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Song P, Xu H, Ye H, Du X, Zhai Y, Bao X, Mehmood I, Tanigawa H, Niu W, Tu Z, Chen P, Zhang T, Zhao X, Yu X. A new function of offset response in the primate auditory cortex: marker of temporal integration. Commun Biol 2024; 7:1350. [PMID: 39424927 PMCID: PMC11489726 DOI: 10.1038/s42003-024-07058-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 10/11/2024] [Indexed: 10/21/2024] Open
Abstract
Offset responses are traditionally viewed as indicators of sound cessation. Here, we investigate offset responses to auditory click trains, examining how they are modulated by inter-click intervals (ICIs) and train duration. Using extracellular recordings and electrocorticography (ECoG) in non-human primates, alongside electroencephalography (EEG) in humans, we show that offset responses are significantly influenced by both ICI and train length, thereby establishing them as markers of temporal integration. We introduce the concept of the 'Neuronal Integrative Window' (NIW), defined as the temporal span during which neurons integrate stimuli to produce or modulate the temporal integration signal. Our data reveal that on the neuronal level, the auditory cortex (AC) exhibits a more expansive NIW than the medial geniculate body (MGB), integrating stimuli over longer durations and showing a preference for larger ICIs. Furthermore, our results indicate that offset responses could serve as potential biomarkers for neurological and psychiatric conditions, highlighted by their sensitivity to pharmacological modulation with ketamine. This study advances our understanding of auditory temporal processing and proposes a novel approach for assessing and monitoring brain health.
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Affiliation(s)
- Peirun Song
- Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
- 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
| | - Haoxuan Xu
- 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
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Hangting Ye
- 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
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Xinyu Du
- Department of Anesthesia, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Yuying Zhai
- Department of Anesthesia, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Xuehui Bao
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Ishrat Mehmood
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Hisashi Tanigawa
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Wanqiu Niu
- Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhiyi Tu
- Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Pei Chen
- Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Tingting Zhang
- Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xuan Zhao
- Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 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.
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang Province, China.
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Shi L, Mastracchio C, Saytashev I, Ye M. Low frequency ultrasound elicits broad cortical responses inhibited by ketamine in mice. COMMUNICATIONS ENGINEERING 2024; 3:120. [PMID: 39192002 DOI: 10.1038/s44172-024-00269-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 08/20/2024] [Indexed: 08/29/2024]
Abstract
The neuromodulatory effects of >250 kHz ultrasound have been well-demonstrated, but the impact of lower-frequency ultrasound, which can transmit better through air and the skull, on the brain is unclear. This study investigates the biological impact of 40 kHz pulsed ultrasound on the brain using calcium imaging and electrophysiology in mice. Our findings reveal burst duration-dependent neural responses in somatosensory and auditory cortices, resembling responses to 12 kHz audible tone, in vivo. In vitro brain slice experiments show no neural responses to 300 kPa 40 kHz ultrasound, implying indirect network effects. Ketamine fully blocks neural responses to ultrasound in both cortices but only partially affects 12 kHz audible tone responses in the somatosensory cortex and has no impact on auditory cortex 12 kHz responses. This suggests that low-frequency ultrasound's cortical effects rely heavily on NMDA receptors and may involve mechanisms beyond indirect auditory cortex activation. This research uncovers potential low-frequency ultrasound effects and mechanisms in the brain, offering a path for future neuromodulation.
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Affiliation(s)
- Linli Shi
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA
| | - Christina Mastracchio
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA
| | - Ilyas Saytashev
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA
| | - Meijun Ye
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA.
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Zhao Z, Feng Y, Wang M, Wei J, Tan T, Li R, Hu H, Wang M, Chen P, Gao X, Wei Y, Wang C, Gao Z, Jiang W, Zhou X, Li M, Wang C, Pang T, Yu Y. Investigating cortical complexity and connectivity in rats with schizophrenia. Front Neuroinform 2024; 18:1392271. [PMID: 39211912 PMCID: PMC11358091 DOI: 10.3389/fninf.2024.1392271] [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: 02/27/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024] Open
Abstract
Background The above studies indicate that the SCZ animal model has abnormal gamma oscillations and abnormal functional coupling ability of brain regions at the cortical level. However, few researchers have focused on the correlation between brain complexity and connectivity at the cortical level. In order to provide a more accurate representation of brain activity, we studied the complexity of electrocorticogram (ECoG) signals and the information interaction between brain regions in schizophrenic rats, and explored the correlation between brain complexity and connectivity. Methods We collected ECoG signal from SCZ rats. The frequency domain and time domain functional connectivity of SCZ rats were evaluated by magnitude square coherence and mutual information (MI). Permutation entropy (PE) and permutation Lempel-Ziv complexity (PLZC) were used to analyze the complexity of ECoG, and the relationship between them was evaluated. In addition, in order to further understand the causal structure of directional information flow among brain regions, we used phase transfer entropy (PTE) to analyze the effective connectivity of the brain. Results Firstly, in the high gamma band, the complexity of brain regions in SCZ rats is higher than that in normal rats, and the neuronal activity is irregularity. Secondly, the information integration ability of SCZ rats decreased and the communication of brain network information was hindered at the cortical level. Finally, compared with normal rats, the causal relationship between brain regions of SCZ rats was closer, but the information interaction center was not clear. Conclusion The above findings suggest that at the cortical level, complexity and connectivity are valid biomarkers for identifying SCZ. This bridges the gap between peak potentials and EEG. This may help to understand the pathophysiological mechanisms at the cortical level in schizophrenics.
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Affiliation(s)
- Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yifan Feng
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Menghan Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Jiarong Wei
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Tao Tan
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Ruijiao Li
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Heshun Hu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Mengke Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Peiqi Chen
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Xudong Gao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yinping Wei
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Chang Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Zhixian Gao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Wenshuai Jiang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Xuezhi Zhou
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Mingcai Li
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Chong Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Ting Pang
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Center of Image and Signal Processing, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Yi Yu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
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Peng F, Harper NS, Mishra AP, Auksztulewicz R, Schnupp JWH. Dissociable Roles of the Auditory Midbrain and Cortex in Processing the Statistical Features of Natural Sound Textures. J Neurosci 2024; 44:e1115232023. [PMID: 38267259 PMCID: PMC10919253 DOI: 10.1523/jneurosci.1115-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 11/23/2023] [Accepted: 12/11/2023] [Indexed: 01/26/2024] Open
Abstract
Sound texture perception takes advantage of a hierarchy of time-averaged statistical features of acoustic stimuli, but much remains unclear about how these statistical features are processed along the auditory pathway. Here, we compared the neural representation of sound textures in the inferior colliculus (IC) and auditory cortex (AC) of anesthetized female rats. We recorded responses to texture morph stimuli that gradually add statistical features of increasingly higher complexity. For each texture, several different exemplars were synthesized using different random seeds. An analysis of transient and ongoing multiunit responses showed that the IC units were sensitive to every type of statistical feature, albeit to a varying extent. In contrast, only a small proportion of AC units were overtly sensitive to any statistical features. Differences in texture types explained more of the variance of IC neural responses than did differences in exemplars, indicating a degree of "texture type tuning" in the IC, but the same was, perhaps surprisingly, not the case for AC responses. We also evaluated the accuracy of texture type classification from single-trial population activity and found that IC responses became more informative as more summary statistics were included in the texture morphs, while for AC population responses, classification performance remained consistently very low. These results argue against the idea that AC neurons encode sound type via an overt sensitivity in neural firing rate to fine-grain spectral and temporal statistical features.
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Affiliation(s)
- Fei Peng
- Department of Neuroscience, City University of Hong Kong, Hong Kong, China
| | - Nicol S Harper
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 2JD, United Kingdom
| | - Ambika P Mishra
- Department of Neuroscience, City University of Hong Kong, Hong Kong, China
| | - Ryszard Auksztulewicz
- Department of Neuroscience, City University of Hong Kong, Hong Kong, China
- Center for Cognitive Neuroscience Berlin, Free University Berlin, Berlin 14195, Germany
| | - Jan W H Schnupp
- Department of Neuroscience, City University of Hong Kong, Hong Kong, China
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O'Reilly JA. Roving oddball paradigm elicits sensory gating, frequency sensitivity, and long-latency response in common marmosets. IBRO Neurosci Rep 2021; 11:128-136. [PMID: 34622244 PMCID: PMC8482433 DOI: 10.1016/j.ibneur.2021.09.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 08/21/2021] [Accepted: 09/18/2021] [Indexed: 12/17/2022] Open
Abstract
Mismatch negativity (MMN) is a candidate biomarker for neuropsychiatric disease. Understanding the extent to which it reflects cognitive deviance-detection or purely sensory processes will assist practitioners in making informed clinical interpretations. This study compares the utility of deviance-detection and sensory-processing theories for describing MMN-like auditory responses of a common marmoset monkey during roving oddball stimulation. The following exploratory analyses were performed on an existing dataset: responses during the transition and repetition sequence of the roving oddball paradigm (standard -> deviant/S1 -> S2 -> S3) were compared; long-latency potentials evoked by deviant stimuli were examined using a double-epoch waveform subtraction; effects of increasing stimulus repetitions on standard and deviant responses were analyzed; and transitions between standard and deviant stimuli were divided into ascending and descending frequency changes to explore contributions of frequency-sensitivity. An enlarged auditory response to deviant stimuli was observed. This decreased exponentially with stimulus repetition, characteristic of sensory gating. A slow positive deflection was viewed over approximately 300–800 ms after the deviant stimulus, which is more difficult to ascribe to afferent sensory mechanisms. When split into ascending and descending frequency transitions, the resulting difference waveforms were disproportionally influenced by descending frequency deviant stimuli. This asymmetry is inconsistent with the general deviance-detection theory of MMN. These findings tentatively suggest that MMN-like responses from common marmosets are predominantly influenced by rapid sensory adaptation and frequency preference of the auditory cortex, while deviance-detection may play a role in long-latency activity.
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Affiliation(s)
- Jamie A O'Reilly
- College of Biomedical Engineering, Rangsit University, 52/347 Muang-Ake, Phaholyothin Road, Pathumthani 12000, Thailand
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