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Central pain disorders, such as central post-stroke pain, remain clinically challenging to treat, despite many decades of pharmacological advances and the evolution of neuromodulation. For treatment refractory cases, previous studies have highlighted some benefits of cortical stimulation. Recent advances in new targets for pain and the optimization of neuromodulation encouraged our group to develop a dual cortical target approach paired with Bayesian optimization to provide a personalized treatment. Here, we present a case report of a woman who developed left-sided facial pain after multiple thalamic strokes. All previous pharmacologic and interventional treatments failed to mitigate the pain, leaving her incapacitated due to pain and medication side effects. She subsequently underwent a single burr hole for placement of motor cortex (M1) and dorsolateral prefrontal cortex (dlPFC) paddles for stimulation with externalization. By using Bayesian optimization to find optimal stimulation parameters and stimulation sites, we were able to reduce pain from an 8.5/10 to a 0/10 during a 5-day inpatient stay, with pain staying at or below a 2/10 one-month post-procedure. We found optimal treatment to be simultaneous stimulation of M1 and dlPFC without any evidence of seizure induction. In addition, we found no worsening in cognitive performance during a working memory task with dlPFC stimulation. This personalized approach using Bayesian optimization may provide a new foundation for treating central pain and other functional disorders through systematic evaluation of stimulation parameters.

Since the first antidepressant in the 1950s, biological treatments for mood disorders have largely focused on altering monoaminergic neurotransmission, with only partial success. In the landmark STAR*D trial, 1 out [...]

Cognitive control, the ability to regulate one’s cognition and actions on the basis of over-riding goals, is impaired in many psychiatric conditions. Although control requires the coordinated function of several prefrontal cortical regions, it has been challenging to determine how they work together, in part because doing so requires simultaneous recordings from multiple regions. Here, we provide a précis of cognitive control and describe the beneficial consequences of recent advances in neurosurgical practice that make large-scale prefrontal cortical network recordings possible in humans. Such recordings implicate inter-regional theta (5–8 Hz) local field potential (LFP) synchrony as a key element in cognitive control. Major open questions include how theta might influence other oscillations within these networks, the precise timing of information flow between these regions, and how perturbations such as brain stimulation might demonstrate the causal role of LFP phenomena. We propose that an increased focus on human electrophysiology is essential for an understanding of the neural basis of cognitive control.

Censored data occur commonly in trial-structured behavioral experiments and many other forms of longitudinal data. They can lead to severe bias and reduction of statistical power in subsequent analyses. Principled approaches for dealing with censored data, such as data imputation and methods based on the complete data’s likelihood, work well for estimating fixed features of statistical models but have not been extended to dynamic measures, such as serial estimates of an underlying latent variable over time. Here we propose an approach to the censored-data problem for dynamic behavioral signals. We developed a state-space modeling framework with a censored observation process at the trial timescale. We then developed a filter algorithm to compute the posterior distribution of the state process using the available data. We showed that special cases of this framework can incorporate the three most common approaches to censored observations: ignoring trials with censored data, imputing the censored data values, or using the full information available in the data likelihood. Finally, we derived a computationally efficient approximate Gaussian filter that is similar in structure to a Kalman filter, but that efficiently accounts for censored data. We compared the performances of these methods in a simulation study and provide recommendations of approaches to use, based on the expected amount of censored data in an experiment. These new techniques can broadly be applied in many research domains in which censored data interfere with estimation, including survival analysis and other clinical trial applications.

Cross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate examples of CFC during a seizure and in response to electrical stimuli.

Mathematical modeling of behavior during a psychophysical task, referred to as “computational psychiatry,” could greatly improve our understanding of mental disorders. One barrier to the broader adoption of computational methods, is that they often require advanced statistical modeling and mathematical skills. Biological and behavioral signals often show skewed or non-Gaussian distributions, and very few toolboxes and analytical platforms are capable of processing such signal categories. We developed the Computational Psychiatry Adaptive State-Space (COMPASS) toolbox, an open-source MATLAB-based software package. This toolbox is easy to use and capable of integrating signals with a variety of distributions. COMPASS has the tools to process signals with continuous-valued and binary measurements, or signals with incomplete—missing or censored—measurements, which makes it well-suited for processing those signals captured during a psychophysical task. After specifying a few parameters in a small set of user-friendly functions, COMPASS allows users to efficiently apply a wide range of computational behavioral models. The model output can be analyzed as an experimental outcome or used as a regressor for neural data and can also be tested using the goodness-of-fit measurement. Here, we demonstrate that COMPASS can replicate two computational behavioral analyses from different groups. COMPASS replicates and can slightly improve on the original modeling results. We also demonstrate the use of COMPASS application in a censored-data problem and compare its performance result with naïve estimation methods. This flexible, general-purpose toolkit should accelerate the use of computational modeling in psychiatric neuroscience.

Objective.: Anxiety can interfere with attention and working memory, which are components that affect learning. Statistical models have been designed to study learning, such as the Bayesian Learning Model, which takes into account prior possibilities and behaviours to determine how much of a new behaviour is determined by learning instead of chance. However, the neurobiological basis underlying how anxiety interferes with learning is not yet known. Accordingly, we aimed to use neuroimaging techniques and apply a Bayesian Learning Model to study learning in individuals with generalised anxiety disorder (GAD). Methods.: Participants were 25 controls and 14 individuals with GAD and comorbid disorders. During fMRI, participants completed a shape-button association learning and reversal task. Using a flexible factorial analysis in SPM, activation in the dorsolateral prefrontal cortex, basal ganglia, and hippocampus was compared between groups during first reversal. Beta values from the peak of these regions were extracted for all learning conditions and submitted to repeated measures analyses in SPSS. Results.: Individuals with GAD showed less activation in the basal ganglia and the hippocampus only in the first reversal compared with controls. This difference was not present in the initial learning and second reversal. Conclusion.: Given that the basal ganglia is associated with initial learning, and the hippocampus with transfer of knowledge from short- to long-term memory, our results suggest that GAD may engage these regions to a lesser extent during early accommodation or consolidation of learning, but have no longer term effects in brain activation patterns during subsequent learning. [ABSTRACT FROM AUTHOR]