Psychotic spectrum disorders (PSD) are difficult to differentially diagnose and treat, typically leaving their victims with lifetime disability. It is increasingly becoming recognized that traditionally distinct disorders such as schizophrenia, schizoaffective disorder and bipolar disorder with psychotic features share overlapping symptoms. For example, in addition to positive symptoms, PSD patients also experience deficits in cognitive control/executive functioning, which likely result from dysregulation of the mesocortical and mesostriatal pathways. Importantly, cognitive deficits contribute to deficits in interpersonal and occupational functioning, more traditional clinical symptoms (e.g., disorganized thinking) and are currently refractory to treatment. The current application will use novel recruiting strategies and novel multivariate analytic techniques to establish empirical, neuronally-based cluster metrics (i.e., circuit-level pathologies) that are associated with impairments in cognitive control (primary outcome) and everyday functioning (secondary outcome) in PSD regardless of traditional diagnoses (DSM-V). Other potential mediating variables evaluated in the current model include negative symptoms and disorganized thinking. We investigate potential causal mechanisms for these circuit- level pathologies by examining the aggregation of specific genetic mutations (single nucleotide polymorphisms; SNPs) within three neurotransmitter (dopamine, glutamate and GABA) signaling pathways, axonal guidance pathway, and synaptic long-term potentiation pathways based on our preliminary data. Finally, an exploratory aim evaluates whether the expression of cognitive control deficits across multiple psychiatric illnesses is mediated by each individual's total number of rare deletions in DNA (copy number variations; CNVs). To evaluate these hypotheses, 175 continuously recruited PSD patients will complete an extensive clinical battery and undergo multimodal neuroimaging. Evoked and intrinsic hemodynamic activity will be used in conjunction with white matter assays (diffusion tensor imaging) to investigate the integrity and connectivity of the cognitive control circuit (dorsal medial prefrontal cortex, lateral prefrontal cortex and caudate nucleus) during a multisensory cognitive control task with real-world validity. PSD patients will be classified into meaningful entities based on univariate and multivariate indices of grey/white matter pathology in the cognitive control network using a K-means algorithm. We will then determine the predictive validity of these clusters for describing deficits in cognitive control and everyday functioning, using the leave-one-out methodology to verify the model. Thus, the current application utilizes multiple units of analyses (genes, circuits, self-report, behavior, and paradigms) from the NIMH Research Domain Criteria to develop a novel classification system based on neurophysiological and genetic biomarkers of impaired cognitive control that spans traditional diagnostic categories. We are confident that moving beyond traditional nosologies will result in more meaningful diagnoses and ultimately more successful treatments for refractory symptoms, leading to substantial improvements in mental health care.
Psychotic spectrum (schizophrenia, schizophreniform disorder, schizoaffective disorder, delusional disorder, brief psychotic disorder, psychotic disorder not otherwise specified, and bipolar disorder I with history of psychotic features) disorders (PSD) are a large public health concern that results in tremendous interpersonal and societal costs. Although traditionally classified as separate entities, these diverse disorders share common psychotic symptoms, as well as similar cognitive deficits, disease courses and genetic determinants. However, the underlying reasons (e.g., abnormalities in brain circuitry, genetic make-up, etc.) why PSD patients tend to exhibit similar phenomenological behaviors (e.g., positive symptoms in conjunction with poor cognitive control) have yet to be elucidated. The current proposal will utilize novel analytic methods and neuroimaging techniques to classify PSD patients into meaningful sub-groups based on objective pathology within frontal brain circuits. We will also examine whether behavioral deficits and brain abnormalities are dependent on genetic factors. At the end of this proposal, we will produce a rich, publically available dataset that can be used to increase our collective understanding of these devastating diseases.
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| Dodd, Andrew B; Ling, Josef M; Bedrick, Edward J et al. (2018) Spatial distribution bias in subject-specific abnormalities analyses. Brain Imaging Behav 12:1828-1834 |
| Mayer, Andrew R; Dodd, Andrew B; Ling, Josef M et al. (2018) An evaluation of Z-transform algorithms for identifying subject-specific abnormalities in neuroimaging data. Brain Imaging Behav 12:437-448 |
| Mayer, Andrew R; Ryman, Sephira G; Hanlon, Faith M et al. (2017) Look Hear! The Prefrontal Cortex is Stratified by Modality of Sensory Input During Multisensory Cognitive Control. Cereb Cortex 27:2831-2840 |
| Houck, Jon M; Çetin, Mustafa S; Mayer, Andrew R et al. (2017) Magnetoencephalographic and functional MRI connectomics in schizophrenia via intra- and inter-network connectivity. Neuroimage 145:96-106 |
| Hanlon, Faith M; McGrew, Christopher A; Mayer, Andrew R (2017) Does a Unique Neuropsychiatric Profile Currently Exist for Chronic Traumatic Encephalopathy? Curr Sports Med Rep 16:30-35 |
| Hanlon, Faith M; Shaff, Nicholas A; Dodd, Andrew B et al. (2016) Hemodynamic response function abnormalities in schizophrenia during a multisensory detection task. Hum Brain Mapp 37:745-55 |
| Mayer, Andrew R; Hanlon, Faith M; Dodd, Andrew B et al. (2016) Proactive response inhibition abnormalities in the sensorimotor cortex of patients with schizophrenia. J Psychiatry Neurosci 41:312-21 |
| Mayer, Andrew R; Hanlon, Faith M; Teshiba, Terri M et al. (2015) An fMRI study of multimodal selective attention in schizophrenia. Br J Psychiatry 207:420-8 |
| Xue, Wenqiong; Bowman, F DuBois; Pileggi, Anthony V et al. (2015) A multimodal approach for determining brain networks by jointly modeling functional and structural connectivity. Front Comput Neurosci 9:22 |
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