Modelling anxiety in humans for drug development

Dynamic Tracking of State Anxiety via Multi-Modal Data and Machine Learning

Anxiety induction is widely used in the investigations of the mechanism and treatment of state anxiety. State anxiety is accompanied by immediate psychological and physiological responses. However, the existing state anxiety measurement, such as the commonly used state anxiety subscale of the State-Trait Anxiety Inventory, mainly relies on questionnaires with low temporal resolution. This study aims to develop a tracking model of state anxiety with high temporal resolution. To capture the dynamic changes of state anxiety levels, we induced the participants' state anxiety through exposure to aversive pictures or the risk of electric shocks and simultaneously recorded multi-modal data, including dimensional emotion ratings, electrocardiogram, and galvanic skin response. Using the paired self-reported state anxiety levels and multi-modal measures, we trained and validated machine learning models to predict state anxiety based on psychological and physiological features extracted from the multi-modal data. The prediction model achieved a high correlation between the predicted and self-reported state anxiety levels. This quantitative model provides fine-grained and sensitive measures of state anxiety levels for future affective brain-computer interaction and anxiety modulation studies.

The research domain criteria framework in drug discovery for neuropsychiatric diseases: focus on negative valence

Drug discovery, particularly in the field of central nervous system, has had very limited success in the last few decades. A likely contributor is the poor translation between preclinical and clinical phases. The Research Domain Criteria of the National Institutes of Mental Health is a framework which aims to identify new ways of classifying mental illnesses that are based on observable behaviour and neurobiological measures, and to provide a guiding and evolving framework to improve the translation from preclinical to clinical research. At the core of the Research Domain Criteria approach is the assumption that the dimensional constructs described can be assessed across different units of analysis, thus enabling a more precise quantitative understanding of their neurobiological underpinnings, increasing the likelihood of identifying new and effective therapeutic approaches. In the present review, we discuss how the Research Domain Criteria can be applied to drug discovery with the domain Negative Valence, construct Potential Threat (‘Anxiety’) as an example. We will discuss the evidence supporting the utility of the Research Domain Criteria approach and evaluate how close we are to achieving a common thread of translational research from gene to self-report.

Effects of lorazepam on brain activity pattern during an anxiety symptom provocation challenge

Human models of anxiety are useful to develop new effective anxiolytics. The objective of this study was to use functional magnetic resonance imaging (fMRI) to test the hypothesis that a single dose of lorazepam modifies brain activation during an anxiety challenge. Eighteen healthy male subjects underwent fMRI associated with a challenge based on the anticipation of aversive electrical stimulations after pretreatment, either with placebo or with 1.0 mg of oral lorazepam. Anxiety was rated before fMRI and after, referring to the threat condition periods, using State Trait Anxiety Inventory (STAI) and Hamilton scales. The conditioning procedure induced anxiety, as indicated by clinical rating score changes. Lorazepam did not modify anxiety rating as compared to placebo. Lorazepam reduced cerebral activity in superior frontal gyrus, anterior insula/inferior frontal gyrus and cingulate gyrus. The current finding provides the first evidence of the modulatory effects of an established anxiolytic agent on brain activation related to anticipatory anxiety.