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Brain activity during shadowing of audiovisual cocktail party speech, contributions of auditory-motor integration and selective attention. Sci Rep 2022; 12:18789. [PMID: 36335137 PMCID: PMC9637225 DOI: 10.1038/s41598-022-22041-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022] Open
Abstract
Selective listening to cocktail-party speech involves a network of auditory and inferior frontal cortical regions. However, cognitive and motor cortical regions are differentially activated depending on whether the task emphasizes semantic or phonological aspects of speech. Here we tested whether processing of cocktail-party speech differs when participants perform a shadowing (immediate speech repetition) task compared to an attentive listening task in the presence of irrelevant speech. Participants viewed audiovisual dialogues with concurrent distracting speech during functional imaging. Participants either attentively listened to the dialogue, overtly repeated (i.e., shadowed) attended speech, or performed visual or speech motor control tasks where they did not attend to speech and responses were not related to the speech input. Dialogues were presented with good or poor auditory and visual quality. As a novel result, we show that attentive processing of speech activated the same network of sensory and frontal regions during listening and shadowing. However, in the superior temporal gyrus (STG), peak activations during shadowing were posterior to those during listening, suggesting that an anterior-posterior distinction is present for motor vs. perceptual processing of speech already at the level of the auditory cortex. We also found that activations along the dorsal auditory processing stream were specifically associated with the shadowing task. These activations are likely to be due to complex interactions between perceptual, attention dependent speech processing and motor speech generation that matches the heard speech. Our results suggest that interactions between perceptual and motor processing of speech relies on a distributed network of temporal and motor regions rather than any specific anatomical landmark as suggested by some previous studies.
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Ren J, Hubbard CS, Ahveninen J, Cui W, Li M, Peng X, Luan G, Han Y, Li Y, Shinn AK, Wang D, Li L, Liu H. Dissociable Auditory Cortico-Cerebellar Pathways in the Human Brain Estimated by Intrinsic Functional Connectivity. Cereb Cortex 2021; 31:2898-2912. [PMID: 33497437 PMCID: PMC8107796 DOI: 10.1093/cercor/bhaa398] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 11/10/2020] [Accepted: 12/11/2020] [Indexed: 12/16/2022] Open
Abstract
The cerebellum, a structure historically associated with motor control, has more recently been implicated in several higher-order auditory-cognitive functions. However, the exact functional pathways that mediate cerebellar influences on auditory cortex (AC) remain unclear. Here, we sought to identify auditory cortico-cerebellar pathways based on intrinsic functional connectivity magnetic resonance imaging. In contrast to previous connectivity studies that principally consider the AC as a single functionally homogenous unit, we mapped the cerebellar connectivity across different parts of the AC. Our results reveal that auditory subareas demonstrating different levels of interindividual functional variability are functionally coupled with distinct cerebellar regions. Moreover, auditory and sensorimotor areas show divergent cortico-cerebellar connectivity patterns, although sensorimotor areas proximal to the AC are often functionally grouped with the AC in previous connectivity-based network analyses. Lastly, we found that the AC can be functionally segmented into highly similar subareas based on either cortico-cerebellar or cortico-cortical functional connectivity, suggesting the existence of multiple parallel auditory cortico-cerebellar circuits that involve different subareas of the AC. Overall, the present study revealed multiple auditory cortico-cerebellar pathways and provided a fine-grained map of AC subareas, indicative of the critical role of the cerebellum in auditory processing and multisensory integration.
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Affiliation(s)
- Jianxun Ren
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, 100084 Beijing, China
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Catherine S Hubbard
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Weigang Cui
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
- Department of Automation Sciences and Electrical Engineering, Beihang University, 100083 Beijing, China
| | - Meiling Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Xiaolong Peng
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Guoming Luan
- Department of Neurosurgery, Comprehensive Epilepsy Center, Sanbo Brain Hospital, Capital Medical University, 100093 Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, 100053 Beijing, China
| | - Yang Li
- Department of Automation Sciences and Electrical Engineering, Beihang University, 100083 Beijing, China
| | - Ann K Shinn
- Psychotic Disorders Division, McLean Hospital, Harvard Medical School, Belmont, MA 02478, USA
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Luming Li
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, 100084 Beijing, China
- Precision Medicine & Healthcare Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, 518055 Shenzhen, China
- IDG/McGovern Institute for Brain Research at Tsinghua University, 100084 Beijing, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
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Wikman P, Sahari E, Salmela V, Leminen A, Leminen M, Laine M, Alho K. Breaking down the cocktail party: Attentional modulation of cerebral audiovisual speech processing. Neuroimage 2020; 224:117365. [PMID: 32941985 DOI: 10.1016/j.neuroimage.2020.117365] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 08/19/2020] [Accepted: 09/07/2020] [Indexed: 12/20/2022] Open
Abstract
Recent studies utilizing electrophysiological speech envelope reconstruction have sparked renewed interest in the cocktail party effect by showing that auditory neurons entrain to selectively attended speech. Yet, the neural networks of attention to speech in naturalistic audiovisual settings with multiple sound sources remain poorly understood. We collected functional brain imaging data while participants viewed audiovisual video clips of lifelike dialogues with concurrent distracting speech in the background. Dialogues were presented in a full-factorial design, comprising task (listen to the dialogues vs. ignore them), audiovisual quality and semantic predictability. We used univariate analyses in combination with multivariate pattern analysis (MVPA) to study modulations of brain activity related to attentive processing of audiovisual speech. We found attentive speech processing to cause distinct spatiotemporal modulation profiles in distributed cortical areas including sensory and frontal-control networks. Semantic coherence modulated attention-related activation patterns in the earliest stages of auditory cortical processing, suggesting that the auditory cortex is involved in high-level speech processing. Our results corroborate views that emphasize the dynamic nature of attention, with task-specificity and context as cornerstones of the underlying neuro-cognitive mechanisms.
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Affiliation(s)
- Patrik Wikman
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland.
| | - Elisa Sahari
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Viljami Salmela
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland; Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University, Espoo, Finland
| | - Alina Leminen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland; Department of Digital Humanities, University of Helsinki, Helsinki, Finland
| | - Miika Leminen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland; Department of Phoniatrics, Helsinki University Hospital, Helsinki, Finland
| | - Matti Laine
- Department of Psychology, Åbo Akademi University, Turku, Finland
| | - Kimmo Alho
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland; Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University, Espoo, Finland
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Zempeltzi MM, Kisse M, Brunk MGK, Glemser C, Aksit S, Deane KE, Maurya S, Schneider L, Ohl FW, Deliano M, Happel MFK. Task rule and choice are reflected by layer-specific processing in rodent auditory cortical microcircuits. Commun Biol 2020; 3:345. [PMID: 32620808 PMCID: PMC7335110 DOI: 10.1038/s42003-020-1073-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 06/11/2020] [Indexed: 01/16/2023] Open
Abstract
The primary auditory cortex (A1) is an essential, integrative node that encodes the behavioral relevance of acoustic stimuli, predictions, and auditory-guided decision-making. However, the realization of this integration with respect to the cortical microcircuitry is not well understood. Here, we characterize layer-specific, spatiotemporal synaptic population activity with chronic, laminar current source density analysis in Mongolian gerbils (Meriones unguiculatus) trained in an auditory decision-making Go/NoGo shuttle-box task. We demonstrate that not only sensory but also task- and choice-related information is represented in the mesoscopic neuronal population code of A1. Based on generalized linear-mixed effect models we found a layer-specific and multiplexed representation of the task rule, action selection, and the animal's behavioral options as accumulating evidence in preparation of correct choices. The findings expand our understanding of how individual layers contribute to the integrative circuit in the sensory cortex in order to code task-relevant information and guide sensory-based decision-making.
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Affiliation(s)
| | - Martin Kisse
- Leibniz Institute for Neurobiology, D-39118, Magdeburg, Germany
| | | | - Claudia Glemser
- Leibniz Institute for Neurobiology, D-39118, Magdeburg, Germany
| | - Sümeyra Aksit
- Leibniz Institute for Neurobiology, D-39118, Magdeburg, Germany
| | - Katrina E Deane
- Leibniz Institute for Neurobiology, D-39118, Magdeburg, Germany
| | - Shivam Maurya
- Leibniz Institute for Neurobiology, D-39118, Magdeburg, Germany
| | - Lina Schneider
- Leibniz Institute for Neurobiology, D-39118, Magdeburg, Germany
| | - Frank W Ohl
- Leibniz Institute for Neurobiology, D-39118, Magdeburg, Germany
- Institute of Biology, Otto von Guericke University, D-39120, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), 39106, Magdeburg, Germany
| | | | - Max F K Happel
- Leibniz Institute for Neurobiology, D-39118, Magdeburg, Germany.
- Center for Behavioral Brain Sciences (CBBS), 39106, Magdeburg, Germany.
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Fu D, Weber C, Yang G, Kerzel M, Nan W, Barros P, Wu H, Liu X, Wermter S. What Can Computational Models Learn From Human Selective Attention? A Review From an Audiovisual Unimodal and Crossmodal Perspective. Front Integr Neurosci 2020; 14:10. [PMID: 32174816 PMCID: PMC7056875 DOI: 10.3389/fnint.2020.00010] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 02/11/2020] [Indexed: 11/13/2022] Open
Abstract
Selective attention plays an essential role in information acquisition and utilization from the environment. In the past 50 years, research on selective attention has been a central topic in cognitive science. Compared with unimodal studies, crossmodal studies are more complex but necessary to solve real-world challenges in both human experiments and computational modeling. Although an increasing number of findings on crossmodal selective attention have shed light on humans' behavioral patterns and neural underpinnings, a much better understanding is still necessary to yield the same benefit for intelligent computational agents. This article reviews studies of selective attention in unimodal visual and auditory and crossmodal audiovisual setups from the multidisciplinary perspectives of psychology and cognitive neuroscience, and evaluates different ways to simulate analogous mechanisms in computational models and robotics. We discuss the gaps between these fields in this interdisciplinary review and provide insights about how to use psychological findings and theories in artificial intelligence from different perspectives.
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Affiliation(s)
- Di Fu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Department of Informatics, University of Hamburg, Hamburg, Germany
| | - Cornelius Weber
- Department of Informatics, University of Hamburg, Hamburg, Germany
| | - Guochun Yang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Matthias Kerzel
- Department of Informatics, University of Hamburg, Hamburg, Germany
| | - Weizhi Nan
- Department of Psychology, Center for Brain and Cognitive Sciences, School of Education, Guangzhou University, Guangzhou, China
| | - Pablo Barros
- Department of Informatics, University of Hamburg, Hamburg, Germany
| | - Haiyan Wu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xun Liu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Stefan Wermter
- Department of Informatics, University of Hamburg, Hamburg, Germany
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Zhang G, Li Y, Zhang J. Tracking the Dynamic Functional Network Interactions During Goal-Directed Auditory Tasks by Brain State Clustering. Front Neurosci 2019; 13:1220. [PMID: 31803006 PMCID: PMC6872968 DOI: 10.3389/fnins.2019.01220] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 10/29/2019] [Indexed: 12/03/2022] Open
Abstract
Both perceiving and processing external sound stimuli as well as actively maintaining and updating relevant information (i.e., working memory) are critical for communication and problem solving in everyday acoustic environments. The translation of sensory information into perceptual decisions for goal-directed tasks hinges on dynamic changes in neural activity. However, the underlying brain network dynamics involved in this process are not well specified. In this study, we collected functional MRI data of participants engaging in auditory discrimination and auditory working memory tasks. Independent component analysis (ICA) was performed to extract the brain networks involved and the sliding-window functional connectivity (FC) among networks was calculated. Next, a temporal clustering technique was used to identify the brain states underlying auditory processing. Our results identified seven networks configured into four brain states. The number of brain state transitions was negatively correlated with auditory discrimination performance, and the fractional dwell time of State 2-which included connectivity among the triple high-order cognitive networks and the auditory network (AN)-was positively correlated with working memory performance. A comparison of the two tasks showed significant differences in the connectivity of the frontoparietal, default mode, and sensorimotor networks (SMNs), which is consistent with previous studies of the modulation of task load on brain network interaction. In summary, the dynamic network analysis employed in this study allowed us to isolate moment-to-moment fluctuations in inter-network synchrony, find network configuration in each state, and identify the specific state that enables fast, effective performance during auditory processing. This information is important for understanding the key neural mechanisms underlying goal-directed auditory tasks.
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Affiliation(s)
- Gaoyan Zhang
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China
| | - Yuexuan Li
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China
| | - Jinliang Zhang
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China
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Tavildar S, Mogen B, Zanos S, Seeman S, Perlmutter S, Fetz E, Ashrafi A. Inferring Cortical Connectivity from ECoG Signals Using Graph Signal Processing. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2019; 7:109349-109362. [PMID: 36883134 PMCID: PMC9988241 DOI: 10.1109/access.2019.2934490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
A novel method to characterize connectivity between sites in the cerebral cortex of primates is proposed in this paper. Connectivity graphs for two macaque monkeys are inferred from Electrocorticographic (ECoG) activity recorded while the animals were alert. The locations of ECoG electrodes are considered as nodes of the graph, the coefficients of the auto-regressive (AR) representation of the signals measured at each node are considered as the signal on the graph and the connectivity strengths between the nodes are considered as the edges of the graph. Maximization of the graph smoothness defined from the Laplacian quadratic form is used to infer the connectivity map (adjacency matrix of the graph). The cortical evoked potential (CEP) map was obtained by stimulating different electrodes and recording the evoked potentials at the other electrodes. The maps obtained by the graph inference and the traditional method of spectral coherence are compared with the CEP map. The results show that the proposed method provides a description of cortical connectivity that is more similar to the stimulation-based measures than spectral coherence. The results are also tested by the surrogate map analysis in which the CEP map is randomly permuted and the distribution of the errors is obtained. It is shown that error between the two maps is comfortably outside the surrogate map error distribution. This indicates that the similarity between the map calculated by the graph inference and the CEP map is statistically significant.
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Affiliation(s)
- Siddhi Tavildar
- Computational Science Research Center, San Diego State University, San Diego CA, USA
- Center for Neurotechnology, Seattle WA, USA
| | - Brian Mogen
- Center for Neurotechnology, Seattle WA, USA
- Department of Bioengineering, Univ of Washington, Seattle WA, USA
| | - Stavros Zanos
- Center for Neurotechnology, Seattle WA, USA
- WA National Primate Research Center, Univ of Washington, Seattle WA, USA
- Center for Bioelectronic Medicine, Feinstein Institute for Medical Research, Manhasset NY, USA
| | - Stephanie Seeman
- Center for Neurotechnology, Seattle WA, USA
- Dept. Physiology & Biophysics, University of Washington, Seattle WA, USA
| | - Steve Perlmutter
- Center for Neurotechnology, Seattle WA, USA
- WA National Primate Research Center, Univ of Washington, Seattle WA, USA
- Dept. Physiology & Biophysics, University of Washington, Seattle WA, USA
| | - Eberhard Fetz
- Center for Neurotechnology, Seattle WA, USA
- WA National Primate Research Center, Univ of Washington, Seattle WA, USA
- Dept. Physiology & Biophysics, University of Washington, Seattle WA, USA
| | - Ashkan Ashrafi
- Computational Science Research Center, San Diego State University, San Diego CA, USA
- Center for Neurotechnology, Seattle WA, USA
- Department of Electrical and Computer Engineering, San Diego State University, San Diego CA, USA
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