Schizophrenia is a heterogeneous group of disorders, which shares some commonality in symptoms and neuropathology. These different syndromes within schizophrenia are characterized by the predominance of distinct symptom profiles. The overarching theme of this proposal is to distinguish biological subtypes from among the clinical syndrome profiles, using the combined approach of functional brain imaging, genetic markers, and neurocognitive testing. Clinically defined syndromes respond differently to antipsychotic treatments; conventional antipsychotics can mitigate positive symptoms, for example, without particularly aiding in treating negative symptoms, while atypical antipsychotics have a small but measurable effect on negative symptoms. Neuroimaging and genetic analyses have begun to elucidate the relationships among the clinical profiles and treatment response. We have found that there are genetic markers that can differentiate certain drug responders from nonresponders. Genetic markers as well can predict the occurrence of side effects to certain pharmacological interventions. These treatment response subtypes could represent more homogeneous forms of schizophrenia that may, in turn, have distinct brain structure/function characteristics.
The aim of this proposal is to investigate the hypothesis that these syndrome profiles are a consequence of dysfunction in separate brain areas or circuits, all of which communicate with the dorsal prefrontal cortex (DPFC). The DPFC includes the dorsolateral prefrontal cortex (DLPFC; BA 46/ventral 9) and anterior superior frontal gyrus (dorsal BA 9). We hypothesize that the DPFC dysfunction commonly found in schizophrenia can arise from abnormalities in the DPFC output, itself, or from various circuits and combinations of circuits that ultimately interact with the DPFC. Our use of fMRI will focus on both local and extended circuitry that we hypothesize is involved in producing different aspects of schizophrenia. In the Background section (below) we discuss the specific brain areas that are implicated in schizophrenia and our hypotheses as to how these areas are functionally linked in producing dysfunction. To test these hypotheses, improved and more accurate methods of identification and segmentation of these brain areas are required. We propose to extend our circuitry analyses by adding clinical measures, neurocognitive assessments, and genetic data to determine common patterns that could serve as endophenotypes. The impetus for this combination is the fact that schizophrenia is heritable and associated with specific neurocognitive deficits. Combining such data is a developing field and brings with it many computational and statistical challenges due to the large numbers of variables relative to the number of subjects. As a Driving Biological Project (DPB), this endeavor requires computational strengths and strategies implementation software in an interactive collaboration among the projects funded by this grant to address the challenges of the emerging field of combined imaging and genetic data analysis.
| Ghayoor, Ali; Vaidya, Jatin G; Johnson, Hans J (2018) Robust automated constellation-based landmark detection in human brain imaging. Neuroimage 170:471-481 |
| Wachinger, Christian; Toews, Matthew; Langs, Georg et al. (2018) Keypoint Transfer for Fast Whole-Body Segmentation. IEEE Trans Med Imaging : |
| Lyu, Ilwoo; Perdomo, Jonathan; Yapuncich, Gabriel S et al. (2018) Group-wise Shape Correspondence of Variable and Complex Objects. Proc SPIE Int Soc Opt Eng 10574: |
| Hong, Sungmin; Fishbaugh, James; Gerig, Guido (2018) 4D CONTINUOUS MEDIAL REPRESENTATION BY GEODESIC SHAPE REGRESSION. Proc IEEE Int Symp Biomed Imaging 2018:1014-1017 |
| Swanson, Meghan R; Wolff, Jason J; Shen, Mark D et al. (2018) Development of White Matter Circuitry in Infants With Fragile X Syndrome. JAMA Psychiatry 75:505-513 |
| Swanson, Meghan R; Shen, Mark D; Wolff, Jason J et al. (2018) Naturalistic Language Recordings Reveal ""Hypervocal"" Infants at High Familial Risk for Autism. Child Dev 89:e60-e73 |
| Sadeghi, Neda; Gilmore, John H; Gerig, Guido (2017) Twin-singleton developmental study of brain white matter anatomy. Hum Brain Mapp 38:1009-1024 |
| Freifeld, Oren; Hauberg, Soren; Batmanghelich, Kayhan et al. (2017) Transformations Based on Continuous Piecewise-Affine Velocity Fields. IEEE Trans Pattern Anal Mach Intell 39:2496-2509 |
| Wachinger, Christian; Brennan, Matthew; Sharp, Greg C et al. (2017) Efficient Descriptor-Based Segmentation of Parotid Glands With Nonlocal Means. IEEE Trans Biomed Eng 64:1492-1502 |
| Norton, Isaiah; Essayed, Walid Ibn; Zhang, Fan et al. (2017) SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research. Cancer Res 77:e101-e103 |
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