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Alnes SL, Bächlin LZM, Schindler K, Tzovara A. Neural complexity and the spectral slope characterise auditory processing in wakefulness and sleep. Eur J Neurosci 2024; 59:822-841. [PMID: 38100263 DOI: 10.1111/ejn.16203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 10/11/2023] [Accepted: 11/10/2023] [Indexed: 12/17/2023]
Abstract
Auditory processing and the complexity of neural activity can both indicate residual consciousness levels and differentiate states of arousal. However, how measures of neural signal complexity manifest in neural activity following environmental stimulation and, more generally, how the electrophysiological characteristics of auditory responses change in states of reduced consciousness remain under-explored. Here, we tested the hypothesis that measures of neural complexity and the spectral slope would discriminate stages of sleep and wakefulness not only in baseline electroencephalography (EEG) activity but also in EEG signals following auditory stimulation. High-density EEG was recorded in 21 participants to determine the spatial relationship between these measures and between EEG recorded pre- and post-auditory stimulation. Results showed that the complexity and the spectral slope in the 2-20 Hz range discriminated between sleep stages and had a high correlation in sleep. In wakefulness, complexity was strongly correlated to the 20-40 Hz spectral slope. Auditory stimulation resulted in reduced complexity in sleep compared to the pre-stimulation EEG activity and modulated the spectral slope in wakefulness. These findings confirm our hypothesis that electrophysiological markers of arousal are sensitive to sleep/wake states in EEG activity during baseline and following auditory stimulation. Our results have direct applications to studies using auditory stimulation to probe neural functions in states of reduced consciousness.
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Affiliation(s)
- Sigurd L Alnes
- Institute of Computer Science, University of Bern, Bern, Switzerland
- Zentrum für Experimentelle Neurologie, Department of Neurology, Inselspital University Hospital Bern, Bern, Switzerland
| | - Lea Z M Bächlin
- Institute of Computer Science, University of Bern, Bern, Switzerland
| | - Kaspar Schindler
- Sleep-Wake-Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Athina Tzovara
- Institute of Computer Science, University of Bern, Bern, Switzerland
- Zentrum für Experimentelle Neurologie, Department of Neurology, Inselspital University Hospital Bern, Bern, Switzerland
- Sleep-Wake-Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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2
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Tzovara A, Fedele T, Sarnthein J, Ledergerber D, Lin JJ, Knight RT. Predictable and unpredictable deviance detection in the human hippocampus and amygdala. Cereb Cortex 2024; 34:bhad532. [PMID: 38216528 PMCID: PMC10839835 DOI: 10.1093/cercor/bhad532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/15/2023] [Accepted: 12/16/2023] [Indexed: 01/14/2024] Open
Abstract
Our brains extract structure from the environment and form predictions given past experience. Predictive circuits have been identified in wide-spread cortical regions. However, the contribution of medial temporal structures in predictions remains under-explored. The hippocampus underlies sequence detection and is sensitive to novel stimuli, sufficient to gain access to memory, while the amygdala to novelty. Yet, their electrophysiological profiles in detecting predictable and unpredictable deviant auditory events remain unknown. Here, we hypothesized that the hippocampus would be sensitive to predictability, while the amygdala to unexpected deviance. We presented epileptic patients undergoing presurgical monitoring with standard and deviant sounds, in predictable or unpredictable contexts. Onsets of auditory responses and unpredictable deviance effects were detected earlier in the temporal cortex compared with the amygdala and hippocampus. Deviance effects in 1-20 Hz local field potentials were detected in the lateral temporal cortex, irrespective of predictability. The amygdala showed stronger deviance in the unpredictable context. Low-frequency deviance responses in the hippocampus (1-8 Hz) were observed in the predictable but not in the unpredictable context. Our results reveal a distributed network underlying the generation of auditory predictions and suggest that the neural basis of sensory predictions and prediction error signals needs to be extended.
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Affiliation(s)
- Athina Tzovara
- Helen Wills Neuroscience Institute, University of California, 450 Li Ka Shing Biomedical Center, Berkeley, CA 94720-3370, United States
- Institute of Computer Science, University of Bern, Bern, Neubrückstrasse 3012, Switzerland
- Center for Experimental Neurology - Sleep Wake Epilepsy Center | NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Freiburgstrasse 3010, Switzerland
| | - Tommaso Fedele
- Neurosurgery Department, University Hospital Zürich, Zürich, Frauenklinikstrasse 8091, Switzerland
| | - Johannes Sarnthein
- Neurosurgery Department, University Hospital Zürich, Zürich, Frauenklinikstrasse 8091, Switzerland
| | - Debora Ledergerber
- Swiss Epilepsy Center, Klinik Lengg, Zürich, Bleulerstrasse 8008, Switzerland
| | - Jack J Lin
- Department of Neurology, University of California, Davis, Folsom Boulevard, Davis, CA 95816, USA
- The Center of Mind and Brain, University of California, Davis, Cousteau Pl, Davis, CA 95618, USA
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California, 450 Li Ka Shing Biomedical Center, Berkeley, CA 94720-3370, United States
- Department of Psychology, University of California, Berkeley, CA 94720-1650, USA
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3
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Aellen FM, Van der Meer J, Dietmann A, Schmidt M, Bassetti CLA, Tzovara A. Disentangling the complex landscape of sleep-wake disorders with data-driven phenotyping: A study of the Bernese center. Eur J Neurol 2024; 31:e16026. [PMID: 37531449 DOI: 10.1111/ene.16026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 07/05/2023] [Accepted: 07/31/2023] [Indexed: 08/04/2023]
Abstract
BACKGROUND AND PURPOSE The diagnosis of sleep-wake disorders (SWDs) is challenging because of the existence of only few accurate biomarkers and the frequent coexistence of multiple SWDs and/or other comorbidities. The aim of this study was to assess in a large cohort of well-characterized SWD patients the potential of a data-driven approach for the identification of SWDs. METHODS We included 6958 patients from the Bernese Sleep Registry and 300 variables/biomarkers including questionnaires, results of polysomnography/vigilance tests, and final clinical diagnoses. A pipeline, based on machine learning, was created to extract and cluster the clinical data. Our analysis was performed on three cohorts: patients with central disorders of hypersomnolence (CDHs), a full cohort of patients with SWDs, and a clean cohort without coexisting SWDs. RESULTS A first analysis focused on the cohort of patients with CDHs and revealed four patient clusters: two clusters for narcolepsy type 1 (NT1) but not for narcolepsy type 2 or idiopathic hypersomnia. In the full cohort of SWDs, nine clusters were found: four contained patients with obstructive and central sleep apnea syndrome, one with NT1, and four with intermixed SWDs. In the cohort of patients without coexisting SWDs, an additional cluster of patients with chronic insomnia disorder was identified. CONCLUSIONS This study confirms the existence of clear clusters of NT1 in CDHs, but mainly intermixed groups in the full spectrum of SWDs, with the exception of sleep apnea syndromes and NT1. New biomarkers are needed for better phenotyping and diagnosis of SWDs.
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Affiliation(s)
- Florence M Aellen
- Institute of Computer Science, University of Bern, Bern, Switzerland
- Center for Experimental Neurology, Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Julia Van der Meer
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Anelia Dietmann
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Markus Schmidt
- Center for Experimental Neurology, Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Claudio L A Bassetti
- Center for Experimental Neurology, Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Athina Tzovara
- Institute of Computer Science, University of Bern, Bern, Switzerland
- Center for Experimental Neurology, Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
- Sleep Wake Epilepsy Center-NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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Monachino G, Zanchi B, Fiorillo L, Conte G, Auricchio A, Tzovara A, Faraci FD. Deep Generative Models: The winning key for large and easily accessible ECG datasets? Comput Biol Med 2023; 167:107655. [PMID: 37976830 DOI: 10.1016/j.compbiomed.2023.107655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/04/2023] [Accepted: 10/31/2023] [Indexed: 11/19/2023]
Abstract
Large high-quality datasets are essential for building powerful artificial intelligence (AI) algorithms capable of supporting advancement in cardiac clinical research. However, researchers working with electrocardiogram (ECG) signals struggle to get access and/or to build one. The aim of the present work is to shed light on a potential solution to address the lack of large and easily accessible ECG datasets. Firstly, the main causes of such a lack are identified and examined. Afterward, the potentials and limitations of cardiac data generation via deep generative models (DGMs) are deeply analyzed. These very promising algorithms have been found capable not only of generating large quantities of ECG signals but also of supporting data anonymization processes, to simplify data sharing while respecting patients' privacy. Their application could help research progress and cooperation in the name of open science. However several aspects, such as a standardized synthetic data quality evaluation and algorithm stability, need to be further explored.
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Affiliation(s)
- Giuliana Monachino
- Institute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Via la Santa 1, Lugano 6900, Switzerland; Institute of Informatics, University of Bern, Neubrückstrasse 10, Bern 3012, Switzerland.
| | - Beatrice Zanchi
- Institute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Via la Santa 1, Lugano 6900, Switzerland; Department of Quantitative Biomedicine, University of Zurich, Schmelzbergstrasse 26, Zurich 8091, Switzerland
| | - Luigi Fiorillo
- Institute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Via la Santa 1, Lugano 6900, Switzerland
| | - Giulio Conte
- Division of Cardiology, Fondazione Cardiocentro Ticino, Via Tesserete 48, Lugano 6900, Switzerland; Centre for Computational Medicine in Cardiology, Faculty of Informatics, Università della Svizzera Italiana, Via la Santa 1, Lugano 6900, Switzerland
| | - Angelo Auricchio
- Division of Cardiology, Fondazione Cardiocentro Ticino, Via Tesserete 48, Lugano 6900, Switzerland; Centre for Computational Medicine in Cardiology, Faculty of Informatics, Università della Svizzera Italiana, Via la Santa 1, Lugano 6900, Switzerland
| | - Athina Tzovara
- Institute of Informatics, University of Bern, Neubrückstrasse 10, Bern 3012, Switzerland; Sleep Wake Epilepsy Center | NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16, Bern 3010, Switzerland
| | - Francesca Dalia Faraci
- Institute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Via la Santa 1, Lugano 6900, Switzerland
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Martinez DRQ, Rubio GF, Bonetti L, Achyutuni KG, Tzovara A, Knight RT, Vuust P. Decoding reveals the neural representation of held and manipulated musical thoughts. bioRxiv 2023:2023.08.15.553456. [PMID: 37645733 PMCID: PMC10462096 DOI: 10.1101/2023.08.15.553456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Imagine a song you know by heart. With little effort you could sing it or play it vividly in your mind. However, we are only beginning to understand how the brain represents, holds, and manipulates these musical "thoughts". Here, we decoded listened and imagined melodies from MEG brain data (N = 71) to show that auditory regions represent the sensory properties of individual sounds, whereas cognitive control (prefrontal cortex, basal nuclei, thalamus) and episodic memory areas (inferior and medial temporal lobe, posterior cingulate, precuneus) hold and manipulate the melody as an abstract unit. Furthermore, the mental manipulation of a melody systematically changes its neural representation, reflecting the volitional control of auditory images. Our work sheds light on the nature and dynamics of auditory representations and paves the way for future work on neural decoding of auditory imagination.
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Affiliation(s)
- David R. Quiroga Martinez
- Helen Wills Neuroscience Institute & Department of Psychology, University of California Berkeley, Berkeley, CA
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus, Denmark
| | - Gemma Fernandez Rubio
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus, Denmark
| | - Leonardo Bonetti
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus, Denmark
- Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford UK
- Department of Psychiatry, University of Oxford, Oxford UK
| | - Kriti G. Achyutuni
- Helen Wills Neuroscience Institute & Department of Psychology, University of California Berkeley, Berkeley, CA
| | - Athina Tzovara
- Helen Wills Neuroscience Institute & Department of Psychology, University of California Berkeley, Berkeley, CA
- Institute of Computer Science, University of Bern, Bern, Switzerland
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Robert T. Knight
- Helen Wills Neuroscience Institute & Department of Psychology, University of California Berkeley, Berkeley, CA
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus, Denmark
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Zubler F, Tzovara A. Deep learning for EEG-based prognostication after cardiac arrest: from current research to future clinical applications. Front Neurol 2023; 14:1183810. [PMID: 37560450 PMCID: PMC10408678 DOI: 10.3389/fneur.2023.1183810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 07/03/2023] [Indexed: 08/11/2023] Open
Abstract
Outcome prognostication in comatose patients after cardiac arrest (CA) remains to date a challenge. The major determinant of clinical outcome is the post-hypoxic/ischemic encephalopathy. Electroencephalography (EEG) is routinely used to assess neural functions in comatose patients. Currently, EEG-based outcome prognosis relies on visual evaluation by medical experts, which is time consuming, prone to subjectivity, and oblivious to complex patterns. The field of deep learning has given rise to powerful algorithms for detecting patterns in large amounts of data. Analyzing EEG signals of coma patients with deep neural networks with the goal of assisting in outcome prognosis is therefore a natural application of these algorithms. Here, we provide the first narrative literature review on the use of deep learning for prognostication after CA. Existing studies show overall high performance in predicting outcome, relying either on spontaneous or on auditory evoked EEG signals. Moreover, the literature is concerned with algorithmic interpretability, and has shown that largely, deep neural networks base their decisions on clinically or neurophysiologically meaningful features. We conclude this review by discussing considerations that the fields of artificial intelligence and neurology will need to jointly address in the future, in order for deep learning algorithms to break the publication barrier, and to be integrated in clinical practice.
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Affiliation(s)
- Frederic Zubler
- Department of Neurology, Spitalzentrum Biel, University of Bern, Biel/Bienne, Switzerland
| | - Athina Tzovara
- Institute of Computer Science, University of Bern, Bern, Switzerland
- Department of Neurology, Zentrum für Experimentelle Neurologie and Sleep Wake Epilepsy Center—Neurotec, Inselspital University Hospital Bern, Bern, Switzerland
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7
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Cusinato R, Alnes SL, van Maren E, Boccalaro I, Ledergerber D, Adamantidis A, Imbach LL, Schindler K, Baud MO, Tzovara A. Intrinsic Neural Timescales in the Temporal Lobe Support an Auditory Processing Hierarchy. J Neurosci 2023; 43:3696-3707. [PMID: 37045604 PMCID: PMC10198454 DOI: 10.1523/jneurosci.1941-22.2023] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/21/2023] [Accepted: 03/02/2023] [Indexed: 04/14/2023] Open
Abstract
During rest, intrinsic neural dynamics manifest at multiple timescales, which progressively increase along visual and somatosensory hierarchies. Theoretically, intrinsic timescales are thought to facilitate processing of external stimuli at multiple stages. However, direct links between timescales at rest and sensory processing, as well as translation to the auditory system are lacking. Here, we measured intracranial EEG in 11 human patients with epilepsy (4 women), while listening to pure tones. We show that, in the auditory network, intrinsic neural timescales progressively increase, while the spectral exponent flattens, from temporal to entorhinal cortex, hippocampus, and amygdala. Within the neocortex, intrinsic timescales exhibit spatial gradients that follow the temporal lobe anatomy. Crucially, intrinsic timescales at baseline can explain the latency of auditory responses: as intrinsic timescales increase, so do the single-electrode response onset and peak latencies. Our results suggest that the human auditory network exhibits a repertoire of intrinsic neural dynamics, which manifest in cortical gradients with millimeter resolution and may provide a variety of temporal windows to support auditory processing.SIGNIFICANCE STATEMENT Endogenous neural dynamics are often characterized by their intrinsic timescales. These are thought to facilitate processing of external stimuli. However, a direct link between intrinsic timing at rest and sensory processing is missing. Here, with intracranial EEG, we show that intrinsic timescales progressively increase from temporal to entorhinal cortex, hippocampus, and amygdala. Intrinsic timescales at baseline can explain the variability in the timing of intracranial EEG responses to sounds: cortical electrodes with fast timescales also show fast- and short-lasting responses to auditory stimuli, which progressively increase in the hippocampus and amygdala. Our results suggest that a hierarchy of neural dynamics in the temporal lobe manifests across cortical and limbic structures and can explain the temporal richness of auditory responses.
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Affiliation(s)
- Riccardo Cusinato
- Institute of Computer Science, University of Bern, Bern 3012, Switzerland
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Sigurd L Alnes
- Institute of Computer Science, University of Bern, Bern 3012, Switzerland
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Ellen van Maren
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Ida Boccalaro
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | | | - Antoine Adamantidis
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Lukas L Imbach
- Swiss Epilepsy Center, Klinik Lengg, Zurich 8008, Switzerland
| | - Kaspar Schindler
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Maxime O Baud
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Athina Tzovara
- Institute of Computer Science, University of Bern, Bern 3012, Switzerland
- Center for Experimental Neurology, Sleep Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Helen Wills Neuroscience Institute, University of California-Berkeley, Berkeley 94720, California
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8
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Fiorillo L, Monachino G, van der Meer J, Pesce M, Warncke JD, Schmidt MH, Bassetti CLA, Tzovara A, Favaro P, Faraci FD. U-Sleep's resilience to AASM guidelines. NPJ Digit Med 2023; 6:33. [PMID: 36878957 PMCID: PMC9988983 DOI: 10.1038/s41746-023-00784-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 02/21/2023] [Indexed: 03/08/2023] Open
Abstract
AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications, e.g., recommended EEG derivations, to detailed sleep scoring rules accordingly to age. Automated sleep scoring systems have always largely exploited the standards as fundamental guidelines. In this context, deep learning has demonstrated better performance compared to classical machine learning. Our present work shows that a deep learning-based sleep scoring algorithm may not need to fully exploit the clinical knowledge or to strictly adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a state-of-the-art sleep scoring algorithm, can be strong enough to solve the scoring task even using clinically non-recommended or non-conventional derivations, and with no need to exploit information about the chronological age of the subjects. We finally strengthen a well-known finding that using data from multiple data centers always results in a better performing model compared with training on a single cohort. Indeed, we show that this latter statement is still valid even by increasing the size and the heterogeneity of the single data cohort. In all our experiments we used 28528 polysomnography studies from 13 different clinical studies.
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Affiliation(s)
- Luigi Fiorillo
- Institute of Informatics, University of Bern, Bern, Switzerland.
- Institute of Digital Technologies for Personalized Healthcare ∣ MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland.
| | - Giuliana Monachino
- Institute of Informatics, University of Bern, Bern, Switzerland
- Institute of Digital Technologies for Personalized Healthcare ∣ MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland
| | - Julia van der Meer
- Sleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Marco Pesce
- Sleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Jan D Warncke
- Sleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Markus H Schmidt
- Sleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Claudio L A Bassetti
- Sleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Athina Tzovara
- Institute of Informatics, University of Bern, Bern, Switzerland
- Sleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Paolo Favaro
- Institute of Informatics, University of Bern, Bern, Switzerland
| | - Francesca D Faraci
- Institute of Digital Technologies for Personalized Healthcare ∣ MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland
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9
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Aellen FM, Alnes SL, Loosli F, Rossetti AO, Zubler F, De Lucia M, Tzovara A. Auditory stimulation and deep learning predict awakening from coma after cardiac arrest. Brain 2023; 146:778-788. [PMID: 36637902 PMCID: PMC9924902 DOI: 10.1093/brain/awac340] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/28/2022] [Accepted: 09/02/2022] [Indexed: 01/14/2023] Open
Abstract
Assessing the integrity of neural functions in coma after cardiac arrest remains an open challenge. Prognostication of coma outcome relies mainly on visual expert scoring of physiological signals, which is prone to subjectivity and leaves a considerable number of patients in a 'grey zone', with uncertain prognosis. Quantitative analysis of EEG responses to auditory stimuli can provide a window into neural functions in coma and information about patients' chances of awakening. However, responses to standardized auditory stimulation are far from being used in a clinical routine due to heterogeneous and cumbersome protocols. Here, we hypothesize that convolutional neural networks can assist in extracting interpretable patterns of EEG responses to auditory stimuli during the first day of coma that are predictive of patients' chances of awakening and survival at 3 months. We used convolutional neural networks (CNNs) to model single-trial EEG responses to auditory stimuli in the first day of coma, under standardized sedation and targeted temperature management, in a multicentre and multiprotocol patient cohort and predict outcome at 3 months. The use of CNNs resulted in a positive predictive power for predicting awakening of 0.83 ± 0.04 and 0.81 ± 0.06 and an area under the curve in predicting outcome of 0.69 ± 0.05 and 0.70 ± 0.05, for patients undergoing therapeutic hypothermia and normothermia, respectively. These results also persisted in a subset of patients that were in a clinical 'grey zone'. The network's confidence in predicting outcome was based on interpretable features: it strongly correlated to the neural synchrony and complexity of EEG responses and was modulated by independent clinical evaluations, such as the EEG reactivity, background burst-suppression or motor responses. Our results highlight the strong potential of interpretable deep learning algorithms in combination with auditory stimulation to improve prognostication of coma outcome.
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Affiliation(s)
- Florence M Aellen
- Correspondence to: Florence Aellen University of Bern; Institute for Computer Science Neubrückstrasse 10; CH-3012 Bern E-mail:
| | - Sigurd L Alnes
- Institute of Computer Science, University of Bern, Bern, Switzerland,Zentrum für Experimentelle Neurologie, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Fabian Loosli
- Institute of Computer Science, University of Bern, Bern, Switzerland
| | - Andrea O Rossetti
- Neurology Service, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Frédéric Zubler
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Marzia De Lucia
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Athina Tzovara
- Correspondence may also be addressed to: Athina Tzovara E-mail:
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10
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Aellen F, Van der Meer J, Dietmann A, Schmidt M, Bassetti C, Tzovara A. The Bern Sleep Database: Clustering of Patients with Sleep Disorders. Sleep Med 2022. [DOI: 10.1016/j.sleep.2022.05.295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Gnarra O, van der Meer J, Warncke J, Wenz E, Fragolente L, Khatami R, Schindler K, Tzovara A, Schmidt M, Bassetti C. SPHYNCS: Longterm monitoring with Fitbit in patients with narcolepsy and its borderland. Sleep Med 2022. [DOI: 10.1016/j.sleep.2022.05.294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Ojala KE, Tzovara A, Poser BA, Lutti A, Bach DR. Asymmetric representation of aversive prediction errors in Pavlovian threat conditioning. Neuroimage 2022; 263:119579. [PMID: 35995374 DOI: 10.1016/j.neuroimage.2022.119579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 11/24/2022] Open
Abstract
Survival in biological environments requires learning associations between predictive sensory cues and threatening outcomes. Such aversive learning may be implemented through reinforcement learning algorithms that are driven by the signed difference between expected and encountered outcomes, termed prediction errors (PEs). While PE-based learning is well established for reward learning, the role of putative PE signals in aversive learning is less clear. Here, we used functional magnetic resonance imaging in humans (21 healthy men and women) to investigate the neural representation of PEs during maintenance of learned aversive associations. Four visual cues, each with a different probability (0, 33, 66, 100%) of being followed by an aversive outcome (electric shock), were repeatedly presented to participants. We found that neural activity at omission (US-) but not occurrence of the aversive outcome (US+) encoded PEs in the medial prefrontal cortex. More expected omission of aversive outcome was associated with lower neural activity. No neural signals fulfilled axiomatic criteria, which specify necessary and sufficient components of PE signals, for signed PE representation in a whole-brain search or in a-priori regions of interest. Our results might suggest that, different from reward learning, aversive learning does not involve signed PE signals that are represented within the same brain region for all conditions.
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Affiliation(s)
- Karita E Ojala
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Lenggstrasse 31, Zurich 8032, Switzerland; Neuroscience Centre Zurich, University of Zurich, Winterthurerstrasse 190, Zürich 8057, Switzerland.
| | - Athina Tzovara
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Lenggstrasse 31, Zurich 8032, Switzerland; Neuroscience Centre Zurich, University of Zurich, Winterthurerstrasse 190, Zürich 8057, Switzerland; Institute of Computer Science, University of Bern, Neubrückstrasse 10, Bern 3012, Switzerland
| | - Benedikt A Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55 EV 6299, Maastricht, the Netherlands
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Chemin de Mont-Paisible 16, Lausanne 1011, Switzerland
| | - Dominik R Bach
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Lenggstrasse 31, Zurich 8032, Switzerland; Neuroscience Centre Zurich, University of Zurich, Winterthurerstrasse 190, Zürich 8057, Switzerland; Wellcome Centre for Human Neuroimaging and Max-Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, 10-12 Russell Square, London WC1B 5EH, United Kingdom.
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13
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Aellen FM, Göktepe-Kavis P, Apostolopoulos S, Tzovara A. Convolutional neural networks for decoding electroencephalography responses and visualizing trial by trial changes in discriminant features. J Neurosci Methods 2021; 364:109367. [PMID: 34563599 DOI: 10.1016/j.jneumeth.2021.109367] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 09/15/2021] [Accepted: 09/17/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Deep learning has revolutionized the field of computer vision, where convolutional neural networks (CNNs) extract complex patterns of information from large datasets. The use of deep networks in neuroscience is mainly focused to neuroimaging or brain computer interface -BCI- applications. In electroencephalography (EEG) research, multivariate pattern analysis (MVPA) mainly relies on linear algorithms, which require a homogeneous dataset and assume that discriminant features appear at consistent latencies and electrodes across trials. However, neural responses may shift in time or space during an experiment, resulting in under-estimation of discriminant features. Here, we aimed at using CNNs to classify EEG responses to external stimuli, by taking advantage of time- and space- unlocked neural activity, and at examining how discriminant features change over the course of an experiment, on a trial by trial basis. NEW METHOD We present a novel pipeline, consisting of data augmentation, CNN training, and feature visualization techniques, fine-tuned for MVPA on EEG data. RESULTS Our pipeline provides high classification performance and generalizes to new datasets. Additionally, we show that the features identified by the CNN for classification are electrophysiologically interpretable and can be reconstructed at the single-trial level to study trial-by-trial evolution of class-specific discriminant activity. COMPARISON WITH EXISTING TECHNIQUES The developed pipeline was compared to commonly used MVPA algorithms like logistic regression and support vector machines, as well as to shallow and deep convolutional neural networks. Our approach yielded significantly higher classification performance than existing MVPA techniques (p = 0.006) and comparable results to other CNNs for EEG data. CONCLUSION In summary, we present a novel deep learning pipeline for MVPA of EEG data, that can extract trial-by-trial discriminative activity in a data-driven way.
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Affiliation(s)
| | | | | | - Athina Tzovara
- Institute of Computer Science, University of Bern, Switzerland; Sleep Wake Epilepsy Center - NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland; Helen Wills Neuroscience Institute, University of California, Berkeley, United States.
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14
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Abstract
Artificial intelligence (AI) has an astonishing potential in assisting clinical decision making and revolutionizing the field of health care. A major open challenge that AI will need to address before its integration in the clinical routine is that of algorithmic bias. Most AI algorithms need big datasets to learn from, but several groups of the human population have a long history of being absent or misrepresented in existing biomedical datasets. If the training data is misrepresentative of the population variability, AI is prone to reinforcing bias, which can lead to fatal outcomes, misdiagnoses, and lack of generalization. Here, we describe the challenges in rendering AI algorithms fairer, and we propose concrete steps for addressing bias using tools from the field of open science.
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Affiliation(s)
- Natalia Norori
- Institute of Computer Science, University of Bern, Neubrückstrasse 10 3012 Bern, Switzerland
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1UD, UK
| | - Qiyang Hu
- Institute of Computer Science, University of Bern, Neubrückstrasse 10 3012 Bern, Switzerland
| | | | - Francesca Dalia Faraci
- Institute of Digital Technologies for Personalized Healthcare (MeDiTech), Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, 6962 Lugano, Switzerland
| | - Athina Tzovara
- Institute of Computer Science, University of Bern, Neubrückstrasse 10 3012 Bern, Switzerland
- Sleep Wake Epilepsy Center | NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA 94720, USA
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15
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Tivadar RI, Knight RT, Tzovara A. Automatic Sensory Predictions: A Review of Predictive Mechanisms in the Brain and Their Link to Conscious Processing. Front Hum Neurosci 2021; 15:702520. [PMID: 34489663 PMCID: PMC8416526 DOI: 10.3389/fnhum.2021.702520] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 07/12/2021] [Indexed: 01/22/2023] Open
Abstract
The human brain has the astonishing capacity of integrating streams of sensory information from the environment and forming predictions about future events in an automatic way. Despite being initially developed for visual processing, the bulk of predictive coding research has subsequently focused on auditory processing, with the famous mismatch negativity signal as possibly the most studied signature of a surprise or prediction error (PE) signal. Auditory PEs are present during various consciousness states. Intriguingly, their presence and characteristics have been linked with residual levels of consciousness and return of awareness. In this review we first give an overview of the neural substrates of predictive processes in the auditory modality and their relation to consciousness. Then, we focus on different states of consciousness - wakefulness, sleep, anesthesia, coma, meditation, and hypnosis - and on what mysteries predictive processing has been able to disclose about brain functioning in such states. We review studies investigating how the neural signatures of auditory predictions are modulated by states of reduced or lacking consciousness. As a future outlook, we propose the combination of electrophysiological and computational techniques that will allow investigation of which facets of sensory predictive processes are maintained when consciousness fades away.
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Affiliation(s)
| | - Robert T. Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
- Department of Psychology, University of California, Berkeley, Berkeley, CA, United States
| | - Athina Tzovara
- Institute of Computer Science, University of Bern, Bern, Switzerland
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
- Sleep-Wake Epilepsy Center | NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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16
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Llorens A, Tzovara A, Bellier L, Bhaya-Grossman I, Bidet-Caulet A, Chang WK, Cross ZR, Dominguez-Faus R, Flinker A, Fonken Y, Gorenstein MA, Holdgraf C, Hoy CW, Ivanova MV, Jimenez RT, Jun S, Kam JWY, Kidd C, Marcelle E, Marciano D, Martin S, Myers NE, Ojala K, Perry A, Pinheiro-Chagas P, Riès SK, Saez I, Skelin I, Slama K, Staveland B, Bassett DS, Buffalo EA, Fairhall AL, Kopell NJ, Kray LJ, Lin JJ, Nobre AC, Riley D, Solbakk AK, Wallis JD, Wang XJ, Yuval-Greenberg S, Kastner S, Knight RT, Dronkers NF. Gender bias in academia: A lifetime problem that needs solutions. Neuron 2021; 109:2047-2074. [PMID: 34237278 PMCID: PMC8553227 DOI: 10.1016/j.neuron.2021.06.002] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 11/19/2020] [Accepted: 06/01/2021] [Indexed: 11/18/2022]
Abstract
Despite increased awareness of the lack of gender equity in academia and a growing number of initiatives to address issues of diversity, change is slow, and inequalities remain. A major source of inequity is gender bias, which has a substantial negative impact on the careers, work-life balance, and mental health of underrepresented groups in science. Here, we argue that gender bias is not a single problem but manifests as a collection of distinct issues that impact researchers' lives. We disentangle these facets and propose concrete solutions that can be adopted by individuals, academic institutions, and society.
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Affiliation(s)
- Anaïs Llorens
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.
| | - Athina Tzovara
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Institute for Computer Science, University of Bern, Bern, Switzerland; Sleep Wake Epilepsy Center | NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland.
| | - Ludovic Bellier
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Ilina Bhaya-Grossman
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
| | - Aurélie Bidet-Caulet
- Brain Dynamics and Cognition Team, Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS UMR 5292, University of Lyon, Lyon, France
| | - William K Chang
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Zachariah R Cross
- Cognitive and Systems Neuroscience Research Hub, University of South Australia, Adelaide, SA, Australia
| | | | | | - Yvonne Fonken
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Mark A Gorenstein
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Chris Holdgraf
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; The Berkeley Institute for Data Science, Berkeley, CA, USA
| | - Colin W Hoy
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Maria V Ivanova
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Richard T Jimenez
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Soyeon Jun
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Department of Brain and Cognitive Science College of Natural Sciences, Seoul National University, Seoul, Korea
| | - Julia W Y Kam
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Department of Psychology, University of Calgary, Calgary, AB, Canada
| | - Celeste Kidd
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Enitan Marcelle
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Deborah Marciano
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Haas School of Business, University of California, Berkeley, Berkeley, CA, USA
| | - Stephanie Martin
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Department of Cognitive Science, University of California, San Diego, San Diego, CA, USA
| | - Nicholas E Myers
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Department of Experimental Psychology and Oxford Centre for Human Brain Activity, Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Karita Ojala
- Institute of Systems Neuroscience, Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Anat Perry
- Department of Psychology, the Hebrew University of Jerusalem, Jerusalem, Israel
| | - Pedro Pinheiro-Chagas
- Laboratory of Behavioral and Cognitive Neuroscience, Stanford Human, Stanford University, Stanford, CA, USA
| | - Stephanie K Riès
- School of Speech, Language, and Hearing Sciences and Center for Clinical and Cognitive Neuroscience, San Diego State University, San Diego, CA, USA
| | - Ignacio Saez
- Department of Neurosurgery, University of California Davis, Sacramento, CA, USA
| | - Ivan Skelin
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Katarina Slama
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Brooke Staveland
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Haas School of Business, University of California, Berkeley, Berkeley, CA, USA
| | - Danielle S Bassett
- Departments of Bioengineering, Electrical & Systems Engineering, Physics & Astronomy, Psychiatry, and Neurology, University of Pennsylvania, Philadelphia, PA, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Elizabeth A Buffalo
- Department of Physiology and Biophysics and School of Medicine, Washington National Primate Research Center, University of Washington, Seattle, WA, USA
| | - Adrienne L Fairhall
- Department of Physiology and Biophysics and Computational Neuroscience Center, University of Washington, Seattle, WA 98195, USA
| | - Nancy J Kopell
- Department of Mathematics & Statistics, Boston University, Boston, MA, USA
| | - Laura J Kray
- Haas School of Business, University of California, Berkeley, Berkeley, CA, USA
| | - Jack J Lin
- Comprehensive Epilepsy Program, Department of Neurology, University of California, Irvine, Irvine, CA, USA; Department of Biomedical Engineering, Henry Samueli School of Engineering, Irvine, CA, USA
| | - Anna C Nobre
- Department of Experimental Psychology and Oxford Centre for Human Brain Activity, Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Dylan Riley
- Department of Sociology, University of California, Berkeley, Berkeley, CA 94720-1980, USA
| | - Anne-Kristin Solbakk
- Department of Psychology, Oslo University Hospital-Rikshospitalet, Oslo, Norway; Department of Neurosurgery, Oslo University Hospital-Rikshospitalet, Oslo, Norway; RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway; Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
| | - Joni D Wallis
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Xiao-Jing Wang
- Center for Neural Science, New York University, 4 Washington Place, New York, NY 10003, USA
| | - Shlomit Yuval-Greenberg
- School of Psychological Sciences and Sagol School of Neuroscience, Tel Aviv University, Ramat Aviv, 6997801 Tel Aviv-Yafo, Israel
| | - Sabine Kastner
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ 08544, USA
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Nina F Dronkers
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA; Department of Neurology, University of California, Davis, Sacramento, CA, USA
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17
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Dietmann A, Wenz E, van der Meer J, Ringli M, Warncke JD, Edwards E, Schmidt MH, Bernasconi CA, Nirkko A, Strub M, Miano S, Manconi M, Acker J, von Manitius S, Baumann CR, Valko PO, Yilmaz B, Brunner AD, Tzovara A, Zhang Z, Largiadèr CR, Tafti M, Latorre D, Sallusto F, Khatami R, Bassetti CLA. The Swiss Primary Hypersomnolence and Narcolepsy Cohort study (SPHYNCS): Study protocol for a prospective, multicentre cohort observational study. J Sleep Res 2021; 30:e13296. [PMID: 33813771 PMCID: PMC8519114 DOI: 10.1111/jsr.13296] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 01/08/2021] [Accepted: 01/13/2021] [Indexed: 12/19/2022]
Abstract
Narcolepsy type 1 (NT1) is a disorder with well‐established markers and a suspected autoimmune aetiology. Conversely, the narcoleptic borderland (NBL) disorders, including narcolepsy type 2, idiopathic hypersomnia, insufficient sleep syndrome and hypersomnia associated with a psychiatric disorder, lack well‐defined markers and remain controversial in terms of aetiology, diagnosis and management. The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a comprehensive multicentre cohort study, which will investigate the clinical picture, pathophysiology and long‐term course of NT1 and the NBL. The primary aim is to validate new and reappraise well‐known markers for the characterization of the NBL, facilitating the diagnostic process. Seven Swiss sleep centres, belonging to the Swiss Narcolepsy Network (SNaNe), joined the study and will prospectively enrol over 500 patients with recent onset of excessive daytime sleepiness (EDS), hypersomnia or a suspected central disorder of hypersomnolence (CDH) during a 3‐year recruitment phase. Healthy controls and patients with EDS due to severe sleep‐disordered breathing, improving after therapy, will represent two control groups of over 50 patients each. Clinical and electrophysiological (polysomnography, multiple sleep latency test, maintenance of wakefulness test) information, and information on psychomotor vigilance and a sustained attention to response task, actigraphy and wearable devices (long‐term monitoring), and responses to questionnaires will be collected at baseline and after 6, 12, 24 and 36 months. Potential disease markers will be searched for in blood, cerebrospinal fluid and stool. Analyses will include quantitative hypocretin measurements, proteomics/peptidomics, and immunological, genetic and microbiota studies. SPHYNCS will increase our understanding of CDH and the relationship between NT1 and the NBL. The identification of new disease markers is expected to lead to better and earlier diagnosis, better prognosis and personalized management of CDH.
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Affiliation(s)
- Anelia Dietmann
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Elena Wenz
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.,Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Julia van der Meer
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Maya Ringli
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Jan D Warncke
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Ellen Edwards
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Markus H Schmidt
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Corrado A Bernasconi
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | | | | | - Silvia Miano
- Sleep and Epilepsy Center, Neurocenter of the Southern Switzerland, Regional Hospital (EOC) of Lugano, Lugano, Switzerland
| | - Mauro Manconi
- Sleep and Epilepsy Center, Neurocenter of the Southern Switzerland, Regional Hospital (EOC) of Lugano, Lugano, Switzerland
| | - Jens Acker
- Clinic for Sleep Medicine, Bad Zurzach, Switzerland
| | | | | | - Philip O Valko
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Bahtiyar Yilmaz
- Department of Visceral Surgery and Medicine, Bern University Hospital, University of Bern, Bern, Switzerland.,Maurice Müller Laboratories, Department for Biomedical Research, University of Bern, Bern, Switzerland
| | - Andreas-David Brunner
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Athina Tzovara
- Institute of Computer Science, University of Bern, Bern, Switzerland.,Department of Neurology, Sleep Wake Epilepsy Center, NeuroTec, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.,Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Zhongxing Zhang
- Clinic Barmelweid, Center for Sleep Medicine and Sleep Research, Barmelweid, Switzerland
| | - Carlo R Largiadèr
- Department of Clinical Chemistry, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Mehdi Tafti
- Department of Biomedical Science, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | | | - Federica Sallusto
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland.,Institute for Research in Biomedicine, Faculty of Biomedical Sciences, USI, Bellinzona, Switzerland
| | - Ramin Khatami
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.,Clinic Barmelweid, Center for Sleep Medicine and Sleep Research, Barmelweid, Switzerland
| | - Claudio L A Bassetti
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
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18
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Tzovara A, Amarreh I, Borghesani V, Chakravarty MM, DuPre E, Grefkes C, Haugg A, Jollans L, Lee HW, Newman SD, Olsen RK, Ratnanather JT, Rippon G, Uddin LQ, Vega MLB, Veldsman M, White T, Badhwar A. Embracing diversity and inclusivity in an academic setting: Insights from the Organization for Human Brain Mapping. Neuroimage 2021; 229:117742. [PMID: 33454405 DOI: 10.1016/j.neuroimage.2021.117742] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 01/07/2021] [Accepted: 01/10/2021] [Indexed: 12/17/2022] Open
Abstract
Scientific research aims to bring forward innovative ideas and constantly challenges existing knowledge structures and stereotypes. However, women, ethnic and cultural minorities, as well as individuals with disabilities, are systematically discriminated against or even excluded from promotions, publications, and general visibility. A more diverse workforce is more productive, and thus discrimination has a negative impact on science and the wider society, as well as on the education, careers, and well-being of individuals who are discriminated against. Moreover, the lack of diversity at scientific gatherings can lead to micro-aggressions or harassment, making such meetings unpleasant, or even unsafe environments for early career and underrepresented scientists. At the Organization for Human Brain Mapping (OHBM), we recognized the need for promoting underrepresented scientists and creating diverse role models in the field of neuroimaging. To foster this, the OHBM has created a Diversity and Inclusivity Committee (DIC). In this article, we review the composition and activities of the DIC that have promoted diversity within OHBM, in order to inspire other organizations to implement similar initiatives. Activities of the committee over the past four years have included (a) creating a code of conduct, (b) providing diversity and inclusivity education for OHBM members, (c) organizing interviews and symposia on diversity issues, and (d) organizing family-friendly activities and providing childcare grants during the OHBM annual meetings. We strongly believe that these activities have brought positive change within the wider OHBM community, improving inclusivity and fostering diversity while promoting rigorous, ground-breaking science. These positive changes could not have been so rapidly implemented without the enthusiastic support from the leadership, including OHBM Council and Program Committee, and the OHBM Special Interest Groups (SIGs), namely the Open Science, Student and Postdoc, and Brain-Art SIGs. Nevertheless, there remains ample room for improvement, in all areas, and even more so in the area of targeted attempts to increase inclusivity for women, individuals with disabilities, members of the LGBTQ+ community, racial/ethnic minorities, and individuals of lower socioeconomic status or from low and middle-income countries. Here, we present an overview of the DIC's composition, its activities, future directions and challenges. Our goal is to share our experiences with a wider audience to provide information to other organizations and institutions wishing to implement similar comprehensive diversity initiatives. We propose that scientific organizations can push the boundaries of scientific progress only by moving beyond existing power structures and by integrating principles of equity and inclusivity in their core values.
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Affiliation(s)
- Athina Tzovara
- Institute for Computer Science, University of Bern, Neubrückstrasse 10, CH-3012 Bern, Switzerland; Helen Wills Neuroscience Institute, University of California Berkeley, USA; Sleep Wake Epilepsy Center
- NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland.
| | | | - Valentina Borghesani
- Memory and Aging Center, Department of Neurology, University of California San Francisco
| | - M Mallar Chakravarty
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Research Centre; Departments of Psychiatry and Biological and Biomedical Engineering at McGill University
| | - Elizabeth DuPre
- NeuroDataScience - ORIGAMI laboratory, McGill University, Montreal, Canada
| | - Christian Grefkes
- University of Cologne, Medical Faculty, and Department of Neurology, University Hospital Cologne, Germany; Institute of Medicine and Neuroscience, Cognitive Neurology (INM-3), Juelich Research Center, Germany
| | - Amelie Haugg
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland
| | - Lee Jollans
- Department of Translational Research in Psychiatry; Max Planck Institute of Psychiatry; Munich, Germany
| | - Hyang Woon Lee
- Departments of Neurology, Medical Science, Computational Medicine and System Health & Engineering Major, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, South Korea
| | - Sharlene D Newman
- Alabama Life Research Institute, University of Alabama, Tuscaloosa, AL, USA
| | - Rosanna K Olsen
- Rotman Research Institute, Baycrest Health Sciences, and Department of Psychology, University of Toronto
| | - J Tilak Ratnanather
- Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Gina Rippon
- Aston Brain Centre, Aston University, Birmingham B4 7ET, UK
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Maria L Bringas Vega
- University of Electronic Sciences and Technology of China, Chengdu China; Cuban Neuroscience Center, La Habana, Cuba
| | - Michele Veldsman
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam; Department of Radiology and Nuclear Medicine, Erasmus University Medical Centre, Rotterdam
| | - AmanPreet Badhwar
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Université de Montréal, Montréal, Quebec H3W 1W5, Canada; Université de Montréal, Département de pharmacologie et physiologie, Montreal, Canada.
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19
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Johnson EL, Kam JWY, Tzovara A, Knight RT. Insights into human cognition from intracranial EEG: A review of audition, memory, internal cognition, and causality. J Neural Eng 2020; 17:051001. [PMID: 32916678 PMCID: PMC7731730 DOI: 10.1088/1741-2552/abb7a5] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
By recording neural activity directly from the human brain, researchers gain unprecedented insight into how neurocognitive processes unfold in real time. We first briefly discuss how intracranial electroencephalography (iEEG) recordings, performed for clinical practice, are used to study human cognition with the spatiotemporal and single-trial precision traditionally limited to non-human animal research. We then delineate how studies using iEEG have informed our understanding of issues fundamental to human cognition: auditory prediction, working and episodic memory, and internal cognition. We also discuss the potential of iEEG to infer causality through the manipulation or 'engineering' of neurocognitive processes via spatiotemporally precise electrical stimulation. We close by highlighting limitations of iEEG, potential of burgeoning techniques to further increase spatiotemporal precision, and implications for future research using intracranial approaches to understand, restore, and enhance human cognition.
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Affiliation(s)
- Elizabeth L Johnson
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
- Life-Span Cognitive Neuroscience Program, Institute of Gerontology, Wayne State University, United States of America
| | - Julia W Y Kam
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
- Department of Psychology, University of Calgary, Canada
- Hotchkiss Brain Institute, University of Calgary, Canada
| | - Athina Tzovara
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
- Institute for Computer Science, University of Bern, Switzerland
- Sleep Wake Epilepsy Center | NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
- Department of Psychology, University of California, Berkeley, United States of America
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20
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Castegnetti G, Tzovara A, Khemka S, Melinščak F, Barnes GR, Dolan RJ, Bach DR. Representation of probabilistic outcomes during risky decision-making. Nat Commun 2020; 11:2419. [PMID: 32415145 PMCID: PMC7229012 DOI: 10.1038/s41467-020-16202-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 04/21/2020] [Indexed: 12/19/2022] Open
Abstract
Goal-directed behaviour requires prospectively retrieving and evaluating multiple possible action outcomes. While a plethora of studies suggested sequential retrieval for deterministic choice outcomes, it remains unclear whether this is also the case when integrating multiple probabilistic outcomes of the same action. We address this question by capitalising on magnetoencephalography (MEG) in humans who made choices in a risky foraging task. We train classifiers to distinguish MEG field patterns during presentation of two probabilistic outcomes (reward, loss), and then apply these to decode such patterns during deliberation. First, decoded outcome representations have a temporal structure, suggesting alternating retrieval of the outcomes. Moreover, the probability that one or the other outcome is being represented depends on loss magnitude, but not on loss probability, and it predicts the chosen action. In summary, we demonstrate decodable outcome representations during probabilistic decision-making, which are sequentially structured, depend on task features, and predict subsequent action.
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Affiliation(s)
- Giuseppe Castegnetti
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.
- Institute of Cognitive Neuroscience, University College London, London, UK.
| | - Athina Tzovara
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
- Department of Computer Science & Faculty of Medicine, University of Bern, Bern, Switzerland
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Saurabh Khemka
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Filip Melinščak
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Raymond J Dolan
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing, University College London, London, UK
| | - Dominik R Bach
- Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing, University College London, London, UK
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21
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Fedele T, Tzovara A, Steiger B, Hilfiker P, Grunwald T, Stieglitz L, Jokeit H, Sarnthein J. The relation between neuronal firing, local field potentials and hemodynamic activity in the human amygdala in response to aversive dynamic visual stimuli. Neuroimage 2020; 213:116705. [PMID: 32165266 DOI: 10.1016/j.neuroimage.2020.116705] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 02/11/2020] [Accepted: 03/03/2020] [Indexed: 10/24/2022] Open
Abstract
The amygdala is a central part of networks of brain regions underlying perception and cognition, in particular related to processing of emotionally salient stimuli. Invasive electrophysiological and hemodynamic measurements are commonly used to evaluate functions of the human amygdala, but a comprehensive understanding of their relation is still lacking. Here, we aimed at investigating the link between fast and slow frequency amygdalar oscillations, neuronal firing and hemodynamic responses. To this aim, we recorded intracranial electroencephalography (iEEG), hemodynamic responses and single neuron activity from the amygdala of patients with epilepsy. Patients were presented with dynamic visual sequences of fearful faces (aversive condition), interleaved with sequences of neutral landscapes (neutral condition). Comparing responses to aversive versus neutral stimuli across participants, we observed enhanced high gamma power (HGP, >60 Hz) during the first 2 s of aversive sequence viewing, and reduced delta power (1-4 Hz) lasting up to 18 s. In 5 participants with implanted microwires, neuronal firing rates were enhanced following aversive stimuli, and exhibited positive correlation with HGP and hemodynamic responses. Our results show that high gamma power, neuronal firing and BOLD responses from the human amygdala are co-modulated. Our findings provide, for the first time, a comprehensive investigation of amygdalar responses to aversive stimuli, ranging from single-neuron spikes to local field potentials and hemodynamic responses.
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Affiliation(s)
- Tommaso Fedele
- National Research University Higher School of Economics, Moscow, Russian Federation.
| | - Athina Tzovara
- Institute for Computer Science, University of Bern, Switzerland
| | | | | | | | - Lennart Stieglitz
- Klinik für Neurochirurgie, UniversitätsSpital Zürich und Universität Zürich, Zurich, Switzerland
| | - Hennric Jokeit
- Schweizerische Epilepsie-Klinik, Zurich, Switzerland; Zentrum für Neurowissenschaften Zürich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, UniversitätsSpital Zürich und Universität Zürich, Zurich, Switzerland; Zentrum für Neurowissenschaften Zürich, Switzerland.
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22
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Tzovara A, Meyer SS, Bonaiuto JJ, Abivardi A, Dolan RJ, Barnes GR, Bach DR. High-precision magnetoencephalography for reconstructing amygdalar and hippocampal oscillations during prediction of safety and threat. Hum Brain Mapp 2019; 40:4114-4129. [PMID: 31257708 PMCID: PMC6772181 DOI: 10.1002/hbm.24689] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 04/09/2019] [Accepted: 05/27/2019] [Indexed: 02/02/2023] Open
Abstract
Learning to associate neutral with aversive events in rodents is thought to depend on hippocampal and amygdala oscillations. In humans, oscillations underlying aversive learning are not well characterised, largely due to the technical difficulty of recording from these two structures. Here, we used high‐precision magnetoencephalography (MEG) during human discriminant delay threat conditioning. We constructed generative anatomical models relating neural activity with recorded magnetic fields at the single‐participant level, including the neocortex with or without the possibility of sources originating in the hippocampal and amygdalar structures. Models including neural activity in amygdala and hippocampus explained MEG data during threat conditioning better than exclusively neocortical models. We found that in both amygdala and hippocampus, theta oscillations during anticipation of an aversive event had lower power compared to safety, both during retrieval and extinction of aversive memories. At the same time, theta synchronisation between hippocampus and amygdala increased over repeated retrieval of aversive predictions, but not during safety. Our results suggest that high‐precision MEG is sensitive to neural activity of the human amygdala and hippocampus during threat conditioning and shed light on the oscillation‐mediated mechanisms underpinning retrieval and extinction of fear memories in humans.
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Affiliation(s)
- Athina Tzovara
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.,Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.,Helen Wills Neuroscience Institute, University of California, Berkeley, California
| | - Sofie S Meyer
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.,UCL Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - James J Bonaiuto
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Aslan Abivardi
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Raymond J Dolan
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Dominik R Bach
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.,Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
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23
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Greshake Tzovaras B, Angrist M, Arvai K, Dulaney M, Estrada-Galiñanes V, Gunderson B, Head T, Lewis D, Nov O, Shaer O, Tzovara A, Bobe J, Price Ball M. Open Humans: A platform for participant-centered research and personal data exploration. Gigascience 2019; 8:giz076. [PMID: 31241153 PMCID: PMC6593360 DOI: 10.1093/gigascience/giz076] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 05/02/2019] [Accepted: 06/03/2019] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Many aspects of our lives are now digitized and connected to the internet. As a result, individuals are now creating and collecting more personal data than ever before. This offers an unprecedented chance for human-participant research ranging from the social sciences to precision medicine. With this potential wealth of data comes practical problems (e.g., how to merge data streams from various sources), as well as ethical problems (e.g., how best to balance risks and benefits when enabling personal data sharing by individuals). RESULTS To begin to address these problems in real time, we present Open Humans, a community-based platform that enables personal data collections across data streams, giving individuals more personal data access and control of sharing authorizations, and enabling academic research as well as patient-led projects. We showcase data streams that Open Humans combines (e.g., personal genetic data, wearable activity monitors, GPS location records, and continuous glucose monitor data), along with use cases of how the data facilitate various projects. CONCLUSIONS Open Humans highlights how a community-centric ecosystem can be used to aggregate personal data from various sources, as well as how these data can be used by academic and citizen scientists through practical, iterative approaches to sharing that strive to balance considerations with participant autonomy, inclusion, and privacy.
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Affiliation(s)
- Bastian Greshake Tzovaras
- Open Humans Foundation, 500 Westover Dr #10553, Sanford, NC, 27330, USA
- Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA
| | - Misha Angrist
- Social Science Research Institute, Duke University, 140 Science Drive, Durham, NC 27708, USA
| | | | - Mairi Dulaney
- Open Humans Foundation, 500 Westover Dr #10553, Sanford, NC, 27330, USA
| | - Vero Estrada-Galiñanes
- QoL Lab, Department of ComputerScience, University of Copenhagen, Sigurdsgade 41, DK-2200 Copenhagen, Denmark
- IDE, University of Stavanger, Kjell Arholmsgate 41, 4036 Stavanger, Norway
| | | | - Tim Head
- Wild Tree Tech, Froehlichstrasse 42 5200 Brugg Switzerland
| | | | - Oded Nov
- Tandon School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY 11201, USA
| | - Orit Shaer
- Wellesley College, 106 Central Street – Wellesley, MA 02481, USA
| | - Athina Tzovara
- Helen Wills Neuroscience Institute, University of California, Berkeley 174 Li Ka Shing Center, Berkeley, CA 94720, USA
- Institute of Computer Science, University of Bern, Neubrückstrasse 10, 3012 Bern, Switzerland
| | - Jason Bobe
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place New York, NY 10029-5674, USA
| | - Mad Price Ball
- Open Humans Foundation, 500 Westover Dr #10553, Sanford, NC, 27330, USA
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24
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25
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Abstract
Learning to predict threat from environmental cues is a fundamental skill in changing environments. This aversive learning process is exemplified by Pavlovian threat conditioning. Despite a plethora of studies on the neural mechanisms supporting the formation of associations between neutral and aversive events, our computational understanding of this process is fragmented. Importantly, different computational models give rise to different and partly opposing predictions for the trial-by-trial dynamics of learning, for example expressed in the activity of the autonomic nervous system (ANS). Here, we investigate human ANS responses to conditioned stimuli during Pavlovian fear conditioning. To obtain precise, trial-by-trial, single-subject estimates of ANS responses, we build on a statistical framework for psychophysiological modelling. We then consider previously proposed non-probabilistic models, a simple probabilistic model, and non-learning models, as well as different observation functions to link learning models with ANS activity. Across three experiments, and both for skin conductance (SCR) and pupil size responses (PSR), a probabilistic learning model best explains ANS responses. Notably, SCR and PSR reflect different quantities of the same model: SCR track a mixture of expected outcome and uncertainty, while PSR track expected outcome alone. In summary, by combining psychophysiological modelling with computational learning theory, we provide systematic evidence that the formation and maintenance of Pavlovian threat predictions in humans may rely on probabilistic inference and includes estimation of uncertainty. This could inform theories of neural implementation of aversive learning.
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Affiliation(s)
- Athina Tzovara
- Clinical Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
- Wellcome Centre for Human Neuroimaging and Max Planck UCL Centre for Computational Psychiatry and Ageing, University College London, London, United Kingdom
- Helen Wills Neuroscience Institute, UC Berkeley, Berkeley, California, United States of America
| | - Christoph W. Korn
- Clinical Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
- Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Dominik R. Bach
- Clinical Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland
- Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
- Wellcome Centre for Human Neuroimaging and Max Planck UCL Centre for Computational Psychiatry and Ageing, University College London, London, United Kingdom
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26
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Bach DR, Tzovara A, Vunder J. Blocking human fear memory with the matrix metalloproteinase inhibitor doxycycline. Mol Psychiatry 2018; 23:1584-1589. [PMID: 28373691 PMCID: PMC5507298 DOI: 10.1038/mp.2017.65] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 02/21/2017] [Accepted: 02/22/2017] [Indexed: 12/23/2022]
Abstract
Learning to predict threat is a fundamental ability of many biological organisms, and a laboratory model for anxiety disorders. Interfering with such memories in humans would be of high clinical relevance. On the basis of studies in cell cultures and slice preparations, it is hypothesised that synaptic remodelling required for threat learning involves the extracellular enzyme matrix metalloproteinase (MMP) 9. However, in vivo evidence for this proposal is lacking. Here we investigate human Pavlovian fear conditioning under the blood-brain barrier crossing MMP inhibitor doxycyline in a pre-registered, randomised, double-blind, placebo-controlled trial. We find that recall of threat memory, measured with fear-potentiated startle 7 days after acquisition, is attenuated by ~60% in individuals who were under doxycycline during acquisition. This threat memory impairment is also reflected in increased behavioural surprise signals to the conditioned stimulus during subsequent re-learning, and already late during initial acquisition. Our findings support an emerging view that extracellular signalling pathways are crucially required for threat memory formation. Furthermore, they suggest novel pharmacological methods for primary prevention and treatment of posttraumatic stress disorder.
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Affiliation(s)
- D R Bach
- Division of Clinical Psychiatry Research, Psychiatric Hospital, University of Zurich, Zurich, Switzerland. .,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland. .,Wellcome Trust Centre for Neuroimaging and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.
| | - A Tzovara
- 0000 0004 1937 0650grid.7400.3Division of Clinical Psychiatry Research, Psychiatric Hospital, University of Zurich, Zurich, Switzerland ,0000 0004 1937 0650grid.7400.3Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland ,0000000121901201grid.83440.3bWellcome Trust Centre for Neuroimaging and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - J Vunder
- 0000 0004 1937 0650grid.7400.3Division of Clinical Psychiatry Research, Psychiatric Hospital, University of Zurich, Zurich, Switzerland ,0000 0004 1937 0650grid.7400.3Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
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27
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Abstract
During fear conditioning, pupil size responses dissociate between conditioned stimuli that are contingently paired (CS+) with an aversive unconditioned stimulus, and those that are unpaired (CS-). Current approaches to assess fear learning from pupil responses rely on ad hoc specifications. Here, we sought to develop a psychophysiological model (PsPM) in which pupil responses are characterized by response functions within the framework of a linear time-invariant system. This PsPM can be written as a general linear model, which is inverted to yield amplitude estimates of the eliciting process in the central nervous system. We first characterized fear-conditioned pupil size responses based on an experiment with auditory CS. PsPM-based parameter estimates distinguished CS+/CS- better than, or on par with, two commonly used methods (peak scoring, area under the curve). We validated this PsPM in four independent experiments with auditory, visual, and somatosensory CS, as well as short (3.5 s) and medium (6 s) CS/US intervals. Overall, the new PsPM provided equal or decisively better differentiation of CS+/CS- than the two alternative methods and was never decisively worse. We further compared pupil responses with concurrently measured skin conductance and heart period responses. Finally, we used our previously developed luminance-related pupil responses to infer the timing of the likely neural input into the pupillary system. Overall, we establish a new PsPM to assess fear conditioning based on pupil responses. The model has a potential to provide higher statistical sensitivity, can be applied to other conditioning paradigms in humans, and may be easily extended to nonhuman mammals.
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Affiliation(s)
- Christoph W. Korn
- Department of Psychiatry, Psychotherapy, and PsychosomaticsUniversity of ZurichZurichSwitzerland
- Neuroscience Center ZurichUniversity of ZurichZurichSwitzerland
| | - Matthias Staib
- Department of Psychiatry, Psychotherapy, and PsychosomaticsUniversity of ZurichZurichSwitzerland
- Neuroscience Center ZurichUniversity of ZurichZurichSwitzerland
| | - Athina Tzovara
- Department of Psychiatry, Psychotherapy, and PsychosomaticsUniversity of ZurichZurichSwitzerland
- Neuroscience Center ZurichUniversity of ZurichZurichSwitzerland
- Wellcome Trust Centre for NeuroimagingUniversity College LondonLondonUK
| | - Giuseppe Castegnetti
- Department of Psychiatry, Psychotherapy, and PsychosomaticsUniversity of ZurichZurichSwitzerland
- Neuroscience Center ZurichUniversity of ZurichZurichSwitzerland
| | - Dominik R. Bach
- Department of Psychiatry, Psychotherapy, and PsychosomaticsUniversity of ZurichZurichSwitzerland
- Neuroscience Center ZurichUniversity of ZurichZurichSwitzerland
- Wellcome Trust Centre for NeuroimagingUniversity College LondonLondonUK
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28
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Castegnetti G, Tzovara A, Staib M, Gerster S, Bach DR. Assessing fear learning via conditioned respiratory amplitude responses. Psychophysiology 2016; 54:215-223. [PMID: 27933608 PMCID: PMC6001548 DOI: 10.1111/psyp.12778] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Accepted: 09/26/2016] [Indexed: 11/27/2022]
Abstract
Respiratory physiology is influenced by cognitive processes. It has been suggested that some cognitive states may be inferred from respiration amplitude responses (RAR) after external events. Here, we investigate whether RAR allow assessment of fear memory in cued fear conditioning, an experimental model of aversive learning. To this end, we built on a previously developed psychophysiological model (PsPM) of RAR, which regards interpolated RAR time series as the output of a linear time invariant system. We first establish that average RAR after CS+ and CS− are different. We then develop the response function of fear‐conditioned RAR, to be used in our PsPM. This PsPM is inverted to yield estimates of cognitive input into the respiratory system. We analyze five validation experiments involving fear acquisition and retention, delay and trace conditioning, short and medium CS‐US intervals, and data acquired with bellows and MRI‐compatible pressure chest belts. In all experiments, CS+ and CS− are distinguished by their estimated cognitive inputs, and the sensitivity of this distinction is higher for model‐based estimates than for peak scoring of RAR. Comparing these data with skin conductance responses (SCR) and heart period responses (HPR), we find that, on average, RAR performs similar to SCR in distinguishing CS+ and CS−, but is less sensitive than HPR. Overall, our work provides a novel and robust tool to investigate fear memory in humans that may allow wide and straightforward application to diverse experimental contexts.
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Affiliation(s)
- Giuseppe Castegnetti
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Athina Tzovara
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.,Wellcome Trust Centre for Neuroimaging, University College London, London, UK
| | - Matthias Staib
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Samuel Gerster
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Dominik R Bach
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.,Wellcome Trust Centre for Neuroimaging, University College London, London, UK
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29
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Khemka S, Tzovara A, Gerster S, Quednow BB, Bach DR. Modeling startle eyeblink electromyogram to assess fear learning. Psychophysiology 2016; 54:204-214. [PMID: 27753123 PMCID: PMC5298047 DOI: 10.1111/psyp.12775] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 09/13/2016] [Indexed: 11/29/2022]
Abstract
Pavlovian fear conditioning is widely used as a laboratory model of associative learning in human and nonhuman species. In this model, an organism is trained to predict an aversive unconditioned stimulus from initially neutral events (conditioned stimuli, CS). In humans, fear memory is typically measured via conditioned autonomic responses or fear‐potentiated startle. For the latter, various analysis approaches have been developed, but a systematic comparison of competing methodologies is lacking. Here, we investigate the suitability of a model‐based approach to startle eyeblink analysis for assessment of fear memory, and compare this to extant analysis strategies. First, we build a psychophysiological model (PsPM) on a generic startle response. Then, we optimize and validate this PsPM on three independent fear‐conditioning data sets. We demonstrate that our model can robustly distinguish aversive (CS+) from nonaversive stimuli (CS‐, i.e., has high predictive validity). Importantly, our model‐based approach captures fear‐potentiated startle during fear retention as well as fear acquisition. Our results establish a PsPM‐based approach to assessment of fear‐potentiated startle, and qualify previous peak‐scoring methods. Our proposed model represents a generic startle response and can potentially be used beyond fear conditioning, for example, to quantify affective startle modulation or prepulse inhibition of the acoustic startle response.
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Affiliation(s)
- Saurabh Khemka
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Athina Tzovara
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.,Wellcome Trust Centre for Neuroimaging, University College London, London, UK
| | - Samuel Gerster
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland
| | - Boris B Quednow
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Dominik R Bach
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.,Wellcome Trust Centre for Neuroimaging, University College London, London, UK
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30
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Bach DR, Gerster S, Tzovara A, Castegnetti G. A linear model for event-related respiration responses. J Neurosci Methods 2016; 270:147-155. [PMID: 27268156 PMCID: PMC4994768 DOI: 10.1016/j.jneumeth.2016.06.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Revised: 05/09/2016] [Accepted: 06/02/2016] [Indexed: 10/27/2022]
Abstract
BACKGROUND Cognitive processes influence respiratory physiology. This may allow inferring cognitive states from measured respiration. Here, we take a first step towards this goal and investigate whether event-related respiratory responses can be identified, and whether they are accessible to a model-based approach. NEW METHOD We regard respiratory responses as the output of a linear time invariant system that receives brief inputs after psychological events. We derive average responses to visual targets, aversive stimulation, and viewing of arousing pictures, in interpolated respiration period (RP), respiration amplitude (RA), and respiratory flow rate (RFR). We then base a Psychophysiological Model (PsPM) on these averaged event-related responses. The PsPM is inverted to yield estimates of cognitive input into the respiratory system. This method is validated in an independent data set. RESULTS All three measures show event-related responses, which are captured as non-zero response amplitudes in the PsPM. Amplitude estimates for RA and RFR distinguish between picture viewing and the other tasks. This pattern is replicated in the validation experiment. COMPARISON WITH EXISTING METHODS Existing respiratory measures are based on relatively short time-intervals after an event while the new method is based on the entire duration of respiratory responses. CONCLUSION Our findings suggest that interpolated respiratory measures show replicable event-related response patterns. PsPM inversion is a suitable approach to analysing these patterns, with a potential to infer cognitive processes from respiration.
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Affiliation(s)
- Dominik R Bach
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom.
| | - Samuel Gerster
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Switzerland
| | - Athina Tzovara
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Switzerland
| | - Giuseppe Castegnetti
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Switzerland
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Tzovara A, Rossetti AO, Juan E, Suys T, Viceic D, Rusca M, Oddo M, Lucia MD. Prediction of awakening from hypothermic postanoxic coma based on auditory discrimination. Ann Neurol 2016; 79:748-757. [DOI: 10.1002/ana.24622] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Revised: 02/17/2016] [Accepted: 02/17/2016] [Indexed: 11/12/2022]
Affiliation(s)
- Athina Tzovara
- Neuroimaging Research Laboratory, Department of Clinical Neurosciences; Lausanne University Hospital and University of Lausanne; Lausanne Switzerland
- Department of Psychiatry, Psychotherapy; and Psychosomatics and Neuroscience Centre Zurich; University of Zurich Switzerland
| | - Andrea O. Rossetti
- Neurology Service, Department of Clinical Neurosciences; Lausanne University Hospital and University of Lausanne; Lausanne Switzerland
| | - Elsa Juan
- Neuroimaging Research Laboratory, Department of Clinical Neurosciences; Lausanne University Hospital and University of Lausanne; Lausanne Switzerland
- Neurology Service, Department of Clinical Neurosciences; Lausanne University Hospital and University of Lausanne; Lausanne Switzerland
| | - Tamarah Suys
- Department of Intensive Care Medicine; Lausanne University Hospital and University of Lausanne; Lausanne Switzerland
| | | | - Marco Rusca
- Intensive Care Medicine Service; Valais Hospital; Sion Switzerland
| | - Mauro Oddo
- Department of Intensive Care Medicine; Lausanne University Hospital and University of Lausanne; Lausanne Switzerland
| | - Marzia De Lucia
- Neuroimaging Research Laboratory, Department of Clinical Neurosciences; Lausanne University Hospital and University of Lausanne; Lausanne Switzerland
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32
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Affiliation(s)
- Marzia De Lucia
- Laboratoire de Recherche en Neuroimagerie (LREN), Department of Clinical Neuroscience, Lausanne University and University Hospital, Lausanne, CH-1011, Switzerland
| | - Athina Tzovara
- Laboratoire de Recherche en Neuroimagerie (LREN), Department of Clinical Neuroscience, Lausanne University and University Hospital, Lausanne, CH-1011, Switzerland Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, CH-8032, Switzerland Neuroscience Centre Zurich University of Zurich, CH-8032, Switzerland
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Castegnetti G, Tzovara A, Staib M, Paulus PC, Hofer N, Bach DR. Modeling fear-conditioned bradycardia in humans. Psychophysiology 2016; 53:930-9. [PMID: 26950648 PMCID: PMC4869680 DOI: 10.1111/psyp.12637] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 02/04/2016] [Indexed: 11/29/2022]
Abstract
Across species, cued fear conditioning is a common experimental paradigm to investigate aversive Pavlovian learning. While fear‐conditioned stimuli (CS+) elicit overt behavior in many mammals, this is not the case in humans. Typically, autonomic nervous system activity is used to quantify fear memory in humans, measured by skin conductance responses (SCR). Here, we investigate whether heart period responses (HPR) evoked by the CS, often observed in humans and small mammals, are suitable to complement SCR as an index of fear memory in humans. We analyze four datasets involving delay and trace conditioning, in which heart beats are identified via electrocardiogram or pulse oximetry, to show that fear‐conditioned heart rate deceleration (bradycardia) is elicited and robustly distinguishes CS+ from CS−. We then develop a psychophysiological model (PsPM) of fear‐conditioned HPR. This PsPM is inverted to yield estimates of autonomic input into the heart. We show that the sensitivity to distinguish CS+ and CS− (predictive validity) is higher for model‐based estimates than peak‐scoring analysis, and compare this with SCR. Our work provides a novel tool to investigate fear memory in humans that allows direct comparison between species.
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Affiliation(s)
- Giuseppe Castegnetti
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Athina Tzovara
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Staib
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Philipp C Paulus
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.,Department of Psychology, Dresden University of Technology, Dresden, Germany
| | - Nicolas Hofer
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland
| | - Dominik R Bach
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.,Neuroscience Centre Zurich, University of Zurich, Zurich, Switzerland.,Wellcome Trust Centre for Neuroimaging, University College London, London, UK
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Tzovara A, Simonin A, Oddo M, Rossetti AO, De Lucia M. Reply: Neural detection of complex sound sequences or of statistical regularities in the absence of consciousness? Brain 2015; 138:e396. [DOI: 10.1093/brain/awv186] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Chouiter L, Tzovara A, Dieguez S, Annoni JM, Magezi D, De Lucia M, Spierer L. Experience-based Auditory Predictions Modulate Brain Activity to Silence as do Real Sounds. J Cogn Neurosci 2015; 27:1968-80. [PMID: 26042500 DOI: 10.1162/jocn_a_00835] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Interactions between stimuli's acoustic features and experience-based internal models of the environment enable listeners to compensate for the disruptions in auditory streams that are regularly encountered in noisy environments. However, whether auditory gaps are filled in predictively or restored a posteriori remains unclear. The current lack of positive statistical evidence that internal models can actually shape brain activity as would real sounds precludes accepting predictive accounts of filling-in phenomenon. We investigated the neurophysiological effects of internal models by testing whether single-trial electrophysiological responses to omitted sounds in a rule-based sequence of tones with varying pitch could be decoded from the responses to real sounds and by analyzing the ERPs to the omissions with data-driven electrical neuroimaging methods. The decoding of the brain responses to different expected, but omitted, tones in both passive and active listening conditions was above chance based on the responses to the real sound in active listening conditions. Topographic ERP analyses and electrical source estimations revealed that, in the absence of any stimulation, experience-based internal models elicit an electrophysiological activity different from noise and that the temporal dynamics of this activity depend on attention. We further found that the expected change in pitch direction of omitted tones modulated the activity of left posterior temporal areas 140-200 msec after the onset of omissions. Collectively, our results indicate that, even in the absence of any stimulation, internal models modulate brain activity as do real sounds, indicating that auditory filling in can be accounted for by predictive activity.
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Affiliation(s)
| | - Athina Tzovara
- University of Lausanne.,University Hospital of Lausanne.,University of Zürich
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Tzovara A, Simonin A, Oddo M, Rossetti AO, De Lucia M. Neural detection of complex sound sequences in the absence of consciousness. Brain 2015; 138:1160-6. [PMID: 25740220 DOI: 10.1093/brain/awv041] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2014] [Accepted: 12/09/2014] [Indexed: 12/14/2022] Open
Abstract
The neural response to a violation of sequences of identical sounds is a typical example of the brain's sensitivity to auditory regularities. Previous literature interprets this effect as a pre-attentive and unconscious processing of sensory stimuli. By contrast, a violation to auditory global regularities, i.e. based on repeating groups of sounds, is typically detectable when subjects can consciously perceive them. Here, we challenge the notion that global detection implies consciousness by testing the neural response to global violations in a group of 24 patients with post-anoxic coma (three females, age range 45-87 years), treated with mild therapeutic hypothermia and sedation. By applying a decoding analysis to electroencephalographic responses to standard versus deviant sound sequences, we found above-chance decoding performance in 10 of 24 patients (Wilcoxon signed-rank test, P < 0.001), despite five of them being mildly hypothermic, sedated and unarousable. Furthermore, consistently with previous findings based on the mismatch negativity the progression of this decoding performance was informative of patients' chances of awakening (78% predictive of awakening). Our results show for the first time that detection of global regularities at neural level exists despite a deeply unconscious state.
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Affiliation(s)
- Athina Tzovara
- 1 Centre for Biomedical Imaging (CIBM), Department of Radiology, Lausanne, University Hospital and University of Lausanne, CH-1011 Lausanne, Switzerland 2 Laboratoire de Recherche en Neuroimagerie (LREN), Department of Clinical Neurosciences, Lausanne University and University Hospital, Lausanne, CH-1011, Switzerland
| | - Alexandre Simonin
- 3 Department of Clinical Neurosciences, University Hospital, Lausanne, CH-1011, Switzerland
| | - Mauro Oddo
- 4 Department of Intensive Care Medicine, CH-1011, Lausanne University Hospital, Switzerland
| | - Andrea O Rossetti
- 3 Department of Clinical Neurosciences, University Hospital, Lausanne, CH-1011, Switzerland
| | - Marzia De Lucia
- 1 Centre for Biomedical Imaging (CIBM), Department of Radiology, Lausanne, University Hospital and University of Lausanne, CH-1011 Lausanne, Switzerland 2 Laboratoire de Recherche en Neuroimagerie (LREN), Department of Clinical Neurosciences, Lausanne University and University Hospital, Lausanne, CH-1011, Switzerland
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De Lucia M, Tzovara A. Decoding auditory EEG responses in healthy and clinical populations: A comparative study. J Neurosci Methods 2014; 250:106-13. [PMID: 25445243 DOI: 10.1016/j.jneumeth.2014.10.019] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Revised: 10/21/2014] [Accepted: 10/22/2014] [Indexed: 12/17/2022]
Abstract
BACKGROUND Analyses of brain responses to external stimuli are typically based on the means computed across conditions. However in many cognitive and clinical applications, taking into account their variability across trials has turned out to be statistically more sensitive than comparing their means. NEW METHOD In this study we present a novel implementation of a single-trial topographic analysis (STTA) for discriminating auditory evoked potentials at predefined time-windows. This analysis has been previously introduced for extracting spatio-temporal features at the level of the whole neural response. Adapting the STTA on specific time windows is an essential step for comparing its performance to other time-window based algorithms. RESULTS We analyzed responses to standard vs. deviant sounds and showed that the new implementation of the STTA gives above-chance decoding results in all subjects (in comparison to 7 out of 11 with the original method). In comatose patients, the improvement of the decoding performance was even more pronounced than in healthy controls and doubled the number of significant results. COMPARISON WITH EXISTING METHOD(S) We compared the results obtained with the new STTA to those based on a logistic regression in healthy controls and patients. We showed that the first of these two comparisons provided a better performance of the logistic regression; however only the new STTA provided significant results in comatose patients at group level. CONCLUSIONS Our results provide quantitative evidence that a systematic investigation of the accuracy of established methods in normal and clinical population is an essential step for optimizing decoding performance.
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Affiliation(s)
- Marzia De Lucia
- Laboratoire de recherche en Neuroimagerie (LREN), Department of Clinical Neurosciences, Lausanne University and University Hospital, 1011 Lausanne, Switzerland; Radiology Department, Vaudois University Hospital and University of Lausanne, 1011, Switzerland; Center for Biomedical Imaging (CIBM) of Lausanne and Geneva, 1011, Switzerland.
| | - Athina Tzovara
- Laboratoire de recherche en Neuroimagerie (LREN), Department of Clinical Neurosciences, Lausanne University and University Hospital, 1011 Lausanne, Switzerland; Radiology Department, Vaudois University Hospital and University of Lausanne, 1011, Switzerland; Center for Biomedical Imaging (CIBM) of Lausanne and Geneva, 1011, Switzerland
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Cossy N, Tzovara A, Simonin A, Rossetti AO, De Lucia M. Robust discrimination between EEG responses to categories of environmental sounds in early coma. Front Psychol 2014; 5:155. [PMID: 24611061 PMCID: PMC3933775 DOI: 10.3389/fpsyg.2014.00155] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Accepted: 02/07/2014] [Indexed: 01/18/2023] Open
Abstract
Humans can recognize categories of environmental sounds, including vocalizations produced by humans and animals and the sounds of man-made objects. Most neuroimaging investigations of environmental sound discrimination have studied subjects while consciously perceiving and often explicitly recognizing the stimuli. Consequently, it remains unclear to what extent auditory object processing occurs independently of task demands and consciousness. Studies in animal models have shown that environmental sound discrimination at a neural level persists even in anesthetized preparations, whereas data from anesthetized humans has thus far provided null results. Here, we studied comatose patients as a model of environmental sound discrimination capacities during unconsciousness. We included 19 comatose patients treated with therapeutic hypothermia (TH) during the first 2 days of coma, while recording nineteen-channel electroencephalography (EEG). At the level of each individual patient, we applied a decoding algorithm to quantify the differential EEG responses to human vs. animal vocalizations as well as to sounds of living vocalizations vs. man-made objects. Discrimination between vocalization types was accurate in 11 patients and discrimination between sounds from living and man-made sources in 10 patients. At the group level, the results were significant only for the comparison between vocalization types. These results lay the groundwork for disentangling truly preferential activations in response to auditory categories, and the contribution of awareness to auditory category discrimination.
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Affiliation(s)
- Natacha Cossy
- Electroencephalography Brain Mapping Core, Center for Biomedical Imaging (CIBM), University Hospital Center, University of Lausanne Lausanne, Switzerland ; Department of Radiology, University Hospital Center, University of Lausanne Lausanne, Switzerland
| | - Athina Tzovara
- Electroencephalography Brain Mapping Core, Center for Biomedical Imaging (CIBM), University Hospital Center, University of Lausanne Lausanne, Switzerland ; Department of Radiology, University Hospital Center, University of Lausanne Lausanne, Switzerland
| | - Alexandre Simonin
- Department of Clinical Neurosciences, University Hospital Center, University of Lausanne Lausanne, Switzerland
| | - Andrea O Rossetti
- Department of Clinical Neurosciences, University Hospital Center, University of Lausanne Lausanne, Switzerland
| | - Marzia De Lucia
- Electroencephalography Brain Mapping Core, Center for Biomedical Imaging (CIBM), University Hospital Center, University of Lausanne Lausanne, Switzerland ; Department of Radiology, University Hospital Center, University of Lausanne Lausanne, Switzerland
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Tzovara A, Rossetti AO, Spierer L, Grivel J, Murray MM, Oddo M, De Lucia M. Progression of auditory discrimination based on neural decoding predicts awakening from coma. ACTA ACUST UNITED AC 2012; 136:81-9. [PMID: 23148350 DOI: 10.1093/brain/aws264] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Auditory evoked potentials are informative of intact cortical functions of comatose patients. The integrity of auditory functions evaluated using mismatch negativity paradigms has been associated with their chances of survival. However, because auditory discrimination is assessed at various delays after coma onset, it is still unclear whether this impairment depends on the time of the recording. We hypothesized that impairment in auditory discrimination capabilities is indicative of coma progression, rather than of the comatose state itself and that rudimentary auditory discrimination remains intact during acute stages of coma. We studied 30 post-anoxic comatose patients resuscitated from cardiac arrest and five healthy, age-matched controls. Using a mismatch negativity paradigm, we performed two electroencephalography recordings with a standard 19-channel clinical montage: the first within 24 h after coma onset and under mild therapeutic hypothermia, and the second after 1 day and under normothermic conditions. We analysed electroencephalography responses based on a multivariate decoding algorithm that automatically quantifies neural discrimination at the single patient level. Results showed high average decoding accuracy in discriminating sounds both for control subjects and comatose patients. Importantly, accurate decoding was largely independent of patients' chance of survival. However, the progression of auditory discrimination between the first and second recordings was informative of a patient's chance of survival. A deterioration of auditory discrimination was observed in all non-survivors (equivalent to 100% positive predictive value for survivors). We show, for the first time, evidence of intact auditory processing even in comatose patients who do not survive and that progression of sound discrimination over time is informative of a patient's chance of survival. Tracking auditory discrimination in comatose patients could provide new insight to the chance of awakening in a quantitative and automatic fashion during early stages of coma.
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Affiliation(s)
- Athina Tzovara
- Electroencephalography Brain Mapping Core, Centre for Biomedical Imaging, Lausanne University Hospital and University of Lausanne, CH-1011 Lausanne, Switzerland
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Tzovara A, Murray MM, Michel CM, De Lucia M. A Tutorial Review of Electrical Neuroimaging From Group-Average to Single-Trial Event-Related Potentials. Dev Neuropsychol 2012; 37:518-44. [DOI: 10.1080/87565641.2011.636851] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Tzovara A, Murray MM, Bourdaud N, Chavarriaga R, Millán JDR, De Lucia M. The timing of exploratory decision-making revealed by single-trial topographic EEGanalyses. Neuroimage 2012; 60:1959-69. [DOI: 10.1016/j.neuroimage.2012.01.136] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2011] [Revised: 01/25/2012] [Accepted: 01/30/2012] [Indexed: 11/15/2022] Open
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