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We present FGES-Merge, a new method for learning the structure of gene regulatory networks via merging locally learned Bayesian networks, based on the fast greedy equivalent search algorithm. The method is competitive with the state of the art in terms of the recall of the true structure while also improving upon it in terms of speed, scaling up to the tens of thousands of variables and being able to use empirical knowledge about the topological structure of gene regulatory networks. We apply this method to learning the gene regulatory network for the full human genome using data from samples of different brain structures (from the Allen Human Brain Atlas). Our goal is to develop a Bayesian network model that predicts interactions between genes in a way that is clear to experts, following the current trends in interpretable artificial intelligence. To achieve this, we also present a new open-access visualization tool that facilitates the exploration of massive networks and can aid in finding nodes of interest for experimental tests.

The dorsolateral striatum plays a major role in stimulus-response habits that are learned in the experimental laboratory. Here, we use meta-analytic procedures to identify the neural circuits activated during the execution of stimulus-response behaviours acquired in everyday-life and those activated by habits acquired in the laboratory. In the case of everyday-life habits we dissociated motor and associative components. We found that motor-dominant stimulus-response associations developed outside the laboratory engaged posterior dorsal putamen, supplementary motor area (SMA) and cerebellum. Associative components were also represented in the posterior putamen. Meanwhile, newly learned habits relied more on the anterior putamen with activation expanding to caudate and nucleus accumbens. Importantly, common neural representations for both naturalistic and laboratory based habits were found in posterior left and anterior right putamen. Our findings suggest a common striatal substrate for behaviours with significant stimulus-response associations, independently of whether they were acquired in the laboratory or everyday-life.

AbstractBayeSuites is the first web framework for learning, visualizing, and interpreting Bayesian networks (BNs) that can scale to tens of thousands of nodes while providing fast and friendly user experience. All the necessary features that enable this are reviewed in this paper; these features include scalability, extensibility, interoperability, ease of use, and interpretability. Scalability is the key factor in learning and processing massive networks within reasonable time; for a maintainable software open to new functionalities, extensibility and interoperability are necessary. Ease of use and interpretability are fundamental aspects of model interpretation, fairly similar to the case of the recent explainable artificial intelligence trend. We present the capabilities of our proposed framework by highlighting a real example of a BN learned from genomic data obtained from Allen Institute for Brain Science. The extensibility properties of the software are also demonstrated with the help of our BN-based probabilistic clustering implementation, together with another genomic-data example.

Aim: Impulse-control disorder is a common neuropsychiatric complication in Parkinson's disease (PD) under dopamine replacement therapy. Prior studies tested the balance between enhanced desire towards reward and cognitive control deficits, hypothesized to be biased towards the former in impulse control disorders. We provide evidence for this hypothesis by measuring behavioral and neural patterns behind the influence of sexual desire over response inhibition and tools towards functional restoration using repetitive transcranial stimulation in patients with hypersexuality as predominant impulsive disorder. Methods:The effect of sexual cues on inhibition was measured with a novel erotic stop-signal task under on and off dopaminergic medication. Task-related functional and anatomical connectivity models were estimated in 16 hypersexual and 17 nonhypersexual patients with PD as well as in 17 healthy controls. Additionally, excitatory neuromodulation using intermittent theta-burst stimulation (sham-controlled) was applied over the pre-supplementary motor area in 20 additional hypersexual patients with PD aiming to improve response inhibition.Results: Compared with their nonhypersexual peers, patients with hypersexuality recruited caudate, pre-supplementary motor area, ventral tegmental area, and anterior cingulate cortex while on medication. Reduced connectivity was found between pre-supplementary motor area and caudate nucleus in hypersexual compared with nonhypersexual patients (while medicated), a result paralleled by compensatory enhanced anatomical connectivity. Furthermore, stimulation over the pre-supplementary motor area improved response inhibition in hypersexual patients with PD when exposed to sexual cues. Conclusion:This study, therefore, has identified a specific fronto-striatal and mesolimbic circuitry underlying uncontrolled sexual responses in medicated patients with PD where cortical neuromodulation halts its expression.

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Electrophysiology data acquisition of single neurons represents a key factor for the understanding of neuronal dynamics. However, the traditional method to acquire this data is through patch-clamp technology, which presents serious scalability flaws due to its slowness and complexity to record at fine-grained spatial precision (dendrites and axon).In silico biophysical models are therefore created for simulating hundreds of experiments that would be impractical to recreate in vitro. The optimal way to create these models is based on the knowledge of the morphological and electrical features for each neuron. Since large-scale data acquisition is often unfeasible for electrical data, previous expert knowledge can be used but it must have an acceptable degree of similarity with the type of neurons that we are trying to model.Here, we present a data-driven machine learning approach to predict the electrophysiological features of single neurons in case of only having their morphology available. To solve this multi-output regression problem, we use an artificial neural network that has the particularity of providing a probability distribution for every output feature, to incorporate uncertainty. Input data to train the model is obtained from from the Allen Cell Types database. The electrical properties can depend on the morphology, whose acquisition technology is highly automated and scalable so there exist large data sets of them. We also provide integrations with the BluePyOpt library to create a biophysical model using the original morphology and the predicted electrical features. Finally, we connect the resulting biophysical model with the Geppetto UI software to run all the simulations in a sophisticated user interface.

One common neuropsychiatric complication in patients with Parkinson's disease treated with dopamine replacement therapy is impulse control disorders. A proportion of patients under dopamine agonist exhibit excessive desire towards appetitive cues accompanied by lost control over behaviour in forms of pathological gambling, hypersexuality or binge eating. The balance between enhanced desire towards sexual cues and cognitive control changes has been hypothesized to be biased toward the former in individuals with hypersexuality and impulse control disorders. Yet, some studies report no behavioural differences in cognitive control differences between impulsive patients and general Parkinson's disease, suggesting possible functional changes along the mesocorticolimbic cortico-subcortical circuitry. Here, we provide evidence for this hypothesis by comparing the neurobiological substrate of sexual disturbance over response inhibition associated to medication states in Parkinson's disease patients with a specific subtype of impulse control disorders (i.e., hypersexuality). We assessed the impact of sexual cues on response inhibition using a novel erotic stop signal task inside an fMRI. A total of 50 participants were included divided in 16 hypersexual and 17 non-hypersexual Parkinson's disease patients and 17 healthy controls. Task-related activations, functional and anatomical connectivity models were performed. Additionally, a separate sample of 20 hypersexual Parkinson's disease patients received excitatory neuromodulation (sham-controlled) over the pre-supplementary motor area (based on fMRI group-based results) aiming to improve response inhibition when exposed to sexual cues. Compared with their non-hypersexual peers, medication perturbed response inhibition upon presentation of sexual cues in patients with hypersexuality, recruiting a network involving caudate, pre-supplementary motor area, ventral tegmental area, and anterior cingulate cortex. Dynamic causal modelling revealed distinct best models to account for cortico-subcortical interactions with reduced task-related inputs in pre-supplementary motor area and descending connectivity to caudate in hypersexual compared to non-hypersexual Parkinson's disease patients (while medicated). This was sustained by enhanced fractional anisotropy and reduced mean diffusivity in the pre-supplementary motor area-caudate pathway. Importantly, stimulation over the pre-supplementary motor area improved response inhibition when exposed to sexual cues in hypersexual Parkinson's disease. We identified a specific fronto-striatal and mesolimbic circuitry underlying uncontrolled sexual behaviours in Parkinson's disease induced by medication, with recovery options by applying neuromodulation.