Dimensionality reduction of calcium-imaged neuronal population activity.

Calcium imaging has been widely adopted for its ability to record from large neuronal populations. To summarize the time course of neural activity, dimensionality reduction methods, which have been applied extensively to population spiking activity, may be particularly useful. However, it is unclear if the dimensionality reduction methods applied to spiking activity are appropriate for calcium imaging. We thus carried out a systematic study of design choices based on standard dimensionality reduction methods. We also developed a method to perform deconvolution and dimensionality reduction simultaneously (Calcium Imaging Linear Dynamical System, CILDS). CILDS most accurately recovered the single-trial, low-dimensional time courses from simulated calcium imaging data. CILDS also outperformed the other methods on calcium imaging recordings from larval zebrafish and mice. More broadly, this study represents a foundation for summarizing calcium imaging recordings of large neuronal populations using dimensionality reduction in diverse experimental settings.

A neural basis of choking under pressure.

Incentives tend to drive improvements in performance. But when incentives get too high, we can "choke under pressure" and underperform when it matters most. What neural processes might lead to choking under pressure? We studied Rhesus monkeys performing a challenging reaching task in which they underperform when an unusually large "jackpot" reward is at stake. We observed a collapse in neural information about upcoming movements for jackpot rewards: in the motor cortex, neural planning signals became less distinguishable for different reach directions when a jackpot reward was made available. We conclude that neural signals of reward and motor planning interact in the motor cortex in a manner that can explain why we choke under pressure. One-Sentence Summary: In response to exceptionally large reward cues, animals can "choke under pressure", and this corresponds to a collapse in the neural information about upcoming movements.

Theoretical principles for illuminating sensorimotor processing with brain-wide neuronal recordings.

Modern recording techniques now permit brain-wide sensorimotor circuits to be observed at single neuron resolution in small animals. Extracting theoretical understanding from these recordings requires principles that organize findings and guide future experiments. Here we review theoretical principles that shed light onto brain-wide sensorimotor processing. We begin with an analogy that conceptualizes principles as streetlamps that illuminate the empirical terrain, and we illustrate the analogy by showing how two familiar principles apply in new ways to brain-wide phenomena. We then focus the bulk of the review on describing three more principles that have wide utility for mapping brain-wide neural activity, making testable predictions from highly parameterized mechanistic models, and investigating the computational determinants of neuronal response patterns across the brain.

Stabilization of a brain-computer interface via the alignment of low-dimensional spaces of neural activity.

The instability of neural recordings can render clinical brain-computer interfaces (BCIs) uncontrollable. Here, we show that the alignment of low-dimensional neural manifolds (low-dimensional spaces that describe specific correlation patterns between neurons) can be used to stabilize neural activity, thereby maintaining BCI performance in the presence of recording instabilities. We evaluated the stabilizer with non-human primates during online cursor control via intracortical BCIs in the presence of severe and abrupt recording instabilities. The stabilized BCIs recovered proficient control under different instability conditions and across multiple days. The stabilizer does not require knowledge of user intent and can outperform supervised recalibration. It stabilized BCIs even when neural activity contained little information about the direction of cursor movement. The stabilizer may be applicable to other neural interfaces and may improve the clinical viability of BCIs.

mTor signaling is required for the formation of proliferating Müller glia-derived progenitor cells in the chick retina.

We investigate the roles of mTor signaling in the formation of Müller glia-derived progenitor cells (MGPCs) in the chick retina. During embryonic development, pS6 (a readout of active mTor signaling) is present in early-stage retinal progenitors, differentiating amacrine and ganglion cells, and late-stage progenitors or maturing Müller glia. By contrast, pS6 is present at low levels in a few scattered cell types in mature, healthy retina. Following retinal damage, in which MGPCs are known to form, mTor signaling is rapidly activated in Müller glia. Inhibition of mTor in damaged retinas prevented the accumulation of pS6 in Müller glia and reduced numbers of proliferating MGPCs. Inhibition of mTor had no effect on MAPK signaling or on upregulation of the stem cell factor Klf4, whereas Pax6 upregulation was significantly reduced. Inhibition of mTor potently blocked the MGPC-promoting effects of Hedgehog, Wnt and glucocorticoid signaling in damaged retinas. In the absence of retinal damage, insulin, IGF1 and FGF2 induced pS6 in Müller glia, and this was blocked by mTor inhibitor. In FGF2-treated retinas, in which MGPCs are known to form, inhibition of mTor blocked the accumulation of pS6, the upregulation of Pax6 and the formation of proliferating MGPCs. We conclude that mTor signaling is required, but not sufficient, to stimulate Müller glia to give rise to proliferating progenitors, and the network of signaling pathways that drive the formation of MGPCs requires activation of mTor.

Self-recalibrating classifiers for intracortical brain-computer interfaces.

OBJECTIVE: Intracortical brain-computer interface (BCI) decoders are typically retrained daily to maintain stable performance. Self-recalibrating decoders aim to remove the burden this may present in the clinic by training themselves autonomously during normal use but have only been developed for continuous control. Here we address the problem for discrete decoding (classifiers). APPROACH: We recorded threshold crossings from 96-electrode arrays implanted in the motor cortex of two rhesus macaques performing center-out reaches in 7 directions over 41 and 36 separate days spanning 48 and 58 days in total for offline analysis. MAIN RESULTS: We show that for the purposes of developing a self-recalibrating classifier, tuning parameters can be considered as fixed within days and that parameters on the same electrode move up and down together between days. Further, drift is constrained across time, which is reflected in the performance of a standard classifier which does not progressively worsen if it is not retrained daily, though overall performance is reduced by more than 10% compared to a daily retrained classifier. Two novel self-recalibrating classifiers produce a ~15% increase in classification accuracy over that achieved by the non-retrained classifier to nearly recover the performance of the daily retrained classifier. SIGNIFICANCE: We believe that the development of classifiers that require no daily retraining will accelerate the clinical translation of BCI systems. Future work should test these results in a closed-loop setting.

(18)F-FDG PET/CT imaging of metastatic atypical fibroxanthoma.

We report the F-FDG PET/CT appearance of a metastatic biopsy-proven malignant fibroxanthoma of the ankle. A 41-year-old female patient with a history of scleroderma presented with a fungating mass in the left ankle. Shave biopsy of the overlying skin showed atypical fibroxanthoma (AFX). Staging FDG PET/CT demonstrated a hypermetabolic exophytic soft tissue mass in the left ankle with local extension to bone and widespread metastatic disease including pulmonary parenchyma, nodes, bone marrow, and skeletal muscle. While rare, knowledge of the potential aggressive nature of AFX is important for accurate diagnosis.

Real-time decision fusion for multimodal neural prosthetic devices.

BACKGROUND: The field of neural prosthetics aims to develop prosthetic limbs with a brain-computer interface (BCI) through which neural activity is decoded into movements. A natural extension of current research is the incorporation of neural activity from multiple modalities to more accurately estimate the user's intent. The challenge remains how to appropriately combine this information in real-time for a neural prosthetic device. METHODOLOGY/PRINCIPAL FINDINGS: Here we propose a framework based on decision fusion, i.e., fusing predictions from several single-modality decoders to produce a more accurate device state estimate. We examine two algorithms for continuous variable decision fusion: the Kalman filter and artificial neural networks (ANNs). Using simulated cortical neural spike signals, we implemented several successful individual neural decoding algorithms, and tested the capabilities of each fusion method in the context of decoding 2-dimensional endpoint trajectories of a neural prosthetic arm. Extensively testing these methods on random trajectories, we find that on average both the Kalman filter and ANNs successfully fuse the individual decoder estimates to produce more accurate predictions. CONCLUSIONS: Our results reveal that a fusion-based approach has the potential to improve prediction accuracy over individual decoders of varying quality, and we hope that this work will encourage multimodal neural prosthetics experiments in the future.

Multi-scale recordings for neuroprosthetic control of finger movements.

We trained a rhesus monkey to perform individuated and combined finger flexions and extensions of the thumb, index, and middle finger. A Utah Electrode Array (UEA) was implanted into the hand region of the motor cortex contralateral to the monkey's trained hand. We also implanted a microwire electrocorticography grid (microECoG) epidurally so that it covered the UEA. The microECoG grid spanned the arm and hand regions of both the primary motor and somatosensory cortices. Previously this monkey had Implantable MyoElectric Sensors (IMES) surgically implanted into the finger muscles of the monkey's forearm. Action potentials (APs), local field potentials (LFPs), and microECoG signals were recorded from wired head-stage connectors for the UEA and microECoG grids, while EMG was recorded wirelessly. The monkey performed a finger flexion/extension task while neural and EMG data were acquired. We wrote an algorithm that uses the spike data from the UEA to perform a real-time decode of the monkey's finger movements. Also, analyses of the LFP and microECoG data indicate that these data show trial-averaged differences between different finger movements, indicating the data are potentially decodeable.

A real-time virtual integration environment for the design and development of neural prosthetic systems.

We have developed a virtual integration environment (VIE) for the development of neural prosthetic systems. The VIE is a software environment that modularizes the core functions of a neural prosthetic system--receiving signals, decoding signals and controlling a real or simulated device. Complete prosthetic systems can be quickly assembled by linking pre-existing modules together through standard interfaces. Systems can be simulated in real-time, and simulated components can be swapped out for real hardware. This paper is the first of two companion papers that describe the VIE and its use. In this paper, we first describe the architecture of the VIE and review implemented modules. We then describe the use of the VIE for the real-time validation of neural decode algorithms from pre-recorded data, the use of the VIE in closed loop primate experiments and the use of the VIE in the clinic.

The use of a virtual integration environment for the real-time implementation of neural decode algorithms.

We have developed a virtual integration environment (VIE) for the development of neural prosthetic systems. This paper, the second of two companion articles, describes the use of the VIE as a common platform for the implementation of neural decode algorithms. In this paper, a linear filter decode and a recursive Bayesian algorithm are implemented as separate signal analysis modules of the VIE for the real-time decode of end effector trajectory. The process of implementing each algorithm is described and the real-time behavior as well as computational cost for each algorithm is examined. This is the first report of the real-time implementation of the Mixture of Trajectory Models decode [10]. These real-time algorithms can be easily interfaced with pre-existing modules of the VIE to control simulated and real devices.

The genomic revolution: what does it mean for human and ecological risk assessment?

The first sequencing of a complete organism genome occurred in 1995. Since then there has been an explosion of information, with a new organism being sequenced nearly every week. This rapid development of genomics is providing unparalleled opportunities in toxicology, ecology, and risk assessment. This paper provides an overview of some possible applications of this new information in ecological and human risk assessment.