Biology subjects

Santacruz, S. R.

Publications and source records attributed to Santacruz, S. R..

11 recordsLinked to original sources

bioRxiv2025

Distinct roles of neuronal phenotypes during neurofeedback adaptation

Learning adaptation allows the brain to refine motor patterns in response to changing environments rapidly. While population-level neural dynamics and single-neuron activity in motor learning have been widely studied, the contributions of individual neuron types remain poorly understood. Here, we employed a brain-machine interface (BMI) task with perturbations of varying difficulty to investigate single-neuron dynamics underlying neurofeedback adaptation in two rhesus macaques. Cortical neurons were classified based on waveform shape into narrow waveform (NW) and broad waveform (BW) categories, representing putative inhibitory interneurons and excitatory pyramidal neurons, respectively. Compared to BW neurons, NW neurons were more active and more strongly involved in the learning process. Moreover, task difficulty modulated neural responsiveness and coordination within both neuron groups, highlighting differential neuron engagement during motor learning. Our findings provide novel insights into single-neuron mechanisms underlying neurofeedback adaptation and emphasize the distinct functional roles of neuronal phenotypes in rapid learning processes. Author SummaryUnderstanding how the brain adapts to changes is crucial for improving treatments for neurological disorders and enhancing brain-machine interface (BMI) technology, which allows control of external devices using neural signals. In our study, we investigated how different types of brain cells contribute to the adaptation process when individuals encounter unexpected challenges during neurofeedback tasks. We discovered that one type of neuron was notably more active and played a more engaged role in rapidly adjusting neural activity compared to another type. These neurons demonstrated stronger coordination in their activity and showed greater responsiveness as the task difficulty increased. Our findings highlight the distinct roles of specific neurons in quickly adapting to neurofeedback tasks, offering insights that could enhance therapies for movement disorders and improve the precision and reliability of brain-controlled prosthetics.

neuroscience↗

bioRxiv2025

Population-level constraints on single neuron tuning changes during behavioral adaptation

In neurofeedback training, select neurons - of the billions that comprise the brain - are able to adapt their activity to produce a desired behavior, even though the feedback signal is low-dimensional. However, the extent that individual neurons are able to alter their activity within the context of the neurofeedback population is unclear. Using a brain-computer interface in monkeys (n=2; Macaca mulatta), we tested how the tuning of individual neurons changed in response to various feedback perturbations. Overall, we found that neurons that co-varied their activity the most with the population and those that contributed most to the behavior prior to the perturbation were most resistant to change- regardless of perturbation type. The degree to which individual neurons counteracted the applied perturbation explained daily differences in how well neurons collectively produced adapted behavior. Our work provides important insight into how population-level constraints impact the ability of individual neurons to adapt and support behavioral flexibility.

bioengineering↗

bioRxiv2025

Long-term neuron tracking reveals balance of stability and plasticity in functional properties

Neural stability is essential for executing learned motor behaviors while plasticity provides the flexibility needed to adapt to new tasks and environments. Although low-dimensional neural population dynamics exhibit long-term stability, the extent to which individual neurons retain their functional properties over time and balance the need for both stability and plasticity remains an open question. Tracking individual neurons across multiple recording sessions is crucial to addressing this question, yet conventional methods face challenges such as electrode drift, waveform variability, and large inter-electrode distances that limit the number of channels a neuron is observed on. Here, we introduce a waveform-based neuron tracking method optimized for standard microelectrode arrays, enabling the identification of the same neurons across sessions without relying on spatial overlap, a strategy commonly leveraged with high-density electrode arrays. We apply this method to assess the longitudinal stability of multiple neural properties, including firing rates, inter-spike intervals, tuning properties, and spike-field interactions. Our findings reveal that while spike waveform properties remain stable, certain functional properties such as ISI and tuning can exhibit gradual shifts, suggesting a balance between neural stability and plasticity. Understanding the persistence of individual neural signals provides insight into learning and adaptation while advancing the study of neural stability and plasticity over extended timescales. Beyond basic neuroscience, this framework has potential to enhance the long-term reliability of brain-machine interfaces and closed-loop deep brain stimulation systems that rely on chronic neural sensing.

neuroscience↗

bioRxiv2024

Volitional Regulation and Transferable Patterns of Midbrain Oscillations

Dopaminergic brain areas are crucial for cognition and their dysregulation is linked to neuropsychiatric disorders typically treated with pharmacological interventions. These treatments often have side effects and variable effectiveness, underscoring the need for alternatives. We introduce the first demonstration of neurofeedback using local field potentials (LFP) from the ventral tegmental area (VTA). This approach leverages the real-time temporal resolution of LFP and ability to target deep brain. In our study, non-human primates learned to regulate VTA beta power using a customized normalized metric to stably quantify VTA LFP signal modulation. The subjects demonstrated flexible and specific control with different strategies for specific frequency bands, revealing new insights into the plasticity of VTA neurons contributing to oscillatory activity that is functionally relevant to many aspects of cognition. Excitingly, the subjects showed transferable patterns, a key criterion for clinical applications beyond training settings. This work provides a foundation for neurofeedback-based treatments, which may be a promising alternative to conventional approaches and open new avenues for understanding and managing neuropsychiatric disorders. Significance statementThis study demonstrates, for the first time, that neurofeedback using local field potentials (LFP) from the ventral tegmental area (VTA) is feasible in non-human primates. By leveraging the temporal resolution and ability to target deep brain regions, this approach provides a novel way to modulate brain activity linked to dopamine-related functions. The findings reveal that subjects can flexibly control VTA LFP signals and transfer learned strategies to new settings, offering potential for developing neurofeedback-based treatments. This research opens new avenues for managing neuropsychiatric disorders, presenting an alternative to traditional pharmacological interventions that often have side effects and limited effectiveness. The study highlights the plasticity of VTA neurons and their relevance to cognition and mood regulation.

neuroscience↗

bioRxiv2024

Neural population variance explains adaptation differences during learning

Variability, a ubiquitous feature of neural activity, plays an integral role in behavior. However, establishing a causal relationship between neural signals and behavior is difficult. By defining a mathematical mapping between neural spiking activity and behavior, we investigate the role of spiking variability in adaptation during a brain-computer interface (BCI) behavior in male rhesus macaques (Macaca mulatta, n=2). Recent BCI evidence demonstrates that creating novel neural patterns is harder than repurposing existing patterns to respond to changes in external input. However, what limits the ability to repurpose, or adapt, patterns under different magnitudes of change is less well-characterized. Here, we present evidence that variance in neural spiking activity reveals differences in learnability between easy and hard adaptation conditions and across sessions. Furthermore, our study illuminates the limitations in neural changes underlying behavior within a neurofeedback paradigm. Significance StatementVariability in neural activity is a major driver of behavioral variability, though it is unclear how variability is balanced with stable neural activity as new behaviors become more practiced. By using a brain-computer interface methodology, we define a mathematical mapping between neural spiking activity and a behavioral control signal. Through thoughtful manipulation of this mapping, we incite the subjects (rhesus macaques) to learn and adapt neural activity to regain behavioral proficiency. We find that metrics of neural population variability are differentially modulated depending on difficulty of the imposed manipulation. Our exciting results provide important implications for brain-computer interface applications as well as our understanding of learning and adaptation more broadly. Our work represents an important step forwards towards understanding population neural dynamics in this critical component of behavior.

bioengineering↗

bioRxiv2024

Identification of Recurrent Dynamics in Distributed Neural Populations

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.

neuroscience↗

bioRxiv2023

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termed multiscale neural dynamics neural ordinary differential equation (msDyNODE), to describe multiscale brain communications governing cognition and behavior. We demonstrate that msDyNODE successfully captures multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived causal interactions between recording locations and scales not only aligned well with the abstraction of the hierarchical neuroanatomy of the mammalian central nervous system but also exhibited behavioral dependences. This work offers a new approach for mechanistic multiscale studies of neural processes. Author SummaryMulti-modal measurements have become an emerging trend in recent years due to the capability of studying brain dynamics at disparate scales. However, an integrative framework to systematically capture the multi-scale nonlinear dynamics in brain networks is lacking. A major challenge for creating a cohesive model is a mismatch in the timescale and subsequent sampling rate of the dynamics for disparate modalities. In this work, we introduce a deep learning-based approach to characterize brain communications between regions and scales. By modeling the continuous dynamics of hidden states using the neural network-based ordinary differential equations, the requirement of downsampling the faster sampling signals is discarded, thus preventing from losing dynamics information. Another advantageous feature of the proposed method is flexibility. An adaptable framework to bridge the gap between scales is necessary. Depending on the neural recording modalities utilized in the experiment, any suitable pair of well-established models can be plugged into the proposed multi-scale modeling framework. Thus, this method can provide insight into the brain computations of multi-scale brain activity.

neuroscience↗

bioRxiv2020

Bi-directional Modeling Between Cross-Scale Neural Activity

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.

neuroscience↗

bioRxiv2020

Deep learning enables rapid and robust analysis of fluorescence lifetime imaging in photon-starved conditions

Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study the molecular states in the complex cellular environment as the lifetime readings are not biased by the fluorophore concentration or the excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE (fluorescence lifetime imaging based on Generative Adversarial Network Estimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is not only 258 times faster than the most popular time-domain least-square estimation (TD_LSE) method but also provide more accurate analysis in barcode identification, cellular structure visualization, Forster resonance energy transfer characterization, and metabolic state analysis. With its advantages in speed and reliability, flimGANE is particularly useful in fundamental biological research and clinical applications, where ultrafast analysis is critical.

cell biology↗

bioRxiv2020

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

A major effort is now underway across the brain sciences to identify, characterize and manipulate mesoscale neural circuits in order to elucidate the mechanisms underlying sensory perception, cognition and behavior. Optical imaging technologies, in conjunction with genetically encoded sensors and actuators, serve as important tools toward these goals, allowing access to large-scale genetically defined neuronal populations. In particular, one-photon miniature microscopes, coupled with genetically encoded calcium indicators and microendoscopic gradient-refractive index (GRIN) lenses, enable unprecedented readout of neural circuit dynamics in cortical and deep subcortical brain regions during active behavior in rodents. This has already led to breakthrough discoveries across a wide array of rodent brain regions and behaviors. However, in order to study the neural circuit mechanisms underlying more complex and clinically relevant human behaviors and cognitive functions, it is crucial to translate this technology to non-human primates. Here, we describe the first successful application of this technology in the rhesus macaque. We identified a viral strategy for robust expression of GCaMP, optimized a surgical protocol for microendoscope GRIN lens insertion, and created a chronic cranial chamber and lens mounting system for imaging in gyral cortex. Using these methods, we demonstrate the ability to perform plug-and-play, head-mounted recordings of cellular-resolution calcium dynamics from over 100 genetically-targeted neurons simultaneously in dorsal premotor cortex while the macaque performs a naturalistic motor reach task with the head unrestrained and freely moving. The recorded population of neurons exhibited calcium dynamics selective to the direction of reach, which we show can be used to decode the animals trial-by-trial motor behavior. Recordings were stable over several months, allowing us to longitudinally track large populations of individual neurons and monitor their relationship to motor behavior over time. Finally, we demonstrate the ability to conduct simultaneous, multi-site imaging in bilateral dorsal premotor cortices, offering an opportunity to study distributed networks underlying complex behavior and cognition. Together, this work establishes head-mounted microendoscopic calcium imaging in macaque as a powerful new approach for studying the neural circuit mechanisms underlying complex and clinically relevant behaviors, and promises to greatly advance our understanding of human brain function, as well as its dysfunction in neurological disease. HighlightsFirst demonstration of head-mounted microendoscopic calcium imaging in behaving macaque. Surgical protocols developed for preparing the animal for calcium imaging, including virus injections to express GCaMP and chronic implantation of a GRIN lens to enable optical access to gyral cortex. Proof of concept plug-and-play calcium imaging in behaving macaques with months long stable recording capability allowing populations of individual neurons to be tracked longitudinally. Bilateral calcium imaging from dorsal premotor cortex exhibited dynamics selective to the animals direction of reach and allowed decoding of the animals motor behavior

neuroscience↗

bioRxiv2018

A high-density carbon fiber neural recording array technology

Abstract.Objective: Microwire and Utah-style neural recording arrays are the predominant devices used for cortical neural recording, but the implanted electrodes cause a significant adverse biological response and suffer from well-studied performance degradation. Recent work has demonstrated that carbon fiber electrodes do not elicit this same adverse response, but these existing designs are not practically scalable to hundreds or thousands of recording sites. We present technology that overcomes these issues while additionally providing fine electrode pitch for spatial oversampling.\n\nApproach: We present a 32-channel carbon fiber monofilament-based intracortical neural recording array fabricated through a combination of bulk silicon microfabrication processing and microassembly. This device represents the first truly two-dimensional carbon fiber neural recording array. The density, channel count, and size scale of this array are enabled by an out-of-plane microassembly technique in which individual fibers are inserted through metallized and isotropically conductive adhesive-filled holes in an oxide-passivated microfabricated silicon substrate.\n\nMain results: Five-micron diameter fibers are spaced at a pitch of 38 microns, four times denser than state of the art one-dimensional arrays. The fine diameter of the carbon fibers affords both minimal cross-section and nearly three orders of magnitude greater lateral compliance than standard tungsten microwires. Typical 1 kHz impedances are on the order of hundreds of kiloohms, and successful in vivo recording is demonstrated in the motor cortex of a rat. 22 total units are recorded on 20 channels, with unit SNR ranging from 0.85 to 4.2.\n\nSignificance: This is the highest density microwire-style electrode array to date, and this fabrication technique is scalable to a larger number of electrodes and allows for the potential future integration of microelectronics. Large-scale carbon fiber neural recording arrays are a promising technology for reducing the inflammatory response and increasing the information density, particularly in neural recording applications where microwire arrays are already used.

bioengineering↗