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Metacognition and Addiction: A New Look Inside

BIOLOGICAL PSYCHIATRY-COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024年第11期9卷 1081-1082页

作者： Bradberry, Charles W. Neural Computation of Motivation and Decision-Making Section National Institute on Drug Abuse Intramural Research Program National Institutes of Health Bethesda Maryland

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Effects of EEG Electrode Numbers on Deep Learning-Based Source Imaging 16th

Effects of EEG Electrode Numbers on Deep Learning-Based Sour...

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16th International Conference on Brain Informatics, BI 2023

作者： Rong, Jesse Sun, Rui Guo, Yuxin He, Bin Department of Biomedical Engineering Carnegie Mellon University PittsburghPA15213 United States Neural Computation Program Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9783031430749

Electrophysiological source imaging (ESI) allows spatio-temporal imaging of brain activity and functional connectivity with high density EEG. The relationship between the performance of ESI methods and the number of scalp EEG electrodes has been a topic of ongoing investigation. Research has shown that high-density EEG is necessary for obtaining accurate and reliable ESI results using conventional ESI solutions, limiting their applications when only low-density EEG is available. In recent years, deep learning-based ESI methods have demonstrated strong performance by learning spatiotemporal patterns of brain activities directly from data. In this work, we examined the effect of the EEG electrode numbers on a recently proposed Deep Learning-based Source Imaging Framework (DeepSIF). We evaluated the ESI performance with 16, 21, 32, 64, and 75 channel EEG configurations using DeepSIF and conventional ESI methods in computer simulations. Our findings suggest that DeepSIF can provide accurate estimations of the source location and extent across various numbers of channels and noise levels, outperforming conventional methods, which indicates its merits for wide applications of ESI, especially for centers/labs without high density EEG devices. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Guiding Brain-to-Vocalization Decoder Design Using Structured Generalization Error 46

Guiding Brain-to-Vocalization Decoder Design Using Structure...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Huang, Jingya Tostado-Marcos, Pablo Narasimha, Sowmya Manojna Patel, Aashish N. Arneodo, Ezequiel. M. Gentner, Timothy Q. Mishne, Gal Gilja, Vikash University of California San Diego Department of Electrical and Computer Engineering La Jolla CA92092 United States University of California San Diego Department of Bioengineering La Jolla CA92092 United States University of California San Diego Institute for Neural Computation La Jolla CA92092 United States University of California San Diego Neuroscience Graduate Program La Jolla CA92092 United States University of California San Diego Department of Physics La Jolla CA92092 United States University of California San Diego Department of Psychology La Jolla CA92092 United States University of California San Diego Halicioǧlu Data Science Institute La Jolla CA92092 United States

ISBN: (纸本)9798350371499

State-of-the-art intracortical neuroprostheses currently enable communication at 60+ words per minute for anarthric individuals by training on over 10K sentences to account for phoneme variability in different word contexts. There is limited understanding about whether this performance can be maintained in decoding naturalistic speech with 40K+ word vocabularies across elicited, spontaneous, and conversational speech contexts. We introduce a vocal-unit-level generalization test to explicitly evaluate neural decoder performance with an expanded and more diverse behavioral repertoire. Tested on neural decoders modeling zebra finch vocalization, an analog to human vocal production, we compare three decoders with different input types: spike trains, neural factors, and firing rates. The factors and rates are latent neural features inferred using trained Latent Factor Analysis via Dynamical Systems (LFADS) models that capture the population neural dynamics during vocal production. While the conventional random holdout generalization error measure is similar for all three decoders, factor- and rate-based decoders outperform spike-based decoders when testing vocal-unit-holdout generalization error. These results suggest the later models better adapt to flexible vocalization inference when trained with partial observation of data variation, motivating further exploration of decoders incorporating latent neural and vocalization dynamics. © 2024 IEEE.

关键词： Errors

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Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG Systems Review

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IEEE Journal of Biomedical and Health Informatics 2023年第8期27卷 3830-3843页

作者： He, Congying Chen, Yu-Yi Phang, Chun-Ren Stevenson, Cory Chen, I-Ping Jung, Tzyy-Ping Ko, Li-Wei Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Applied Arts National Yang Ming Chiao Tung University Hsinchu30010 Taiwan International Ph.D. Program in Interdisciplinary Neuroscience National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San DiegoCA90093 United States National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Electrical and Control Engineering Department of Electronics and Electrical Engineering Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Biomedical Science and Environment Biology Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung80708 Taiwan

Wireless electroencephalography (EEG) systems have been attracting increasing attention in recent times. Both the number of articles discussing wireless EEG and their proportion relative to general EEG publications have increased over years. These trends indicate that wireless EEG systems could be more accessible to researchers and the research community has recognized the potential of wireless EEG systems. To explore the development and diverse applications of wireless EEG systems, this review highlights the trends in wearable and wireless EEG systems over the past decade and compares the specifications and research applications of the major wireless systems marketed by 16 companies. For each product, five parameters (number of channels, sampling rate, cost, battery life, and resolution) were assessed for comparison. Currently, these wearable and portable wireless EEG systems have three main application areas: consumer, clinical, and research. To address this multitude of options, the article also discussed the thought process to find a suitable device that meets personalization and use cases specificities. These investigations suggest that low-price and convenience are key factors for consumer applications, wireless EEG systems with FDA or CE-certification may be more suitable for clinical settings, and devices that provide raw EEG data with high-density channels are important for laboratory research. This article presents an overview of the current state of the wireless EEG systems specifications and possible applications and serves as a guide point as it is expected that more influential and novel research will cyclically promote the development of such EEG systems. © 2023 IEEE.

关键词： Electroencephalography

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Guiding Brain-to-Vocalization Decoder Design Using Structured Generalization Error

Guiding Brain-to-Vocalization Decoder Design Using Structure...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jingya Huang Pablo Tostado-Marcos Sowmya Manojna Narasimha Aashish N. Patel Ezequiel. M. Arneodo Timothy Q. Gentner Gal Mishne Vikash Gilja Department of Electrical and Computer Engineering University of California San Diego CA Department of Bioengineering University of California San Diego CA Institute for Neural Computation University of California San Diego CA Neuroscience Graduate Program University of California San Diego CA Department of Physics University of California San Diego CA Department of Psychology University of California San Diego CA Halıcıoğlu Data Science Institute University of California San Diego CA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

关键词： Training Adaptation models Vocabulary Firing Measurement uncertainty Neuroprostheses Production Decoding Engineering in medicine and biology Testing

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The effect of Zentangle on cognitive focus, emotional well-being, and stress levels: A neural perspective

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Brain and Behavior 2024年第8期14卷 e3628页

作者： Usman, Muhammad Jung, Tzzy-Ping Hsin, Ding-Yun Lin, Chun-Ling International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San Diego San Diego CA United States Taipei Municipal Yangming High School Taipei City Taiwan Department of Electrical Engineering Ming Chi University of Technology New Taipei City Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan

Purpose: This study aimed to assess how a Zentangle intervention influences cognitive focus, emotional well-being, stress levels, and neural activity patterns across brain regions and frequency bands. Method: A cohort of 30 healthy adults, all without prior Zentangle experience, participated in this study. Electroencephalography (EEG) was used to measure their brain activity, and self-reported data were collected through questionnaires to assess subjects’ concentration levels, emotional calm, and stress and anxiety. Findings: Participants reported enhanced cognitive focus and emotional well-being, evidenced by increased self-reported concentration and emotional calmness, and reduced stress and anxiety levels during the intervention. EEG analyses revealed notable changes in neural activity patterns, including decreased delta power and increased theta, alpha, beta, and gamma bands. Functional connectivity analysis also highlighted alterations in the brain's functional connectivity, suggesting potential effects on neural communication and information processing. Conclusion: This study provides compelling evidence of Zentangle's impact on EEG data, aligning it with equanimity and tranquility consistent with previous mindfulness research. These findings underscore Zentangle as an effective mindfulness practice, potentially enhancing cognitive focus and emotional well-being, and emerging as a valuable intervention for improving mental health and overall well-being. © 2024 The Author(s). Brain and Behavior published by Wiley Periodicals LLC.

关键词： art therapy electroencephalography mediation mindfulness well-being Zentangle

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Machine learning identification of enhancers in the rhesus macaque genome

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Neuron 2025年第10期113卷 1548-1561.e8页

作者： He, Jing Phan, BaDoi N. Kerkhoff, Willa G. Alikaya, Aydin Hong, Tao Brull, Olivia R. Fredericks, J. Megan Sedorovitz, Morgan Srinivasan, Chaitanya Leone, Michael J. Wirfel, Olivia M. Brown, Ashley Dauby, Samuel Tittle, Rachel K. Lin, Meng K. Hooks, Bryan M. Bostan, Andreea C. Gharbawie, Omar A. Byrne, Leah C. Pfenning, Andreas R. Stauffer, William R. Department of Neurobiology University of Pittsburgh Pittsburgh 15213 PA United States Computational Biology Department Carnegie Mellon University Pittsburgh 15213 PA United States Neuroscience Institute Carnegie Mellon University Pittsburgh 15213 PA United States University of Pittsburgh-Carnegie Mellon University Medical Scientist Training Program Pittsburgh 15213 PA United States Program in Neural Computation Carnegie Mellon University Pittsburgh 15213 PA United States Department of Ophthalmology University of Pittsburgh Pittsburgh 15213 PA United States

Nonhuman primate (NHP) neuroanatomy and cognitive complexity make NHPs ideal models to study human neurobiology and disease. However, NHP circuit-function investigations are limited by the availability of molecular reagents that are effective in NHPs. This calls for reagent development approaches that prioritize NHPs. Therefore, we derived enhancers from the NHP genome. We defined cell-type-specific open chromatin regions (OCRs) in single-cell data from rhesus macaques. We trained machine-learning models to rank those OCRs according to their potential as cell-type-specific enhancers for cells in the dorsolateral prefrontal cortex (DLPFC). We packaged the top-ranked layer-3-pyramidal-neuron enhancer into AAV and injected it into the macaque DLPFC. Expression was mostly restricted to layers 2 and 3 and confirmed with light-driven activation of channelrhodopsin. These results provide a crucial tool for studying the causal functions of DLPFC and provide a roadmap for optimized gene delivery in primates. © 2025 The Authors

关键词： AAV cell types cell-type-specific cognition DLPFC dorsolateral prefrontal cortex enhancer machine learning optogenetics rhesus monkey

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A Parallel RatSlam C++ Library Implementation 2nd

A Parallel RatSlam C++ Library Implementation

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2nd Latin American Workshop on computational Neuroscience, LAWCN 2019

作者： de Souza Muñoz, Mauro Enrique Menezes, Matheus Chaves de Freitas, Edison Pignaton Cheng, Sen de Almeida Neto, Areolino de Oliveira, Alexandre César Muniz de Almeida Ribeiro, Paulo Rogério Federal University of Maranhão Av. dos Portugueses 1966 Bacanga São LuísMaranhão Brazil Graduate Program on Computer Science Federal University of Rio Grande do Sul Porto Alegre Brazil Computational Neuroscience Institute for Neural Computation Ruhr University Bochum Bochum Germany

ISBN: (纸本)9783030366353

RatSlam is a bio-inspired Simultaneous Location and Mapping (SLAM) algorithm used for autonomous mobile robots navigation tasks. This work presents a RatSlam algorithm implementation as a C++ library designed to take advantage of internal RatSlam modules parallelization. The RatSlam algorithm is presented with principal aspects of the library architecture design. Furthermore, its results using a well known RatSlam data set with a standard RatSlam implementation (OpenRatSLAM - Robot Operating System), and a Python implementation. The mapping found with the previous approaches and the proposed on this work were similar. Moreover, the execution times between the OpenRatSLAM and this C++ library was compared, with the proposed implementation having a lower execution time. Thus, the current implementation was validated and has some advantages against previous ones, which can be very relevant for real-time applications. © Springer Nature Switzerland AG 2019.

关键词： Robot Operating System

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Mechanisms for spontaneous symmetry breaking in developing visual cortex

arXiv

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arXiv 2021年

作者： Fumarola, Francesco Hein, Bettina Miller, Kenneth D. Laboratory for Neural Computation and Adaptation RIKEN Center for Brain Science 2-1 Hirosawa Saitama Wako351-0198 Japan Center for Theoretical Neuroscience College of Physicians and Surgeons Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New YorkNY United States Dept. of Neuroscience Swartz Program in Theoretical Neuroscience Kavli Institute for Brain Science College of Physicians and Surgeons Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New YorkNY United States

For the brain to recognize local orientations within images, neurons must spontaneously break the translation and rotation symmetry of their response functions – an archetypal example of unsupervised learning. The dominant framework for unsupervised learning in biology is Hebb’s principle, but how Hebbian learning could break such symmetries is a longstanding biophysical riddle. Theoretical studies agree that this should require inputs to visual cortex to invert the relative magnitude of their correlations at long distances. Empirical measurements have searched in vain for such an inversion, and report the opposite to be true. We formally approach the question through the hermitianization of a multi-layer model, which maps it into a problem of zero-temperature phase transitions. In the emerging phase diagram, both symmetries break spontaneously as long as (1) recurrent interactions are sufficiently long-range and (2) Hebbian competition is duly accounted for. The relevant mechanism for symmetry breaking is competition among connections sprouting from the same afferent cell. Such competition, and not the structure of the inputs, is capable of triggering the broken-symmetry phase required by image processing. We provide analytic predictions on the relative magnitudes of the relevant length-scales needed for this novel mechanism to occur. These results reconcile experimental observations to the Hebbian paradigm, shed light on a new mechanism for visual cortex development, and contribute to our growing understanding of the relationship between learning and symmetry breaking. © 2021, CC BY.

关键词： Phase diagrams

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Multimodal classification of Parkinson’s disease using delay differential analysis

Multimodal classification of Parkinson’s disease using dela...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jonathan Weyhenmeyer Manuel E. Hernandez Claudia Lainscsek Howard Poizner Terrence J. Sejnowski Computational Neurobiology Laboratory The Salk Institute for Biological Studies La Jolla CA USA Indiana University School of Medicine Indianapolis IN USA University of Illinois at Urbana-Champaign Urbana USA Institute for Neural Computation University of California at San Diego La Jolla CA USA Graduate Program in Neurosciences University of California San Diego La Jolla CA USA

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder in the world. PD is known to lead to marked alterations in cortico-thalamo-basal ganglia activity and subsequent movements, which may provide a biomarker for PD diagnosis. Delay differential analysis (DDA) is a time domain analysis framework based on embedding theory in nonlinear dynamics. An embedding reveals the nonlinear invariant properties of an unknown dynamical system (here the brain) from a single time series electroencephalography (EEG) or behavioral signals. The DDA models serve as a low-dimensional nonlinear functional basis onto which the data are mapped. The combination of behavioral and neurological observations gives rise to a multimodal analysis framework that could improve our understanding and classification of PD using time series data from physical systems. We demonstrate how 750 ms of multimodal data can be used to improve DDA classification performance of PD, over clean EEG or behavioral time series data on their own, in two distinct virtual reach to grasp tasks in an uncertain and dynamic virtual reality environment. Thus, multimodal DDA may provide a tool for aiding the clinician in the diagnosis of PD and bolster classification performance through the combination of a wide array of neural or behavioral signals.

关键词： Electroencephalography Delays Robots Task analysis Indexes Handheld computers Brain modeling

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