Song-making In a Group (SING)

Auditory verbal hallucinations (AVH) are among the most distressing and disabling aspects of psychotic illness. They increase the risk of suicide and are only 70% likely to respond to antipsychotics. Despite statistical dissociation of positive and negative psychotic symptoms4, AVH form and foment in the context of social isolation. Furthermore, these social challenges do not respond to current pharmacotherapies, which may even iatrogenically worsen them, leading to challenges with adherence. There is a need for improved treatments, for both AVH and social difficulties, with a favorable side effect profile. Musical intervention (MI) is one such candidate. According to some small qualitative and quantitative studies, MI improves hallucinations and negative symptoms and it is remarkably well tolerated. However, it is unknown how musical interventions lead to symptomatic recovery in psychosis. The overarching aim of the proposed work is to align a promising treatment lead - MI - with a promising mechanistic account of psychosis - Predictive Processing. The R61 phase will investigate the impact of group musical intervention on predictive processing metrics of hallucinations and social dysfunction. Armed with a mechanistic understanding of musical intervention for psychosis, the investigators will be well placed, in the R33 phase, to optimize its administration (Is active participation more effective than passive listening? Does creation of new music help more than performing others' creations?). By tracking the interrelation between symptom mechanisms and MI, the investigators can use those metrics to prospectively assign patients to particular MI.

Go/No Go Decisions: Do metrics of hallucinations and social processing change with musical intervention?

Music and Psychosis: MIs mollify the salient features of auditory verbal hallucinations - like their duration with improvements lasting years into follow-up in some cases. Meta-analysis of 19 studies showed MI to be effective for negative and cognitive symptoms of psychosis (d = 0.71), particularly for popular music over classical. There were no significant differences between groups that passively listened versus those that produced music, nor between music selected by therapist or patient, all helped. However, the dependent variables were subjective ratings scales that often failed to capture both AVH and negative symptoms in the same participants. There is a real need for objective measures of hallucinations and negative symptoms, which the investigators feel their recent computational psychiatry work provides (see below). The investigators propose to employ these metrics in a new, appropriately powered study of MI. Comparisons of active and passive engagement, with music that participants do and don't feel ownership for will be made. It is these factors - ownership and activity - the investigators believe - based on their preliminary data - are the active ingredients of MI.

Mechanisms of Psychosis: Computational modeling of perceptual and decision-making processes offers one approach to identifying objective metrics of processes relevant to AVH and social challenges. The investigators recent work has provided such a computational understanding of AVH. Perception is not simply the passive reception of inputs. Humans actively infer the causes of our sensations. These inferences are influenced by our prior experiences. Priors and inputs are combined according to Bayes' rule. Prediction errors, the mismatch between priors and inputs, contribute to belief updating. Hallucinations (percepts without external stimulus) may arise when strong priors cause a percept in the absence of customary input. The investigators recently tested this theory by engendering new priors about auditory stimuli in human observers using Pavlovian conditioning. Even in healthy individuals, the repeated co-occurrence of visual and auditory stimuli can induce auditory hallucinations. The investigators examined this effect with functional imaging. They used computational modeling to infer the strength of participants' perceptual beliefs about stimuli, associations between stimuli, and the volatility of those associations. Importantly, the model captured how priors are combined with sensory evidence, allowing us to directly test the strong prior hypothesis. First, the investigators determined individual thresholds for detection and psychometric curves. Next, participants worked to detect a 1-Kilo Hertz tone occurring concurrently with presentation of a checkerboard visual stimulus. At the start of conditioning, the tone was presented frequently at threshold, engendering a belief in audio-visual association. This belief was then tested with increasingly frequent sub-threshold and target-absent trials. Conditioned hallucinations occurred when subjects reported tones that were not presented, conditional upon the visual stimulus.

After learning the association between the visual and auditory stimuli, all groups reported hearing tones that had not been presented (conditioned hallucinations), although the H+ groups did so significantly more frequently. To understand these results in the context of our formal model of perception, the investigators employed a three-tiered Hierarchical Gaussian Filter (HGF), which uses participant responses and the task structure to model estimate perceptual beliefs across three levels of abstraction. The first level of the model (X1) represents whether the subject believes that a tone was present or not on each trial. The second level (X2) is their belief that visual cues predict tones. The third level (X3) is the change in belief about the contingency between visual and auditory stimuli (i.e., volatility of X2). HGF modeling of conditioned hallucinations in our participants resulted in two findings critical to the present proposal: