. The parent R01 (R01MH094524) is focused on identifying links between gray matter networks, genomics, and symptom data in a larger aggregated dataset of schizophrenia and bipolar patients.
The aims of the R01 are:
aim 1) to determine gray matter networks underlying psychosis, 2) to identify homogeneous subclusters with similar gray matter and symptom profiles, 3) to identify the genomic patterns underlying gray matter maps, and 4) to extract genetic and structurally similar individual with psychosis. In this supplement, we are proposing to extend the initial techniques for identifying gray matter patterns underlying symptom profiles, to test both their specificity and generalizability across diagnostic categories. We apply them to two neurodegenerative disorders, Alzheimer?s disease and Huntington?s disease, using the ADNI and PREDICT-HD datasets21-24. Building on the research domain criteria (RDoC) dimensional approach we will focus on similar cognitive constructs with related symptoms in an extended sample of individuals. This supplemental work serves the purpose of the parent grant by determining whether the measures we find are specific to a neuropsychiatric population or if they generalize to neurodegenerative disorders; and it extends the understanding of neuropsychiatric symptoms and brain changes in Alzheimer?s disease (AD), an area of growing need. As many as 80% of AD patients may be affected by neuropsychiatric symptoms including delusions, apathy, and depression, with hallucinations possible but less frequent2. Understanding why these arise in some individuals but not others; how they relate to disease progression or response to treatment; and the underlying physiology of these symptoms are questions that are commonly addressed in psychosis research?but do the same answers hold for AD?
The recognition that bipolar disorder and schizophrenia are closely linked phenomenologically, physiologically and genetically, rather than clearly separated disorders, leads us to perform a diagnosis-blind three-way analysis of structural neuroimaging, genome-wide scan, and symptom data on an aggregated dataset of over 4000 individuals with diagnoses of bipolar disorder or schizophrenia. We identify the linked patterns of gray matter effects and symptom presentation across diagnostic groups, distinguishing genetic networks and brain patterns underlying the clinically disparate presentations of positive and negative psychotic symptoms. In this supplement, we are proposing to extend the initial techniques for identifying gray matter patterns underlying symptom profiles, to test both their specificity and generalizability across diagnostic categories. We apply them to two neurodegenerative disorders, Alzheimer?s disease and Huntington?s disease, using the ADNI and PREDICT-HD datasets21-24. .
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