Extracting Generalizable Hierarchical Patterns Of Functional Connectivity In The Brain

The study of the functional organization of the human brain using resting-state functional MRI (rsfMRI) has been of significant interest in cognitive neuroscience for over two decades. The functional organization is characterized by patterns that are believed to be hierarchical in nature. From a clinical context, studying these patterns has become important for understanding various disorders such as Major Depressive Disorder, Autism, Schizophrenia, etc. However, extraction of these interpretable patterns might face challenges in multi-site rsfMRI studies due to variability introduced due to confounding variability introduced by different sites and scanners. This can reduce the predictive power and reproducibility of the patterns, affecting the confidence in using these patterns as biomarkers for assessing and predicting disease. In this thesis, we focus on the problem of robustly extracting hierarchical patterns that can be used as biomarkers for diseases. We propose a matrix factorization based method to extract interpretable hierarchical decomposition of the rsfRMI data. We couple the method with adversarial learning to improve inter-site robustness in multi-site studies, removing non-biological variability that can result in less interpretable and discriminative biomarkers. Finally, a generative-discriminative model is built on top of the proposed framework to extract robust patterns/biomarkers characterizing Major Depressive Disorder. Results on large multi-site rsfMRI studies show the effectiveness of our method in uncovering reproducible connectivity patterns across individuals with high predictive power while maintaining clinical interpretability. Our framework robustly identifies brain patterns characterizing MDD and provides an understanding of the manifestation of the disorder from a functional networks perspective which can be crucial for effective diagnosis, treatment and prevention. The results demonstrate the method\u27s utility and facilitate a broader understanding of the human brain from a functional perspective

Large Eddy Simulations for Film Cooling Assessment of Cylindrical and Laidback Fan-Shaped Holes With Reverse Injection

The large eddy simulations (LES) are performed to access the film cooling performance of cylindrical and reverse shaped hole for forward and reverse injection configurations. In the case of reverse/backward injection, the secondary flow is injected in such a way that its axial velocity component is in the direction opposite to mainstream flow. The study is carried out for a blowing ratio (M =1), density ratio (DR =2.42), and injection angle (α = 35 deg). Formation of counter-rotating vortex pair (CRVP) is one of the major issues in the film cooling. This study revealed that the CRVP found in the case of forward cylindrical hole which promotes coolant jet "liftoff" is completely mitigated in the case of the reverse shaped hole. The coolant coverage for reverse cylindrical and reverse shaped holes is uniform and higher. The reverse shaped hole shows promising results among investigated configurations. The lateral averaged film cooling effectiveness of reverse shaped hole is 1.16-1.42 times higher as compared to the forward shaped holes. The improvement in the lateral averaged film cooling effectiveness of reverse cylindrical hole (RCH) injection over forward cylindrical hole (FCH) injection is 1.33-2 times

Extracting Generalizable Hierarchical Patterns of Functional Connectivity in the Brain

The study of the functional organization of the human brain using resting-state functional MRI (rsfMRI) has been of significant interest in cognitive neuroscience for over two decades. The functional organization is characterized by patterns that are believed to be hierarchical in nature. From a clinical context, studying these patterns has become important for understanding various disorders such as Major Depressive Disorder, Autism, Schizophrenia, etc. However, extraction of these interpretable patterns might face challenges in multi-site rsfMRI studies due to variability introduced due to confounding variability introduced by different sites and scanners. This can reduce the predictive power and reproducibility of the patterns, affecting the confidence in using these patterns as biomarkers for assessing and predicting disease. In this thesis, we focus on the problem of robustly extracting hierarchical patterns that can be used as biomarkers for diseases. We propose a matrix factorization based method to extract interpretable hierarchical decomposition of the rsfRMI data. We couple the method with adversarial learning to improve inter-site robustness in multi-site studies, removing non-biological variability that can result in less interpretable and discriminative biomarkers. Finally, a generative-discriminative model is built on top of the proposed framework to extract robust patterns/biomarkers characterizing Major Depressive Disorder. Results on large multi-site rsfMRI studies show the effectiveness of our method in uncovering reproducible connectivity patterns across individuals with high predictive power while maintaining clinical interpretability. Our framework robustly identifies brain patterns characterizing MDD and provides an understanding of the manifestation of the disorder from a functional networks perspective which can be crucial for effective diagnosis, treatment and prevention. The results demonstrate the method\u27s utility and facilitate a broader understanding of the human brain from a functional perspective

Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience

Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to a broader family called generative methods, which generate new data with a probabilistic model by learning sample distribution from real examples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases

Image1_Efficient and eco-friendly treatment of wastewater through sustainable purification using agricultural waste and coagulation kinetic modelling.JPEG

This scientific manuscript presents a comprehensive investigation into the purification of municipal sewage water through the utilization of agricultural waste materials [Arachis hypogaea shells (AHS), Triticum aestivum straw (TAS), and Gossypium herbaceum shells (GHS)]. The treatment process involved a modified approach with 24 hs of aeration and the addition of 1 gm of agricultural waste biomaterials. The performance of the bio-coagulant was evaluated by monitoring the reduction of physico−chemical parameters. AHS exhibited remarkable turbidity removal efficiency of 93.37%, supported by pseudo−first and pseudo−second−order kinetic modelling. The application of agricultural waste materials significantly reduced key parameters, including solids (up to 70%–80%), dissolved oxygen (DO) (50%), biological oxygen demand (BOD) and chemical oxygen demand (COD) (up to 90%). Principal Component Analysis (PCA) showed the significant positive loading of PC1 (84.71%) that influencing the dual treatments of wastewater. Statistical analysis (p ≤ 0.05) confirmed the effectiveness of agricultural biomaterials in sewage water treatment compared to pre−treated water. The turbidity coagulation pseudo−first−order and pseudo−second−order kinetic modelling also revealed the efficiency against turbidity reduction in municipal sewage water. The findings underscore the significance of utilizing agricultural waste materials for sustainable and efficient purification of municipal sewage water, addressing water pollution and enhancing wastewater treatment processes.</p