1
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Mohammadi Z, Denman DJ, Klug A, Lei TC. A fully automatic multichannel neural spike sorting algorithm with spike reduction and positional feature. J Neural Eng 2024; 21:046039. [PMID: 39019065 PMCID: PMC11298775 DOI: 10.1088/1741-2552/ad647d] [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: 02/14/2024] [Revised: 05/31/2024] [Accepted: 07/17/2024] [Indexed: 07/19/2024]
Abstract
Objective: The sorting of neural spike data recorded by multichannel and high channel neural probes such as Neuropixels, especially in real-time, remains a significant technical challenge. Most neural spike sorting algorithms focus on sorting neural spikes post-hoc for high sorting accuracy-but reducing the processing delay for fast sorting, potentially even live sorting, is generally not possible with these algorithms.Approach: Here we report our Graph nEtwork Multichannel sorting (GEMsort) algorithm, which is largely based on graph network, to allow rapid neural spike sorting for multiple neural recording channels. This was accomplished by two innovations: In GEMsort, duplicated neural spikes recorded from multiple channels were eliminated from duplicate channels by only selecting the highest amplitude neural spike in any channel for subsequent processing. In addition, the channel from which the representative neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features.Main results: Synthetic and experimentally recorded multichannel neural recordings were used to evaluate the sorting performance of GEMsort. The sorting results of GEMsort were also compared with two other state-of-the-art sorting algorithms (Kilosort and Mountainsort) in sorting time and sorting agreements.Significance: GEMsort allows rapidly sort neural spikes and is highly suitable to be implemented with digital circuitry for high processing speed and channel scalability.
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Affiliation(s)
- Zeinab Mohammadi
- Department of Electrical Engineering, University of Colorado Denver, Denver, CO, United States of America
| | - Daniel J Denman
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Achim Klug
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Tim C Lei
- Department of Electrical Engineering, University of Colorado Denver, Denver, CO, United States of America
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2
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Li M, Yang L, Liu Y, Shang Z, Wan H. Dynamic temporal neural patterns based on multichannel LFPs Identify different brain states during anesthesia in pigeons: comparison of three anesthetics. Med Biol Eng Comput 2024:10.1007/s11517-024-03132-w. [PMID: 38819673 DOI: 10.1007/s11517-024-03132-w] [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: 01/08/2024] [Accepted: 05/15/2024] [Indexed: 06/01/2024]
Abstract
Anesthetic-induced brain activity study is crucial in avian cognitive-, consciousness-, and sleep-related research. However, the neurobiological mechanisms underlying the generation of brain rhythms and specific connectivity of birds during anesthesia are poorly understood. Although different kinds of anesthetics can be used to induce an anesthesia state, a comparison study of these drugs focusing on the neural pattern evolution during anesthesia is lacking. Here, we recorded local field potentials (LFPs) using a multi-channel micro-electrode array inserted into the nidopallium caudolateral (NCL) of adult pigeons (Columba livia) anesthetized with chloral hydrate, pelltobarbitalum natricum or urethane. Power spectral density (PSD) and functional connectivity analyses were used to measure the dynamic temporal neural patterns in NCL during anesthesia. Neural decoding analysis was adopted to calculate the probability of the pigeon's brain state and the kind of injected anesthetic. In the NCL during anesthesia, we found elevated power activity and functional connectivity at low-frequency bands and depressed power activity and connectivity at high-frequency bands. Decoding results based on the spectral and functional connectivity features indicated that the pigeon's brain states during anesthesia and the injected anesthetics can be effectively decoded. These findings provide an important foundation for future investigations on how different anesthetics induce the generation of specific neural patterns.
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Affiliation(s)
- Mengmeng Li
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China
| | - Lifang Yang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China
| | - Yuhuai Liu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China.
- National Center for International Joint Research of Electronic Materials and Systems, Zhengzhou, 450001, China.
- International Joint Laboratory of Electronic Materials and Systems of Henan Province, Zhengzhou, 450001, China.
| | - Zhigang Shang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China.
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China.
- Institute of Medical Engineering Technology and Data Mining, Zhengzhou University, Zhengzhou, 450001, China.
| | - Hong Wan
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China.
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China.
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3
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Yang L, Jin F, Yang L, Li J, Li Z, Li M, Shang Z. The Hippocampus in Pigeons Contributes to the Model-Based Valuation and the Relationship between Temporal Context States. Animals (Basel) 2024; 14:431. [PMID: 38338074 PMCID: PMC10854895 DOI: 10.3390/ani14030431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 01/25/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Model-based decision-making guides organism behavior by the representation of the relationships between different states. Previous studies have shown that the mammalian hippocampus (Hp) plays a key role in learning the structure of relationships among experiences. However, the hippocampal neural mechanisms of birds for model-based learning have rarely been reported. Here, we trained six pigeons to perform a two-step task and explore whether their Hp contributes to model-based learning. Behavioral performance and hippocampal multi-channel local field potentials (LFPs) were recorded during the task. We estimated the subjective values using a reinforcement learning model dynamically fitted to the pigeon's choice of behavior. The results show that the model-based learner can capture the behavioral choices of pigeons well throughout the learning process. Neural analysis indicated that high-frequency (12-100 Hz) power in Hp represented the temporal context states. Moreover, dynamic correlation and decoding results provided further support for the high-frequency dependence of model-based valuations. In addition, we observed a significant increase in hippocampal neural similarity at the low-frequency band (1-12 Hz) for common temporal context states after learning. Overall, our findings suggest that pigeons use model-based inferences to learn multi-step tasks, and multiple LFP frequency bands collaboratively contribute to model-based learning. Specifically, the high-frequency (12-100 Hz) oscillations represent model-based valuations, while the low-frequency (1-12 Hz) neural similarity is influenced by the relationship between temporal context states. These results contribute to our understanding of the neural mechanisms underlying model-based learning and broaden the scope of hippocampal contributions to avian behavior.
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Affiliation(s)
- Lifang Yang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; (L.Y.); (F.J.); (L.Y.); (J.L.); (Z.L.)
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Fuli Jin
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; (L.Y.); (F.J.); (L.Y.); (J.L.); (Z.L.)
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Long Yang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; (L.Y.); (F.J.); (L.Y.); (J.L.); (Z.L.)
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Jiajia Li
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; (L.Y.); (F.J.); (L.Y.); (J.L.); (Z.L.)
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Zhihui Li
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; (L.Y.); (F.J.); (L.Y.); (J.L.); (Z.L.)
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
- Institute of Medical Engineering Technology and Data Mining, Zhengzhou University, Zhengzhou 450001, China
| | - Mengmeng Li
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; (L.Y.); (F.J.); (L.Y.); (J.L.); (Z.L.)
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Zhigang Shang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; (L.Y.); (F.J.); (L.Y.); (J.L.); (Z.L.)
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
- Institute of Medical Engineering Technology and Data Mining, Zhengzhou University, Zhengzhou 450001, China
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Zhu JY, Li MM, Zhang ZH, Liu G, Wan H. Performance Baseline of Phase Transfer Entropy Methods for Detecting Animal Brain Area Interactions. ENTROPY (BASEL, SWITZERLAND) 2023; 25:994. [PMID: 37509941 PMCID: PMC10378602 DOI: 10.3390/e25070994] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023]
Abstract
Objective: Phase transfer entropy (TEθ) methods perform well in animal sensory-spatial associative learning. However, their advantages and disadvantages remain unclear, constraining their usage. Method: This paper proposes the performance baseline of the TEθ methods. Specifically, four TEθ methods are applied to the simulated signals generated by a neural mass model and the actual neural data from ferrets with known interaction properties to investigate the accuracy, stability, and computational complexity of the TEθ methods in identifying the directional coupling. Then, the most suitable method is selected based on the performance baseline and used on the local field potential recorded from pigeons to detect the interaction between the hippocampus (Hp) and nidopallium caudolaterale (NCL) in visual-spatial associative learning. Results: (1) This paper obtains a performance baseline table that contains the most suitable method for different scenarios. (2) The TEθ method identifies an information flow preferentially from Hp to NCL of pigeons at the θ band (4-12 Hz) in visual-spatial associative learning. Significance: These outcomes provide a reference for the TEθ methods in detecting the interactions between brain areas.
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Affiliation(s)
- Jun-Yao Zhu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Meng-Meng Li
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Zhi-Heng Zhang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Gang Liu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Hong Wan
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
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Zhao K, Zhu J, Yang L, Shang Z, Wan H. Goal given moment modulates the time period of gamma oscillations in nidopallium caudolaterale during the goal-directed behavior of pigeon. Brain Res 2023; 1806:148288. [PMID: 36801453 DOI: 10.1016/j.brainres.2023.148288] [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: 11/11/2022] [Revised: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 02/21/2023]
Abstract
The cognitive processes of goal-directed navigation are believed to be organized around and serve the identification and selection of goals. Differences in LFP signals in avian nidopallium caudolaterale (NCL) under different goal location/distance information in the goal-directed behavior have been studied. However, for goals that are multifarious constructs that include various information, the modulation of goal time information on the LFP of NCL during goal-directed behavior remains unclear. In this study, we recorded the LFP activity from the NCL of eight pigeons as they performed two goal-directed decision-making tasks in a plus-maze. During the two tasks with different goal time information, spectral analysis revealed significant LFP power selectively enhanced in the slow gamma band (40-60 Hz), while the slow gamma band of LFP which could effectively decode the behavioral goal of the pigeons existed in different time periods. These findings suggest that the LFP activity in the gamma band correlates with the goal-time information, and help to shed light on the contribution of the gamma rhythm recorded from the NCL in goal-directed behavior.
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Affiliation(s)
- Kun Zhao
- School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450000, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450000, China
| | - Junyao Zhu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450000, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450000, China
| | - Lifang Yang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450000, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450000, China
| | - Zhigang Shang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450000, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450000, China.
| | - Hong Wan
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450000, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450000, China.
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6
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Liu D, Li S, Ren L, Liu X, Li X, Wang Z. Different coding characteristics between flight and freezing in dorsal periaqueductal gray of mice during exposure to innate threats. Animal Model Exp Med 2022; 5:491-501. [PMID: 36225094 PMCID: PMC9773308 DOI: 10.1002/ame2.12276] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 09/09/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Flight and freezing are two vital defensive behaviors that mice display to avoid natural enemies. When they are exposed to innate threats, visual cues are processed and transmitted by the visual system into the emotional nuclei and finally transmitted to the periaqueductal gray (PAG) to induce defensive behaviors. However, how the dorsal PAG (dPAG) encodes the two defensive behaviors is unclear. METHODS Multi-array electrodes were implanted in the dPAG nuclei of C57BL/6 mice. Two kinds of visual stimuli (looming and sweeping) were used to induce defensive behaviors in mice. Neural signals under different defense behaviors were recorded, and the encoding characteristics of the two behaviors were extracted and analyzed from spike firing and frequency oscillations. Finally, synchronization of neural activity during the defense process was analyzed. RESULTS The neural activity between flight and freezing behaviors showed different firing patterns, and the differences in the inter-spike interval distribution were mainly reflected in the 2-10 ms period. The frequency band activities under both defensive behaviors were concentrated in the theta band; the active frequency of flight was ~8 to 10 Hz, whereas that of freezing behavior was ~6 to 8 Hz. The network connection density under both defense behaviors was significantly higher than the period before and after defensive behavior occurred, indicating that there was a high synchronization of neural activity during the defense process. CONCLUSIONS The dPAG nuclei of mice have different coding features between flight and freezing behaviors; during strong looming stimulation, fast neuro-instinctive decision making is required while encountering weak sweeping stimulation, and computable planning late behavior is predicted in the early stage. The frequency band activities under both defensive behaviors were concentrated in the theta band. There was a high synchronization of neural activity during the defense process, which may be a key factor triggering different defensive behaviors.
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Affiliation(s)
- Denghui Liu
- School of Electrical and Information EngineeringZhengzhou UniversityZhengzhouChina
| | - Shouhao Li
- School of Electrical and Information EngineeringZhengzhou UniversityZhengzhouChina
| | - Liqing Ren
- School of Electrical and Information EngineeringZhengzhou UniversityZhengzhouChina
| | - Xinyu Liu
- School of Intelligent ManufacturingHuanghuai UniversityZhumadianChina
| | - Xiaoyuan Li
- School of Electrical and Information EngineeringZhengzhou UniversityZhengzhouChina
| | - Zhenlong Wang
- School of Life SciencesZhengzhou UniversityZhengzhouChina
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Zhu J, Chen M, Lu J, Zhao K, Cui E, Zhang Z, Wan H. A Fast and Efficient Ensemble Transfer Entropy and Applications in Neural Signals. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1118. [PMID: 36010782 PMCID: PMC9407540 DOI: 10.3390/e24081118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/27/2022] [Accepted: 08/09/2022] [Indexed: 06/15/2023]
Abstract
The ensemble transfer entropy (TEensemble) refers to the transfer entropy estimated from an ensemble of realizations. Due to its time-resolved analysis, it is adapted to analyze the dynamic interaction between brain regions. However, in the traditional TEensemble, multiple sets of surrogate data should be used to construct the null hypothesis distribution, which dramatically increases the computational complexity. To reduce the computational cost, a fast, efficient TEensemble with a simple statistical test method is proposed here, in which just one set of surrogate data is involved. To validate the improved efficiency, the simulated neural signals are used to compare the characteristics of the novel TEensemble with those of the traditional TEensemble. The results show that the time consumption is reduced by two or three magnitudes in the novel TEensemble. Importantly, the proposed TEensemble could accurately track the dynamic interaction process and detect the strength and the direction of interaction robustly even in the presence of moderate noises. The novel TEensemble reaches its steady state with the increased samples, which is slower than the traditional method. Furthermore, the effectiveness of the novel TEensemble was verified in the actual neural signals. Accordingly, the TEensemble proposed in this work may provide a suitable way to investigate the dynamic interactions between brain regions.
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Affiliation(s)
- Junyao Zhu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Mingming Chen
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Junfeng Lu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Kun Zhao
- School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450001, China
| | - Enze Cui
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Zhiheng Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Hong Wan
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
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Liu D, Li S, Ren L, Li X, Wang Z. The superior colliculus/lateral posterior thalamic nuclei in mice rapidly transmit fear visual information through the theta frequency band. Neuroscience 2022; 496:230-240. [PMID: 35724770 DOI: 10.1016/j.neuroscience.2022.06.021] [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: 12/15/2021] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 10/18/2022]
Abstract
Animals perceive threat information mainly from vision, and the subcortical visual pathway plays a critical role in the rapid processing of fear visual information. The superior colliculus (SC) and lateral posterior (LP) nuclei of the thalamus are key components of the subcortical visual pathway; however, how animals encode and transmit fear visual information is unclear. To evaluate the response characteristics of neurons in SC and LP thalamic nuclei under fear visual stimuli, extracellular action potentials (spikes) and local field potential signals were recorded under looming and dimming visual stimuli. The results showed that both SC and LP thalamic nuclei were strongly responsive to looming visual stimuli but not sensitive to dimming visual stimuli. Under the looming visual stimulus, the theta (θ) frequency bands of both nuclei showed obvious oscillations, which markedly enhanced the synchronization between neurons. The functional network characteristics also indicated that the network connection density and information transmission efficiency were higher under fear visual stimuli. These findings suggest that both SC and LP thalamic nuclei can effectively identify threatening fear visual information and rapidly transmit it between nuclei through the θ frequency band. This discovery can provide a basis for subsequent coding and decoding studies in the subcortical visual pathways.
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Affiliation(s)
- Denghui Liu
- School of Electric Engineering, Zhengzhou University, 450001, Zhengzhou, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology
| | - Shouhao Li
- School of Electric Engineering, Zhengzhou University, 450001, Zhengzhou, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology
| | - Liqing Ren
- School of Electric Engineering, Zhengzhou University, 450001, Zhengzhou, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology
| | - Xiaoyuan Li
- School of Electric Engineering, Zhengzhou University, 450001, Zhengzhou, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology.
| | - Zhenlong Wang
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology; School of Life Sciences, Zhengzhou University, 450001, Zhengzhou, China.
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Many heads are better than one: A multiscale neural information feature fusion framework for spatial route selections decoding from multichannel neural recordings of pigeons. Brain Res Bull 2022; 184:1-12. [DOI: 10.1016/j.brainresbull.2022.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 02/07/2022] [Accepted: 03/10/2022] [Indexed: 11/22/2022]
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10
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Ju J, Bi L, Feleke AG. Noninvasive neural signal-based detection of soft and emergency braking intentions of drivers. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103330] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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11
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Cruttenden CE, Zhu W, Zhang Y, Soon SH, Zhu XH, Chen W, Rajamani R. Adaptive virtual referencing for the extraction of extracellularly recorded action potentials in noisy environments. J Neural Eng 2020; 17:056011. [PMID: 33043903 DOI: 10.1088/1741-2552/abb73c] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Removal of common mode noise and artifacts from extracellularly measured action potentials, herein referred to as spikes, recorded with multi-electrode arrays (MEAs) which included severe noise and artifacts generated by an ultrahigh field (UHF) 16.4 Tesla magnetic resonance imaging (MRI) scanner. APPROACH An adaptive virtual referencing (AVR) algorithm is used to remove artifacts and thus enable extraction of neural spike signals from extracellular recordings in anesthetized rat brains. A 16-channel MEA with 150-micron inter-site spacing is used, and a virtual reference is created by spatially averaging the 16 signal channels which results in a reference signal without extracellular spiking activity while preserving common mode noise and artifacts. This virtual reference signal is then used as the input to an adaptive FIR filter which optimally scales and time-shifts the reference to each specific electrode site to remove the artifacts and noise. MAIN RESULTS By removing artifacts and reducing noise, the neural spikes at each electrode site can be well extracted, even from data originally recorded with a high noise floor due to electromagnetic interference and artifacts generated by a 16.4T MRI scanner. The AVR method enables many more spikes to be detected than would otherwise be possible. Further, the filtered spike waveforms can be well separated from each other using PCA feature extraction and semi-supervised k-means clustering. While data in a 16.4T MRI scanner contains significantly more noise and artifacts, the developed AVR method enables similar data quality to be extracted as recorded on benchtop experiments outside the MRI scanner. SIGNIFICANCE AVR of extracellular spike signals recorded with MEAs has not been previously reported and fills a technical need by enabling low-noise extracellular spike extraction in noisy and challenging environments such as UHF MRI that will enable further study of neuro-vascular coupling at UHF.
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Affiliation(s)
- Corey E Cruttenden
- Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN, United States of America. Center for Magnetic Resonance Research (CMRR), Radiology Department, University of Minnesota, Minneapolis, MN, United States of America
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Yang L, Li M, Yang L, Wang H, Wan H, Shang Z. Functional connectivity changes in the intra- and inter-brain during the construction of the multi-brain network of pigeons. Brain Res Bull 2020; 161:147-157. [DOI: 10.1016/j.brainresbull.2020.04.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 04/15/2020] [Accepted: 04/20/2020] [Indexed: 02/07/2023]
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13
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Li M, Liang Y, Yang L, Wang H, Yang Z, Zhao K, Shang Z, Wan H. Automatic bad channel detection in implantable brain-computer interfaces using multimodal features based on local field potentials and spike signals. Comput Biol Med 2020; 116:103572. [DOI: 10.1016/j.compbiomed.2019.103572] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 11/12/2019] [Accepted: 12/01/2019] [Indexed: 11/29/2022]
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14
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Shang Z, Liang Y, Li M, Zhao K, Yang L, Wan H. Sequential neural information processing in nidopallium caudolaterale of pigeons during the acquisition process of operant conditioning. Neuroreport 2019; 30:966-973. [DOI: 10.1097/wnr.0000000000001312] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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15
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Zhao K, Nie J, Yang L, Liu X, Shang Z, Wan H. Hippocampus-nidopallium caudolaterale interactions exist in the goal-directed behavior of pigeon. Brain Res Bull 2019; 153:257-265. [PMID: 31541677 DOI: 10.1016/j.brainresbull.2019.09.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 09/12/2019] [Accepted: 09/16/2019] [Indexed: 01/19/2023]
Abstract
Avian hippocampus (Hp) and nidopallium caudolaterale (NCL) are believed to play key roles in goal-directed behavior. However, it is still unclear whether there are interactions between the two brain regions in the goal-directed behavior of pigeons. To investigate the interactions between the Hp and the NCL in the goal-directed behavior, we recorded local field potential (LFP) signals from the two regions simultaneously when the pigeons performed a goal-directed decision-making task. Amplitude-amplitude coupling analysis revealed that the coupling value between the LFP recorded from the Hp and that from the NCL increased significantly (P < 0.05) in slow gamma-band (40-60 Hz) during the turning area. In addition, the LFP functional network analysis demonstrated the LFP functional connections between the Hp and the NCL increased significantly (P < 0.05) in the turning area. The result of partial directed coherence (PDC) analysis showed that the predominant direction of information flow is thought to be from the Hp to the NCL. These findings suggest that there are causal functional interactions between the Hp and the NCL by which information is transmitted between the two regions relevant to goal-directed behavior.
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Affiliation(s)
- Kun Zhao
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450000, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou, 450000, China
| | - Jiejie Nie
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450000, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou, 450000, China
| | - Lifang Yang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450000, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou, 450000, China
| | - Xinyu Liu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450000, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou, 450000, China; School of Intelligent Manufacturing, Huanghuai University, Zhumadian, 463000, China
| | - Zhigang Shang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450000, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou, 450000, China.
| | - Hong Wan
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450000, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou, 450000, China.
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16
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Liu X, Ping Y, Wang D, Yao R, Wan H. [Comparison of decoding performance between spike and local field potential signals during goal-directed decision-making task of pigeons]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2018; 35:786-793. [PMID: 30370720 PMCID: PMC9935269 DOI: 10.7507/1001-5515.201712038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Indexed: 11/03/2022]
Abstract
Both spike and local field potential (LFP) signals are two of the most important candidate signals for neural decoding. At present there are numerous studies on their decoding performance in mammals, but the decoding performance in birds is still not clear. We analyzed the decoding performance of both signals recorded from nidopallium caudolaterale area in six pigeons during the goal-directed decision-making task using the decoding algorithm combining leave-one-out and k-nearest neighbor (LOO- kNN). And the influence of the parameters, include the number of channels, the position and size of decoding window, and the nearest neighbor k value, on the decoding performance was also studied. The results in this study have shown that the two signals can effectively decode the movement intention of pigeons during the this task, but in contrast, the decoding performance of LFP signal is higher than that of spike signal and it is less affected by the number of channels. The best decoding window is in the second half of the goal-directed decision-making process, and the optimal decoding window size of LFP signal (0.3 s) is shorter than that of spike signal (1 s). For the LOO- kNN algorithm, the accuracy is inversely proportional to the k value. The smaller the k value is, the larger the accuracy of decoding is. The results in this study will help to parse the neural information processing mechanism of brain and also have reference value for brain-computer interface.
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Affiliation(s)
- Xinyu Liu
- School of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, P.R.China;School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P.R.China;Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450001,
| | - Yanna Ping
- School of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, P.R.China
| | - Dongyun Wang
- School of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, P.R.China
| | - Ruxian Yao
- School of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, P.R.China
| | - Hong Wan
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P.R.China;Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450001, P.R.China
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17
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Goal-directed behavior elevates gamma oscillations in nidopallium caudolaterale of pigeon. Brain Res Bull 2018; 137:10-16. [DOI: 10.1016/j.brainresbull.2017.10.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2017] [Revised: 10/19/2017] [Accepted: 10/23/2017] [Indexed: 11/21/2022]
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18
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Chen Y, Liu X, Li S, Wan H. Decoding Pigeon Behavior Outcomes Using Functional Connections among Local Field Potentials. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018; 2018:3505371. [PMID: 29666632 PMCID: PMC5832173 DOI: 10.1155/2018/3505371] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 01/09/2018] [Accepted: 01/14/2018] [Indexed: 01/01/2023]
Abstract
Recent studies indicate that the local field potential (LFP) carries information about an animal's behavior, but issues regarding whether there are any relationships between the LFP functional networks and behavior tasks as well as whether it is possible to employ LFP network features to decode the behavioral outcome in a single trial remain unresolved. In this study, we developed a network-based method to decode the behavioral outcomes in pigeons by using the functional connectivity strength values among LFPs recorded from the nidopallium caudolaterale (NCL). In our method, the functional connectivity strengths were first computed based on the synchronization likelihood. Second, the strength values were unwrapped into row vectors and their dimensions were then reduced by principal component analysis. Finally, the behavioral outcomes in single trials were decoded using leave-one-out combined with the k-nearest neighbor method. The results showed that the LFP functional network based on the gamma-band was related to the goal-directed behavior of pigeons. Moreover, the accuracy of the network features (74 ± 8%) was significantly higher than that of the power features (61 ± 12%). The proposed method provides a powerful tool for decoding animal behavior outcomes using a neural functional network.
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Affiliation(s)
- Yan Chen
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Xinyu Liu
- School of Information Engineering, Huanghuai University, Zhumadian, Henan, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, China
| | - Shan Li
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Hong Wan
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, China
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