This K23 proposal aims to understand auditory, speech, and cognitive networks underlying proneness to auditory hallucinations (AH) across the continuum of psychosis. The PI, Dr. Shinn, is a clinically trained psychiatrist with four years of post-residency training in psychosis functional connectivity (FC) research. She proposes to investigate both structural and dynamic aspects of connectivity, focusing on dorsal anterior cingulate cortex (dACC), auditory cortex (A1), voice perception (Vper), and voice production (Vpro) areas in AH- prone patients across the diagnostic categories of schizophrenia, schizoaffective disorder, bipolar psychosis, and DSM-IV psychotic disorder not otherwise specified (NOS). There is a clinical need to understand AH pathophysiology better, as AH are often distressing and associated with increased suicide risk. While antipsychotic medications can reduce their severity, in 25-30% of patients AH are refractory to antipsychotic medications. More effective interventions targeted at underlying AH pathophysiology are necessary. However, patient heterogeneity presents a major challenge to biological research in this area. Dr. Shinn proposes to directly address the issue of patient heterogeneity by: (a) Taking a dimensional and cross-diagnostic approach. AH are more homogeneous than diagnoses, and the current proposal aims to identify a neurobiological AH signature that transcends diagnosis. (b) Identifying subject- and functionally specific brain regions of interest and conducting analyses at the individual subject level; (c) Using multimodal neuroimaging within a single cohort to generate more cohesive understanding. Her approach, combining stochastic tractography, resting state fMRI (rsfMRI), and fMRI during a cognitive interference task, can inform about structural, baseline, and dynamic task-related aspects of connectivity. Dr. Shinn's hypothesis, which attempts to reconcile conflicting cognitive models of AH, is that AH-prone patients have increased baseline connectivity between A1, Vper, Vpro, and dACC, and an inability to modulate these connections during the auditory Stroop, a task that requires suppression of attention to irrelevant aspects of auditory stimuli. Such tonic hyper- and phasic hypo- connectivity may reflect a network that is biased towards hearing internally generated voices and less able to allocate resources when challenged. The current proposal may provide better understanding of circuitry abnormalities in AH, and expand possibilities for AH treatment using interventions such as repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation (tDCS), or neurofeedback, with an eye towards personalized medicine approaches. In parallel with the research proposed, Dr. Shinn will engage in multiple career development activities to vertically and horizontally expand her ability to develop mechanistic insights about AH. These include new training in cognitive neuroscience, better understanding of mechanisms involved in auditory and speech/language processing, and a stronger grasp of basic neuroscience. She will also receive advanced training in rsfMRI and new training in stochastic tractography.

Public Health Relevance

Auditory hallucinations are common, transcend diagnoses, and current antipsychotic medications used to attenuate auditory hallucinations are limited by intolerable side effects. Studying the dimension of auditory hallucinations across the psychosis spectrum (schizophrenia, schizoaffective disorder, bipolar psychosis, and psychosis not otherwise specified) may provide better leverage into understanding the pathophysiology of this often distressing symptom, and enable the identification of more targeted treatment interventions. In this proposal, we will examine connectivity abnormalities associated with auditory, speech-related, and cognitive networks using a subject-specific approach in patients with proneness to auditory hallucinations, and this may provide a basis for more personalized pathophysiology-based treatment interventions.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Mentored Patient-Oriented Research Career Development Award (K23)
Project #
5K23MH100611-04
Application #
9248448
Study Section
Neural Basis of Psychopathology, Addictions and Sleep Disorders Study Section (NPAS)
Program Officer
Chavez, Mark
Project Start
2014-04-01
Project End
2019-03-31
Budget Start
2017-04-01
Budget End
2018-03-31
Support Year
4
Fiscal Year
2017
Total Cost
$195,240
Indirect Cost
$13,240
Name
Mclean Hospital
Department
Type
Independent Hospitals
DUNS #
046514535
City
Belmont
State
MA
Country
United States
Zip Code
02478
Kim, Sang-Young; Cohen, Bruce M; Chen, Xi et al. (2017) Redox Dysregulation in Schizophrenia Revealed by in vivo NAD+/NADH Measurement. Schizophr Bull 43:197-204
Shinn, Ann K; Roh, Youkyung S; Ravichandran, Caitlin T et al. (2017) Aberrant cerebellar connectivity in bipolar disorder with psychosis. Biol Psychiatry Cogn Neurosci Neuroimaging 2:438-448
Shinn, Ann K; Bolton, Kirsten W; Karmacharya, Rakesh et al. (2017) McLean OnTrack: a transdiagnostic program for early intervention in first-episode psychosis. Early Interv Psychiatry 11:83-90
Ravichandran, Caitlin; Shinn, Ann K; Öngür, Dost et al. (2017) Frequency of non-right-handedness in bipolar disorder and schizophrenia. Psychiatry Res 253:267-269
Kani, Ayse Sakalli; Shinn, Ann K; Lewandowski, Kathryn E et al. (2017) Converging effects of diverse treatment modalities on frontal cortex in schizophrenia: A review of longitudinal functional magnetic resonance imaging studies. J Psychiatr Res 84:256-276
Ekstrom, Tor; Maher, Stephen; Shinn, Ann et al. (2016) Face identity discrimination in schizophrenia: Impairments to faces with high exposure in society. Schizophr Res 171:237-8
Shinn, Ann K; Baker, Justin T; Lewandowski, Kathryn E et al. (2015) Aberrant cerebellar connectivity in motor and association networks in schizophrenia. Front Hum Neurosci 9:134
Chyzhyk, Darya; Graña, Manuel; Öngür, Döst et al. (2015) Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI. Int J Neural Syst 25:1550007