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Human local field potentials in motor and non-motor brain areas encode upcoming movement direction

COMMUNICATIONS BIOLOGY 2024年第1期7卷 506页

作者： Combrisson, Etienne Di Rienzo, Franck Saive, Anne-Lise Perrone-Bertolotti, Marcela Soto, Juan L. P. Kahane, Philippe Lachaux, Jean-Philippe Guillot, Aymeric Jerbi, Karim Univ Montreal Psychol Dept Montreal PQ Canada Univ Lyon UCBL Lyon 1 UR 7424 Lab Interuniv Biol Motr F-69622 Villeurbanne France Aix Marseille Univ Inst Neurosci Timone CNRS UMR 7289 F-13005 Marseille France Lyfe Res & Innovat Ctr Cognit Sci Dept Ecully France Univ Savoie Mont Blanc Univ Grenoble Alpes CNRS LPNC F-38000 Grenoble France Univ Sao Paulo Telecommun & Control Engn Dept Sao Paulo Brazil Univ Grenoble Alpes CHU Grenoble Alpes INSERM U1216Grenoble Inst NeurosciGIN Grenoble France Univ Lyon Univ Claude Bernard Lyon 1 CNRS Lyon Neurosci Res CtrINSERMEDUWELL TeamUMRS 102 F-69000 Lyon France Mila Quebec AI Inst Montreal PQ Canada Quebec Neuroai Res Ctr UNIQUE Ctr Montreal PQ Canada

Limb movement direction can be inferred from local field potentials in motor cortex during movement execution. Yet, it remains unclear to what extent intended hand movements can be predicted from brain activity recorded during movement planning. Here, we set out to probe the directional-tuning of oscillatory features during motor planning and execution, using a machine learning framework on multi-site local field potentials (LFPs) in humans. We recorded intracranial EEG data from implanted epilepsy patients as they performed a four-direction delayed center-out motor task. Fronto-parietal LFP low-frequency power predicted hand-movement direction during planning while execution was largely mediated by higher frequency power and low-frequency phase in motor areas. By contrast, Phase-Amplitude Coupling showed uniform modulations across directions. Finally, multivariate classification led to an increase in overall decoding accuracy (>80%). The novel insights revealed here extend our understanding of the role of neural oscillations in encoding motor plans.

关键词： Brain-machine interface neural decoding

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decoding motor plans using a closed-loop ultrasonic brain-machine interface

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NATURE NEUROSCIENCE 2024年第1期27卷 196-207页

作者： Griggs, Whitney S. Norman, Sumner L. Deffieux, Thomas Segura, Florian Osmanski, Bruno-Felix Chau, Geeling Christopoulos, Vasileios Liu, Charles Tanter, Mickael Shapiro, Mikhail G. Andersen, Richard A. CALTECH Div Biol & Biol Engn Pasadena CA 91125 USA Univ California Los Angeles David Geffen Sch Med Los Angeles CA 90095 USA PSL Res Univ Phys Med Paris INSERM ESPCI ParisCNRS Paris France Inserm Technol Res Accelerator Biomed Ultrasound Paris France Iconeus Paris France CALTECH T&C Chen Brain Machine Interface Ctr Pasadena CA USA Univ Calif Riverside Dept Bioengn Riverside CA USA USC Dept Neurol Surg Keck Sch Med Los Angeles CA USA USC USC Neurorestorat Ctr Keck Sch Med Los Angeles CA USA Rancho Los Amigos Natl Rehabil Ctr Downey CA USA CALTECH Div Chem & Chem Engn Pasadena CA USA CALTECH Andrew & Peggy Cherng Dept Med Engn Pasadena CA USA Howard Hughes Med Inst Pasadena CA USA

Brain-machine interfaces (BMIs) enable people living with chronic paralysis to control computers, robots and more with nothing but thought. Existing BMIs have trade-offs across invasiveness, performance, spatial coverage and spatiotemporal resolution. Functional ultrasound (fUS) neuroimaging is an emerging technology that balances these attributes and may complement existing BMI recording technologies. In this study, we use fUS to demonstrate a successful implementation of a closed-loop ultrasonic BMI. We streamed fUS data from the posterior parietal cortex of two rhesus macaque monkeys while they performed eye and hand movements. After training, the monkeys controlled up to eight movement directions using the BMI. We also developed a method for pretraining the BMI using data from previous sessions. This enabled immediate control on subsequent days, even those that occurred months apart, without requiring extensive recalibration. These findings establish the feasibility of ultrasonic BMIs, paving the way for a new class of less-invasive (epidural) interfaces that generalize across extended time periods and promise to restore function to people with neurological impairments. Griggs, Norman et al. demonstrate a closed-loop ultrasound-based brain-machine interface (BMI) in rhesus macaques. The ultrasound BMI is capable of controlling eight independent movement directions and is stable across months without retraining.

关键词： Brain–machine interface Motor control neural decoding Ultrasound

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A unifying theory explains seemingly contradictory biases in perceptual estimation

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NATURE NEUROSCIENCE 2024年第4期27卷 793-804页

作者： Hahn, Michael Wei, Xue-Xin Saarland Univ Saarbrucken Germany Univ Texas Austin Ctr Perceptual Syst Ctr Learning & Memory Ctr Theoret & Computat NeurosciDept NeurosciDept Austin TX 78712 USA

Perceptual biases are widely regarded as offering a window into the neural computations underlying perception. To understand these biases, previous work has proposed a number of conceptually different, and even seemingly contradictory, explanations, including attraction to a Bayesian prior, repulsion from the prior due to efficient coding and central tendency effects on a bounded range. We present a unifying Bayesian theory of biases in perceptual estimation derived from first principles. We demonstrate theoretically an additive decomposition of perceptual biases into attraction to a prior, repulsion away from regions with high encoding precision and regression away from the boundary. The results reveal a simple and universal rule for predicting the direction of perceptual biases. Our theory accounts for, and yields, new insights regarding biases in the perception of a variety of stimulus attributes, including orientation, color and magnitude. These results provide important constraints on the neural implementations of Bayesian computations. Despite a long history of studying perceptual biases in neuroscience, many of the biases remain difficult to explain and even appear to be contradictory. The authors propose a unifying theory that sheds new light on such puzzling perceptual biases.

关键词： neural decoding neural encoding Perception Visual system

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Reconstructing Visual Stimulus Representation From EEG Signals Based on Deep Visual Representation Model

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IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2024年第6期54卷 711-722页

作者： Pan, Hongguang Li, Zhuoyi Fu, Yunpeng Qin, Xuebin Hu, Jianchen Xian Univ Sci & Technol Coll Elect & Control Engn Xian 710054 Peoples R China Xian Key Lab Elect Equipment Condit Monitoring & P Xian 710054 Peoples R China Xi An Jiao Tong Univ Sch Automat Sci & Engn Xian 710049 Peoples R China

Reconstructing visual stimulus representation is a significant task in neural decoding. Until now, most studies have considered functional magnetic resonance imaging (fMRI) as the signal source. However, fMRI-based image reconstruction methods are challenging to apply widely due to the complexity and high cost of acquisition equipment. Taking into account the advantages of the low cost and easy portability of electroencephalogram (EEG) acquisition equipment, we propose a novel image reconstruction method based on EEG signals in this article. First, to meet the high recognizability of visual stimulus images in a fast-switching manner, we construct a visual stimuli image dataset and obtain the corresponding EEG dataset through EEG signals collection experiment. Second, we introduce the deep visual representation model (DVRM), comprising a primary encoder and a subordinate decoder, to reconstruct visual stimuli representation. The encoder is designed based on residual-in-residual dense blocks to learn the distribution characteristics between EEG signals and visual stimulus images. Meanwhile, the decoder is designed using a deep neural network to reconstruct the visual stimulus representation from the learned deep visual representation. The DVRM can accommodate the deep and multiview visual features of the human natural state, resulting in more precise reconstructed images. Finally, we evaluate the DVRM based on the quality of the generated images using our EEG dataset. The results demonstrate that the DVRM exhibits an excellent performance in learning deep visual representation from EEG signals, generating reconstructed representation of images that are realistic and highly resemble the original images.

关键词： Visualization Electroencephalography Image reconstruction Feature extraction Brain modeling Functional magnetic resonance imaging decoding Deep visual representation model (DVRM) electroencephalogram (EEG) dataset image reconstruction neural decoding

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decoding Human Interaction Type From Inter-Brain Synchronization by Using EEG Brain Network

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第1期28卷 204-215页

作者： Wang, Xiangcun Shi, Ran Wu, Xia Zhang, Jiacai Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

Cooperation and competition are two common forms of interpersonal interactions and exploring inter-brain synchronization in these two forms can help to further deliberate the underlying neural mechanisms of interpersonal interactions. Recently, studies revealed that electrode-paired inter-brain synchronization plays an important role in human interactions. This study investigated the neural correlates of interpersonal synchronization at the brain network scale and interaction type. Firstly, the network-wise inter-brain synchronization (NIBS) index reflecting cross-brain network synchronization from the global brain perspective was advanced. Secondly, statistical analysis demonstrated that there are differences in NIBS activities between cooperative and competitive interactions. And a row-filtered depthwise separable convolution network was proposed to classify the NIBS features. Results of EEG hyper-scanning data showed significant differences in NIBS between cooperative and competitive tasks, and a comparative study manifested that the cross-brain synchronization in cooperative tasks is more consistent than that of competitive tasks. The neural decoder using a modified convolution network achieved a peak accuracy of 96.05% under the binary classification(cooperation vs competition).

关键词： EEG hyper-scanning interpersonal interaction inter-brain synchronization neural decoding

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Cortical reactivations predict future sensory responses

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NATURE 2024年第7993期625卷 110-+页

作者： Nghia D. Nguyen Lutas, Andrew Amsalem, Oren Fernando, Jesseba Ahn, Andy Young-Eon Hakim, Richard Vergara, Josselyn McMahon, Justin Dimidschstein, Jordane Sabatini, Bernardo L. Andermann, Mark L. Harvard Univ Program Neurosci Boston MA 02138 USA Beth Israel Deaconess Med Ctr Div Endocrinol Dept Med Boston MA 02215 USA NIH NIDDKD Diabet Endocrinol & Obes Branch Bethesda MD USA Harvard Med Sch Howard Hughes Med Inst Dept Neurobiol Boston MA USA Broad Inst Harvard & MIT Stanley Ctr Psychiat Res Cambridge MA USA Harvard Med Sch Dept Neurobiol Boston MA 02115 USA

Many theories of offline memory consolidation posit that the pattern of neurons activated during a salient sensory experience will be faithfully reactivated, thereby stabilizing the pattern(1,2). However, sensory-evoked patterns are not stable but, instead, drift across repeated experiences(3-6). Here, to investigate the relationship between reactivations and the drift of sensory representations, we imaged the calcium activity of thousands of excitatory neurons in the mouse lateral visual cortex. During the minute after a visual stimulus, we observed transient, stimulus-specific reactivations, often coupled with hippocampal sharp-wave ripples. Stimulus-specific reactivations were abolished by local cortical silencing during the preceding stimulus. Reactivations early in a session systematically differed from the pattern evoked by the previous stimulus-they were more similar to future stimulus response patterns, thereby predicting both within-day and across-day representational drift. In particular, neurons that participated proportionally more or less in early stimulus reactivations than in stimulus response patterns gradually increased or decreased their future stimulus responses, respectively. Indeed, we could accurately predict future changes in stimulus responses and the separation of responses to distinct stimuli using only the rate and content of reactivations. Thus, reactivations may contribute to a gradual drift and separation in sensory cortical response patterns, thereby enhancing sensory discrimination(7).

关键词： Consolidation Cortex neural decoding Sensory processing Visual system

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Sequential sparse autoencoder for dynamic heading representation in ventral intraparietal area

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期163卷 107114-107114页

作者： Gao, Wei Shen, Jiangrong Lin, Yipeng Wang, Kejun Lin, Zheng Tang, Huajin Chen, Xiaodong Zhejiang Univ Affiliated Hosp 2 Interdisciplinary Inst Neurosci & Technol Dept Neurol & PsychiatColl Biomed Engn & Instrume 268 Kaixuan Rd Hangzhou 310029 Peoples R China Zhejiang Univ Coll Comp Sci & Technol 38 Zheda Rd Hangzhou 310027 Peoples R China Zhejiang Univ Sch Software Technol 38 Zheda Rd Hangzhou 310027 Peoples R China Zhejiang Univ Affiliated Hosp 2 Sch Med Dept Psychiat 88 Jiefang Rd Hangzhou 310009 Peoples R China

To navigate in space, it is important to predict headings in real-time from neural responses in the brain to vestibular and visual signals, and the ventral intraparietal area (VIP) is one of the critical brain areas. However, it remains unexplored in the population level how the heading perception is represented in VIP. And there are no commonly used methods suitable for decoding the headings from the population responses in VIP, given the large spatiotemporal dynamics and heterogeneity in the neural responses. Here, responses were recorded from 210 VIP neurons in three rhesus monkeys when they were performing a heading perception task. And by specifically and separately modelling the both dynamics with sparse representation, we built a sequential sparse autoencoder (SSAE) to do the population decoding on the recorded dataset and tried to maximize the decoding performance. The SSAE relies on a three-layer sparse autoencoder to extract temporal and spatial heading features in the dataset via unsupervised learning, and a softmax classifier to decode the headings. Compared with other pop-ulation decoding methods, the SSAE achieves a leading accuracy of 96.8% & PLUSMN;2.1%, and shows the advantages of robustness, low storage and computing burden for real-time prediction. Therefore, our SSAE model performs well in learning neurobiologically plausible features comprising dynamic navigational information.

关键词： Heading neural decoding neural dynamics Sparse autoencoder Ventral intraparietal area Vestibular

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Intracortical Hindlimb Brain-Computer Interface Systems: A Systematic Review

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IEEE ACCESS 2023年 11卷 28119-28139页

作者： Ghodrati, Mohammad Taghi Mirfathollahi, Alavie Shalchyan, Vahid Daliri, Mohammad Reza Iran Univ Sci & Technol IUST Sch Elect Engn Biomed Engn Dept Neurosci & Neuroengn Res Lab Tehran *** Iran Inst Cognit Sci Studies ICSS Tehran *** Iran

Brain-computer interfaces (BCI) can help people with motor disorders to regain their ability to communicate and interact with the surrounding environment. The majority of studies in this field pursue the development of BCI systems to enhance or restore the movement functionality of people with disability. Although the studies on the development of BCIs to restore hindlimb movements have shorter backgrounds compared to forelimb, several studies have investigated hindlimb BCIs and their results were promising. In the present study, we systematically reviewed the studies investigating the decoding of hindlimb movement parameters using intracortical signals. Three scientific databases (PubMed, Scopus, and Embase) were used to extract the articles and the experiment, recording, processing methods, and results of the included studies were discussed. Although several studies on upper-limb intracortical BCIs have been conducted on human subjects, almost all studies in hindlimb intracortical BCI field were performed on animal subjects. The most investigated task was walking on a treadmill, and the position of hindlimb joints and gait phase were the most studied continuous and discrete parameters, respectively. The included studies have mainly used spikes and linear decoders, which leaves the question of the effectiveness of using local field potentials and nonlinear decoders in this field unanswered. Although the results imply that hindlimb movement decoding using brain signals is feasible in laboratory conditions, further investigations are required to examine the hindlimb BCIs in real-life conditions.

关键词： decoding Legged locomotion Robots Databases Task analysis Recording Brain-computer interfaces Brain-computer interface motor cortex hindlimb neural decoding intracortical

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Channel Coding Toward 6G: Technical Overview and Outlook

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 2585-2685页

作者： Rowshan, Mohammad Qiu, Min Xie, Yixuan Gu, Xinyi Yuan, Jinhong Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Channel coding plays a pivotal role in ensuring reliable communication over wireless channels. With the growing need for ultra-reliable communication in emerging wireless use cases, the significance of channel coding has amplified. Furthermore, minimizing decoding latency is crucial for critical-mission applications, while optimizing energy efficiency is paramount for mobile and the Internet of Things (IoT) communications. As the fifth generation (5G) of mobile communications is currently in operation and 5G-advanced is on the horizon, the objective of this paper is to assess prominent channel coding schemes in the context of recent advancements and the anticipated requirements for the sixth generation (6G). In this paper, after considering the potential impact of channel coding on key performance indicators (KPIs) of wireless networks, we review the evolution of mobile communication standards and the organizations involved in the standardization, from the first generation (1G) to the current 5G, highlighting the technologies integral to achieving targeted KPIs such as reliability, data rate, latency, energy efficiency, spectral efficiency, connection density, and traffic capacity. Following this, we delve into the anticipated requirements for potential use cases in 6G. The subsequent sections of the paper focus on a comprehensive review of three primary coding schemes utilized in past generations and their recent advancements: low-density parity-check (LDPC) codes, turbo codes (including convolutional codes), and polar codes (alongside Reed-Muller codes). Additionally, we examine alternative coding schemes like Fountain codes (also known as rate-less codes), sparse regression codes, among others. Our evaluation includes a comparative analysis of error correction performance and the performance of hardware implementation for these coding schemes, providing insights into their potential and suitability for the upcoming 6G era. Lastly, we will briefly explore consider

关键词： Codes 5G mobile communication 3GPP 6G mobile communication Reliability Iterative decoding 3G mobile communication Channel coding error control coding error correction codes wireless mobile communications 5th generation 5G 6th generation 6G encoding decoding channel polarization polar codes PAD codes monomial codes CRC low-density parity-check codes LDPC codes convolutional codes turbo codes spatially coupled codes fountain codes spinal codes raptor codes Luby transform codes LT codes lattice codes nonbinary codes sparse regression codes SPARC machine learning neural codes neural decoding successive cancellation decoding beleif propagation decoing Message Passing decoding BCJR decoding iterative decoding Viterbi decoding Fano decoding automorphism ensemble decoding min-sum algorithm bit-flipping PPV bound normal approximation coded modulation bit-interleaving puncturing shortening repetition ratecompatible codes application layer channel coding physical layer waveform non-terrestrial networks free space optical links modulation block error rate BLER frame error rate FER reliability latency complexity enhanced mobile broadband eMBB machine-type communications MTC ultra-reliable low-latency URLLC key performance indicator KPI hardware architecture waveform design

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A distinct cortical code for socially learned threat

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NATURE 2024年第8001期626卷 1066-1072页

作者： Silverstein, Shana E. O'Sullivan, Ruairi Bukalo, Olena Pati, Dipanwita Schaffer, Julia A. Limoges, Aaron Zsembik, Leo Yoshida, Takayuki O'Malley, John J. Paletzki, Ronald F. Lieberman, Abby G. Nonaka, Mio Deisseroth, Karl Gerfen, Charles R. Penzo, Mario A. Kash, Thomas L. Holmes, Andrew NIAAA Lab Behav & Genom Neurosci NIH Bethesda MD 20892 USA Univ N Carolina Dept Pharmacol Chapel Hill NC USA NIMH Unit Neurobiol Affect Memory NIH Bethesda MD USA NIMH NIH Bethesda MD USA Stanford Univ Dept Bioengn Stanford CA USA Stanford Univ Dept Psychiat & Behav Sci Stanford CA USA Stanford Univ Howard Hughes Med Inst Stanford CA USA

Animals can learn about sources of danger while minimizing their own risk by observing how others respond to threats. However, the distinct neural mechanisms by which threats are learned through social observation (known as observational fear learning1-4 (OFL)) to generate behavioural responses specific to such threats remain poorly understood. The dorsomedial prefrontal cortex (dmPFC) performs several key functions that may underlie OFL, including processing of social information and disambiguation of threat cues5-11. Here we show that dmPFC is recruited and required for OFL in mice. Using cellular-resolution microendoscopic calcium imaging, we demonstrate that dmPFC neurons code for observational fear and do so in a manner that is distinct from direct experience. We find that dmPFC neuronal activity predicts upcoming switches between freezing and moving state elicited by threat. By combining neuronal circuit mapping, calcium imaging, electrophysiological recordings and optogenetics, we show that dmPFC projections to the midbrain periaqueductal grey (PAG) constrain observer freezing, and that amygdalar and hippocampal inputs to dmPFC opposingly modulate observer freezing. Together our findings reveal that dmPFC neurons compute a distinct code for observational fear and coordinate long-range neural circuits to select behavioural responses. Studies in mice show that observational fear learning is encoded by neurons in the dorsomedial prefrontal cortex in a manner that is distinct from the encoding of fear learned by direct experience.

关键词： Empathy Fear conditioning neural decoding Prefrontal cortex

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