Samantha R. Santacruz

The University of Texas at Austin

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Yin-Jui Chang

The University of Texas at Austin

Jose M. Carmena

University of California, Berkeley

Yuan‐I Chen

The University of Texas at Austin

Hsin‐Chih Yeh

The University of Texas at Austin

Hung‐Yun Lu

The University of Texas at Austin

Hannah M. Stealey

The University of Texas at Austin

Yi Zhao

The University of Texas at Austin

Shih‐Chu Liao

Institute of Semitic Studies

Philippe N. Tobler

University of Zurich

Enrique Contreras-Hernandez

The University of Texas at Austin

Cooperative Institutions

The University of Texas at Austin

University of California, Berkeley

University of California, Davis

University of California, San Francisco

University of California System

The University of Texas MD Anderson Cancer Center

University of California, San Diego

Palo Alto Institute

Oregon National Primate Research Center

Neuroscience Institute

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Inferring system-level brain communication through multi-scale neural activity

bioRxiv (Cold Spring Harbor Laboratory) (2020)

Yin-Jui Chang Yuan‐I Chen Hsin‐Chih Yeh Jose M. Carmena Samantha R. Santacruz

Abstract Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas the population brain activity has been studied in the micro-to meso-scale in building the connections between the dynamical patterns and the behaviors, such studies were often done at a single length scale and lacked an explanatory theory that identifies the neuronal origin across multiple scales. Here we introduce the NeuroBondGraph Network, a dynamical system incorporating both biological-inspired components and deep learning techniques to capture cross-scale dynamics that can infer and map the neural data from multiple scales. We demonstrated our model is not only 3.5 times more accurate than the popular sphere head model but also able to predict synchronous neural activity and extract correlated low-dimensional latent dynamics. We also showed that we can extend our methods to robustly predict held-out data across several weeks. The extracted effective connectivity agreed with the neuroanatomical hierarchy of motor control. Accordingly, the NeuroBondGraph Network opens the door to revealing comprehensive understanding of the brain computation, where network mechanisms of multi-scale communications are critical.

Neural Activity

10.1101/2020.11.30.404244

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Citations (4)

WAND: A 128-channel, closed-loop, wireless artifact-free neuromodulation device

arXiv (Cornell University) (2017)

Andy Zhou Samantha R. Santacruz Benjamin C. Johnson George Alexandrov Ali Moin

Closed-loop neuromodulation systems aim to treat a variety of neurological conditions by dynamically delivering and adjusting therapeutic electrical stimulation in response to a patient's neural state, recorded in real-time. Existing systems are limited by low channel counts, lack of algorithmic flexibility, and distortion of recorded signals from large, persistent stimulation artifacts. Here, we describe a device that enables new research applications requiring high-throughput data streaming, low-latency biosignal processing, and truly simultaneous sensing and stimulation. The Wireless Artifact-free Neuromodulation Device (WAND) is a miniaturized, wireless neural interface capable of recording and stimulating on 128 channels with on-board processing to fully cancel stimulation artifacts, detect neural biomarkers, and automatically adjust stimulation parameters in a closed-loop fashion. It combines custom application specific integrated circuits (ASICs), an on-board FPGA, and a low-power bidirectional radio. We validate wireless, long-term recordings of local field potentials (LFP) and real-time cancellation of stimulation artifacts in a behaving nonhuman primate (NHP). We use WAND to demonstrate a closed-loop stimulation paradigm to disrupt movement preparatory activity during a delayed-reach task in a NHP in vivo. This wireless device, leveraging custom ASICs for both neural recording and electrical stimulation modalities, makes possible a neural interface platform technology to significantly advance both neuroscientific discovery and preclinical investigations of stimulation-based therapeutic interventions.

Neuromodulation

Biosignal

Neuroprosthetics

Artifact (error)

Local field potential

Interface (matter)

10.48550/arxiv.1708.00556

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Citations (11)

Short-training Algorithm for Online Brain-machine Interfaces Using One-photon Microendoscopic Calcium Imaging

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (2021)

Hung‐Yun Lu Anil Bollimunta Ryan W. Eaton John H. Morrison Karen A. Moxon

Calcium imaging has great potential to be applied to online brain-machine interfaces (BMIs). As opposed to two-photon imaging settings, a one-photon microendoscopic imaging device can be chronically implanted and is subject to little motion artifacts. Traditionally, one-photon microendoscopic calcium imaging data are processed using the constrained nonnegative matrix factorization (CNMFe) algorithm, but this batched processing algorithm cannot be applied in real-time. An online analysis of calcium imaging data algorithm (or OnACIDe) has been proposed, but OnACIDe updates the neural components by repeatedly performing neuron identification frame-by-frame, which may decelerate the update speed if applying to online BMIs. For BMI applications, the ability to track a stable population of neurons in real-time has a higher priority over accurately identifying all the neurons in the field of view. By leveraging the fact that 1) microendoscopic recordings are rather stable with little motion artifacts and 2) the number of neurons identified in a short training period is sufficient for potential online BMI tasks such as cursor movements, we proposed the short-training CNMFe algorithm (stCNMFe) that skips motion correction and neuron identification processes to enable a more efficient BMI training program in a one-photon microendoscopic setting.

Calcium imaging

Frame rate

Identification

10.1109/embc46164.2021.9629838

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Citations (0)

Hydrogen-bonded organic framework nanotransducers enabled sono-optogenetics for Parkinsonian rats

bioRxiv (Cold Spring Harbor Laboratory) (2025)

Wenliang Wang Ilya Pyatnitskiy Yanshu Shi Kai Wing Kevin Tang Yi Xie

Cell-type-specific activation of parvalbumin (PV)-expressing neurons in the external globus pallidus (GPe) through optogenetics has shown promise in facilitating long-lasting movement dysfunction recovery in mice with Parkinson's disease. However, its translational potential is hindered by adverse effects stemming from the invasive implantation of optical fibers into the brain. In this study, we have developed a non-invasive optogenetics approach, utilizing focused ultrasound-triggered mechanoluminescent nanotransducers to enable remote photon delivery deep in the brain for genetically targeted neuromodulation. These mechanoluminescent nanotransducers consist of sonosensitized hydrogen-bonded frameworks and chemiluminescent L012, serving as a nanoscale light source through ultrasound-induced cascade reactions. This system offers high ultrasound-triggered brightness and long-lasting light emission, facilitating repeatable deep brain stimulation. Our sono-optogenetics technology demonstrated effective modulation in the mouse motor cortex for limb motion control and activation of PV-GPe neurons for rescuing movement dysfunction over time in dopamine-depleted Parkinson's disease rats. This approach demonstrates the pathway for achieving genetically targeted and non-invasive neuromodulation for long-lasting treatment of Parkinson's disease, towards non-human primate models and clinical applications.

Neuromodulation

Channelrhodopsin

10.1101/2025.01.03.631271

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Citations (0)

Recent advances in neural dust: towards a neural interface platform

Current Opinion in Neurobiology (2018)

Ryan Neely David K. Piech Samantha R. Santacruz Michel M. Maharbiz Jose M. Carmena

Interfacing

Interface (matter)

Neural Prosthesis

10.1016/j.conb.2017.12.010

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Citations (103)

How Your Sense of Touch Can Change Your Brain

Frontiers for Young Minds (2022)

Hannah M. Stealey Zhao Yi Yin-Jui Chang Samantha R. Santacruz

Your brain can be divided into various areas, one of which is responsible for your sense of touch. This part of your brain can be divided into even smaller areas that communicate with each body part. We can use a special map of the human body, called a sensory homunculus, to help us understand the various sizes of these parts of the brain. We will explain how this map was created and tell you about research showing how these brain areas can change. One study showed that brain areas can be recycled, meaning that the brain areas that no longer receive messages from the body can be used by other functioning brain areas. Another study showed that these changes can even occur within a single day! These studies can help scientists to better understand the brain and to help people who have problems with the sense of touch.

Sense of Place

Motor area

Human brain

10.3389/frym.2022.772919

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Citations (0)

Head-mounted microendoscopic calcium imaging in dorsal premotor cortex of behaving rhesus macaque

Cell Reports (2021)

Anil Bollimunta Samantha R. Santacruz Ryan W. Eaton Pei Sabrina Xu John H. Morrison

Microendoscopic calcium imaging with one-photon miniature microscopes enables unprecedented readout of neural circuit dynamics during active behavior in rodents. In this study, we describe successful application of this technology in the rhesus macaque, demonstrating plug-and-play, head-mounted recordings of cellular-resolution calcium dynamics from large populations of neurons simultaneously in bilateral dorsal premotor cortices during performance of a naturalistic motor reach task. Imaging is stable over several months, allowing us to longitudinally track individual neurons and monitor their relationship to motor behavior over time. We observe neuronal calcium dynamics selective for reach direction, which we could use to decode the animal's trial-by-trial motor behavior. This work establishes head-mounted microendoscopic calcium imaging in macaques as a powerful approach for studying the neural circuit mechanisms underlying complex and clinically relevant behaviors, and it promises to greatly advance our understanding of human brain function, as well as its dysfunction in neurological disease.

Premotor cortex

Rhesus macaque

Calcium imaging

10.1016/j.celrep.2021.109239

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Citations (46)

Inferring system-level brain communication through multi-scale neural activity

bioRxiv (Cold Spring Harbor Laboratory) (2021)

Yin-Jui Chang Yuan‐I Chen Hsin‐Chih Yeh Jose M. Carmena Samantha R. Santacruz

Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas the population brain activity has been studied in the micro- to meso-scale in building the connections between the dynamical patterns and the behaviors, such studies were often done at a single length scale and lacked an explanatory theory that identifies the neuronal origin across multiple scales. Here we introduce the NeuroBondGraph Network, a dynamical system incorporating both biological-inspired components and deep learning techniques to capture cross-scale dynamics that can infer and map the neural data from multiple scales. We demonstrated our model is not only 3.5 times more accurate than the popular sphere head model but also able to predict synchronous neural activity and extract correlated low-dimensional latent dynamics. We also showed that we can extend our methods to robustly predict held-out data across several weeks. The extracted effective connectivity agreed with the neuroanatomical hierarchy of motor control. Accordingly, the NeuroBondGraph Network opens the door to revealing comprehensive understanding of the brain computation, where network mechanisms of multi-scale communications are critical.

Source

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Citations (1)

Identification of Recurrent Dynamics in Distributed Neural Populations

bioRxiv (Cold Spring Harbor Laboratory) (2024)

Rodrigo Osuna-Orozco Edward Castillo Kameron Decker Harris Samantha R. Santacruz

Abstract Large-scale recordings of neural activity over broad anatomical areas with high spatial and temporal resolution are increasingly common in modern experimental neuroscience. Recently, recurrent switching dynamical systems have been used to tackle the scale and complexity of these data. However, an important challenge remains in providing insights into the existence and structure of recurrent linear dynamics in neural time series data. Here we test a scalable approach to time-varying autoregression with low-rank tensors to recover the recurrent dynamics in stochastic neural mass models with multiple stable attractors. We demonstrate that the sparse representation of time-varying system matrices in terms of temporal modes can recover the attractor structure of simple systems via clustering. We then consider simulations based on a human brain connectivity matrix in high and low global connection strength regimes, and reveal the hierarchical clustering structure of the dynamics. Finally, we explain the impact of the forecast time delay on the estimation of the underlying rank and temporal variability of the time series dynamics. This study illustrates that prediction error minimization is not sufficient to recover meaningful dynamic structure and that it is crucial to account for the three key timescales arising from dynamics, noise processes, and attractor switching.

Identification

Dynamics

10.1101/2024.05.27.596062

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Citations (0)

Multi-scale neural decoding and analysis

Journal of Neural Engineering (2021)

Hung‐Yun Lu Elizabeth S. Lorenc Hanlin Zhu Justin Kilmarx James Sulzer

Abstract Objective . Complex spatiotemporal neural activity encodes rich information related to behavior and cognition. Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code. Multi-modal techniques can overcome tradeoffs in the spatial and temporal resolution of a single modality to reveal deeper and more comprehensive understanding of system-level neural mechanisms. Uncovering multi-scale dynamics is essential for a mechanistic understanding of brain function and for harnessing neuroscientific insights to develop more effective clinical treatment. Approach . We discuss conventional methodologies used for characterizing neural activity at different scales and review contemporary examples of how these approaches have been combined. Then we present our case for integrating activity across multiple scales to benefit from the combined strengths of each approach and elucidate a more holistic understanding of neural processes. Main results . We examine various combinations of neural activity at different scales and analytical techniques that can be used to integrate or illuminate information across scales, as well the technologies that enable such exciting studies. We conclude with challenges facing future multi-scale studies, and a discussion of the power and potential of these approaches. Significance . This roadmap will lead the readers toward a broad range of multi-scale neural decoding techniques and their benefits over single-modality analyses. This Review article highlights the importance of multi-scale analyses for systematically interrogating complex spatiotemporal mechanisms underlying cognition and behavior.

Modalities

Modality (human–computer interaction)

Neural decoding

Temporal scales

10.1088/1741-2552/ac160f

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Citations (33)