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Jiang Z, An X, Liu S, Yin E, Yan Y, Ming D. Beyond alpha band: prestimulus local oscillation and interregional synchrony of the beta band shape the temporal perception of the audiovisual beep-flash stimulus. J Neural Eng 2024; 21:036035. [PMID: 37419108 DOI: 10.1088/1741-2552/ace551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 07/07/2023] [Indexed: 07/09/2023]
Abstract
Objective.Multisensory integration is more likely to occur if the multimodal inputs are within a narrow temporal window called temporal binding window (TBW). Prestimulus local neural oscillations and interregional synchrony within sensory areas can modulate cross-modal integration. Previous work has examined the role of ongoing neural oscillations in audiovisual temporal integration, but there is no unified conclusion. This study aimed to explore whether local ongoing neural oscillations and interregional audiovisual synchrony modulate audiovisual temporal integration.Approach.The human participants performed a simultaneity judgment (SJ) task with the beep-flash stimuli while recording electroencephalography. We focused on two stimulus onset asynchrony (SOA) conditions where subjects report ∼50% proportion of synchronous responses in auditory- and visual-leading SOA (A50V and V50A).Main results.We found that the alpha band power is larger in synchronous response in the central-right posterior and posterior sensors in A50V and V50A conditions, respectively. The results suggested that the alpha band power reflects neuronal excitability in the auditory or visual cortex, which can modulate audiovisual temporal perception depending on the leading sense. Additionally, the SJs were modulated by the opposite phases of alpha (5-10 Hz) and low beta (14-20 Hz) bands in the A50V condition while the low beta band (14-18 Hz) in the V50A condition. One cycle of alpha or two cycles of beta oscillations matched an auditory-leading TBW of ∼86 ms, while two cycles of beta oscillations matched a visual-leading TBW of ∼105 ms. This result indicated the opposite phases in the alpha and beta bands reflect opposite cortical excitability, which modulated the audiovisual SJs. Finally, we found stronger high beta (21-28 Hz) audiovisual phase synchronization for synchronous response in the A50V condition. The phase synchrony of the beta band might be related to maintaining information flow between visual and auditory regions in a top-down manner.Significance.These results clarified whether and how the prestimulus brain state, including local neural oscillations and functional connectivity between brain regions, affects audiovisual temporal integration.
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Affiliation(s)
- Zeliang Jiang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, People's Republic of China
| | - Xingwei An
- Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, People's Republic of China
| | - Shuang Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, People's Republic of China
| | - Erwei Yin
- Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, People's Republic of China
- Defense Innovation Institute, Academy of Military Sciences (AMS), 100071 Beijing, People's Republic of China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), 300457 Tianjin, People's Republic of China
| | - Ye Yan
- Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, People's Republic of China
- Defense Innovation Institute, Academy of Military Sciences (AMS), 100071 Beijing, People's Republic of China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), 300457 Tianjin, People's Republic of China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, People's Republic of China
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Castilla E, Ghosh A. Robust Minimum Divergence Estimation for the Multinomial Circular Logistic Regression Model. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1422. [PMID: 37895543 PMCID: PMC10606857 DOI: 10.3390/e25101422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 09/29/2023] [Accepted: 10/05/2023] [Indexed: 10/29/2023]
Abstract
Circular data are extremely important in many different contexts of natural and social science, from forestry to sociology, among many others. Since the usual inference procedures based on the maximum likelihood principle are known to be extremely non-robust in the presence of possible data contamination, in this paper, we develop robust estimators for the general class of multinomial circular logistic regression models involving multiple circular covariates. Particularly, we extend the popular density-power-divergence-based estimation approach for this particular set-up and study the asymptotic properties of the resulting estimators. The robustness of the proposed estimators is illustrated through extensive simulation studies and few important real data examples from forest science and meteorology.
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Affiliation(s)
- Elena Castilla
- Departamento de Matematica Aplicada, Rey Juan Carlos University, Mostoles Campus, 28933 Madrid, Spain
| | - Abhik Ghosh
- Indian Statistical Institute, Kolkata 700108, India;
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Engelen T, Solcà M, Tallon-Baudry C. Interoceptive rhythms in the brain. Nat Neurosci 2023; 26:1670-1684. [PMID: 37697110 DOI: 10.1038/s41593-023-01425-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 08/08/2023] [Indexed: 09/13/2023]
Abstract
Sensing internal bodily signals, or interoception, is fundamental to maintain life. However, interoception should not be viewed as an isolated domain, as it interacts with exteroception, cognition and action to ensure the integrity of the organism. Focusing on cardiac, respiratory and gastric rhythms, we review evidence that interoception is anatomically and functionally intertwined with the processing of signals from the external environment. Interactions arise at all stages, from the peripheral transduction of interoceptive signals to sensory processing and cortical integration, in a network that extends beyond core interoceptive regions. Interoceptive rhythms contribute to functions ranging from perceptual detection up to sense of self, or conversely compete with external inputs. Renewed interest in interoception revives long-standing issues on how the brain integrates and coordinates information in distributed regions, by means of oscillatory synchrony, predictive coding or multisensory integration. Considering interoception and exteroception in the same framework paves the way for biological modes of information processing specific to living organisms.
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Affiliation(s)
- Tahnée Engelen
- Cognitive and Computational Neuroscience Laboratory, Inserm, Ecole Normale Supérieure PSL University, Paris, France
| | - Marco Solcà
- Cognitive and Computational Neuroscience Laboratory, Inserm, Ecole Normale Supérieure PSL University, Paris, France
| | - Catherine Tallon-Baudry
- Cognitive and Computational Neuroscience Laboratory, Inserm, Ecole Normale Supérieure PSL University, Paris, France.
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Jiang Z, An X, Liu S, Wang L, Yin E, Yan Y, Ming D. The effect of prestimulus low-frequency neural oscillations on the temporal perception of audiovisual speech. Front Neurosci 2023; 17:1067632. [PMID: 36816126 PMCID: PMC9935937 DOI: 10.3389/fnins.2023.1067632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 01/17/2023] [Indexed: 02/05/2023] Open
Abstract
Objective Perceptual integration and segregation are modulated by the phase of ongoing neural oscillation whose frequency period is broader than the size of the temporal binding window (TBW). Studies have shown that the abstract beep-flash stimuli with about 100 ms TBW were modulated by the alpha band phase. Therefore, we hypothesize that the temporal perception of speech with about hundreds of milliseconds of TBW might be affected by the delta-theta phase. Methods Thus, we conducted a speech-stimuli-based audiovisual simultaneity judgment (SJ) experiment. Twenty human participants (12 females) attended this study, recording 62 channels of EEG. Results Behavioral results showed that the visual leading TBWs are broader than the auditory leading ones [273.37 ± 24.24 ms vs. 198.05 ± 19.28 ms, (mean ± sem)]. We used Phase Opposition Sum (POS) to quantify the differences in mean phase angles and phase concentrations between synchronous and asynchronous responses. The POS results indicated that the delta-theta phase was significantly different between synchronous and asynchronous responses in the A50V condition (50% synchronous responses in auditory leading SOA). However, in the V50A condition (50% synchronous responses in visual leading SOA), we only found the delta band effect. In the two conditions, we did not find a consistency of phases over subjects for both perceptual responses by the post hoc Rayleigh test (all ps > 0.05). The Rayleigh test results suggested that the phase might not reflect the neuronal excitability which assumed that the phases within a perceptual response across subjects concentrated on the same angle but were not uniformly distributed. But V-test showed the phase difference between synchronous and asynchronous responses across subjects had a significant phase opposition (all ps < 0.05) which is compatible with the POS result. Conclusion These results indicate that the speech temporal perception depends on the alignment of stimulus onset with an optimal phase of the neural oscillation whose frequency period might be broader than the size of TBW. The role of the oscillatory phase might be encoding the temporal information which varies across subjects rather than neuronal excitability. Given the enriched temporal structures of spoken language stimuli, the conclusion that phase encodes temporal information is plausible and valuable for future research.
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Affiliation(s)
- Zeliang Jiang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Xingwei An
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China,Xingwei An,
| | - Shuang Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Lu Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Erwei Yin
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China,Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China
| | - Ye Yan
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China,Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China,*Correspondence: Dong Ming,
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Allen M, Levy A, Parr T, Friston KJ. In the Body’s Eye: The computational anatomy of interoceptive inference. PLoS Comput Biol 2022; 18:e1010490. [PMID: 36099315 PMCID: PMC9506608 DOI: 10.1371/journal.pcbi.1010490] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 09/23/2022] [Accepted: 08/13/2022] [Indexed: 11/24/2022] Open
Abstract
A growing body of evidence highlights the intricate linkage of exteroceptive perception to the rhythmic activity of the visceral body. In parallel, interoceptive inference theories of affective perception and self-consciousness are on the rise in cognitive science. However, thus far no formal theory has emerged to integrate these twin domains; instead, most extant work is conceptual in nature. Here, we introduce a formal model of cardiac active inference, which explains how ascending cardiac signals entrain exteroceptive sensory perception and uncertainty. Through simulated psychophysics, we reproduce the defensive startle reflex and commonly reported effects linking the cardiac cycle to affective behaviour. We further show that simulated ‘interoceptive lesions’ blunt affective expectations, induce psychosomatic hallucinations, and exacerbate biases in perceptual uncertainty. Through synthetic heart-rate variability analyses, we illustrate how the balance of arousal-priors and visceral prediction errors produces idiosyncratic patterns of physiological reactivity. Our model thus offers a roadmap for computationally phenotyping disordered brain-body interaction. Understanding interactions between the brain and the body has become a topic of increased interest in computational neuroscience and psychiatry. A particular question here concerns how visceral, homeostatic rhythms such as the heart beat influence sensory, affective, and cognitive processing. To better understand these and other oscillatory brain-body interactions, we here introduce a novel computational model of interoceptive inference in which a synthetic agent’s perceptual beliefs are coupled to the rhythm of the heart. Our model both helps to explain emerging empirical data indicating that perceptual inference depends upon beat-to-beat heart rhythms, and can be used to better quantify intra- and inter-individual differences in heart-brain coupling. Using proof-of-principle simulations, we demonstrate how future empirical works could utilize our model to better understand and stratify disorders of interoception and brain-body interaction.
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Affiliation(s)
- Micah Allen
- Centre of Functionally Integrative Neuroscience, Aarhus University Hospital, Denmark
- Cambridge Psychiatry, Cambridge University, Cambridge, United Kingdom
- * E-mail:
| | - Andrew Levy
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
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Criscuolo A, Schwartze M, Kotz SA. Cognition through the lens of a body–brain dynamic system. Trends Neurosci 2022; 45:667-677. [DOI: 10.1016/j.tins.2022.06.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 06/07/2022] [Accepted: 06/13/2022] [Indexed: 12/01/2022]
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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Keil A, Bernat EM, Cohen MX, Ding M, Fabiani M, Gratton G, Kappenman ES, Maris E, Mathewson KE, Ward RT, Weisz N. Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series. Psychophysiology 2022; 59:e14052. [PMID: 35398913 PMCID: PMC9717489 DOI: 10.1111/psyp.14052] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 01/29/2023]
Abstract
Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time-frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.
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Affiliation(s)
- Andreas Keil
- Department and Psychology and Center for the Study of Emotion and Attention, University of Florida, Gainesville, Florida, USA
| | - Edward M. Bernat
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | - Michael X. Cohen
- Radboud University and University Medical Center, Nijmegen, the Netherlands
| | - Mingzhou Ding
- J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA
| | - Monica Fabiani
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA,Psychology Department, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Gabriele Gratton
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA,Psychology Department, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Emily S. Kappenman
- Department of Psychology, San Diego State University, San Diego, California, USA
| | - Eric Maris
- Donders Institute for Brain, Cognition, and Behaviour & Faculty of Social Sciences Radboud University, Nijmegen, the Netherlands
| | - Kyle E. Mathewson
- Department of Psychology, Faculty of Science, University of Alberta, Edmonton, Alberta, Canada
| | - Richard T. Ward
- Department and Psychology and Center for the Study of Emotion and Attention, University of Florida, Gainesville, Florida, USA
| | - Nathan Weisz
- Psychology, University of Salzburg, Salzburg, Austria,Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
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