Medical Imaging has been increasingly expanding as the forefront of neurological research thanks to the multi-faceted understanding it renders us with. Stemming from my primary undergraduate education in Biomedical Engineering, my research interest has been consistently focused on medical imaging. In the years since, I have utilized many mathematical techniques in applications across the board ranging from simple classifier implementations to multivariate analyses techniques. I have also had the opportunity to work with different brain MR images such as structural images, resting state and task based functional images as well as diffusion tensor images in different populations such as schizophrenia, late-life depression and methamphetamine addiction. Through the expansive learning from these research projects, I have developed a purposeful interest in understanding cognitive implications in psychological disorders specifically autism, using functional imaging and analysis techniques applied to different disorders. The overarching problem my research seeks to contribute to is twofold - to explore typical variability in functional neuroimaging features and to exploit this knowledge of variability to better characterize autism spectrum disorders.
The holy grail of functional neuroimaging studies is the identification of a biomarker, which can identify abnormal neuronal activity that can be used to diagnose disease and track the effectiveness of treatment and disease progression. Typically, approaches that search for biomarkers start by identifying mean activation differences between groups of patients and healthy controls. The aim of these studies is to identify brain regions with significant differences in regional signal modulation across groups either during the resting state or in response to certain stimuli. However, combining such large data from different subjects and groups to make meaningful statistical inferences is not trivial. Individual variability has already been considered a source of information in both structure and function. Studies show that show that individuals in a group have broadly similar functional organization to the group but have distinct topological factors that might be network specific to those involved in higher-order functioning. This variability has important implications for the evolution of higher-order cognitive abilities and might relate to an increased susceptibility to the formation of abnormal circuitry as manifested in neuropsychiatric disorders. Such revelations are possibly associated with network maturation and developmental differences, and warrant further inspection.
While researchers have used different strategies to characterize the variability representative of the typically developing population as well as to highlight deviations in this variability stemming from disorders, enough evidence to clearly characterize either the anatomical variability or variance in cognitive abilities and functional activation patterns is not available. Furthermore, disorders such as autism and schizophrenia that are characterized as heterogeneous within the population provide additional justification to the notion that functional variability has implications in the multifarious manifestations of these disorders. The diverse symptoms exhibited by diagnosed patients lend support to this hypothesis.
My research seeks improved measures to quantify biologically-meaningful spatial, spectral and connectivity based variability and to identify associated cognitive or behavioral differences that correlate with those measures so as to better understand the pathology and distribution of brain networks characterizing autism and other heterogeneous disorders. The focus is on intrinsic functional variability that manifests in resting state networks to exploit the relationship between functional activation and cognitive or behavioral differences in individuals.