Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs.

Incorrect drug target identification is a major obstacle in drug discovery. Only 15% of drugs advance from Phase II to approval, with ineffective targets accounting for over 50% of these failures1-3. Advances in data fusion and computational modeling have independently progressed towards addressing this issue. Here, we capitalize on both these approaches with Rosalind, a comprehensive gene prioritization method that combines heterogeneous knowledge graph construction with relational inference via tensor factorization to accurately predict disease-gene links. Rosalind demonstrates an increase in performance of 18%-50% over five comparable state-of-the-art algorithms. On historical data, Rosalind prospectively identifies 1 in 4 therapeutic relationships eventually proven true. Beyond efficacy, Rosalind is able to accurately predict clinical trial successes (75% recall at rank 200) and distinguish likely failures (74% recall at rank 200). Lastly, Rosalind predictions were experimentally tested in a patient-derived in-vitro assay for Rheumatoid arthritis (RA), which yielded 5 promising genes, one of which is unexplored in RA.

The structural connectivity of subthalamic deep brain stimulation correlates with impulsivity in Parkinson's disease.

Subthalamic deep brain stimulation (STN-DBS) for Parkinson's disease treats motor symptoms and improves quality of life, but can be complicated by adverse neuropsychiatric side-effects, including impulsivity. Several clinically important questions remain unclear: can 'at-risk' patients be identified prior to DBS; do neuropsychiatric symptoms relate to the distribution of the stimulation field; and which brain networks are responsible for the evolution of these symptoms? Using a comprehensive neuropsychiatric battery and a virtual casino to assess impulsive behaviour in a naturalistic fashion, 55 patients with Parkinson's disease (19 females, mean age 62, mean Hoehn and Yahr stage 2.6) were assessed prior to STN-DBS and 3 months postoperatively. Reward evaluation and response inhibition networks were reconstructed with probabilistic tractography using the participant-specific subthalamic volume of activated tissue as a seed. We found that greater connectivity of the stimulation site with these frontostriatal networks was related to greater postoperative impulsiveness and disinhibition as assessed by the neuropsychiatric instruments. Larger bet sizes in the virtual casino postoperatively were associated with greater connectivity of the stimulation site with right and left orbitofrontal cortex, right ventromedial prefrontal cortex and left ventral striatum. For all assessments, the baseline connectivity of reward evaluation and response inhibition networks prior to STN-DBS was not associated with postoperative impulsivity; rather, these relationships were only observed when the stimulation field was incorporated. This suggests that the site and distribution of stimulation is a more important determinant of postoperative neuropsychiatric outcomes than preoperative brain structure and that stimulation acts to mediate impulsivity through differential recruitment of frontostriatal networks. Notably, a distinction could be made amongst participants with clinically-significant, harmful changes in mood and behaviour attributable to DBS, based upon an analysis of connectivity and its relationship with gambling behaviour. Additional analyses suggested that this distinction may be mediated by the differential involvement of fibres connecting ventromedial subthalamic nucleus and orbitofrontal cortex. These findings identify a mechanistic substrate of neuropsychiatric impairment after STN-DBS and suggest that tractography could be used to predict the incidence of adverse neuropsychiatric effects. Clinically, these results highlight the importance of accurate electrode placement and careful stimulation titration in the prevention of neuropsychiatric side-effects after STN-DBS.

Subjective estimates of uncertainty during gambling and impulsivity after subthalamic deep brain stimulation for Parkinson's disease.

Subthalamic deep brain stimulation (DBS) for Parkinson's disease (PD) may modulate chronometric and instrumental aspects of choice behaviour, including motor inhibition, decisional slowing, and value sensitivity. However, it is not well known whether subthalamic DBS affects more complex aspects of decision-making, such as the influence of subjective estimates of uncertainty on choices. In this study, 38 participants with PD played a virtual casino prior to subthalamic DBS (whilst 'on' medication) and again, 3-months postoperatively (whilst 'on' stimulation). At the group level, there was a small but statistically significant decrease in impulsivity postoperatively, as quantified by the Barratt Impulsiveness Scale (BIS). The gambling behaviour of participants (bet increases, slot machine switches and double or nothing gambles) was associated with this self-reported measure of impulsivity. However, there was a large variance in outcome amongst participants, and we were interested in whether individual differences in subjective estimates of uncertainty (specifically, volatility) were related to differences in pre- and postoperative impulsivity. To examine these individual differences, we fit a computational model (the Hierarchical Gaussian Filter, HGF), to choices made during slot machine game play as well as a simpler reinforcement learning model based on the Rescorla-Wagner formalism. The HGF was superior in accounting for the behaviour of our participants, suggesting that participants incorporated beliefs about environmental uncertainty when updating their beliefs about gambling outcome and translating these beliefs into action. A specific aspect of subjective uncertainty, the participant's estimate of the tendency of the slot machine's winning probability to change (volatility), increased subsequent to DBS. Additionally, the decision temperature of the response model decreased post-operatively, implying greater stochasticity in the belief-to-choice mapping of participants. Model parameter estimates were significantly associated with impulsivity; specifically, increased uncertainty was related to increased postoperative impulsivity. Moreover, changes in these parameter estimates were significantly associated with the maximum post-operative change in impulsivity over a six month follow up period. Our findings suggest that impulsivity in PD patients may be influenced by subjective estimates of uncertainty (environmental volatility) and implicate a role for the subthalamic nucleus in the modulation of outcome certainty. Furthermore, our work outlines a possible approach to characterising those persons who become more impulsive after subthalamic DBS, an intervention in which non-motor outcomes can be highly variable.

The structural connectivity of discrete networks underlies impulsivity and gambling in Parkinson's disease.

Impulsivity in Parkinson's disease may be mediated by faulty evaluation of rewards or the failure to inhibit inappropriate choices. Despite prior work suggesting that distinct neural networks underlie these cognitive operations, there has been little study of these networks in Parkinson's disease, and their relationship to inter-individual differences in impulsivity. High-resolution diffusion MRI data were acquired from 57 individuals with Parkinson's disease (19 females, mean age 62, mean Hoehn and Yahr stage 2.6) prior to surgery for deep brain stimulation. Reward evaluation and response inhibition networks were reconstructed with seed-based probabilistic tractography. Impulsivity was evaluated using two approaches: (i) neuropsychiatric instruments were used to assess latent constructs of impulsivity, including trait impulsiveness and compulsivity, disinhibition, and also impatience; and (ii) participants gambled in an ecologically-valid virtual casino to obtain a behavioural read-out of explorative, risk-taking, impulsive behaviour. Multivariate analyses revealed that different components of impulsivity were associated with distinct variations in structural connectivity, implicating both reward evaluation and response inhibition networks. Larger bet sizes in the virtual casino were associated with greater connectivity of the reward evaluation network, particularly bilateral fibre tracts between the ventral striatum and ventromedial prefrontal cortex. In contrast, weaker connectivity of the response inhibition network was associated with increased exploration of alternative slot machines in the virtual casino, with right-hemispheric tracts between the subthalamic nucleus and the pre-supplementary motor area contributing most strongly. Further, reduced connectivity of the reward evaluation network was associated with more 'double or nothing' gambles, weighted by connections between the subthalamic nucleus and ventromedial prefrontal cortex. Notably, the variance explained by structural connectivity was higher for behavioural indices of impulsivity, derived from clinician-administered tasks and the gambling paradigm, as compared to questionnaire data. Lastly, a clinically-meaningful distinction could be made amongst participants with a history of impulse control behaviours based on the interaction of their network connectivity with medication dosage and gambling behaviour. In summary, we report structural brain-behaviour covariation in Parkinson's disease with distinct reward evaluation and response inhibition networks that underlie dissociable aspects of impulsivity (cf. choosing and stopping). More broadly, our findings demonstrate the potential of using naturalistic paradigms and neuroimaging techniques in clinical settings to assist in the identification of those susceptible to harmful behaviours.

Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression.

This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future.

Inversion of hierarchical Bayesian models using Gaussian processes.

Over the past decade, computational approaches to neuroimaging have increasingly made use of hierarchical Bayesian models (HBMs), either for inferring on physiological mechanisms underlying fMRI data (e.g., dynamic causal modelling, DCM) or for deriving computational trajectories (from behavioural data) which serve as regressors in general linear models. However, an unresolved problem is that standard methods for inverting the hierarchical Bayesian model are either very slow, e.g. Markov Chain Monte Carlo Methods (MCMC), or are vulnerable to local minima in non-convex optimisation problems, such as variational Bayes (VB). This article considers Gaussian process optimisation (GPO) as an alternative approach for global optimisation of sufficiently smooth and efficiently evaluable objective functions. GPO avoids being trapped in local extrema and can be computationally much more efficient than MCMC. Here, we examine the benefits of GPO for inverting HBMs commonly used in neuroimaging, including DCM for fMRI and the Hierarchical Gaussian Filter (HGF). Importantly, to achieve computational efficiency despite high-dimensional optimisation problems, we introduce a novel combination of GPO and local gradient-based search methods. The utility of this GPO implementation for DCM and HGF is evaluated against MCMC and VB, using both synthetic data from simulations and empirical data. Our results demonstrate that GPO provides parameter estimates with equivalent or better accuracy than the other techniques, but at a fraction of the computational cost required for MCMC. We anticipate that GPO will prove useful for robust and efficient inversion of high-dimensional and nonlinear models of neuroimaging data.

A model-based analysis of impulsivity using a slot-machine gambling paradigm.

Impulsivity plays a key role in decision-making under uncertainty. It is a significant contributor to problem and pathological gambling (PG). Standard assessments of impulsivity by questionnaires, however, have various limitations, partly because impulsivity is a broad, multi-faceted concept. What remains unclear is which of these facets contribute to shaping gambling behavior. In the present study, we investigated impulsivity as expressed in a gambling setting by applying computational modeling to data from 47 healthy male volunteers who played a realistic, virtual slot-machine gambling task. Behaviorally, we found that impulsivity, as measured independently by the 11th revision of the Barratt Impulsiveness Scale (BIS-11), correlated significantly with an aggregate read-out of the following gambling responses: bet increases (BIs), machines switches (MS), casino switches (CS), and double-ups (DUs). Using model comparison, we compared a set of hierarchical Bayesian belief-updating models, i.e., the Hierarchical Gaussian Filter (HGF) and Rescorla-Wagner reinforcement learning (RL) models, with regard to how well they explained different aspects of the behavioral data. We then examined the construct validity of our winning models with multiple regression, relating subject-specific model parameter estimates to the individual BIS-11 total scores. In the most predictive model (a three-level HGF), the two free parameters encoded uncertainty-dependent mechanisms of belief updates and significantly explained BIS-11 variance across subjects. Furthermore, in this model, decision noise was a function of trial-wise uncertainty about winning probability. Collectively, our results provide a proof of concept that hierarchical Bayesian models can characterize the decision-making mechanisms linked to the impulsive traits of an individual. These novel indices of gambling mechanisms unmasked during actual play may be useful for online prevention measures for at-risk players and future assessments of PG.