Jessica Aylward

A striking reduction of simple loudness adaptation in autism

Reports of sensory disturbance, such as loudness sensitivity or sound intolerance, are ubiquitous in Autism Spectrum Disorder (ASD) but a mechanistic explanation for these perceptual differences is lacking. Here we tested adaptation to loudness, a process that regulates incoming sensory input, in adults with ASD and matched controls. Simple loudness adaptation (SLA) is a fundamental adaptive process that reduces the subjective loudness of quiet steady-state sounds in the environment over time, whereas induced loudness adaptation (ILA) is a means of generating a reduction in the perceived volume of louder sounds. ASD participants showed a striking reduction in magnitude and rate of SLA relative to age and ability-matched typical adults, but in contrast ILA remained intact. Furthermore, rate of SLA predicted sensory sensitivity coping strategies in the ASD group. These results provide the first evidence that compromised neural mechanisms governing fundamental adaptive processes might account for sound sensitivity in ASD.

Enhanced Risk Aversion, But Not Loss Aversion, in Unmedicated Pathological Anxiety

Background Anxiety disorders are associated with disruptions in both emotional processing and decision making. As a result, anxious individuals often make decisions that favor harm avoidance. However, this bias could be driven by enhanced aversion to uncertainty about the decision outcome (e.g., risk) or aversion to negative outcomes (e.g., loss). Distinguishing between these possibilities may provide a better cognitive understanding of anxiety disorders and hence inform treatment strategies. Methods To address this question, unmedicated individuals with pathological anxiety (n = 25) and matched healthy control subjects (n = 23) completed a gambling task featuring a decision between a gamble and a safe (certain) option on every trial. Choices on one type of gamble—involving weighing a potential win against a potential loss (mixed)—could be driven by both loss and risk aversion, whereas choices on the other type—featuring only wins (gain only)—were exclusively driven by risk aversion. By fitting a computational prospect theory model to participants’ choices, we were able to reliably estimate risk and loss aversion and their respective contribution to gambling decisions. Results Relative to healthy control subjects, pathologically anxious participants exhibited enhanced risk aversion but equivalent levels of loss aversion. Conclusions Individuals with pathological anxiety demonstrate clear avoidance biases in their decision making. These findings suggest that this may be driven by a reduced propensity to take risks rather than a stronger aversion to losses. This important clarification suggests that psychological interventions for anxiety should focus on reducing risk sensitivity rather than reducing sensitivity to negative outcomes per se.

Modeling Avoidance in Mood and Anxiety Disorders Using Reinforcement Learning

Background Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5% of all adult years lived with disability in the developed world. Avoidance behavior—avoiding social situations for fear of embarrassment, for instance—is a core feature of such anxiety. However, as for many other psychiatric symptoms the biological mechanisms underlying avoidance remain unclear. Methods Reinforcement learning models provide formal and testable characterizations of the mechanisms of decision making; here, we examine avoidance in these terms. A total of 101 healthy participants and individuals with mood and anxiety disorders completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock. Results We show an increased reliance in the mood and anxiety group on a parameter of our reinforcement learning model that characterizes a prepotent (pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the mood and anxiety group was under stress. Conclusions This formal description of avoidance within the reinforcement learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of mood and anxiety disorders.

Adaptation of social and non-social cues to direction in adults with autism spectrum disorder and neurotypical adults with autistic traits

Highlights • Autistic traits negatively predict adaptation magnitude for social and non-social cues.• Only adaptation magnitude for social eye-gaze is diminished in adults with ASD.• High ADOS scores predict smaller aftereffects for head and eye-gaze direction.• Diminished adaptation in autistic adults may only affect impaired perceptual domains.

Towards an emotional 'stress test': a reliable, non-subjective cognitive measure of anxious responding

Response to stress or external threats is a key factor in mood and anxiety disorder aetiology. Current measures of anxious responding to threats are limited because they largely rely on retrospective self-report. Objectively quantifying individual differences in threat response would be a valuable step towards improving our understanding of anxiety disorder vulnerability. Our goal is to therefore develop a reliable, objective, within-subject ‘stress-test’ of anxious responding. To this end, we examined threat-potentiated performance on an inhibitory control task from baseline to 2–4 weeks (n = 50) and again after 5–9 months (n = 22). We also describe single session data for a larger sample (n = 157) to provide better population-level estimates of task performance variance. Replicating previous findings, threat of shock improved distractor accuracy and slowed target reaction time on our task. Critically, both within-subject self-report measures of anxiety (ICC = 0.66) and threat-potentiated task performance (ICC = 0.58) showed clinically useful test-retest reliability. Threat-potentiated task performance may therefore hold promise as a non-subjective measure of individual anxious responding.

Back-Translating A Rodent Measure Of Negative Bias Into Humans: The Impact Of Induced Anxiety And Unmedicated Mood And Anxiety Disorders

Background Mood and anxiety disorders are ubiquitous but current treatment options are ineffective for large numbers of sufferers. Moreover, recent years have seen a number of promising pre-clinical interventions fail to translate into clinical efficacy in humans. Improved treatments are unlikely without better animal-human translational pipelines. Here, we directly adapt–i.e. back-translate - a rodent measure of negative affective bias into humans, and explore its relationship with a)pathological mood and anxiety symptoms (study one) and b)transient induced anxiety (study two). Method Participants who met criteria for mood or anxiety disorder symptomatology according to a face-to-face neuropsychiatric interview were included in the symptomatic group. N = 77(47 asymptomatic; Female = 21; 30 symptomatic; Female = 25) participants completed study one and N = 47 asymptomatic participants (25 female) completed study two. Outcome measures were choice ratios, reaction times and parameters recovered from a computational model of reaction time; the drift diffusion model (DDM). Results Symptomatic individuals demonstrated increased negative affective bias relative to asymptomatic individuals (proportion high reward = 0.42(SD = 0.14), and 0.53(SD = 0.17), respectively) as well as reduced DDM drift rate (p = 0.004). No significant effects were observed for the within-subjects anxiety-induction in study 2. Conclusion Humans with pathological anxiety symptoms directly mimic rodents undergoing anxiogenic manipulation. The lack of sensitivity to transient anxiety suggests the paradigm may, moreover, be primarily sensitive to clinically relevant symptoms. Our results establish a direct translational pipeline (and candidate therapeutics screen) from negative affective bias in rodents to pathological mood and anxiety symptoms in humans, and link it to a computational model of reaction time.

645. Neural, Cognitive, and Clinical Effects of Prefrontal Cortex Stimulation in Depression Combined with Psychological Therapy: A Double-Blind Randomized Controlled Trial

Transcranial direct current stimulation (tDCS) of the dorsolateral prefrontal cortex (DLPFC) has recently shown efficacy as a treatment for depression. We combined tDCS with psychological therapy to determine whether tDCS of the DLPFC could enhance therapeutic outcome in depression.

The impact of induced anxiety on affective response inhibition

Studying the effects of experimentally induced anxiety in healthy volunteers may increase our understanding of the mechanisms underpinning anxiety disorders. Experimentally induced stress (via threat of unpredictable shock) improves accuracy at withholding a response on the sustained attention to response task (SART), and in separate studies improves accuracy to classify fearful faces, creating an affective bias. Integrating these findings, participants at two public science engagement events (n = 46, n = 55) were recruited to explore the effects of experimentally induced stress on an affective version of the SART. We hypothesized that we would see an improved accuracy at withholding a response to affectively congruent stimuli (i.e. increased accuracy at withholding a response to fearful ‘no-go’ distractors) under threat of shock. Induced anxiety slowed reaction time, and at the second event quicker responses were made to fearful stimuli. However, we did not observe improved inhibition overall during induced anxiety, and there was no evidence to suggest an interaction between induced anxiety and stimulus valence on response accuracy. Indeed Bayesian analysis provided decisive evidence against this hypothesis. We suggest that the presence of emotional stimuli might make the safe condition more anxiogenic, reducing the differential between conditions and knocking out any threat-potentiated improvement.

Modelling avoidance in pathologically anxious humans using reinforcement-learning

Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5% of all adult ‘years lived with disability’ in the developed world. Avoidance behaviour –avoiding social situations for fear of embarrassment, for instance–is a core feature of such anxiety. However, as for many other psychiatric symptoms, the biological mechanisms underlying avoidance remain unclear. Reinforcement-learning models provide formal and testable characterizations of the mechanisms of decision-making; here, we examine avoidance in these terms. One hundred and one healthy and pathologically anxious individuals completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock. We show an increased reliance in the anxious group on a parameter of our reinforcement-learning model that characterizes a prepotent (Pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the anxious individuals were under stress. This formal description of avoidance within the reinforcement-learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of pathological anxiety.