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Shimizu R, Wu HT. Unveil sleep spindles with concentration of frequency and time (ConceFT). Physiol Meas 2024; 45:085003. [PMID: 39042095 DOI: 10.1088/1361-6579/ad66aa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 07/19/2024] [Indexed: 07/24/2024]
Abstract
Objective.Sleep spindles contain crucial brain dynamics information. We introduce the novel non-linear time-frequency (TF) analysis tool 'Concentration of Frequency and Time' (ConceFT) to create an interpretable automated algorithm for sleep spindle annotation in EEG data and to measure spindle instantaneous frequencies (IFs).Approach.ConceFT effectively reduces stochastic EEG influence, enhancing spindle visibility in the TF representation. Our automated spindle detection algorithm, ConceFT-Spindle (ConceFT-S), is compared to A7 (non-deep learning) and SUMO (deep learning) using Dream and Montreal Archive of Sleep Studies (MASS) benchmark databases. We also quantify spindle IF dynamics.Main results.ConceFT-S achieves F1 scores of 0.765 in Dream and 0.791 in MASS, which surpass A7 and SUMO. We reveal that spindle IF is generally nonlinear.Significance.ConceFT offers an accurate, interpretable EEG-based sleep spindle detection algorithm and enables spindle IF quantification.
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Affiliation(s)
- Riki Shimizu
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
| | - Hau-Tieng Wu
- Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, United States of America
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2
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Ray LB, Baena D, Fogel SM. "Counting sheep PSG": EEGLAB-compatible open-source matlab software for signal processing, visualization, event marking and staging of polysomnographic data. J Neurosci Methods 2024; 407:110162. [PMID: 38740142 DOI: 10.1016/j.jneumeth.2024.110162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 05/06/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Progress in advancing sleep research employing polysomnography (PSG) has been negatively impacted by the limited availability of widely available, open-source sleep-specific analysis tools. NEW METHOD Here, we introduce Counting Sheep PSG, an EEGLAB-compatible software for signal processing, visualization, event marking and manual sleep stage scoring of PSG data for MATLAB. RESULTS Key features include: (1) signal processing tools including bad channel interpolation, down-sampling, re-referencing, filtering, independent component analysis, artifact subspace reconstruction, and power spectral analysis, (2) customizable display of polysomnographic data and hypnogram, (3) event marking mode including manual sleep stage scoring, (4) automatic event detections including movement artifact, sleep spindles, slow waves and eye movements, and (5) export of main descriptive sleep architecture statistics, event statistics and publication-ready hypnogram. COMPARISON WITH EXISTING METHODS Counting Sheep PSG was built on the foundation created by sleepSMG (https://sleepsmg.sourceforge.net/). The scope and functionalities of the current software have made significant advancements in terms of EEGLAB integration/compatibility, preprocessing, artifact correction, event detection, functionality and ease of use. By comparison, commercial software can be costly and utilize proprietary data formats and algorithms, thereby restricting the ability to distribute and share data and analysis results. CONCLUSIONS The field of sleep research remains shackled by an industry that resists standardization, prevents interoperability, builds-in planned obsolescence, maintains proprietary black-box data formats and analysis approaches. This presents a major challenge for the field of sleep research. The need for free, open-source software that can read open-format data is essential for scientific advancement to be made in the field.
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Affiliation(s)
- L B Ray
- School of Psychology, University of Ottawa, Ottawa K1N 6N5, Canada
| | - D Baena
- School of Psychology, University of Ottawa, Ottawa K1N 6N5, Canada; Sleep Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa K1Z 7K4, Canada
| | - S M Fogel
- School of Psychology, University of Ottawa, Ottawa K1N 6N5, Canada; Sleep Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa K1Z 7K4, Canada; University of Ottawa Brain & Mind Research Institute, Ottawa K1H 8M5, Canada.
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3
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Jafarzadeh Esfahani M, Sikder N, Ter Horst R, Daraie AH, Appel K, Weber FD, Bevelander KE, Dresler M. Citizen neuroscience: Wearable technology and open software to study the human brain in its natural habitat. Eur J Neurosci 2024; 59:948-965. [PMID: 38328991 DOI: 10.1111/ejn.16227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 11/09/2023] [Accepted: 11/30/2023] [Indexed: 02/09/2024]
Abstract
Citizen science allows the public to participate in various stages of scientific research, including study design, data acquisition, and data analysis. Citizen science has a long history in several fields of the natural sciences, and with recent developments in wearable technology, neuroscience has also become more accessible to citizen scientists. This development was largely driven by the influx of minimal sensing systems in the consumer market, allowing more do-it-yourself (DIY) and quantified-self (QS) investigations of the human brain. While most subfields of neuroscience require sophisticated monitoring devices and laboratories, the study of sleep characteristics can be performed at home with relevant noninvasive consumer devices. The strong influence of sleep quality on waking life and the accessibility of devices to measure sleep are two primary reasons citizen scientists have widely embraced sleep research. Their involvement has evolved from solely contributing to data collection to engaging in more collaborative or autonomous approaches, such as instigating ideas, formulating research inquiries, designing research protocols and methodology, acting upon their findings, and disseminating results. In this article, we introduce the emerging field of citizen neuroscience, illustrating examples of such projects in sleep research. We then provide overviews of the wearable technologies for tracking human neurophysiology and various open-source software used to analyse them. Finally, we discuss the opportunities and challenges in citizen neuroscience projects and suggest how to improve the study of the human brain outside the laboratory.
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Affiliation(s)
| | - Niloy Sikder
- Donders Institute for Brain, Behaviour, and Cognition, Radboudumc, Nijmegen, The Netherlands
- Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, Kleve, Germany
| | - Rob Ter Horst
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Amir Hossein Daraie
- Donders Institute for Brain, Behaviour, and Cognition, Radboudumc, Nijmegen, The Netherlands
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Frederik D Weber
- Donders Institute for Brain, Behaviour, and Cognition, Radboudumc, Nijmegen, The Netherlands
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Kirsten E Bevelander
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
- Primary and Community Care, Radboud University and Medical Center, Nijmegen, The Netherlands
| | - Martin Dresler
- Donders Institute for Brain, Behaviour, and Cognition, Radboudumc, Nijmegen, The Netherlands
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4
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Deng M, Gluck A, Zhao Y, Li D, Menassa CC, Kamat VR, Brinkley J. An analysis of physiological responses as indicators of driver takeover readiness in conditionally automated driving. ACCIDENT; ANALYSIS AND PREVENTION 2024; 195:107372. [PMID: 37979464 DOI: 10.1016/j.aap.2023.107372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 10/12/2023] [Accepted: 11/03/2023] [Indexed: 11/20/2023]
Abstract
By the year 2045, it is projected that Autonomous Vehicles (AVs) will make up half of the new vehicle market. Successful adoption of AVs can reduce drivers' stress and fatigue, curb traffic congestion, and improve safety, mobility, and economic efficiency. Due to the limited intelligence in relevant technologies, human-in-the-loop modalities are still necessary to ensure the safety of AVs at current or near future stages, because the vehicles may not be able to handle all emergencies. Therefore, it is important to know the takeover readiness of the drivers to ensure the takeover quality and avoid any potential accidents. To achieve this, a comprehensive understanding of the drivers' physiological states is crucial. However, there is a lack of systematic analysis of the correlation between different human physiological responses and takeover behaviors which could serve as important references for future studies to determine the types of data to use. This paper provides a comprehensive analysis of the effects of takeover behaviors on the common physiological indicators. A program for conditional automation was developed based on a game engine and applied to a driving simulator. The experiment incorporated three types of secondary tasks, three takeover events, and two traffic densities. Brain signals, Skin Conductance Level (SCL), and Heart Rate (HR) of the participants were collected while they were performing the driving simulations. The Frontal Asymmetry Index (FAI) (as an indicator of engagement) and Mental Workload (MWL) were calculated from the brain signals to indicate the mental states of the participants. The results revealed that the FAI of the drivers would slightly decrease after the takeover alerts were issued when they were doing secondary tasks prior to the takeover activities, and the higher difficulty of the secondary tasks could lead to lower overall FAI during the takeover periods. In contrast, The MWL and SCL increased during the takeover periods. The HR also increased rapidly at the beginning of the takeover period but dropped back to a normal level quickly. It was found that a fake takeover alert would lead to lower overall HR, slower increase, and lower peak of SCL during the takeover periods. Moreover, the higher traffic density scenarios were associated with higher MWL, and a more difficult secondary task would lead to higher MWL and HR during the takeover activities. A preliminary discussion of the correlation between the physiological data, takeover scenario, and vehicle data (that relevant to takeover readiness) was then conducted, revealing that although takeover event, SCL, and HR had slightly higher correlations with the maximum acceleration and reaction time, none of them dominated the takeover readiness. In addition, the analysis of the data across different participants was conducted, which emphasized the importance of considering standardization or normalization of the data when they were further used as input features for estimating takeover readiness. Overall, the results presented in this paper offer profound insights into the patterns of physiological data changes during takeover periods. These findings can be used as benchmarks for utilizing these variables as indicators of takeover preparedness and performance in future research endeavors.
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Affiliation(s)
- Min Deng
- Department of Civil and Environmental Engineering, University of Michigan, MI 48109, United States.
| | - Aaron Gluck
- School of Computing, Clemson University, SC 29631, United States.
| | - Yijin Zhao
- Department of Civil Engineering, Clemson University, South Carolina, SC 29634, United States.
| | - Da Li
- Department of Civil Engineering, Clemson University, South Carolina, SC 29634, United States.
| | - Carol C Menassa
- Department of Civil and Environmental Engineering, University of Michigan, MI 48109, United States.
| | - Vineet R Kamat
- Department of Civil and Environmental Engineering, University of Michigan, MI 48109, United States.
| | - Julian Brinkley
- School of Computing, Clemson University, SC 29631, United States.
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Kersanté F, Purple RJ, Jones MW. The GABA A receptor modulator zolpidem augments hippocampal-prefrontal coupling during non-REM sleep. Neuropsychopharmacology 2023; 48:594-604. [PMID: 35717464 PMCID: PMC9938179 DOI: 10.1038/s41386-022-01355-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 05/25/2022] [Accepted: 05/27/2022] [Indexed: 01/16/2023]
Abstract
Benzodiazepines and 'Z-drugs' (including zolpidem and zopiclone) are GABAA receptor (GABAAR) positive modulators commonly prescribed as hypnotics to treat insomnia and/or anxiety. However, alongside sedation, augmenting GABAAR function may also alter coordinated neuronal activity during sleep, thereby influencing sleep-dependent processes including memory consolidation. We used simultaneous recordings of neural population activity from the medial prelimbic cortex (PrL) and CA1 of the dorsal hippocampus (dCA1) of naturally sleeping rats to detail the effects of zolpidem on network activity during the cardinal oscillations of non-REM sleep. For comparison, we also characterized the effects of diazepam and 4,5,6,7-tetrahydroisoxazolo(5,4-c)pyridin-3-ol (THIP/gaboxadol), which acts predominantly at extra-synaptic GABAARs. Zolpidem and THIP significantly increased the amplitudes of slow-waves, which were attenuated by diazepam. Zolpidem increased hippocampal ripple density whereas diazepam decreased both ripple density and intrinsic frequency. While none of the drugs affected thalamocortical spindles in isolation, zolpidem augmented the temporal coordination between slow-waves and spindles. At the cellular level, analyses of spiking activity from 523 PrL and 579 dCA1 neurons revealed that zolpidem significantly enhanced synchronized pauses in cortical firing during slow-wave down states, while increasing correlated activity within and between dCA1 and PrL populations. Of the drugs compared here, zolpidem was unique in augmenting coordinated activity within and between hippocampus and neocortex during non-REM sleep. Zolpidem's enhancement of hippocampal-prefrontal coupling may reflect the cellular basis of its potential to modulate offline memory processing.
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Affiliation(s)
- Flavie Kersanté
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Biomedical Sciences Building, University Walk, Bristol, BS8 1TD, UK
| | - Ross J Purple
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Biomedical Sciences Building, University Walk, Bristol, BS8 1TD, UK
| | - Matthew W Jones
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Biomedical Sciences Building, University Walk, Bristol, BS8 1TD, UK.
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6
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Recurrent Hippocampo-neocortical sleep-state divergence in humans. Proc Natl Acad Sci U S A 2022; 119:e2123427119. [PMID: 36279474 PMCID: PMC9636919 DOI: 10.1073/pnas.2123427119] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Sleep is assumed to be a unitary, global state in humans and most other animals that is coordinated by executive centers in the brain stem, hypothalamus, and basal forebrain. However, the common observation of unihemispheric sleep in birds and marine mammals, as well as the recently discovered nonpathological regional sleep in rodents, calls into question whether the whole human brain might also typically exhibit different states between brain areas at the same time. We analyzed sleep states independently from simultaneously recorded hippocampal depth electrodes and cortical scalp electrodes in eight human subjects who were implanted with depth electrodes for pharmacologically intractable epilepsy evaluation. We found that the neocortex and hippocampus could be in nonsimultaneous states, on average, one-third of the night and that the hippocampus often led in asynchronous state transitions. Nonsimultaneous bout lengths varied from 30 s to over 30 min. These results call into question the conclusions of studies, across phylogeny, that measure only surface cortical state but seek to assess the functions and drivers of sleep states throughout the brain.
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7
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da Silva Souto CF, Pätzold W, Paul M, Debener S, Wolf KI. Pre-gelled Electrode Grid for Self-Applied EEG Sleep Monitoring at Home. Front Neurosci 2022; 16:883966. [PMID: 35812225 PMCID: PMC9263847 DOI: 10.3389/fnins.2022.883966] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
The need for diagnostic capabilities for sleep disorders such as sleep apnea and insomnia far exceeds the capacity of inpatient sleep laboratories. Some home monitoring systems omit electroencephalography (EEG) because trained personnel may be needed to apply EEG sensors. Since EEG is essential for the detailed evaluation of sleep, better systems supporting the convenient and robust recording of sleep EEG at home are desirable. Recent advances in EEG acquisition with flex-printed sensors promise easier application of EEG sensor arrays for chronic recordings, yet these sensor arrays were not designed for sleep EEG. Here we explored the self-applicability of a new sleep EEG sensor array (trEEGrid) without prior training. We developed a prototype with pre-gelled neonatal ECG electrodes placed on a self-adhesive grid shape that guided the fast and correct positioning of a total of nine electrodes on the face and around the ear. Positioning of the sensors was based on the results of a previous ear-EEG sleep study (da Silva Souto et al., 2021), and included electrodes around the ear, one eye, and the chin. For comparison, EEG and electrooculogram channels placed according to the American Academy of Sleep Medicine criteria, as well as respiratory inductance plethysmography on thorax and abdomen, oxygen saturation, pulse and body position were included with a mobile polysomnography (PSG) system. Two studies with 32 individuals were conducted to compare the signal quality of the proposed flex-printed grid with PSG signals and to explore self-application of the new grid at home. Results indicate that the new array is self-applicable by healthy participants without on-site hands-on support. A comparison of the hypnogram annotations obtained from the data of both systems revealed an overall substantial agreement on a group level (Cohen’s κ = 0.70 ± 0.01). These results suggest that flex-printed pre-gelled sensor arrays designed for sleep EEG acquisition can facilitate self-recording at home.
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Affiliation(s)
- Carlos F. da Silva Souto
- Fraunhofer Institute for Digital Media Technology IDMT, Branch Hearing, Speech and Audio Technology HSA, Oldenburg, Germany
- *Correspondence: Carlos F. da Silva Souto,
| | - Wiebke Pätzold
- Fraunhofer Institute for Digital Media Technology IDMT, Branch Hearing, Speech and Audio Technology HSA, Oldenburg, Germany
| | | | - Stefan Debener
- Fraunhofer Institute for Digital Media Technology IDMT, Branch Hearing, Speech and Audio Technology HSA, Oldenburg, Germany
- Neuropsychology Laboratory, Department of Psychology, University of Oldenburg, Oldenburg, Germany
- Cluster of Excellence Hearing4All, University of Oldenburg, Oldenburg, Germany
| | - Karen Insa Wolf
- Fraunhofer Institute for Digital Media Technology IDMT, Branch Hearing, Speech and Audio Technology HSA, Oldenburg, Germany
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8
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Hwang J, Lee T, Lee H, Byun S. A Clinical Decision Support System for Sleep Staging Tasks With Explanations From Artificial Intelligence: User-Centered Design and Evaluation Study. J Med Internet Res 2022; 24:e28659. [PMID: 35044311 PMCID: PMC8811694 DOI: 10.2196/28659] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 06/30/2021] [Accepted: 12/01/2021] [Indexed: 12/11/2022] Open
Abstract
Background Despite the unprecedented performance of deep learning algorithms in clinical domains, full reviews of algorithmic predictions by human experts remain mandatory. Under these circumstances, artificial intelligence (AI) models are primarily designed as clinical decision support systems (CDSSs). However, from the perspective of clinical practitioners, the lack of clinical interpretability and user-centered interfaces hinders the adoption of these AI systems in practice. Objective This study aims to develop an AI-based CDSS for assisting polysomnographic technicians in reviewing AI-predicted sleep staging results. This study proposed and evaluated a CDSS that provides clinically sound explanations for AI predictions in a user-centered manner. Methods Our study is based on a user-centered design framework for developing explanations in a CDSS that identifies why explanations are needed, what information should be contained in explanations, and how explanations can be provided in the CDSS. We conducted user interviews, user observation sessions, and an iterative design process to identify three key aspects for designing explanations in the CDSS. After constructing the CDSS, the tool was evaluated to investigate how the CDSS explanations helped technicians. We measured the accuracy of sleep staging and interrater reliability with macro-F1 and Cohen κ scores to assess quantitative improvements after our tool was adopted. We assessed qualitative improvements through participant interviews that established how participants perceived and used the tool. Results The user study revealed that technicians desire explanations that are relevant to key electroencephalogram (EEG) patterns for sleep staging when assessing the correctness of AI predictions. Here, technicians wanted explanations that could be used to evaluate whether the AI models properly locate and use these patterns during prediction. On the basis of this, information that is closely related to sleep EEG patterns was formulated for the AI models. In the iterative design phase, we developed a different visualization strategy for each pattern based on how technicians interpreted the EEG recordings with these patterns during their workflows. Our evaluation study on 9 polysomnographic technicians quantitatively and qualitatively investigated the helpfulness of the tool. For technicians with <5 years of work experience, their quantitative sleep staging performance improved significantly from 56.75 to 60.59 with a P value of .05. Qualitatively, participants reported that the information provided effectively supported them, and they could develop notable adoption strategies for the tool. Conclusions Our findings indicate that formulating clinical explanations for automated predictions using the information in the AI with a user-centered design process is an effective strategy for developing a CDSS for sleep staging.
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Affiliation(s)
| | | | | | - Seonjeong Byun
- Department of Neuropsychiatry, Uijeongbu St Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu-si, Republic of Korea
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9
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da Silva Souto CF, Pätzold W, Wolf KI, Paul M, Matthiesen I, Bleichner MG, Debener S. Flex-Printed Ear-EEG Sensors for Adequate Sleep Staging at Home. Front Digit Health 2021; 3:688122. [PMID: 34713159 PMCID: PMC8522006 DOI: 10.3389/fdgth.2021.688122] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/01/2021] [Indexed: 12/03/2022] Open
Abstract
A comfortable, discrete and robust recording of the sleep EEG signal at home is a desirable goal but has been difficult to achieve. We investigate how well flex-printed electrodes are suitable for sleep monitoring tasks in a smartphone-based home environment. The cEEGrid ear-EEG sensor has already been tested in the laboratory for measuring night sleep. Here, 10 participants slept at home and were equipped with a cEEGrid and a portable amplifier (mBrainTrain, Serbia). In addition, the EEG of Fpz, EOG_L and EOG_R was recorded. All signals were recorded wirelessly with a smartphone. On average, each participant provided data for M = 7.48 h. An expert sleep scorer created hypnograms and annotated grapho-elements according to AASM based on the EEG of Fpz, EOG_L and EOG_R twice, which served as the baseline agreement for further comparisons. The expert scorer also created hypnograms using bipolar channels based on combinations of cEEGrid channels only, and bipolar cEEGrid channels complemented by EOG channels. A comparison of the hypnograms based on frontal electrodes with the ones based on cEEGrid electrodes (κ = 0.67) and the ones based on cEEGrid complemented by EOG channels (κ = 0.75) both showed a substantial agreement, with the combination including EOG channels showing a significantly better outcome than the one without (p = 0.006). Moreover, signal excerpts of the conventional channels containing grapho-elements were correlated with those of the cEEGrid in order to determine the cEEGrid channel combination that optimally represents the annotated grapho-elements. The results show that the grapho-elements were well-represented by the front-facing electrode combinations. The correlation analysis of the grapho-elements resulted in an average correlation coefficient of 0.65 for the most suitable electrode configuration of the cEEGrid. The results confirm that sleep stages can be identified with electrodes placement around the ear. This opens up opportunities for miniaturized ear-EEG systems that may be self-applied by users.
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Affiliation(s)
- Carlos F da Silva Souto
- Branch for Hearing, Speech and Audio Technology HSA, Fraunhofer Institute for Digital Media Technology IDMT, Oldenburg, Germany
| | - Wiebke Pätzold
- Branch for Hearing, Speech and Audio Technology HSA, Fraunhofer Institute for Digital Media Technology IDMT, Oldenburg, Germany
| | - Karen Insa Wolf
- Branch for Hearing, Speech and Audio Technology HSA, Fraunhofer Institute for Digital Media Technology IDMT, Oldenburg, Germany
| | | | - Ida Matthiesen
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Martin G Bleichner
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany.,Neurophysiology of Everyday Life Group, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Stefan Debener
- Branch for Hearing, Speech and Audio Technology HSA, Fraunhofer Institute for Digital Media Technology IDMT, Oldenburg, Germany.,Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
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10
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Carrier-Ruiz A, Sugaya Y, Kumar D, Vergara P, Koyanagi I, Srinivasan S, Naoi T, Kano M, Sakaguchi M. Calcium imaging of adult-born neurons in freely moving mice. STAR Protoc 2021; 2:100238. [PMID: 33458703 PMCID: PMC7797924 DOI: 10.1016/j.xpro.2020.100238] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Adult-born neurons (ABNs) in the dentate gyrus bestow unique cellular plasticity to the mammalian brain. We recently found that the activity of ABNs during sleep is necessary for memory consolidation. Here, we describe our method for Ca2+ imaging of ABN activity using a miniaturized fluorescent microscope and sleep recordings. As preparatory surgery and post-recording data processing can be major obstacles, we provide detailed descriptions and problem-solving tips. For complete details on the use and execution of this protocol, please refer to Kumar et al. (2020).
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Affiliation(s)
- Alvaro Carrier-Ruiz
- Department of Neurophysiology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo 113-0033, Japan
- Department of Neuroscience, Karolinska Institute, Stockholm 17165, Sweden
| | - Yuki Sugaya
- Department of Neurophysiology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo 113-0033, Japan
| | - Deependra Kumar
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki 305-0006, Japan
| | - Pablo Vergara
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki 305-0006, Japan
| | - Iyo Koyanagi
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki 305-0006, Japan
| | - Sakthivel Srinivasan
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki 305-0006, Japan
| | - Toshie Naoi
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki 305-0006, Japan
| | - Masanobu Kano
- Department of Neurophysiology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo 113-0033, Japan
| | - Masanori Sakaguchi
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki 305-0006, Japan
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11
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Vallat R, Nicolas A, Ruby P. Brain functional connectivity upon awakening from sleep predicts interindividual differences in dream recall frequency. Sleep 2020; 43:5864676. [PMID: 32597973 DOI: 10.1093/sleep/zsaa116] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 05/14/2020] [Indexed: 12/28/2022] Open
Abstract
Why do some individuals recall dreams every day while others hardly ever recall one? We hypothesized that sleep inertia-the transient period following awakening associated with brain and cognitive alterations-could be a key mechanism to explain interindividual differences in dream recall at awakening. To test this hypothesis, we measured the brain functional connectivity (combined electroencephalography-functional magnetic resonance imaging) and cognition (memory and mental calculation) of high dream recallers (HR, n = 20) and low dream recallers (LR, n = 18) in the minutes following awakening from an early-afternoon nap. Resting-state scans were acquired just after or before a 2 min mental calculation task, before the nap, 5 min after awakening from the nap, and 25 min after awakening. A comic was presented to the participants before the nap with no explicit instructions to memorize it. Dream(s) and comic recall were collected after the first post-awakening scan. As expected, between-group contrasts of the functional connectivity at 5 min post-awakening revealed a pattern of enhanced connectivity in HR within the default mode network (DMN) and between regions of the DMN and regions involved in memory processes. At the behavioral level, a between-group difference was observed in dream recall, but not comic recall. Our results provide the first evidence that brain functional connectivity right after awakening is associated with interindividual trait differences in dream recall and suggest that the brain connectivity of HR at awakening facilitates the maintenance of the short-term memory of the dream during the sleep-wake transition.
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Affiliation(s)
- Raphael Vallat
- Department of Psychology, Center for Human Sleep Science, University of California, Berkeley, CA.,Lyon Neuroscience Research Center (CRNL), Brain Dynamics and Cognition team (DYCOG), INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Alain Nicolas
- Lyon Neuroscience Research Center (CRNL), Brain Dynamics and Cognition team (DYCOG), INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Perrine Ruby
- Lyon Neuroscience Research Center (CRNL), Brain Dynamics and Cognition team (DYCOG), INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
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12
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Combrisson E, Nest T, Brovelli A, Ince RAA, Soto JLP, Guillot A, Jerbi K. Tensorpac: An open-source Python toolbox for tensor-based phase-amplitude coupling measurement in electrophysiological brain signals. PLoS Comput Biol 2020; 16:e1008302. [PMID: 33119593 PMCID: PMC7654762 DOI: 10.1371/journal.pcbi.1008302] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 11/10/2020] [Accepted: 09/02/2020] [Indexed: 12/12/2022] Open
Abstract
Despite being the focus of a thriving field of research, the biological mechanisms that underlie information integration in the brain are not yet fully understood. A theory that has gained a lot of traction in recent years suggests that multi-scale integration is regulated by a hierarchy of mutually interacting neural oscillations. In particular, there is accumulating evidence that phase-amplitude coupling (PAC), a specific form of cross-frequency interaction, plays a key role in numerous cognitive processes. Current research in the field is not only hampered by the absence of a gold standard for PAC analysis, but also by the computational costs of running exhaustive computations on large and high-dimensional electrophysiological brain signals. In addition, various signal properties and analyses parameters can lead to spurious PAC. Here, we present Tensorpac, an open-source Python toolbox dedicated to PAC analysis of neurophysiological data. The advantages of Tensorpac include (1) higher computational efficiency thanks to software design that combines tensor computations and parallel computing, (2) the implementation of all most widely used PAC methods in one package, (3) the statistical analysis of PAC measures, and (4) extended PAC visualization capabilities. Tensorpac is distributed under a BSD-3-Clause license and can be launched on any operating system (Linux, OSX and Windows). It can be installed directly via pip or downloaded from Github (https://github.com/EtienneCmb/tensorpac). By making Tensorpac available, we aim to enhance the reproducibility and quality of PAC research, and provide open tools that will accelerate future method development in neuroscience.
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Affiliation(s)
- Etienne Combrisson
- Psychology Department, University of Montréal, QC, Canada
- Institut de Neurosciences de la Timone, UMR 7289, Aix Marseille Université, CNRS, 13385 Marseille, France
| | - Timothy Nest
- Psychology Department, University of Montréal, QC, Canada
- Département d’informatique et de recherche opérationnelle, University of Montréal, QC, Canada
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, UMR 7289, Aix Marseille Université, CNRS, 13385 Marseille, France
| | - Robin A. A. Ince
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
| | - Juan L. P. Soto
- Telecommunications and Control Engineering Department, University of Sao Paulo, Sao Paulo, Brazil
| | - Aymeric Guillot
- Univ. Lyon, UCBL-Lyon 1, Laboratoire Interuniversitaire de Biologie de la Motricité, EA 7424, F-69622 Villeurbanne, France
| | - Karim Jerbi
- Psychology Department, University of Montréal, QC, Canada
- MEG Center, University of Montréal, QC, Canada
- Mila - Quebec Artificial Intelligence Institute, QC, Canada
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13
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Jacobsen S, Meiron O, Salomon DY, Kraizler N, Factor H, Jaul E, Tsur EE. Integrated Development Environment for EEG-Driven Cognitive-Neuropsychological Research. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2020; 8:2200208. [PMID: 32431963 PMCID: PMC7233754 DOI: 10.1109/jtehm.2020.2989768] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 03/23/2020] [Accepted: 04/17/2020] [Indexed: 11/29/2022]
Abstract
Background: EEG-driven research is paramount in cognitive-neuropsychological studies, as it provides a non-invasive window to the underlying neural mechanisms of cognition and behavior. A myriad collection of software and hardware frameworks has been developed to alleviate some of the technical barriers involved in EEG-driven research. Methods: we propose an integrated development environment which encompasses the entire technical “data-collection pipeline” of cognitive-neuropsychological research, including experiment design, data acquisition, data exploration and analysis in a state-of-the-art user interface. Our framework is based on a unique integration between a python-based web framework, time-oriented databases and object-based data schemes. Results: we demonstrated our framework with the recording and analysis of an n-Back task completed by 15 elderly (ages 50 to 80) participants. This case study demonstrates the highly utilized nature of our integrated framework with a challenging target population. Furthermore, our results may provide new insights into the correlation between brain activity and working memory performance in elderly people, who are prone to experience accelerated decline in executive prefrontal cortex functioning. Conclusion: our framework extends the range of EEG-driven experimental methods for assessing cognition available for cognitive-neuroscientists, allowing them to concentrate on the creative part of their work instead of technical aspects.
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Affiliation(s)
- Shoham Jacobsen
- 1Department of Computer ScienceJerusalem College of TechnologyJerusalem91160Israel
| | - Oded Meiron
- 2Clinical Research Center for Brain SciencesHerzog Medical CenterJerusalem91120Israel
| | - David Yoel Salomon
- 1Department of Computer ScienceJerusalem College of TechnologyJerusalem91160Israel
| | - Nir Kraizler
- 1Department of Computer ScienceJerusalem College of TechnologyJerusalem91160Israel
| | - Hagai Factor
- 2Clinical Research Center for Brain SciencesHerzog Medical CenterJerusalem91120Israel
| | - Efraim Jaul
- 3Geriatric Skilled Nursing DepartmentHerzog Medical CenterJerusalem91120Israel
| | - Elishai Ezra Tsur
- 1Department of Computer ScienceJerusalem College of TechnologyJerusalem91160Israel
- 4Neuro-Biomorphic Engineering Laboratory (NBEL)Department of Mathematics and Computer ScienceThe Open University of IsraelRa'anana4353701Israel
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14
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Macroscopic information-based taste representations in insular cortex are shaped by stimulus concentration. Proc Natl Acad Sci U S A 2020; 117:7409-7417. [PMID: 32179687 DOI: 10.1073/pnas.1916329117] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Taste processing is an essential ability in all animals signaling potential harm or benefit of ingestive behavior. However, current evidence for cortical taste representations remains contradictory. To address this issue, high-resolution functional MRI (fMRI) and multivariate pattern analysis were used to characterize taste-related informational content in human insular cortex, which contains primary gustatory cortex. Human participants judged pleasantness and intensity of low- and high-concentration tastes (salty, sweet, sour, and bitter) in two fMRI experiments on two different days to test for task- and concentration-invariant taste representations. We observed patterns of fMRI activity within insular cortex narrowly tuned to specific tastants consistently across tasks in all participants. Fewer patterns responded to more than one taste category. Importantly, changes in taste concentration altered the spatial layout of putative taste-specific patterns with distinct, almost nonoverlapping patterns for each taste category at different concentration levels. Together, our results point at macroscopic representations in human insular cortex as a complex function of taste category and concentration rather than representations based solely on taste identity.
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15
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Muehlroth BE, Werkle-Bergner M. Understanding the interplay of sleep and aging: Methodological challenges. Psychophysiology 2020; 57:e13523. [PMID: 31930523 DOI: 10.1111/psyp.13523] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/21/2019] [Accepted: 12/12/2019] [Indexed: 12/16/2022]
Abstract
In quest of new avenues to explain, predict, and treat pathophysiological conditions during aging, research on sleep and aging has flourished. Despite the great scientific potential to pinpoint mechanistic pathways between sleep, aging, and pathology, only little attention has been paid to the suitability of analytic procedures applied to study these interrelations. On the basis of electrophysiological sleep and structural brain data of healthy younger and older adults, we identify, illustrate, and resolve methodological core challenges in the study of sleep and aging. We demonstrate potential biases in common analytic approaches when applied to older populations. We argue that uncovering age-dependent alterations in the physiology of sleep requires the development of adjusted and individualized analytic procedures that filter out age-independent interindividual differences. Age-adapted methodological approaches are thus required to foster the development of valid and reliable biomarkers of age-associated cognitive pathologies.
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Affiliation(s)
- Beate E Muehlroth
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Markus Werkle-Bergner
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
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16
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Özbay PS, Chang C, Picchioni D, Mandelkow H, Chappel-Farley MG, van Gelderen P, de Zwart JA, Duyn J. Sympathetic activity contributes to the fMRI signal. Commun Biol 2019; 2:421. [PMID: 31754651 PMCID: PMC6861267 DOI: 10.1038/s42003-019-0659-0] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 10/21/2019] [Indexed: 12/15/2022] Open
Abstract
The interpretation of functional magnetic resonance imaging (fMRI) studies of brain activity is often hampered by the presence of brain-wide signal variations that may arise from a variety of neuronal and non-neuronal sources. Recent work suggests a contribution from the sympathetic vascular innervation, which may affect the fMRI signal through its putative and poorly understood role in cerebral blood flow (CBF) regulation. By analyzing fMRI and (electro-) physiological signals concurrently acquired during sleep, we found that widespread fMRI signal changes often co-occur with electroencephalography (EEG) K-complexes, signatures of sub-cortical arousal, and episodic drops in finger skin vascular tone; phenomena that have been associated with intermittent sympathetic activity. These findings support the notion that the extrinsic sympathetic innervation of the cerebral vasculature contributes to CBF regulation and the fMRI signal. Accounting for this mechanism could help separate systemic from local signal contributions and improve interpretation of fMRI studies.
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Affiliation(s)
- Pinar Senay Özbay
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
| | | | - Dante Picchioni
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
| | - Hendrik Mandelkow
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
| | | | - Peter van Gelderen
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
| | | | - Jeff Duyn
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
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17
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Beier M, Penzel T, Krefting D. A Performant Web-Based Visualization, Assessment, and Collaboration Tool for Multidimensional Biosignals. Front Neuroinform 2019; 13:65. [PMID: 31607882 PMCID: PMC6769110 DOI: 10.3389/fninf.2019.00065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 09/09/2019] [Indexed: 11/13/2022] Open
Abstract
Biosignal-based research is often multidisciplinary and benefits greatly from multi-site collaboration. This requires appropriate tooling that supports collaboration, is easy to use, and is accessible. However, current software tools do not provide the necessary functionality, usability, and ubiquitous availability. The latter is particularly crucial in environments, such as hospitals, which often restrict users' permissions to install software. This paper introduces a new web-based application for interactive biosignal visualization and assessment. A focus has been placed on performance to allow for handling files of any size. The proposed solution can load local and remote files. It parses data locally on the client, and harmonizes channel labels. The data can then be scored, annotated, pseudonymized and uploaded to a clinical data management system for further analysis. The data and all actions can be interactively shared with a second party. This lowers the barrier to quickly visually examine data, collaborate and make informed decisions.
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Affiliation(s)
- Maximilian Beier
- Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Center for Biomedical Image and Information Processing, University of Applied Sciences, Berlin, Germany
| | - Thomas Penzel
- Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Dagmar Krefting
- Center for Biomedical Image and Information Processing, University of Applied Sciences, Berlin, Germany.,Department of Medical Informatics, University Medical Center Goettingen, Göttingen, Germany
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18
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Combrisson E, Vallat R, O'Reilly C, Jas M, Pascarella A, Saive AL, Thiery T, Meunier D, Altukhov D, Lajnef T, Ruby P, Guillot A, Jerbi K. Visbrain: A Multi-Purpose GPU-Accelerated Open-Source Suite for Multimodal Brain Data Visualization. Front Neuroinform 2019; 13:14. [PMID: 30967769 PMCID: PMC6439346 DOI: 10.3389/fninf.2019.00014] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 02/19/2019] [Indexed: 11/13/2022] Open
Abstract
We present Visbrain, a Python open-source package that offers a comprehensive visualization suite for neuroimaging and electrophysiological brain data. Visbrain consists of two levels of abstraction: (1) objects which represent highly configurable neuro-oriented visual primitives (3D brain, sources connectivity, etc.) and (2) graphical user interfaces for higher level interactions. The object level offers flexible and modular tools to produce and automate the production of figures using an approach similar to that of Matplotlib with subplots. The second level visually connects these objects by controlling properties and interactions through graphical interfaces. The current release of Visbrain (version 0.4.2) contains 14 different objects and three responsive graphical user interfaces, built with PyQt: Signal, for the inspection of time-series and spectral properties, Brain for any type of visualization involving a 3D brain and Sleep for polysomnographic data visualization and sleep analysis. Each module has been developed in tight collaboration with end-users, i.e., primarily neuroscientists and domain experts, who bring their experience to make Visbrain as transparent as possible to the recording modalities (e.g., intracranial EEG, scalp-EEG, MEG, anatomical and functional MRI). Visbrain is developed on top of VisPy, a Python package providing high-performance 2D and 3D visualization by leveraging the computational power of the graphics card. Visbrain is available on Github and comes with a documentation, examples, and datasets (http://visbrain.org).
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Affiliation(s)
- Etienne Combrisson
- Computational and Cognitive Neuroscience Lab (CoCo Lab), Psychology Department, University of Montreal, Montreal, QC, Canada
| | - Raphael Vallat
- Lyon Neuroscience Research Center, Brain Dynamics and Cognition team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Christian O'Reilly
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Mainak Jas
- Computational and Cognitive Neuroscience Lab (CoCo Lab), Psychology Department, University of Montreal, Montreal, QC, Canada
| | - Annalisa Pascarella
- Institute for Applied Mathematics Mauro Picone, National Research Council, Rome, Italy
| | - Anne-Lise Saive
- Computational and Cognitive Neuroscience Lab (CoCo Lab), Psychology Department, University of Montreal, Montreal, QC, Canada
| | - Thomas Thiery
- Computational and Cognitive Neuroscience Lab (CoCo Lab), Psychology Department, University of Montreal, Montreal, QC, Canada
| | - David Meunier
- Aix Marseille Univ, CNRS, INT, Inst Neurosci Timone, Marseille, France
| | - Dmitrii Altukhov
- National Research University Higher School of Economics, Moscow, Russia.,MEG Center, Moscow State University of Pedagogics and Education, Moscow, Russia
| | - Tarek Lajnef
- Computational and Cognitive Neuroscience Lab (CoCo Lab), Psychology Department, University of Montreal, Montreal, QC, Canada.,Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montreal, QC, Canada
| | - Perrine Ruby
- Lyon Neuroscience Research Center, Brain Dynamics and Cognition team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Aymeric Guillot
- Inter-University Laboratory of Human Movement Biology, University of Lyon, University Claude Bernard Lyon 1, Villeurbanne, France
| | - Karim Jerbi
- Computational and Cognitive Neuroscience Lab (CoCo Lab), Psychology Department, University of Montreal, Montreal, QC, Canada.,MEG Unit, University of Montreal, Montreal, QC, Canada
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19
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Vallat R, Meunier D, Nicolas A, Ruby P. Hard to wake up? The cerebral correlates of sleep inertia assessed using combined behavioral, EEG and fMRI measures. Neuroimage 2019; 184:266-278. [DOI: 10.1016/j.neuroimage.2018.09.033] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 08/21/2018] [Accepted: 09/12/2018] [Indexed: 12/23/2022] Open
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