute at Baycrest Centre, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada
| | - Kenneth R Evans
- Indoc Research, 258 Adelaide St. East, Suite 200, Toronto, ON, M5A 1N1, Canada.,Department of Pathology and Molecular Medicine, Queen's University, 88 Stuart Street, Kingston, ON, K7L 3N6, Canada
| | - Susan Rotzinger
- Department of Psychiatry, University of Toronto, 250 College Street, 8th floor, Toronto, ON, M5T 1R8, Canada.,University Health Network, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, University of Toronto, 250 College Street, 8th floor, Toronto, ON, M5T 1R8, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Medical Sciences Building, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada.,University Health Network, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada.,St. Michael's Hospital, 193 Yonge St, Toronto, ON, M5B 1M4, Canada
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15
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Bigdely-Shamlo N, Cockfield J, Makeig S, Rognon T, La Valle C, Miyakoshi M, Robbins KA. Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG. Front Neuroinform 2016; 10:42. [PMID: 27799907 PMCID: PMC5065975 DOI: 10.3389/fninf.2016.00042] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Accepted: 09/21/2016] [Indexed: 11/13/2022] Open
Abstract
Real-world brain imaging by EEG requires accurate annotation of complex subject-environment interactions in event-rich tasks and paradigms. This paper describes the evolution of the Hierarchical Event Descriptor (HED) system for systematically describing both laboratory and real-world events. HED version 2, first described here, provides the semantic capability of describing a variety of subject and environmental states. HED descriptions can include stimulus presentation events on screen or in virtual worlds, experimental or spontaneous events occurring in the real world environment, and events experienced via one or multiple sensory modalities. Furthermore, HED 2 can distinguish between the mere presence of an object and its actual (or putative) perception by a subject. Although the HED framework has implicit ontological and linked data representations, the user-interface for HED annotation is more intuitive than traditional ontological annotation. We believe that hiding the formal representations allows for a more user-friendly interface, making consistent, detailed tagging of experimental, and real-world events possible for research users. HED is extensible while retaining the advantages of having an enforced common core vocabulary. We have developed a collection of tools to support HED tag assignment and validation; these are available at hedtags.org. A plug-in for EEGLAB (sccn.ucsd.edu/eeglab), CTAGGER, is also available to speed the process of tagging existing studies.
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Affiliation(s)
| | - Jeremy Cockfield
- Department of Computer Science, University of Texas at San Antonio San Antonio, TX, USA
| | - Scott Makeig
- Swartz Center for Computational Neuroscience, University of California, San Diego San Diego, CA, USA
| | - Thomas Rognon
- Department of Computer Science, University of Texas at San Antonio San Antonio, TX, USA
| | - Chris La Valle
- Department of Computer Science, University of Texas at San Antonio San Antonio, TX, USA
| | - Makoto Miyakoshi
- Swartz Center for Computational Neuroscience, University of California, San Diego San Diego, CA, USA
| | - Kay A Robbins
- Department of Computer Science, University of Texas at San Antonio San Antonio, TX, USA
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16
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Hege MA, Kullmann S, Heni M, Schleger F, Linder K, Fritsche A, Preissl H. Electro/magnetoencephalographic signatures of human brain insulin resistance. Curr Opin Behav Sci 2016. [DOI: 10.1016/j.cobeha.2016.05.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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