Song-making In a Group (SING)
Condition: Schizophrenia
MeSH terms: Schizophrenia
Study type: Interventional

Summary: 
      The overarching aim of the proposed work is to align a promising treatment lead - Musical
      Intervention (MI) - with a promising mechanistic account of psychosis - Predictive
      Processing. The R61 phase (that this registration covers) will investigate the impact of
      group musical intervention on predictive processing metrics of hallucinations and social
      dysfunction.
    

Detailed description: 
      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:

      Those with hallucinations demonstrate higher degrees of perceptual belief on the first two
      layers (X1 and X2) and an over-reliance on prior beliefs ('prior over-weighting' p<0.0019).
      Those with psychosis, regardless of whether they hallucinate or not, are less likely to
      detect changes in the statistical structure of the task (X3) compared to non-psychotic
      participants ('change insensitivity'. Furthermore, there was a significant negative
      correlation between change sensitivity and illness burden and a significant positive
      correlation between prior weighting and hallucination severity score. For the first time, the
      investigators have an objective, laboratory-based measure of AVH, with component processes
      relevant to different features of hallucinations. The investigators propose to examine
      whether and how those AVH components change with the experience of MI.

      Social Learning in Mental Illness: Distrust and relational turbulence are core features of
      social problems in serious mental illness. These features can be modeled experimentally to
      interrogate their mechanistic basis. To assay social behavior, our research subjects play
      computer-based tasks with a partner (or confederate). The investigators record behavior and
      calculate trial-by-trial learning about partner trustworthiness, which varies over time
      (social volatility). Computational models can describe the details of how learning combines
      prior beliefs with new social experience during this task. For example, one can measure how
      quickly subjects learn about trustworthiness. The investigators expect learning rates to be
      slow early in the task when social volatility is low, and faster when social volatility is
      higher; players should change quickly to keep up. In the investigators first paper describing
      this approach, recently published in Biological Psychiatry, they found that both control
      subjects significantly increase their learning rates when social volatility is high but
      people with social challenges do not. The investigators hypothesize that MI will reduce
      social learning deficits in people with serious mental illnesses.

      Combining Quantitative and Qualitative Approaches: Quantitative and qualitative approaches
      may be differently appropriate for different study phases (exploration versus hypothesis
      testing). They also have fundamentally different conceptions of the scientific process
      (removed, objective versus engaged, subjective). The investigators believe that these
      approaches are not fundamentally incompatible, rather, they can be mutually informative and
      enriching. For example, the move toward peer support and engagement in mental health research
      has highlighted the shortcomings of the patrician expert-by-education-led approach to AVH
      research. In brief, clinical trials often employ tools to assess AVH severity which conflate
      salient features of AVH into single metrics, and thus do not distinguish which features
      change with treatment. Clinical trials have also assumed that the goal of AVH treatment is
      the eradication of voices by decreasing their frequency. Peer-led advocacy groups like The
      Hearing Voices Network (HVN), comprised of experts-by-experience, suggest instead that some
      voices can be positive and supportive, that even the negative voices carry important meaning
      and that the goal of treatment should be tailored toward the individual and honor that
      meaning. The investigators have argued that whilst HVN and computational psychiatry may
      appear strange bedfellows, their shared focus on plurality of explanation (across levels of
      analysis) and focus on AVH phenomenology suggest a powerful and mutually beneficial
      collaboration is possible. The proposed work, aligning quantitative computational work with
      qualitative analyses of AVH changes, social engagement and self-representation, will ensure
      that the investigators capture the ways in which MI changes AVH and social challenges in ways
      that are meaningful to service users, whilst grounding those changes in the mechanistic
      model-based understanding of AVH that computational psychiatry provides.

      Music & Predictive Processing: According to the predictive processing framework, backward
      predictions are passed down cortical hierarchies to resolve prediction errors at lower
      levels. Unresolved prediction errors can ascend the hierarchy to evince better predictions,
      based on their relative precision (inverse variance). This computational motif subsumes
      sensorimotor, autonomic, and memory systems. And prediction errors serve as imperatives to
      act within these systems (engaging in actions and homeostatic regulation that minimizes them
      across systems). Music affords competing predictions and then dispels uncertainty by
      confirming a particular prediction. Generating music is quintessentially enactive. Music
      perception is likewise. As with language, humans predict music based on how they might
      generate it themselves. Humans feel the drive to move our bodies to the beat to establish
      appropriate auditory predictions. Predictive processing implies the existence of a
      hierarchical generative model of precision that spans modalities. Attending to external music
      attenuates interoceptive and proprioceptive predictions of the sort one would encounter when
      generating music ourselves. In this way, music perception is more akin to language
      processing. The investigators suggest, based on preliminary data, that hallucinations and
      social dysfunction involve imbalances in the relative precisions of perceptual,
      proprioceptive and social priors and prediction errors. Music impacts hierarchies of dynamic
      precision, particularly when it is self-produced. In so doing, they hypothesize it will
      impact the pathophysiological mechanisms underlying AVH and social deficits.

      Song-making in a Group (SING): Preliminary qualitative interviews and ethnographic
      observations who frequented our MI program's drop-in site and participated in music-making
      and performance activities included twenty-one people, approximately 60% of whom reported
      currently receiving or having received mental health services. Analysis of the in-depth
      interviews and ethnographic field notes revealed four major characteristics of the musical
      intervention space and music making experience: 1) the importance of a nonclinical
      therapeutic and sober environment; 2) opportunities for social engagement and integration; 3)
      opportunities for identity (re)invention; and 4) an outlet for artistic and musical
      expression. For this proposal, the investigators have adapted that MI to facilitate the
      examination of predictive coding relevant mechanisms. This adapted intervention is called
      SING - Song-Making In a Group. In a one-hour session, 5 individuals work together with a
      trained facilitator to experience and/or produce music. The investigators propose to
      manipulate the SING group tasks to identify the impact of certain activities on AVH and
      social processing.

      The SING Team is unique, uniting people with lived experience of psychosis, quantitative and
      qualitative researchers, clinician scientists, and musicologists. This unity is made possible
      by the Connecticut Mental Health Center, a state mental health facility whose tripartite
      goals are treatment, education and research and whose unique partnership with Yale University
      is embodied in the two research centers connected by this application; the Yale Program for
      Recovery and Community Health and the Clinical Neuroscience Research Unit in the Abraham
      Ribicoff Research Facilities. Together these units have the real and virtual infrastructures,
      staff and experience to make the proposed work a success.
    

Intervention name(s): Musical Intervention
Intervention type(s): Behavioral
Intervention description(s): A trained musician-facilitator will convene a series of four weekly two-hour sessions to which groups of five participants will be invited. The facilitator provides keyboard, professional microphone, recording interface, headphones, guitar, computer and a Digital Audio Workstation (DAW) for recording.
Intervention model: Single Group Assignment
Masking: None (Open Label)
Primary purpose: Basic Science
Keywords: Hallucinations