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Poublan-Couzardot A, Lecaignard F, Fucci E, Davidson RJ, Mattout J, Lutz A, Abdoun O. Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response. PLoS Comput Biol 2023; 19:e1010557. [PMID: 38091350 PMCID: PMC10752554 DOI: 10.1371/journal.pcbi.1010557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 12/27/2023] [Accepted: 11/20/2023] [Indexed: 12/28/2023] Open
Abstract
Despite attempts to unify the different theoretical accounts of the mismatch negativity (MMN), there is still an ongoing debate on the neurophysiological mechanisms underlying this complex brain response. On one hand, neuronal adaptation to recurrent stimuli is able to explain many of the observed properties of the MMN, such as its sensitivity to controlled experimental parameters. On the other hand, several modeling studies reported evidence in favor of Bayesian learning models for explaining the trial-to-trial dynamics of the human MMN. However, direct comparisons of these two main hypotheses are scarce, and previous modeling studies suffered from methodological limitations. Based on reports indicating spatial and temporal dissociation of physiological mechanisms within the timecourse of mismatch responses in animals, we hypothesized that different computational models would best fit different temporal phases of the human MMN. Using electroencephalographic data from two independent studies of a simple auditory oddball task (n = 82), we compared adaptation and Bayesian learning models' ability to explain the sequential dynamics of auditory deviance detection in a time-resolved fashion. We first ran simulations to evaluate the capacity of our design to dissociate the tested models and found that they were sufficiently distinguishable above a certain level of signal-to-noise ratio (SNR). In subjects with a sufficient SNR, our time-resolved approach revealed a temporal dissociation between the two model families, with high evidence for adaptation during the early MMN window (from 90 to 150-190 ms post-stimulus depending on the dataset) and for Bayesian learning later in time (170-180 ms or 200-220ms). In addition, Bayesian model averaging of fixed-parameter models within the adaptation family revealed a gradient of adaptation rates, resembling the anatomical gradient in the auditory cortical hierarchy reported in animal studies.
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Affiliation(s)
- Arnaud Poublan-Couzardot
- Cente de Recherche en Neurosciences de Lyon (CRNL), CNRS UMRS5292, INSERM U1028, Université Claude Bernard Lyon 1, Bron, France
| | - Françoise Lecaignard
- Cente de Recherche en Neurosciences de Lyon (CRNL), CNRS UMRS5292, INSERM U1028, Université Claude Bernard Lyon 1, Bron, France
| | - Enrico Fucci
- 2 Institute for Globally Distributed Open Research and Education (IGDORE), Sweden
| | - Richard J. Davidson
- Center for Healthy Minds, University of Wisconsin, Madison, Wisconsin, United States of America
- Department of Psychology, University of Wisconsin, Madison, Wisconsin, United States of America
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, Wisconsin, United States of America
- Department of Psychiatry, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Jérémie Mattout
- Cente de Recherche en Neurosciences de Lyon (CRNL), CNRS UMRS5292, INSERM U1028, Université Claude Bernard Lyon 1, Bron, France
| | - Antoine Lutz
- Cente de Recherche en Neurosciences de Lyon (CRNL), CNRS UMRS5292, INSERM U1028, Université Claude Bernard Lyon 1, Bron, France
| | - Oussama Abdoun
- Cente de Recherche en Neurosciences de Lyon (CRNL), CNRS UMRS5292, INSERM U1028, Université Claude Bernard Lyon 1, Bron, France
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Song P, Zhai Y, Yu X. Stimulus-Specific Adaptation (SSA) in the Auditory System: Functional Relevance and Underlying Mechanisms. Neurosci Biobehav Rev 2023; 149:105190. [PMID: 37085022 DOI: 10.1016/j.neubiorev.2023.105190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 04/23/2023]
Abstract
Rapid detection of novel stimuli that appear suddenly in the surrounding environment is crucial for an animal's survival. Stimulus-specific adaptation (SSA) may be an important mechanism underlying novelty detection. In this review, we discuss the latest advances in SSA research by addressing four main aspects: 1) the frequency dependence of SSA and the origin of SSA in the auditory cortex: 2) spatial SSA and its comparison with frequency SSA: 3) feature integration in SSA and its implications in novelty detection: 4) functional significance and the physiological mechanism of SSA. Although SSA has been extensively investigated, the cognitive insights from SSA studies are extremely limited. Future work should aim to bridge these gaps.
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Affiliation(s)
- Peirun Song
- Department of Anesthesia, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China; Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China; Interdisciplinary Institute of Neuroscience and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Yuying Zhai
- Department of Anesthesia, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Xiongjie Yu
- Department of Anesthesia, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China; Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China; Interdisciplinary Institute of Neuroscience and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang Province, China.
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Oddball-irrelevant visual stimuli cross-modally attenuate auditory mismatch negativity in rats. Neuroreport 2022; 33:363-368. [DOI: 10.1097/wnr.0000000000001793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Lecaignard F, Bertrand R, Brunner P, Caclin A, Schalk G, Mattout J. Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals. Front Hum Neurosci 2022; 15:794654. [PMID: 35221952 PMCID: PMC8866734 DOI: 10.3389/fnhum.2021.794654] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/07/2021] [Indexed: 11/21/2022] Open
Abstract
Recent computational models of perception conceptualize auditory oddball responses as signatures of a (Bayesian) learning process, in line with the influential view of the mismatch negativity (MMN) as a prediction error signal. Novel MMN experimental paradigms have put an emphasis on neurophysiological effects of manipulating regularity and predictability in sound sequences. This raises the question of the contextual adaptation of the learning process itself, which on the computational side speaks to the mechanisms of gain-modulated (or precision-weighted) prediction error. In this study using electrocorticographic (ECoG) signals, we manipulated the predictability of oddball sound sequences with two objectives: (i) Uncovering the computational process underlying trial-by-trial variations of the cortical responses. The fluctuations between trials, generally ignored by approaches based on averaged evoked responses, should reflect the learning involved. We used a general linear model (GLM) and Bayesian Model Reduction (BMR) to assess the respective contributions of experimental manipulations and learning mechanisms under probabilistic assumptions. (ii) To validate and expand on previous findings regarding the effect of changes in predictability using simultaneous EEG-MEG recordings. Our trial-by-trial analysis revealed only a few stimulus-responsive sensors but the measured effects appear to be consistent over subjects in both time and space. In time, they occur at the typical latency of the MMN (between 100 and 250 ms post-stimulus). In space, we found a dissociation between time-independent effects in more anterior temporal locations and time-dependent (learning) effects in more posterior locations. However, we could not observe any clear and reliable effect of our manipulation of predictability modulation onto the above learning process. Overall, these findings clearly demonstrate the potential of trial-to-trial modeling to unravel perceptual learning processes and their neurophysiological counterparts.
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Affiliation(s)
- Françoise Lecaignard
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
- University Lyon 1, Lyon, France
| | - Raphaëlle Bertrand
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
- University Lyon 1, Lyon, France
| | - Peter Brunner
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neurology, Albany Medical College, Albany, NY, United States
- National Center for Adaptive Neurotechnologies, Albany, NY, United States
| | - Anne Caclin
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
- University Lyon 1, Lyon, France
| | - Gerwin Schalk
- National Center for Adaptive Neurotechnologies, Albany, NY, United States
| | - Jérémie Mattout
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
- University Lyon 1, Lyon, France
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Malmierca MS. How the bat brain detects novel sounds (commentary on Wetekam et al., 2021). Eur J Neurosci 2022; 55:895-897. [PMID: 35075703 PMCID: PMC9304208 DOI: 10.1111/ejn.15606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 01/12/2022] [Accepted: 01/15/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Manuel S. Malmierca
- Cognitive and Auditory Neuroscience Laboratory (CANELAB), Institute of Neuroscience of Castilla y León (INCYL) University of Salamanca Salamanca Spain
- Institute for Biomedical Research of Salamanca University of Salamanca Salamanca Spain
- Department of Cell Biology and Pathology, School of Medicine University of Salamanca Salamanca Spain
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