Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces.

Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method to measure instability in neural data without needing to label user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.

Optimizing Nitrogen Fertilization to Enhance Productivity and Profitability of Upland Rice Using CSM-CERES-Rice.

Nitrogen (N) deficiency can limit rice productivity, whereas the over- and underapplication of N results in agronomic and economic losses. Process-based crop models are useful tools and could assist in optimizing N management, enhancing the production efficiency and profitability of upland rice production systems. The study evaluated the ability of CSM-CERES-Rice to determine optimal N fertilization rate for different sowing dates of upland rice. Field experimental data from two growing seasons (2018-2019 and 2019-2020) were used to simulate rice responses to four N fertilization rates (N30, N60, N90 and a control-N0) applied under three different sowing windows (SD1, SD2 and SD3). Cultivar coefficients were calibrated with data from N90 under all sowing windows in both seasons and the remaining treatments were used for model validation. Following model validation, simulations were extended up to N240 to identify the sowing date's specific economic optimum N fertilization rate (EONFR). Results indicated that CSM-CERES-Rice performed well both in calibration and validation, in simulating rice performance under different N fertilization rates. The d-index and nRMSE values for grain yield (0.90 and 16%), aboveground dry matter (0.93 and 13%), harvest index (0.86 and 7%), grain N contents (0.95 and 18%), total crop N uptake (0.97 and 15%) and N use efficiencies (0.94-0.97 and 11-15%) during model validation indicated good agreement between simulated and observed data. Extended simulations indicated that upland rice yield was responsive to N fertilization up to 180 kg N ha-1 (N180), where the yield plateau was observed. Fertilization rates of 140, 170 and 130 kg N ha-1 were identified as the EONFR for SD1, SD2 and SD3, respectively, based on the computed profitability, marginal net returns and N utilization. The model results suggested that N fertilization rate should be adjusted for different sowing windows rather than recommending a uniform N rate across sowing windows. In summary, CSM-CERES-Rice can be used as a decision support tool for determining EONFR for seasonal sowing windows to maximize the productivity and profitability of upland rice production.

Prefrontal network engagement by deep brain stimulation in limbic hubs.

Prefrontal circuits in the human brain play an important role in cognitive and affective processing. Neuromodulation therapies delivered to certain key hubs within these circuits are being used with increasing frequency to treat a host of neuropsychiatric disorders. However, the detailed neurophysiological effects of stimulation to these hubs are largely unknown. Here, we performed intracranial recordings across prefrontal networks while delivering electrical stimulation to two well-established white matter hubs involved in cognitive regulation and depression: the subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VC/VS). We demonstrate a shared frontotemporal circuit consisting of the ventromedial prefrontal cortex, amygdala, and lateral orbitofrontal cortex where gamma oscillations are differentially modulated by stimulation target. Additionally, we found participant-specific responses to stimulation in the dorsal anterior cingulate cortex and demonstrate the capacity for further tuning of neural activity using current-steered stimulation. Our findings indicate a potential neurophysiological mechanism for the dissociable therapeutic effects seen across the SCC and VC/VS targets for psychiatric neuromodulation and our results lay the groundwork for personalized, network-guided neurostimulation therapy.

Periodic Artifact Removal With Applications to Deep Brain Stimulation.

Deep brain stimulation (DBS) therapies have shown clinical success in the treatment of a number of neurological illnesses, including obsessive-compulsive disorder, epilepsy, and Parkinson's disease. An emerging strategy for increasing the efficacy of DBS therapies is to develop closed-loop, adaptive DBS systems that can sense biomarkers associated with particular symptoms and in response, adjust DBS parameters in real-time. The development of such systems requires extensive analysis of the underlying neural signals while DBS is on, so that candidate biomarkers can be identified and the effects of varying the DBS parameters can be better understood. However, DBS creates high amplitude, high frequency stimulation artifacts that prevent the underlying neural signals and thus the biological mechanisms underlying DBS from being analyzed. Additionally, DBS devices often require low sampling rates, which alias the artifact frequency, and rely on wireless data transmission methods that can create signal recordings with missing data of unknown length. Thus, traditional artifact removal methods cannot be applied to this setting. We present a novel periodic artifact removal algorithm for DBS applications that can accurately remove stimulation artifacts in the presence of missing data and in some cases where the stimulation frequency exceeds the Nyquist frequency. The numerical examples suggest that, if implemented on dedicated hardware, this algorithm has the potential to be used in embedded closed-loop DBS therapies to remove DBS stimulation artifacts and hence, to aid in the discovery of candidate biomarkers in real-time. Code for our proposed algorithm is publicly available on Github.

PELP: Accounting for Missing Data in Neural Time Series by Periodic Estimation of Lost Packets.

Recent advances in wireless data transmission technology have the potential to revolutionize clinical neuroscience. Today sensing-capable electrical stimulators, known as "bidirectional devices", are used to acquire chronic brain activity from humans in natural environments. However, with wireless transmission come potential failures in data transmission, and not all available devices correctly account for missing data or provide precise timing for when data losses occur. Our inability to precisely reconstruct time-domain neural signals makes it difficult to apply subsequent neural signal processing techniques and analyses. Here, our goal was to accurately reconstruct time-domain neural signals impacted by data loss during wireless transmission. Towards this end, we developed a method termed Periodic Estimation of Lost Packets (PELP). PELP leverages the highly periodic nature of stimulation artifacts to precisely determine when data losses occur. Using simulated stimulation waveforms added to human EEG data, we show that PELP is robust to a range of stimulation waveforms and noise characteristics. Then, we applied PELP to local field potential (LFP) recordings collected using an implantable, bidirectional DBS platform operating at various telemetry bandwidths. By effectively accounting for the timing of missing data, PELP enables the analysis of neural time series data collected via wireless transmission-a prerequisite for better understanding the brain-behavior relationships underlying neurological and psychiatric disorders.

Evaluating the Impact of Nitrogen Application on Growth and Productivity of Maize Under Control Conditions.

Climatic conditions significantly affect the maize productivity. Among abiotic factors, nitrogen (N) fertilizer and temperature are the two important factors which dominantly affect the maize (Zea mays L.) production during the early crop growth stages. Two experiments were conducted to determine the impact of N fertilizer and temperature on the maize growth and yield. In the first experiment, the maize hybrids were screened for their sensitivity to temperature variations. The screening was based on the growth performance of the hybrids under three temperatures (T 1 = ambient open-air temperature, T 2 = 1°C higher than the ambient temperature, and T 3 = 1°C lower than the ambient temperature) range. The results showed that an increase in temperature was resulted less 50% emergence and mean emergence (4.1 and 6.3 days, respectively), while emergence energy and full emergence were higher (25.4 and 75.2%, respectively) under the higher temperature exposure. The results showed that Syngenta 7720 and Muqabla S 25W87 were temperature tolerant and sensitive maize hybrids, respectively. The second experiment was carried out to study the response of the two selected maize hybrids (Syngenta 7720 and Muqabla S 25W87) to four N fertilizer applications. The results revealed that the maximum N use efficiency (19.5 kg kg-1) was achieved in maize hybrids with low N application (75 kg N ha-1 equivalent to 1.13 g N plant-1). However, the maximum maize grain yield (86.4 g plant-1), dry weight (203 g plant-1), and grain protein content (15.0%) were observed in maize hybrids that were grown with the application of 300 kg N ha-1 (equivalent to 4.52 g N plant-1). Therefore, it is recommended that the application of 300 kg N ha-1 to temperature tolerant maize hybrid may be considered best agricultural management practices for obtaining optimum maize grain yield under present changing climate.

How do packet losses affect measures of averaged neural signalsƒ.

Recent advances in implanted device development have enabled chronic streaming of neural data to external devices allowing for long timescale, naturalistic recordings. However, characteristic data losses occur during wireless transmission. Estimates for the duration of these losses are typically uncertain reducing signal quality and impeding analyses. To characterize the effect of these losses on recovery of averaged neural signals, we simulated neural time series data for a typical event-related potential (ERP) experiment. We investigated how the signal duration and the degree of timing uncertainty affected the offset of the ERP, its duration in time, its amplitude, and the ability to resolve small differences corresponding to different task conditions. Simulations showed that long timescale signals were generally robust to the effects of packet losses apart from timing offsets while short timescale signals were significantly delocalized and attenuated. These results provide clarity on the types of signals that can be resolved using these datasets and provide clarity on the restrictions imposed by data losses on typical analyses.

Long-term ecological assessment of intracranial electrophysiology synchronized to behavioral markers in obsessive-compulsive disorder.

Detection of neural signatures related to pathological behavioral states could enable adaptive deep brain stimulation (DBS), a potential strategy for improving efficacy of DBS for neurological and psychiatric disorders. This approach requires identifying neural biomarkers of relevant behavioral states, a task best performed in ecologically valid environments. Here, in human participants with obsessive-compulsive disorder (OCD) implanted with recording-capable DBS devices, we synchronized chronic ventral striatum local field potentials with relevant, disease-specific behaviors. We captured over 1,000 h of local field potentials in the clinic and at home during unstructured activity, as well as during DBS and exposure therapy. The wide range of symptom severity over which the data were captured allowed us to identify candidate neural biomarkers of OCD symptom intensity. This work demonstrates the feasibility and utility of capturing chronic intracranial electrophysiology during daily symptom fluctuations to enable neural biomarker identification, a prerequisite for future development of adaptive DBS for OCD and other psychiatric disorders.

Uncovering biomarkers during therapeutic neuromodulation with PARRM: Period-based Artifact Reconstruction and Removal Method.

Advances in therapeutic neuromodulation devices have enabled concurrent stimulation and electrophysiology in the central nervous system. However, stimulation artifacts often obscure the sensed underlying neural activity. Here, we develop a method, termed Period-based Artifact Reconstruction and Removal Method (PARRM), to remove stimulation artifacts from neural recordings by leveraging the exact period of stimulation to construct and subtract a high-fidelity template of the artifact. Benchtop saline experiments, computational simulations, five unique in vivo paradigms across animal and human studies, and an obscured movement biomarker are used for validation. Performance is found to exceed that of state-of-the-art filters in recovering complex signals without introducing contamination. PARRM has several advantages: (1) it is superior in signal recovery; (2) it is easily adaptable to several neurostimulation paradigms; and (3) it has low complexity for future on-device implementation. Real-time artifact removal via PARRM will enable unbiased exploration and detection of neural biomarkers to enhance efficacy of closed-loop therapies.

Does face-selective cortex show a left visual field bias for centrally-viewed faces?

The left half of a centrally-viewed face contributes more strongly to recognition performance than the right. This left visual field (LVF) advantage is typically attributed to an untested assumption that face-selective cortex in the right hemisphere (RH) exhibits a contralateral bias, even for centrally-viewed faces. We tested the validity of this assumption using a behavioral measure of the LVF advantage and an fMRI experiment that measured laterality of face-selective cortex and neural contralateral bias. In the behavioral experiment, participants performed a chimeric face-matching task (Harrison and Strother, 2019). In the fMRI experiment, participants viewed chimeric faces comprised of face halves that either repeated or changed simultaneously in both hemifields, or repeated in one hemifield and changed in the other. This enabled us to measure lateralization of fMRI face-repetition suppression and hemifield-specific half-face sensitivity in face-selective cortex. We found that LVF bias in the fusiform face area (FFA) and right-lateralization of the FFA for changing versus repeated faces were both positively correlated with a behavioral measure of the LVF advantage for upright (but not inverted) faces. Results from regression analyses showed that LVF bias in the right FFA and FFA laterality make separable contributions to the prediction of our behavioral measure of the LVF bias for upright faces. Our results confirm a ubiquitous but previously untested assumption that RH superiority combined with contralateral bias in face-selective cortex explains the LVF advantage in face recognition. Specifically, our results show that neural LVF bias in the right FFA is sufficient to explain the relationship between FFA laterality and the perceptual LVF bias for centrally-viewed faces.

Network structure and rapid HIV transmission among people who inject drugs: A simulation-based analysis.

As HIV incidence among people who inject drugs grows in the context of an escalating drug overdose epidemic in North America, investigating how network structure may affect vulnerability to rapid HIV transmission is necessary for preventing outbreaks. We compared the characteristics of the observed contact tracing network from the 2015 outbreak in rural Indiana with 1000 networks generated by an agent-based network model with approximately the same number of individuals (n = 420) and ties between them (n = 913). We introduced an initial HIV infection into the simulated networks and compared the subsequent epidemic behavior (e.g., cumulative HIV infections over 5 years). The model was able to produce networks with largely comparable characteristics and total numbers of incident HIV infections. Although the model was unable to produce networks with comparable cohesiveness (where the observed network had a transitivity value 35.7 standard deviations from the mean of the simulated networks), the structural variability of the simulated networks allowed for investigation into their potential facilitation of HIV transmission. These findings emphasize the need for continued development of injection network simulation studies in tandem with empirical data collection to further investigate how network characteristics played a role in this and future outbreaks.

Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils.

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models.

The Kalman filter provides a simple and efficient algorithm to compute the posterior distribution for state-space models where both the latent state and measurement models are linear and gaussian. Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for models where the observation model p(observation|state) is nonlinear. We argue that in many cases, a model for p(state|observation) proves both easier to learn and more accurate for latent state estimation. Approximating p(state|observation) as gaussian leads to a new filtering algorithm, the discriminative Kalman filter (DKF), which can perform well even when p(observation|state) is highly nonlinear and/or nongaussian. The approximation, motivated by the Bernstein-von Mises theorem, improves as the dimensionality of the observations increases. The DKF has computational complexity similar to the Kalman filter, allowing it in some cases to perform much faster than particle filters with similar precision, while better accounting for nonlinear and nongaussian observation models than Kalman-based extensions. When the observation model must be learned from training data prior to filtering, off-the-shelf nonlinear and nonparametric regression techniques can provide a gaussian model for p(observation|state) that cleanly integrates with the DKF. As part of the BrainGate2 clinical trial, we successfully implemented gaussian process regression with the DKF framework in a brain-computer interface to provide real-time, closed-loop cursor control to a person with a complete spinal cord injury. In this letter, we explore the theory underlying the DKF, exhibit some illustrative examples, and outline potential extensions.

Does right hemisphere superiority sufficiently explain the left visual field advantage in face recognition?

The tendency to perceive the identity of the left half of a centrally viewed face more strongly than that of the right half is associated with visual processing of faces in the right hemisphere (RH). Here we investigate conditions under which this well-known left visual field (LVF) half-face advantage fails to occur. Our findings challenge the sufficiency of its explanation as a function of RH specialization for face processing coupled with LVF-RH correspondence. In two experiments we show that the LVF half-face advantage occurs for normal faces and chimeric faces composed of different half-face identities. In a third experiment, we show that face inversion disrupts the LVF half-face advantage. In two additional experiments we show that half-faces viewed in isolation or paired with inverted half-faces fail to show the LVF advantage. Consistent with previous explanations of the LVF half-face advantage, our findings suggest that the LVF half-face advantage reflects RH superiority for processing faces and direct transfer of LVF face information to visual cortex in the RH. Critically, however, our findings also suggest the operation of a third factor, which involves the prioritization of face-processing resources to the LVF, but only when two upright face-halves compete for these resources. We therefore conclude that RH superiority alone does not suffice to explain the LVF advantage in face recognition. We also discuss the implications of our findings for specialized visual processing of faces by the right hemisphere, and we distinguish LVF advantages for faces viewed centrally and peripherally in divided field studies.

Fixed choice design and augmented fixed choice design for network data with missing observations.

The statistical analysis of social networks is increasingly used to understand social processes and patterns. The association between social relationships and individual behaviors is of particular interest to sociologists, psychologists, and public health researchers. Several recent network studies make use of the fixed choice design (FCD), which induces missing edges in the network data. Because of the complex dependence structure inherent in networks, missing data can pose very difficult problems for valid statistical inference. In this article, we introduce novel methods for accounting for the FCD censoring and introduce a new survey design, which we call the augmented fixed choice design (AFCD). The AFCD adds considerable information to analyses without unduly burdening the survey respondent, resulting in improvements over the FCD, and other existing estimators. We demonstrate this new method through simulation studies and an analysis of alcohol use in a network of undergraduate students living in a residence hall.

Robust Closed-Loop Control of a Cursor in a Person with Tetraplegia using Gaussian Process Regression.

Intracortical brain computer interfaces can enable individuals with paralysis to control external devices through voluntarily modulated brain activity. Decoding quality has been previously shown to degrade with signal nonstationarities-specifically, the changes in the statistics of the data between training and testing data sets. This includes changes to the neural tuning profiles and baseline shifts in firing rates of recorded neurons, as well as nonphysiological noise. While progress has been made toward providing long-term user control via decoder recalibration, relatively little work has been dedicated to making the decoding algorithm more resilient to signal nonstationarities. Here, we describe how principled kernel selection with gaussian process regression can be used within a Bayesian filtering framework to mitigate the effects of commonly encountered nonstationarities. Given a supervised training set of (neural features, intention to move in a direction)-pairs, we use gaussian process regression to predict the intention given the neural data. We apply kernel embedding for each neural feature with the standard radial basis function. The multiple kernels are then summed together across each neural dimension, which allows the kernel to effectively ignore large differences that occur only in a single feature. The summed kernel is used for real-time predictions of the posterior mean and variance under a gaussian process framework. The predictions are then filtered using the discriminative Kalman filter to produce an estimate of the neural intention given the history of neural data. We refer to the multiple kernel approach combined with the discriminative Kalman filter as the MK-DKF. We found that the MK-DKF decoder was more resilient to nonstationarities frequently encountered in-real world settings yet provided similar performance to the currently used Kalman decoder. These results demonstrate a method by which neural decoding can be made more resistant to nonstationarities.

Mixture models with a prior on the number of components.

A natural Bayesian approach for mixture models with an unknown number of components is to take the usual finite mixture model with symmetric Dirichlet weights, and put a prior on the number of components-that is, to use a mixture of finite mixtures (MFM). The most commonly-used method of inference for MFMs is reversible jump Markov chain Monte Carlo, but it can be nontrivial to design good reversible jump moves, especially in high-dimensional spaces. Meanwhile, there are samplers for Dirichlet process mixture (DPM) models that are relatively simple and are easily adapted to new applications. It turns out that, in fact, many of the essential properties of DPMs are also exhibited by MFMs-an exchangeable partition distribution, restaurant process, random measure representation, and stick-breaking representation-and crucially, the MFM analogues are simple enough that they can be used much like the corresponding DPM properties. Consequently, many of the powerful methods developed for inference in DPMs can be directly applied to MFMs as well; this simplifies the implementation of MFMs and can substantially improve mixing. We illustrate with real and simulated data, including high-dimensional gene expression data used to discriminate cancer subtypes.

Visual recognition of mirrored letters and the right hemisphere advantage for mirror-invariant object recognition.

Unlike most objects, letter recognition is closely tied to orientation and mirroring, which in some cases (e.g., b and d), defines letter identity altogether. We combined a divided field paradigm with a negative priming procedure to examine the relationship between mirror generalization, its suppression during letter recognition, and language-related visual processing in the left hemisphere. In our main experiment, observers performed a centrally viewed letter-recognition task, followed by an object-recognition task performed in either the right or the left visual hemifield. The results show clear evidence of inhibition of mirror generalization for objects viewed in either hemifield but a right hemisphere advantage for visual recognition of mirrored and repeated objects. Our findings are consistent with an opponent relationship between symmetry-related visual processing in the right hemisphere and neurally recycled mechanisms in the left hemisphere used for visual processing of written language stimuli.

Rapid calibration of an intracortical brain-computer interface for people with tetraplegia.

OBJECTIVE: Brain-computer interfaces (BCIs) can enable individuals with tetraplegia to communicate and control external devices. Though much progress has been made in improving the speed and robustness of neural control provided by intracortical BCIs, little research has been devoted to minimizing the amount of time spent on decoder calibration. APPROACH: We investigated the amount of time users needed to calibrate decoders and achieve performance saturation using two markedly different decoding algorithms: the steady-state Kalman filter, and a novel technique using Gaussian process regression (GP-DKF). MAIN RESULTS: Three people with tetraplegia gained rapid closed-loop neural cursor control and peak, plateaued decoder performance within 3 min of initializing calibration. We also show that a BCI-naïve user (T5) was able to rapidly attain closed-loop neural cursor control with the GP-DKF using self-selected movement imagery on his first-ever day of closed-loop BCI use, acquiring a target 37 s after initiating calibration. SIGNIFICANCE: These results demonstrate the potential for an intracortical BCI to be used immediately after deployment by people with paralysis, without the need for user learning or extensive system calibration.