From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation

Stimulation optimization has garnered considerable interest in recent years in order to efficiently parametrize neuromodulation-based therapies. To date, efforts focused on automatically identifying settings from parameter spaces that do not change over time. A limitation of these approaches, however, is that they lack consideration for time dependent factors that may influence therapy outcomes. Disease progression and biological rhythmicity are two sources of variation that may influence optimal stimulation settings over time. To account for this, we present a novel time-varying Bayesian optimization (TV-BayesOpt) for tracking the optimum parameter set for neuromodulation therapy. We evaluate the performance of TV-BayesOpt for tracking gradual and periodic slow variations over time. The algorithm was investigated within the context of a computational model of phase-locked deep brain stimulation for treating oscillopathies representative of common movement disorders such as Parkinson's disease and Essential Tremor. When the optimal stimulation settings changed due to gradual and periodic sources, TV-BayesOpt outperformed standard time-invariant techniques and was able to identify the appropriate stimulation setting. Through incorporation of both a gradual "forgetting" and periodic covariance functions, the algorithm maintained robust performance when a priori knowledge differed from observed variations. This algorithm presents a broad framework that can be leveraged for the treatment of a range of neurological and psychiatric conditions and can be used to track variations in optimal stimulation settings such as amplitude, pulse-width, frequency and phase for invasive and non-invasive neuromodulation strategies.

Dynamics of cortical and corticomuscular connectivity during planning and execution of visually guided steps in humans

The cortical mechanisms underlying the act of taking a step-including planning, execution, and modification-are not well understood. We hypothesized that oscillatory communication in a parieto-frontal and corticomuscular network is involved in the neural control of visually guided steps. We addressed this hypothesis using source reconstruction and lagged coherence analysis of electroencephalographic and electromyographic recordings during visually guided stepping and 2 control tasks that aimed to investigate processes involved in (i) preparing and taking a step and (ii) adjusting a step based on visual information. Steps were divided into planning, initiation, and execution phases. Taking a step was characterized by an upregulation of beta/gamma coherence within the parieto-frontal network during planning followed by a downregulation of alpha and beta/gamma coherence during initiation and execution. Step modification was characterized by bidirectional modulations of alpha and beta/gamma coherence in the parieto-frontal network during the phases leading up to step execution. Corticomuscular coherence did not exhibit task-related effects. We suggest that these task-related modulations indicate that the brain makes use of communication through coherence in the context of large-scale, whole-body movements, reflecting a process of flexibly fine-tuning inter-regional communication to achieve precision control during human stepping.

Inference of brain networks with approximate Bayesian computation - assessing face validity with an example application in Parkinsonism

This paper describes and validates a novel framework using the Approximate Bayesian Computation (ABC) algorithm for parameter estimation and model selection in models of mesoscale brain network activity. We provide a proof of principle, first pass validation of this framework using a set of neural mass models of the cortico-basal ganglia thalamic circuit inverted upon spectral features from experimental, in vivo recordings. This optimization scheme relaxes an assumption of fixed-form posteriors (i.e. the Laplace approximation) taken in previous approaches to inverse modelling of spectral features. This enables the exploration of model dynamics beyond that approximated from local linearity assumptions and so fit to explicit, numerical solutions of the underlying non-linear system of equations. In this first paper, we establish a face validation of the optimization procedures in terms of: (i) the ability to approximate posterior densities over parameters that are plausible given the known causes of the data; (ii) the ability of the model comparison procedures to yield posterior model probabilities that can identify the model structure known to generate the data; and (iii) the robustness of these procedures to local minima in the face of different starting conditions. Finally, as an illustrative application we show (iv) that model comparison can yield plausible conclusions given the known neurobiology of the cortico-basal ganglia-thalamic circuit in Parkinsonism. These results lay the groundwork for future studies utilizing highly nonlinear or brittle models that can explain time dependant dynamics, such as oscillatory bursts, in terms of the underlying neural circuits.

Programmable Transcranial Magnetic Stimulation: A Modulation Approach for the Generation of Controllable Magnetic Stimuli

OBJECTIVE

A transcranial magnetic stimulation system with programmable stimulus pulses and patterns is presented. The stimulus pulses of the implemented system expand beyond conventional damped cosine or near-rectangular pulses and approach an arbitrary waveform.

METHODS

The desired stimulus waveform shape is defined as a reference signal. This signal controls the semiconductor switches of an H-bridge inverter to generate a high-power imitation of the reference. The design uses a new paradigm for TMS, applying pulse-width modulation with a non-resonant, high-frequency switching architecture to synthesize waveforms that leverages the low-pass filtering properties of neuronal cells. The modulation technique enables control of the waveform, frequency, pattern, and intensity of the stimulus.

RESULTS

A system prototype was developed to demonstrate the technique. The experimental measurements demonstrate that the system is capable of generating stimuli up to 4 kHz with peak voltage and current values of ±1000 V and ±3600 A, respectively. The maximum transferred energy measured in the experimental validation was 100.4 Joules. To characterize repetitive TMS modalities, the efficiency of generating consecutive pulse triplets and quadruplets with interstimulus intervals of 1 ms was tested and verified.

CONCLUSION

The implemented TMS device can generate consecutive rectangular pulses with a predetermined time interval, widths and polarities, enables the synthesis of a wide range of magnetic stimuli.

SIGNIFICANCE

New waveforms promise functional advantages over the waveforms generated by current-generation TMS systems for clinical neuroscience research.

Object recognition is enabled by an experience-dependent appraisal of visual features in the brain's value system

This paper addresses perceptual synthesis by comparing responses evoked by visual stimuli before and after they are recognized, depending on prior exposure. Using magnetoencephalography, we analyzed distributed patterns of neuronal activity - evoked by Mooney figures - before and after they were recognized as meaningful objects. Recognition induced changes were first seen at 100-120 ​ms, for both faces and tools. These early effects - in right inferior and middle occipital regions - were characterized by an increase in power in the absence of any changes in spatial patterns of activity. Within a later 210-230 ​ms window, a quite different type of recognition effect appeared. Regions of the brain's value system (insula, entorhinal cortex and cingulate of the right hemisphere for faces and right orbitofrontal cortex for tools) evinced a reorganization of their neuronal activity without an overall power increase in the region. Finally, we found that during the perception of disambiguated face stimuli, a face-specific response in the right fusiform gyrus emerged at 240-290 ​ms, with a much greater latency than the well-known N170m component, and, crucially, followed the recognition effect in the value system regions. These results can clarify one of the most intriguing issues of perceptual synthesis, namely, how a limited set of high-level predictions, which is required to reduce the uncertainty when resolving the ill-posed inverse problem of perception, can be available before category-specific processing in visual cortex. We suggest that a subset of local spatial features serves as partial cues for a fast re-activation of object-specific appraisal by the value system. The ensuing top-down feedback from value system to visual cortex, in particular, the fusiform gyrus enables high levels of processing to form category-specific predictions. This descending influence of the value system was more prominent for faces than for tools, the fact that reflects different dependence of these categories on value-related information.

Measuring directed functional connectivity using non-parametric directionality analysis: Validation and comparison with non-parametric Granger Causality

Background

‘Non-parametric directionality’ (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015).

Methods

This work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive models to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) data length, and f) parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording.

Results

We demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD measure is compared with non-parametric estimators of Granger causality (NPG), a well-established methodology for model-free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG, albeit with an increased sensitivity to data length. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate amounts of instantaneous signal mixing.

Conclusions

The results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging.

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Non-parametric directionality (NPD) is a novel directed connectivity measure.

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NPD estimates are equivalent to estimates of Granger causality but are more robust to signal confounds.

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Multivariate extensions of NPD can correctly identify signal routing.

Propagation of beta/gamma rhythms in the cortico-basal ganglia circuits of the parkinsonian rat

Much of the motor impairment associated with Parkinson’s disease is thought to arise from pathological activity in the networks formed by the basal ganglia (BG) and motor cortex. To evaluate several hypotheses proposed to explain the emergence of pathological oscillations in parkinsonism, we investigated changes to the directed connectivity in BG networks following dopamine depletion. We recorded local field potentials (LFPs) in the cortex and basal ganglia of rats rendered parkinsonian by injection of 6-hydroxydopamine (6-OHDA) and in dopamine-intact controls. We performed systematic analyses of the networks using a novel tool for estimation of directed interactions (nonparametric directionality, NPD). We used a “conditioned” version of the NPD analysis that reveals the dependence of the correlation between two signals on a third reference signal. We find evidence of the dopamine dependency of both low-beta (14–20 Hz) and high-beta/low-gamma (20–40 Hz) directed network interactions. Notably, 6-OHDA lesions were associated with enhancement of the cortical “hyperdirect” connection to the subthalamic nucleus (STN) and its feedback to the cortex and striatum. We find that pathological beta synchronization resulting from 6-OHDA lesioning is widely distributed across the network and cannot be located to any individual structure. Furthermore, we provide evidence that high-beta/gamma oscillations propagate through the striatum in a pathway that is independent of STN. Rhythms at high beta/gamma show susceptibility to conditioning that indicates a hierarchical organization compared with those at low beta. These results further inform our understanding of the substrates for pathological rhythms in salient brain networks in parkinsonism.

NEW & NOTEWORTHY We present a novel analysis of electrophysiological recordings in the cortico-basal ganglia network with the aim of evaluating several hypotheses concerning the origins of abnormal brain rhythms associated with Parkinson’s disease. We present evidence for changes in the directed connections within the network following chronic dopamine depletion in rodents. These findings speak to the plausibility of a “short-circuiting” of the network that gives rise to the conditions from which pathological synchronization may arise.

Net carbon flux from agricultural ecosystems: methodology for full carbon cycle analyses

Agricultural ecosystems have the potential to sequester carbon in soils by altering agricultural management practices (i.e. tillage practice, cover crops, and crop rotation) and using agricultural inputs (i.e. fertilizers and irrigation) more efficiently. Changes in agricultural practices can also cause changes in CO2 emissions associated with these practices. In order to account for changes in net CO2 emissions, and thereby estimate the overall impact of carbon sequestration initiatives on the atmospheric CO2 pool, we use a methodology for full carbon cycle analysis of agricultural ecosystems. The analysis accounts for changes in carbon sequestration and emission rates with time, and results in values representing a change in net carbon flux. Comparison among values of net carbon flux for two or more systems, using the initial system as a baseline value, results in a value for relative net carbon flux. Some results from using the full carbon cycle methodology, along with US national average values for agricultural inputs, indicate that the net carbon flux averaged over all crops following conversion from conventional tillage to no-till is -189 kg C ha(-1) year(-1) (a negative value indicates net transfer of carbon from the atmosphere). The relative net carbon flux, using conventional tillage as the baseline, is -371 kg C ha(-1) year(-1), which represents the total atmospheric CO2 reduction caused by changing tillage practices. The methodology used here illustrates the importance of (1) delineating system boundaries, (2) including CO2 emissions associated with sequestration initiatives in the accounting process, and (3) comparing the new management practices associated with sequestration initiatives with the original management practices to obtain the true impact of sequestration projects on the atmospheric CO2 pool.