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In this paper, we present a novel Bayesian adaptive dual controller (ADC) for autonomously programming deep brain stimulation devices. We evaluated the Bayesian ADC’s performance in the context of reducing beta power in a computational model of Parkinson’s disease, in which it was tasked with finding the set of stimulation parameters which optimally reduced beta power as fast as possible. Here, the Bayesian ADC has dual goals: (a) to minimize beta power by exploiting the best parameters found so far, and (b) to explore the space to find better parameters, thus allowing for better control in the future. The Bayesian ADC is composed of two parts: an inner parameterized feedback stimulator and an outer parameter adjustment loop. The inner loop operates on a short time scale, delivering stimulus based upon the phase and power of the beta oscillation. The outer loop operates on a long time scale, observing the effects of the stimulation parameters and using Bayesian optimization to intelligently select new parameters to minimize the beta power. We show that the Bayesian ADC can efficiently optimize stimulation parameters, and is superior to other optimization algorithms. The Bayesian ADC provides a robust and general framework for tuning stimulation parameters, can be adapted to use any feedback signal, and is applicable across diseases and stimulator designs. [ABSTRACT FROM AUTHOR]

This paper discusses modeling and automatic feedback control of (postural and rest) tremor for adaptive-control-methodology-based estimation of deep brain stimulation (DBS) parameters. The simplest linear oscillator-based tremor model, between stimulation amplitude and tremor, is investigated by utilizing input-output knowledge. Further, a nonlinear generalization of the oscillator-based tremor model, useful for derivation of a control strategy involving incorporation of parametric-bound knowledge, is provided. Using the Lyapunov method, a robust adaptive output feedback control law, based on measurement of the tremor signal from the fingers of a patient, is formulated to estimate the stimulation amplitude required to control the tremor. By means of the proposed control strategy, an algorithm is developed for estimation of DBS parameters such as amplitude, frequency and pulse width, which provides a framework for development of an automatic clinical device for control of motor symptoms. The DBS parameter estimation results for the proposed control scheme are verified through numerical simulations. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) is known to be an effective treatment for a variety of neurological disorders, including Parkinson’s disease and essential tremor (ET). At present, it involves administering a train of pulses with constant frequency via electrodes implanted into the brain. New ‘closed-loop’ approaches involve delivering stimulation according to the ongoing symptoms or brain activity and have the potential to provide improvements in terms of efficiency, efficacy and reduction of side effects. The success of closed-loop DBS depends on being able to devise a stimulation strategy that minimizes oscillations in neural activity associated with symptoms of motor disorders. A useful stepping stone towards this is to construct a mathematical model, which can describe how the brain oscillations should change when stimulation is applied at a particular state of the system. Our work focuses on the use of coupled oscillators to represent neurons in areas generating pathological oscillations. Using a reduced form of the Kuramoto model, we analyse how a patient should respond to stimulation when neural oscillations have a given phase and amplitude, provided a number of conditions are satisfied. For such patients, we predict that the best stimulation strategy should be phase specific but also that stimulation should have a greater effect if applied when the amplitude of brain oscillations is lower. We compare this surprising prediction with data obtained from ET patients. In light of our predictions, we also propose a new hybrid strategy which effectively combines two of the closed-loop strategies found in the literature, namely phase-locked and adaptive DBS. [ABSTRACT FROM AUTHOR]

Neuroscience investigations are most often focused on the prediction of future perception or decisions based on prior brain states or stimulus presentations. However, the brain can also process information retroactively, such that later stimuli impact conscious percepts of the stimuli that have already occurred (called “postdiction”). Postdictive effects have thus far been mostly unimodal (such as apparent motion), and the models for postdiction have accordingly been limited to early sensory regions of one modality. We have discovered two related multimodal illusions in which audition instigates postdictive changes in visual perception. In the first illusion (called the “Illusory Audiovisual Rabbit”), the location of an illusory flash is influenced by an auditory beep-flash pair that follows the perceived illusory flash. In the second illusion (called the “Invisible Audiovisual Rabbit”), a beep-flash pair following a real flash suppresses the perception of the earlier flash. Thus, we showed experimentally that these two effects are influenced significantly by postdiction. The audiovisual rabbit illusions indicate that postdiction can bridge the senses, uncovering a relatively-neglected yet critical type of neural processing underlying perceptual awareness. Furthermore, these two new illusions broaden the Double Flash Illusion, in which a single real flash is doubled by two sounds. Whereas the double flash indicated that audition can create an illusory flash, these rabbit illusions expand audition’s influence on vision to the suppression of a real flash and the relocation of an illusory flash. These new additions to auditory-visual interactions indicate a spatio-temporally fine-tuned coupling of the senses to generate perception. [ABSTRACT FROM AUTHOR]

We develop of a line of work initiated by Curto and Itskov towards understanding the amount of information contained in the spike trains of hippocampal place cells via topology considerations. Previously, it was established that simply knowing which groups of place cells fire together in an animal’s hippocampus is sufficient to extract the global topology of the animal’s physical environment. We model a system where collections of place cells group and ungroup according to short-term plasticity rules. In particular, we obtain the surprising result that in experiments with spurious firing, the accuracy of the extracted topological information decreases with the persistence (beyond a certain regime) of the cell groups. This suggests that synaptic transience, or forgetting, is a mechanism by which the brain counteracts the effects of spurious place cell activity. [ABSTRACT FROM AUTHOR]

Background and objective: To determine rates of adverse events (AEs) related to deep brain stimulation (DBS) surgery or implanted devices from a large series from a single institution. Sound comparisons with the literature require the definition of unambiguous categories, since there is no consensus on the reporting of such AEs. Patients and methods: 123 consecutive patients (median age 63 yrs; female 45.5%) treated with DBS in the subthalamic nucleus (78 patients), ventrolateral thalamus (24), internal pallidum (20), and centre médian-parafascicular nucleus (1) were analyzed retrospectively. Both mean and median follow-up time was 4.7 years (578 patient-years). AEs were assessed according to three unambiguous categories: (i) hemorrhages including other intracranial complications because these might lead to neurological deficits or death, (ii) infections and similar AEs necessitating the explantation of hardware components as this results in the interruption of DBS therapy, and (iii) lead revisions for various reasons since this involves an additional intracranial procedure. For a systematic review of the literature AE rates were calculated based on primary data presented in 103 publications. Heterogeneity between studies was assessed with the I2 statistic and analyzed further by a random effects meta-regression. Publication bias was analyzed with funnel plots. Results: Surgery- or hardware-related AEs (23) affected 18 of 123 patients (14.6%) and resolved without permanent sequelae in all instances. In 2 patients (1.6%), small hemorrhages in the striatum were associated with transient neurological deficits. In 4 patients (3.3%; 0.7% per patient-year) impulse generators were removed due to infection. In 2 patients electrodes were revised (1.6%; 0.3% per patient-year). There was no lead migration or surgical revision because of lead misplacement. Age was not statistically significant different (p>0.05) between patients affected by AEs or not. AE rates did not decline over time and similar incidences were found among all patients (423) implanted with DBS systems at our institution until December 2016. A systematic literature review revealed that exact AE rates could not be determined from many studies, which could not be attributed to study designs. Average rates for intracranial complications were 3.8% among studies (per-study analysis) and 3.4% for pooled analysis of patients from different studies (per-patient analysis). Annual hardware removal rates were 3.6 and 2.4% for per-study and per-patient analysis, respectively, and lead revision rates were 4.1 and 2.6%, respectively. There was significant heterogeneity between studies (I2 ranged between 77% and 91% for the three categories; p< 0.0001). For hardware removal heterogeneity (I2 = 87.4%) was reduced by taking study size (p< 0.0001) and publication year (p< 0.01) into account, although a significant degree of heterogeneity remained (I2 = 80.0%; p< 0.0001). Based on comparisons with health care-related databases there appears to be publication bias with lower rates for hardware-related AEs in published patient cohorts. Conclusions: The proposed categories are suited for an unequivocal assessment of AEs even in a retrospective manner and useful for benchmarking. AE rates in the present cohorts from our institution compare favorable with the literature. [ABSTRACT FROM AUTHOR]

Brain stimulation can modulate the activity of neural circuits impaired by Alzheimer’s disease (AD), having promising clinical benefit. However, all individuals with the same condition currently receive identical brain stimulation, with limited theoretical basis for this generic approach. In this study, we introduce a control theory framework for obtaining exogenous signals that revert pathological electroencephalographic activity in AD at a minimal energetic cost, while reflecting patients’ biological variability. We used anatomical networks obtained from diffusion magnetic resonance images acquired by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) as mediators for the interaction between Duffing oscillators. The nonlinear nature of the brain dynamics is preserved, given that we extend the so-called state-dependent Riccati equation control to reflect the stimulation objective in the high-dimensional neural system. By considering nonlinearities in our model, we identified regions for which control inputs fail to correct abnormal activity. There are changes to the way stimulated regions are ranked in terms of the energetic cost of controlling the entire network, from a linear to a nonlinear approach. We also found that limbic system and basal ganglia structures constitute the top target locations for stimulation in AD. Patients with highly integrated anatomical networks–namely, networks having low average shortest path length, high global efficiency–are the most suitable candidates for the propagation of stimuli and consequent success on the control task. Other diseases associated with alterations in brain dynamics and the self-control mechanisms of the brain can be addressed through our framework. [ABSTRACT FROM AUTHOR]

Considerable debate has focused on whether adults possess an implicit system for representing others’ mental states. Some argue that people automatically represent the perspective of others using evidence from altercentric interference—cases in which another agent’s perspective affects the speed with which one can report one’s own perspective. Others have argued that altercentric interference is not always specific to social stimuli and thus may represent a simpler process such as submentalizing. To distinguish between these positions, Study 1 developed a novel measure of altercentric interference—a “sandbox” measure—that allowed us to more sensitively assess altercentric interference across social and non-social conditions. We replicated previous findings showing that participants experience both egocentric and altercentric interference, but we found that these effects emerge equally in social and non-social conditions. To further test whether altercentric interference emerges in social perspective-taking situations, Study 2 conducted a conceptual replication of a study which used a novel “goggle” paradigm to assess whether individuals implicitly represent others’ perspectives. Although we failed to find evidence of altercentric interference in response times, participants’ accuracy reflected the possibility of interference from others’ perspectives. We argue that these findings provide support for the idea that altercentric interference in response to social stimuli (an avatar) is driven by perspective-taking mechanisms, while such interference in response to non-social stimuli (an arrow) is driven by attention-cuing mechanisms. [ABSTRACT FROM AUTHOR]

Essential tremor (ET) is the most common age-related disease leading to abnormal tremulous behaviors in the upper and lower extremities. Non-invasive brain stimulation (NIBS) may be an effective ET therapy by modulating the oscillating network of the brain. The current systematic review and meta-analysis examined the effects of NIBS interventions on tremor symptoms in ET patients. Our comprehensive search identified eight studies that used 1 Hz of rTMS, cTBS, or ctDCS protocols. Twenty total comparisons from the eight qualified studies were statistically synthesized, and the meta-analytic findings revealed that NIBS techniques reduced tremulous behaviors in individuals with ET. Moreover, the four moderator variable analyses demonstrated that the positive therapeutic effects of NIBS appeared across the following subgroups: (a) tremor assessment (clinical test vs. quantitative tremor assessment), (b) stimulation site (cerebellum vs. motor cortex), (c) session number (single session vs. multiple sessions), and (d) sustained positive treatment effect (posttest vs. retention test). This comprehensive systematic review and meta-analysis provided evidence that support positive treatment effects of NIBS techniques on ET motor therapy. [ABSTRACT FROM AUTHOR]

Pursuit of one goal typically precludes simultaneous pursuit of another. Thus, each exclusive activity entails an “opportunity cost:” the forgone benefits from the next-best activity eschewed. The present experiment estimates, in laboratory rats, the function that maps objective opportunity costs into subjective ones. In an operant chamber, rewarding electrical brain stimulation was delivered when the cumulative time a lever had been depressed reached a criterion duration. The value of the activities forgone during this duration is the opportunity cost of the electrical reward. We determined which of four functions best describes how objective opportunity costs, expressed as the required duration of lever depression, are translated into their subjective equivalents. The simplest account is the identity function, which equates subjective and objective opportunity costs. A variant of this function called the “sigmoidal-slope function,” converges on the identity function at longer durations but deviates from it at shorter durations. The sigmoidal-slope function has the form of a hockey stick. The flat “blade” denotes a range over which opportunity costs are subjectively equivalent; these durations are too short to allow substitution of more beneficial activities. The blade extends into an upward-curving portion over which costs become discriminable and finally into the straight “handle,” over which objective and subjective costs match. The two remaining functions are based on hyperbolic and exponential temporal discounting, respectively. The results are best described by the sigmoidal-slope function. That this is so suggests that different principles of intertemporal choice are involved in the evaluation of time spent working for a reward or waiting for its delivery. The subjective opportunity-cost function plays a key role in the evaluation and selection of goals. An accurate description of its form and parameters is essential to successful modeling and prediction of instrumental performance and reward-related decision making. [ABSTRACT FROM AUTHOR]

Driven by clinical needs and progress in neurotechnology, targeted interaction with neuronal networks is of increasing importance. Yet, the dynamics of interaction between intrinsic ongoing activity in neuronal networks and their response to stimulation is unknown. Nonetheless, electrical stimulation of the brain is increasingly explored as a therapeutic strategy and as a means to artificially inject information into neural circuits. Strategies using regular or event-triggered fixed stimuli discount the influence of ongoing neuronal activity on the stimulation outcome and are therefore not optimal to induce specific responses reliably. Yet, without suitable mechanistic models, it is hardly possible to optimize such interactions, in particular when desired response features are network-dependent and are initially unknown. In this proof-of-principle study, we present an experimental paradigm using reinforcement-learning (RL) to optimize stimulus settings autonomously and evaluate the learned control strategy using phenomenological models. We asked how to (1) capture the interaction of ongoing network activity, electrical stimulation and evoked responses in a quantifiable ‘state’ to formulate a well-posed control problem, (2) find the optimal state for stimulation, and (3) evaluate the quality of the solution found. Electrical stimulation of generic neuronal networks grown from rat cortical tissue in vitro evoked bursts of action potentials (responses). We show that the dynamic interplay of their magnitudes and the probability to be intercepted by spontaneous events defines a trade-off scenario with a network-specific unique optimal latency maximizing stimulus efficacy. An RL controller was set to find this optimum autonomously. Across networks, stimulation efficacy increased in 90% of the sessions after learning and learned latencies strongly agreed with those predicted from open-loop experiments. Our results show that autonomous techniques can exploit quantitative relationships underlying activity-response interaction in biological neuronal networks to choose optimal actions. Simple phenomenological models can be useful to validate the quality of the resulting controllers. [ABSTRACT FROM AUTHOR]

In brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) the number of selectable targets is rather limited when each target has its own stimulation frequency. One way to remedy this is by combining frequency- with phase encoding. We introduce a new multivariate spatiotemporal filter, based on Linearly Constrained Minimum Variance (LCMV) beamforming, for discriminating between frequency-phase encoded targets more accurately, even when using short signal lengths than with (extended) Canonical Correlation Analysis (CCA), which is traditionally posited for this stimulation paradigm. [ABSTRACT FROM AUTHOR]

The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles (“fingerprints”), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified from these spectral profiles with high accuracy. Our results suggest that each brain area engages in different spectral modes that are characteristic for individual areas. Clustering of brain areas according to similarity of spectral profiles reveals well-known brain networks. Furthermore, we demonstrate task-specific modulations of auditory spectral profiles during auditory processing. These findings have important implications for the classification of regional spectral activity and allow for novel approaches in neuroimaging and neurostimulation in health and disease. [ABSTRACT FROM AUTHOR]

Oscillatory neuronal activity is implicated in many cognitive functions, and its phase coupling between sensors may reflect networks of communicating neuronal populations. Oscillatory activity is often studied using extracranial recordings and compared between experimental conditions. This is challenging, because there is overlap between sensor-level activity generated by different sources, and this can obscure differential experimental modulations of these sources. Additionally, in extracranial data, sensor-level phase coupling not only reflects communicating populations, but can also be generated by a current dipole, whose sensor-level phase coupling does not reflect source-level interactions. We present a novel method, which is capable of separating and characterizing sources on the basis of their phase coupling patterns as a function of space, frequency and time (trials). Importantly, this method depends on a plausible model of a neurobiological rhythm. We present this model and an accompanying analysis pipeline. Next, we demonstrate our approach, using magnetoencephalographic (MEG) recordings during a cued tactile detection task as a case study. We show that the extracted components have overlapping spatial maps and frequency content, which are difficult to resolve using conventional pairwise measures. Because our decomposition also provides trial loadings, components can be readily contrasted between experimental conditions. Strikingly, we observed heterogeneity in alpha and beta sources with respect to whether their activity was suppressed or enhanced as a function of attention and performance, and this happened both in task relevant and irrelevant regions. This heterogeneity contrasts with the common view that alpha and beta amplitude over sensory areas are always negatively related to attention and performance. [ABSTRACT FROM AUTHOR]

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system. [ABSTRACT FROM AUTHOR]

Sleep-wake cycling is controlled by the complex interplay between two brain systems, one which controls vigilance state, regulating the transition between sleep and wake, and the other circadian, which communicates time-of-day. Together, they align sleep appropriately with energetic need and the day-night cycle. Neural circuits connect brain stem sites that regulate vigilance state with the suprachiasmatic nucleus (SCN), the master circadian clock, but the function of these connections has been unknown. Coupling discrete stimulation of pontine nuclei controlling vigilance state with analytical chemical measurements of intra-SCN microdialysates in mouse, we found significant neurotransmitter release at the SCN and, concomitantly, resetting of behavioral circadian rhythms. Depending upon stimulus conditions and time-of-day, SCN acetylcholine and/or glutamate levels were augmented and generated shifts of behavioral rhythms. These results establish modes of neurochemical communication from brain regions controlling vigilance state to the central circadian clock, with behavioral consequences. They suggest a basis for dynamic integration across brain systems that regulate vigilance states, and a potential vulnerability to altered communication in sleep disorders. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) is a common therapy for treating movement disorders, such as Parkinson’s disease (PD), and provides a unique opportunity to study the neural activity of various subcortical structures in human patients. Local field potential (LFP) recordings are often performed with either intraoperative microelectrodes or DBS leads and reflect oscillatory activity within nuclei of the basal ganglia. These LFP recordings have numerous clinical implications and might someday be used to optimize DBS outcomes in closed-loop systems. However, the origin of the recorded LFP is poorly understood. Therefore, the goal of this study was to theoretically analyze LFP recordings within the context of clinical DBS applications. This goal was achieved with a detailed recording model of beta oscillations (∼20 Hz) in the subthalamic nucleus. The recording model consisted of finite element models of intraoperative microelectrodes and DBS macroelectrodes implanted in the brain along with multi-compartment cable models of STN projection neurons. Model analysis permitted systematic investigation into a number of variables that can affect the composition of the recorded LFP (e.g. electrode size, electrode impedance, recording configuration, and filtering effects of the brain, electrode-electrolyte interface, and recording electronics). The results of the study suggest that the spatial reach of the LFP can extend several millimeters. Model analysis also showed that variables such as electrode geometry and recording configuration can have a significant effect on LFP amplitude and spatial reach, while the effects of other variables, such as electrode impedance, are often negligible. The results of this study provide insight into the origin of the LFP and identify variables that need to be considered when analyzing LFP recordings in clinical DBS applications. [ABSTRACT FROM AUTHOR]

Background: Myocardial ischemia in the anterior wall of the left ventricule (LV) and in the inferior wall and/or right ventricle (RV) shows different manifestations that can be explained by the different innervations of cardiac afferent nerves. However, it remains unclear whether information from different areas of the heart, such as the LV and RV, are differently processed in the brain. In this study, we investigated the brain regions that process information from the LV or RV using cardiac electrical stimulation and functional magnetic resonance imaging (fMRI) in anesthetized rats because the combination of these two approaches cannot be used in humans. Methodology/Principal Findings: An electrical stimulation catheter was inserted into the LV or RV (n = 12 each). Brain fMRI scans were recorded during LV or RV stimulation (9 Hz and 0.3 ms width) over 10 blocks consisting of alternating periods of 2 mA for 30 sec followed by 0.2 mA for 60 sec. The validity of fMRI signals was confirmed by first and second-level analyses and temporal profiles. Increases in fMRI signals were observed in the anterior cingulate cortex and the right somatosensory cortex under LV stimulation. In contrast, RV stimulation activated the right somatosensory cortex, which was identified more anteriorly compared with LV stimulation but did not activate the anterior cingulate cortex. Conclusion/Significance: This study provides the first evidence for differences in brain activation under LV and RV stimulation. These different brain processes may be associated with different clinical manifestations between anterior wall and inferoposterior wall and/or RV myocardial ischemia. [ABSTRACT FROM AUTHOR]