IDENTIFICATION OF NEUROMARKERS USING STRUCTURAL AND FUNCTIONAL NEUROIMAGING

One of the grand challenges in science is understanding the human brain. Recent advances in magnetic resonance imaging have opened unmatched opportunities to demystify neural circuitry. In this research, the spatially and temporally complex neuroimaging data were used to identify neuromarkers that aids in quantifying a brain’s health. In the first part, a comparison of static and dynamic functional connectivities was made to study their efficacies in identifying intrinsic individual connectivity patterns. Results show that the intrinsic individual connectivity pattern can be used as a ‘fingerprint’ to distinguish among and identify subjects and their biological sex and is more accurately captured with partial correlation and assuming static connectivity. The neuromarkers involved in identifying subjects and their sex were distinguished using edge consistency, variability, and differential power measures. The second part maps neuronal and functional complexities estimated using multi-scale entropy at various levels of the brain’s organization. The work introduces functional complexity, and neuromarkers were identified to predict fluid intelligence. The third part explores brain abnormalities associated with early life stress and how resilience helps in mitigating its effects. Results reveal that the neural correlates of reward processing may serve as neuroimaging phenotypes. The fourth part utilizes multimodal ensemble deep learning to predict disruptive behavior disorders in children. Results show the potential of the deep learning model to predict disruptive behavior disorders and to identify neuroimaging phenotypes using gradient class activation maps. Future research can build on this study to investigate brain neuromarkers in health, diseases and disorders --Abstract, p. i

Modular Processing of Two-Dimensional Significance Map for Efficient Feature Extraction

Scientific visualization is an essential and indispensable tool for the systematic study of computational (CFD) datasets. There are numerous methods currently used for the unwieldy task of processing and visualizing the characteristically large datasets. Feature extraction is one such technique and has become a significant means for enabling effective visualization. This thesis proposes different modules to refine the maps which are generated from a feature detection on a dataset. The specific example considered in this work is the vortical flow in a two-dimensional oceanographic dataset. This thesis focuses on performing feature extraction by detecting the features and processing the feature maps in three different modules, namely, denoising, segmenting and ranking. The denoising module exploits a wavelet-based multiresolution analysis (MRA). Although developed for two-dimensional datasets, these techniques are directly extendable to three-dimensional cases. A comparative study of the performance of Optimal Feature-Preserving (OFP) filters and non-OFP filters for denoising is presented. A computationally economical implementation for segmenting the feature maps as well as different algorithms for ranking the regions of interest (ROI\u27s) are also discussed in this work

A Study of Brain Neuronal and Functional Complexities Estimated Using Multiscale Entropy in Healthy Young Adults

Brain complexity estimated using sample entropy and multiscale entropy (MSE) has recently gained much attention to compare brain function between diseased or neurologically impaired groups and healthy control groups. Using resting-state functional magnetic resonance imaging (rfMRI) blood oxygen-level dependent (BOLD) signals in a large cohort (n = 967) of healthy young adults, the present study maps neuronal and functional complexities estimated by using MSE of BOLD signals and BOLD phase coherence connectivity, respectively, at various levels of the brain’s organization. The functional complexity explores patterns in a higher dimension than neuronal complexity and may better discern changes in brain functioning. The leave-one-subject-out cross-validation method is used to predict fluid intelligence using neuronal and functional complexity MSE values as features. While a wide range of scales was selected with neuronal complexity, only the first three scales were selected with functional complexity. Fewer scales are advantageous as they preclude the need for long BOLD signals to calculate good estimates of MSE. The presented results corroborate with previous findings and provide a baseline for other studies exploring the use of MSE to examine changes in brain function related to aging, diseases, and clinical disorders

A Comparison of Static and Dynamic Functional Connectivities for Identifying Subjects and Biological Sex using Intrinsic Individual Brain Connectivity

Functional magnetic resonance imaging has revealed correlated activities in brain regions even in the absence of a task. Initial studies assumed this resting-state functional connectivity (FC) to be stationary in nature, but recent studies have modeled these activities as a dynamic network. Dynamic spatiotemporal models better model the brain activities, but are computationally more involved. A comparison of static and dynamic FCs was made to quantitatively study their efficacies in identifying intrinsic individual connectivity patterns using data from the Human Connectome project. Results show that the intrinsic individual brain connectivity pattern can be used as a ‘fingerprint’ to distinguish among and identify subjects and is more accurately captured with partial correlation and assuming static FC. It was also seen that the intrinsic individual brain connectivity patterns were invariant over a few months. Additionally, biological sex identification was successfully performed using the intrinsic individual connectivity patterns, and group averages of male and female FC matrices. Edge consistency, edge variability and differential power measures were used to identify the major resting-state networks involved in identifying subjects and their sex

Molecular Mechanisms of Cannabinoids as Anti-cancer Agents

Cancer is a growing health concern world-wide and is the second most common cause of death after heart diseases. Current treatment strategies such as surgery, chemotherapy and radiation provide some relief to cancer patients but the toxic side effects associated with chemotherapy and radiation often lead to further adverse health effects. Hence there is a need for drugs with better safety profile and improved efficacy. Cannabinoids are a group of compounds with several therapeutic properties and besides their appetite stimulant, anti-emetic and analgesic effects, cannabinoids can inhibit tumor growth, survival and metastasis. The mechanisms of action of cannabinoids as anticancer agents are highly complex and not completely understood. Studies from our laboratory indicate that the specificity protein (Sp) transcription factors, Sp1, Sp3 and Sp4 that belong to the Sp/KLF family of transcription factors are overexpressed in many tumors and regulate critical factors responsible for cancer cell proliferation, growth, angiogenesis and survival. Hence, we hypothesized that cannabinoids elicit their responses on cancer cells by downregulating the expression of Sp proteins and Sp-regulated gene products. Treatment of colon and prostate cancer cells with the cannabinoids WIN and cannabidiol (CBD) inhibited cancer cell proliferation, induced apoptosis and downregulated Sp proteins and Sp-dependent genes. Furthermore, we demonstrated that WIN and CBD-mediated induction of apoptosis and repression of Sp proteins were mediated by phosphatases and that the phosphatase involved in WIN- dependent downregulation of Sp proteins was protein phosphatase 2A (PP2a). In addition WIN induced expression of ZBTB-10, an Sp repressor and downregulated microRNA-27a (miR27a) and these effects were PP2a-dependent indicating that WIN transcriptionally represses Sp protein expression by activating the phosphatase, PP2a. We also investigated the effects of 1,1-bis(3'-indolyl)-1-(p-bromophenyl)methane (DIM-C-pPhBr) and the 2,2'-dimethyl analog (2,2'-diMeDIM-C-pPhBr), on survivin expression in colon and pancreatic cancer cells. Survivin is an anti-apoptotic protein associated with cancer cell survival and confers radiation-resistance in patients receiving radiotherapy. In addition radiation induces survivin, leading to radioresistance in tumors. In this study we demonstrated that DIM-C-pPhBr and 2,2'-diMeDIM-C-pPhBr inhibit cell proliferation and induce apoptosis in colon and pancreatic cancer cells and in combination with radiotherapy, these drugs suppress radioresistance by inhibiting radiation induced survivin

Consumer Preferences on eating-out at Fast Food Restaurants: A quantitative study of Pizza Hut Outlets in India

The concept of meal as eating a variety of kinds of food at regular intervals have now been replaced by breaks of fast individual eating, and mostly the young crowd nowadays do not have a proper eating pattern and prefer quick and tasty food. The fast food Industry identifies the preferences of adolescents and children, and targets a major part on their marketing schemes on them, since they have an important portion of their customer base. Young adults, another major important segment of their consumer base are equally targeted. The Fast food industry incorporates attributes into their outlets on the basis of their understanding and interpretation of the preferences of consumers in the environment they are operating in. These preferences vary across nations and hence the attributes that needs to be incorporated into the fast food outlets vary accordingly in each country of operation. Dominant consumer preferences in a country are given more importance. Certain fast food attributes facilitate consumer’s decision to dine at these outlets more effectively in a particular country compared to another. A well structured survey is conducted to solicit response from fast food consumers of one fast food chain in India, for analysis. These values are then analyzed using the corresponding analytical methods to understand the relationship between fast food outlets attributes and the frequency of visits to these fast food outlets by consumers, and thereby, to find the most influential fast food attributes resulting in the frequent visits to these outlets

Neuroinflammation and white matter alterations in obesity assessed by Diffusion Basis Spectrum Imaging

Human obesity is associated with low-grade chronic systemic inflammation, alterations in brain structure and function, and cognitive impairment. Rodent models of obesity show that high-calorie diets cause brain inflammation (neuroinflammation) in multiple regions, including the hippocampus, and impairments in hippocampal-dependent memory tasks. To determine if similar effects exist in humans with obesity, we applied Diffusion Basis Spectrum Imaging (DBSI) to evaluate neuroinflammation and axonal integrity. We examined diffusion-weighted magnetic resonance imaging (MRI) data in two independent cohorts of obese and non-obese individuals (Cohort 1: 25 obese/21 non-obese; Cohort 2: 18 obese/41 non-obese). We applied Tract-based Spatial Statistics (TBSS) to allow whole-brain white matter (WM) analyses and compare DBSI-derived isotropic and anisotropic diffusion measures between the obese and non-obese groups. In both cohorts, the obese group had significantly greater DBSI-derived restricted fraction (DBSI-RF; an indicator of neuroinflammation-related cellularity), and significantly lower DBSI-derived fiber fraction (DBSI-FF; an indicator of apparent axonal density) in several WM tracts (all correcte