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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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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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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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Robustness tests for biomedical foundation models should tailor to specification

arXiv

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arXiv 2025年

作者： Xian, R. Patrick Baker, Noah R. David, Tom Cui, Qiming Holmgren, A. Jay Bauer, Stefan Sushil, Madhumita Abbasi-Asl, Reza Weill Institute for Neurosciences University of California San Francisco 1651 4th Street San FranciscoCA94158 United States Biological and Medical Informatics Graduate Program University of California San Francisco 550 16th Street 3rd Floor San FranciscoCA94158 United States PRISM Eval 10 Rue de Penthièvre Paris75008 France Department of Bioengineering University of California Berkeley 306 Stanley Hall University Drive BerkeleyCA94720 United States Division of Clinical Informatics and Digital Transformation University of California San Francisco 10 Koret Way San FranciscoCA94117 United States School of Computation Information and Technology Technical University of Munich & Helmholtz AI Arcisstrasse 21 Munich80333 Germany Bakar Computational Health Sciences Institute University of California San Francisco 490 Illinois Street San FranciscoCA94158 United States

Existing regulatory frameworks for biomedical AI include robustness as a key component but lack detailed implementational guidance. The recent rise of biomedical foundation models creates new hurdles in testing and certification given their broad capabilities and susceptibility to complex distribution shifts. To balance test feasibility and effectiveness, we suggest a priority-based, task-oriented approach to tailor robustness evaluation objectives to a predefined specification. We urge concrete policies to adopt a granular categorization of robustness concepts in the specification. Our approach promotes the standardization of risk assessment and monitoring, which guides technical developments and mitigation efforts. © 2025, CC BY-NC-SA.

关键词： Specifications

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An integrated transcriptomic cell atlas of human neural organoids (vol 635, pg 690, 2024)

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NATURE 2025年第8045期637卷 E21-E21页

作者： He, Zhisong Dony, Leander Fleck, Jonas Simon Szalata, Artur Li, Katelyn X. Sliskovic, Irena Lin, Hsiu-Chuan Santel, Malgorzata Atamian, Alexander Quadrato, Giorgia Sun, Jieran Pasca, Sergiu P. Camp, J. Gray Theis, Fabian J. Treutlein, Barbara Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Institute of Computational Biology Computational Health Center Helmholtz Munich Neuherberg Germany Department Genes and Environment Max Planck Institute of Psychiatry Munich Germany International Max Planck Research School for Translational Psychiatry (IMPRS-TP) Munich Germany TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Physiology and Pharmacology Karolinska Institutet Stockholm Sweden Institute of Human Biology (IHB) Roche Pharma Research and Early Development Roche Innovation Center Basel Basel Switzerland Biozentrum University of Basel Basel Switzerland Department of Stem Cell Biology and Regenerative Medicine Keck School of Medicine University of Southern California Los Angeles CA USA Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC Keck School of Medicine University of Southern California Los Angeles CA USA Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA Stanford Brain Organogenesis Program Wu Tsai Neurosciences Institute and Bio-X Stanford CA USA Neural Stem Cell Institute Albany NY USA UK Dementia Research Institute at the University of Edinburgh Edinburgh Bioquarter Edinburgh UK Centre for Discovery Brain Sciences School of Biomedical Sciences College of Medicine and Veterinary Medicine The University of Edinburgh Edinburgh UK Human Technopole Milan Italy Department of Oncology and Hemato-Oncology University of Milan Milan Italy

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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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A consensus protocol for functional connectivity analysis in the rat brain (vol 26, pg 673, 2023)

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NATURE NEUROSCIENCE 2023年第6期26卷 1127-1128页

作者： Grandjean, Joanes Desrosiers-Gregoire, Gabriel Anckaerts, Cynthia Angeles-Valdez, Diego Ayad, Fadi Barriere, David A. Blockx, Ines Bortel, Aleksandra Broadwater, Margaret Cardoso, Beatriz M. Celestine, Marina Chavez-Negrete, Jorge E. Choi, Sangcheon Christiaen, Emma Clavijo, Perrin Colon-Perez, Luis Cramer, Samuel Daniele, Tolomeo Dempsey, Elaine Diao, Yujian Doelemeyer, Arno Dopfel, David Dvorakova, Lenka Falfan-Melgoza, Claudia Fernandes, Francisca F. Fowler, Caitlin F. Fuentes-Ibanez, Antonio Garin, Clement M. Gelderman, Eveline Golden, Carla E. M. Guo, Chao C. G. Henckens, Marloes J. A. G. Hennessy, Lauren A. Herman, Peter Hofwijks, Nita Horien, Corey Ionescu, Tudor M. Jones, Jolyon Kaesser, Johannes Kim, Eugene Lambers, Henriette Lazari, Alberto Lee, Sung-Ho Lillywhite, Amanda Liu, Yikang Liu, Yanyan Y. Lopez-Castro, Alejandra Lopez-Gil, Xavier Ma, Zilu MacNicol, Eilidh Madularu, Dan Mandino, Francesca Marciano, Sabina McAuslan, Matthew J. McCunn, Patrick McIntosh, Alison Meng, Xianzong Meyer-Baese, Lisa Missault, Stephan Moro, Federico Naessens, Daphne M. P. Nava-Gomez, Laura J. Nonaka, Hiroi Ortiz, Juan J. Paasonen, Jaakko Peeters, Lore M. Pereira, Mickael Perez, Pablo D. Pompilus, Marjory Prior, Malcolm Rakhmatullin, Rustam Reimann, Henning M. Reinwald, Jonathan Del Rio, Rodrigo Triana Rivera-Olvera, Alejandro Ruiz-Perez, Daniel Russo, Gabriele Rutten, Tobias J. Ryoke, Rie Sack, Markus Salvan, Piergiorgio Sanganahalli, Basavaraju G. Schroeter, Aileen Seewoo, Bhedita J. Selingue, Erwan Seuwen, Aline Shi, Bowen Sirmpilatze, Nikoloz Smith, Joanna A. B. Smith, Corrie Sobczak, Filip Stenroos, Petteri J. Straathof, Milou Strobelt, Sandra Sumiyoshi, Akira Takahashi, Kengo Torres-Garcia, Maria E. Tudela, Raul van den Berg, Monica van der Marel, Kajo van Hout, Aran T. B. Vertullo, Roberta Vidal, Benjamin Vrooman, Roel M. Wang, Victora X. Wank, Isabel Watson, David J. G. Yin, Ting Zhang, Yongzhi Zurbruegg, Stefan Achard, Sophie Alcauter, Sarael Auer, Dorothee P. Barbier, Emmanuel L. Baudewig, Juergen Beckmann, Chri Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada Integrated Program in Neuroscience McGill University Montreal QC Canada Bio-imaging Lab University of Antwerp Antwerp Belgium µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico Biological and Biomedical Engineering McGill University Montreal QC Canada McConnell Brain Imaging Centre McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France Department of Neurology and Neurosurgery McGill University Montreal QC Canada Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tuebingen Germany Graduate Training Centre of Neuroscience International Max

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

关键词： Databases neural circuits

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One Tap at a Time: Correlating Sensorimotor Synchronization with Brain Signatures of Temporal Processing

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Cerebral cortex communications 2020年第1期1卷 tgaa036页

作者： Gina M D'Andrea-Penna John R Iversen Andrea A Chiba Alexander K Khalil Victor H Minces Neurosciences Graduate Program UC San Diego La Jolla CA 92093 USA. Institute for Neural Computation UC San Diego La Jolla CA 92093 USA. Department of Cognitive Science UC San Diego La Jolla CA 92093 USA.

The ability to integrate our perceptions across sensory modalities and across time, to execute and coordinate movements, and to adapt to a changing environment rests on temporal processing. Timing is essential for basic daily tasks, such as walking, social interaction, speech and language comprehension, and attention. Impaired temporal processing may contribute to various disorders, from attention-deficit hyperactivity disorder and schizophrenia to Parkinson's disease and dementia. The foundational importance of timing ability has yet to be fully understood; and popular tasks used to investigate behavioral timing ability, such as sensorimotor synchronization (SMS), engage a variety of processes in addition to the neural processing of time. The present study utilizes SMS in conjunction with a separate passive listening task that manipulates temporal expectancy while recording electroencephalographic data. Participants display a larger N1-P2 evoked potential complex to unexpected beats relative to temporally predictable beats, a differential we call the timing response index (TRI). The TRI correlates with performance on the SMS task: better synchronizers show a larger brain response to unexpected beats. The TRI, derived from the perceptually driven N1-P2 complex, disentangles the perceptual and motor components inherent in SMS and thus may serve as a neural marker of a more general temporal processing.

关键词： electroencephalography (EEG) rhythm sensorimotor synchronization (SMS) sensory integration timing

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Conserved brain-wide emergence of emotional response from sensory experience in humans and mice

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Science (New York, N.Y.) 2025年第6750期388卷 eadt3971页

作者： Isaac Kauvar Ethan B Richman Tony X Liu Chelsea Li Sam Vesuna Adelaida Chibukhchyan Lisa Yamada Adam Fogarty Ethan Solomon Eun Young Choi Leili Mortazavi Gustavo Chau Loo Kung Pavithra Mukunda Cephra Raja Dariana Gil-Hernández Kishandra Patron Xue Zhang Jacob Brawer Shenandoah Wrobel Zoe Lusk Dian Lyu Anish Mitra Laura Hack Liqun Luo Logan Grosenick Peter van Roessel Leanne M Williams Boris D Heifets Jaimie M Henderson Jennifer A McNab Carolyn I Rodríguez Vivek Buch Paul Nuyujukian Karl Deisseroth Human Neural Circuitry program Stanford University Stanford CA USA. Department of Bioengineering Stanford University Stanford CA USA. Graduate School of Education Stanford University Stanford CA USA. Neurosciences Graduate Program Stanford University Stanford CA USA. Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA. Department of Neurosurgery Stanford University Stanford CA USA. Department of Electrical Engineering Stanford University Stanford CA USA. Department of Psychology Stanford University Stanford CA USA. Department of Radiology Stanford University Stanford CA USA. Department of Neurology and Neurological Sciences Stanford CA USA. Veterans Affairs Palo Alto Health Care System Palo Alto CA USA. Department of Biology Stanford University Stanford CA USA. Howard Hughes Medical Institute Stanford University Stanford CA USA. Department of Psychiatry Weill Cornell Medicine New York NY USA. Department of Anesthesiology Perioperative and Pain Medicine Stanford University Stanford CA USA.

Emotional responses to sensory experience are central to the human condition in health and disease. We hypothesized that principles governing the emergence of emotion from sensation might be discoverable through their conservation across the mammalian lineage. We therefore designed a cross-species neural activity screen, applicable to humans and mice, combining precise affective behavioral measurements, clinical medication administration, and brain-wide intracranial electrophysiology. This screen revealed conserved biphasic dynamics in which emotionally salient sensory signals are swiftly broadcast throughout the brain and followed by a characteristic persistent activity pattern. Medication-based interventions that selectively blocked persistent dynamics while preserving fast broadcast selectively inhibited emotional responses in humans and mice. Mammalian emotion appears to emerge as a specifically distributed neural context, driven by persistent dynamics and shaped by a global intrinsic timescale.

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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