Biology subjects

Miller, K. J.

Publications and source records attributed to Miller, K. J..

46 records · Page 3Linked to original sources

bioRxiv2021

Distributed Brain Co-Processor for Neurophysiologic Tracking and Adaptive Stimulation: Application to Drug Resistant Epilepsy

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioral inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behavior and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a hand-held device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes, and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from 9 humans and 8 canines with epilepsy, and then implemented prospectively in out-of-sample testing in 2 pet canines and 4 humans with drug resistant epilepsy living in their natural environments.

bioengineering↗

bioRxiv2021

Typical somatomotor physiology of the hand is preserved in a patient with an amputated arm

Electrophysiological signals in the human motor system may change in different ways after deafferentation, with some studies emphasizing reorganization while others propose retained physiology. Understanding whether motor electrophysiology is retained over longer periods of time can be invaluable for patients with paralysis (e.g. ALS or brainstem stroke) when signals from sensorimotor areas may be used for communication or control over neural prosthetic devices. In addition, a maintained electrophysiology can potentially benefit the treatment of phantom limb pains through prolonged use of these signals in a brain-machine interface (BCI). Here, we were presented with the unique opportunity to investigate the physiology of the sensorimotor cortex in a patient with an amputated arm using electrocorticographic (ECoG) measurements. While implanted with an ECoG grid for clinical evaluation of electrical stimulation for phantom limb pain, the patient performed attempted finger movements with the contralateral (lost) hand and executed finger movements with the ipsilateral (healthy) hand. The electrophysiology of the sensorimotor cortex contralateral to the amputated hand remained very similar to that of hand movement in healthy people, with a spatially focused increase of high-frequency band (65-175Hz; HFB) power over the hand region and a distributed decrease in low-frequency band (15-28Hz; LFB) power. The representation of the three different fingers (thumb, index and little) remained intact and HFB patterns could be decoded using support vector learning at single-trial classification accuracies of >90%, based on the first 1-3s of the HFB response. These results demonstrate that hand representations are largely retained in the motor cortex. The intact physiological response of the amputated hand, the high distinguishability of the fingers and fast temporal peak are encouraging for neural prosthetic devices that target the sensorimotor cortex.

neuroscience↗

bioRxiv2021

Basis profile curve identification to understand electrical stimulation effects in human brain networks

Brain networks can be explored by delivering brief pulses of electrical current in one area while measuring voltage responses in other areas. We propose a convergent paradigm to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites. Viewed in this manner, visually-apparent motifs in the temporal response shape emerge from adjacent stimulation sites. This work constructs and illustrates a data-driven approach to determine characteristic spatiotemporal structure in these response shapes, summarized by a set of unique "basis profile curves" (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. Our technique is demonstrated for an array of implanted brain surface electrodes in a human patient. This framework enables straightforward interpretation of single-pulse brain stimulation data, and can be applied generically to explore the diverse milieu of interactions that comprise the connectome. Author summaryWe present a new machine learning framework to probe how brain regions interact using single-pulse electrical stimulation. Unlike previous studies, this approach does not assume a form for how one brain area will respond to stimulation in another area, but rather discovers the shape of the response in time from the data. We call the set of characteristic discovered response shapes "basis profile curves" (BPCs), and show how these can be mapped back onto the brain quantitatively. An illustrative example is included from one of our human patients to characterize inputs to the parahippocampal gyrus. A code package is downloadable from https://purl.stanford.edu/rc201dv0636 so the reader may explore the technique for own data, or study sample data provided to reproduce the illustrative case presented in the manuscript.

neuroscience↗

bioRxiv2020

Functional MRI based simulations of ECoG grid configurations for optimal measurement of spatially distributed hand-gesture information

In electrocorticography (ECoG), the physical characteristics of the electrode grid determine which aspect of the neurophysiology is measured. For particular cases, the ECoG grid may be tailored to capture specific features, such as in the development and use of brain-computer-interfaces (BCI). Neural representations of hand movement are increasingly used to control ECoG based BCIs. However, it remains unclear which grid configurations are the most optimal to capture the dynamics of hand gesture information. Here, we investigate how the design and surgical placement of grids would affect the usability of ECoG measurements. High resolution 7T functional MRI was used as a proxy for neural activity in ten healthy participants to simulate various grid configurations, and evaluated the performance of each configuration for decoding hand gestures. The grid configurations varied in number of electrodes, electrode distance and electrode size. Optimal decoding of hand gestures occurred in grid configurations with a higher number of densely-packed, large-size, electrodes up to a grid of ~5x5 electrodes. When restricting the grid placement to a highly informative region of primary sensorimotor cortex, optimal parameters converged to about 3x3 electrodes, an inter-electrode distance of 8mm, and an electrode size of 3mm radius (performing at ~70% 3-class classification accuracy). Our approach might be used to identify the most informative region, find the optimal grid configuration and assist in positioning of the grid to achieve high BCI performance for the decoding of hand-gestures prior to surgical implantation.

bioengineering↗

bioRxiv2019

Broadband Signal Rather than Frequency-Specific Rhythms Underlies Prediction Error in the Primate Auditory Cortex

Detection of statistical irregularities, measured as a prediction error response, is fundamental to the perceptual monitoring of the environment. We studied whether prediction error response is associated with neural oscillations or asynchronous broadband activity. Electrocorticography (ECoG) was carried out in three male monkeys, who passively listened to the auditory roving oddball stimuli. Local field potentials (LFP) recorded over the auditory cortex underwent spectral principal component analysis, which decoupled broadband and rhythmic components of the LFP signal. We found that the broadband component captured the prediction error response, whereas none of the rhythmic components were associated with statistical irregularities of sounds. The broadband component displayed more stochastic, asymmetrical multifractal properties than the rhythmic components, which revealed more self-similar dynamics. We thus conclude that the prediction error response is captured by neuronal populations generating asynchronous broadband activity, defined by irregular dynamical states which, unlike oscillatory rhythms, appear to enable the neural representation of auditory prediction error response. Significance StatementThis study aimed to examine the contribution of oscillatory and asynchronous components of auditory local field potentials in the generation of prediction error responses to sensory irregularities, as this has not been directly addressed in the previous studies. Here, we show that mismatch negativity - an auditory prediction error response - is driven by the asynchronous broadband component of potentials recorded in the auditory cortex. This finding highlights the importance of non-oscillatory neural processes in the predictive monitoring of the environment. At a more general level, the study demonstrates that stochastic neural processes, which are often disregarded as neural noise, do have a functional role in the processing of sensory information.

neuroscience↗

bioRxiv2018

From predictive models to cognitive models: An analysis of rat behavior in the two-armed bandit task

Cognitive models are a fundamental tool in computational neuroscience, embodying in software precise hypotheses about the algorithms by which the brain gives rise to behavior. The development of such models is often a hypothesis-first process, drawing on inspiration from the literature and the creativity of the individual researcher to construct a model, and afterwards testing the model against experimental data. Here, we adopt a complementary approach, in which richly characterizing and summarizing the patterns present in a dataset reveals an appropriate cognitive model, without recourse to an a priori hypothesis. We apply this approach to a large behavioral dataset from rats performing a dynamic reward learning task. The revealed model suggests that behavior in this task can be understood as a mixture of three components with different timescales: a quick-learning reward-seeking component, a slower-learning perseverative component, and a very slow "gamblers fallacy" component.

animal behavior and cognition↗

bioRxiv2018

Value Representations in Orbitofrontal Cortex Drive Learning, but not Choice

Humans and animals make predictions about the rewards they expect to receive in different situations. In formal models of behavior, these predictions are known as value representations, and they play two very different roles. Firstly, they drive choice: the expected values of available options are compared to one another, and the best option is selected. Secondly, they support learning: expected values are compared to rewards actually received, and future expectations are updated accordingly. Whether these different functions are mediated by different neural representations remains an open question. Here we employ a recently-developed multi-step task for rats that computationally separates learning from choosing. We investigate the role of value representations in the rodent orbitofrontal cortex, a key structure for value-based cognition. Electrophysiological recordings and optogenetic perturbations indicate that these representations do not directly drive choice. Instead, they signal expected reward information to a learning process elsewhere in the brain that updates choice mechanisms.

neuroscience↗

bioRxiv2017

Sequence Transfer Learning for Neural Decoding

A fundamental challenge in designing brain-computer interfaces (BCIs) is decoding behavior from time-varying neural oscillations. in typical applications, decoders are constructed for individual subjects and with limited data leading to restrictions on the types of models that can be utilized. currently, the best performing decoders are typically linear models capable of utilizing rigid timing constraints with limited training data. Here we demonstrate the use of Long Short-Term Memory (LSTM) networks to take advantage of the temporal information present in sequential neural data collected from subjects implanted with electrocorticographic (ECoG) electrode arrays performing a finger flexion task. our constructed models are capable of achieving accuracies that are comparable to existing techniques while also being robust to variation in sample data size. Moreover, we utilize the LSTM networks and an affine transformation layer to construct a novel architecture for transfer learning. We demonstrate that in scenarios where only the affine transform is learned for a new subject, it is possible to achieve results comparable to existing state-of-the-art techniques. The notable advantage is the increased stability of the model during training on novel subjects. Relaxing the constraint of only training the affine transformation, we establish our model as capable of exceeding performance of current models across all training data sizes. Overall, this work demonstrates that LSTMS are a versatile model that can accurately capture temporal patterns in neural data and can provide a foundation for transfer learning in neural decoding.

neuroscience↗

bioRxiv2016

Dorsal hippocampus plays a causal role in model-based planning

Planning can be defined as a process of action selection that leverages an internal model of the environment. Such models provide information about the likely outcomes that will follow each selected action, and their use is a key function underlying complex adaptive behavior. However, the neural mechanisms supporting this ability remain poorly understood. In the present work, we adapt for rodents recent advances from work on human planning, presenting for the first time a task for animals which produces many trials of planned behavior per session, allowing the experimental toolkit available for use in trial-by-trial tasks for rodents to be applied to the study of planning. We take advantage of one part of this toolkit to address a perennially controversial issue in planning research: the role of the dorsal hippocampus. Although prospective representations in the hippocampus have been proposed to support model-based planning, intact planning in hippocampally damaged animals has been observed in a number of assays. Combining formal algorithmic behavioral analysis with muscimol inactivation, we provide the first causal evidence directly linking dorsal hippocampus with planning behavior. The results reported, and the methods introduced, open the door to new and more detailed investigations of the neural mechanisms of planning, in the hippocampus and throughout the brain.

neuroscience↗

bioRxiv2016

Identifying Model-Based and Model-Free Patterns in Behavior on Multi-Step Tasks

Recent years have seen a surge of research into the neuroscience of planning. Much of this work has taken advantage of a two-step sequential decision task developed by Daw et al. (2011), which gives the ability to diagnose whether or not subjects behavior is the result of planning. Here, we present simulations which suggest that the techniques most commonly used to analyze data from this task may be confounded in important ways. We introduce a new analysis technique, which suffers from fewer of these issues. This technique also presents a richer view of behavior, making it useful for characterizing patterns in behavior in a theory-neutral manner. This allows it to provide an important check on the assumptions of more theory-driven analysis such as agent-based model-fitting.

neuroscience↗