Gray Matter Surface-Based Spatial Statistics in Neuroimaging Studies

Neuroimaging provides an opportunity to gain valuable insight onto the microstructural changes in the brain associated with healthy growth and neurological disorders when conducting longitudinal or cross sectional studies. However, the data interpretation in this area need to be approached with extreme care as it poses challenges because of group bias and variance arising from various factors. The desire to better understand structural-functional relationship drives the need for robust frameworks to analyze structural and functional data, especially ones that can be generalized to novel types of neuroimaging data. This dissertation develops image analysis strategies that focus on improving statistical power in quantifying brain microarchitecture features for conducting group studies in gray matter. The gray matter cerebral cortex is less than 5 mm thick, yet is key to many brain functions. To overcome the challenges of alignment issues and partial volume effects in low-resolution images like diffusion/functional MRI, the gray matter surface based spatial statistics (GSBSS) approach was developed to perform statistical analysis of multi-modal data using gray matter surfaces. Application of this technique was shown in both diffusion and functional MRI modalities in psychosis population. The main contributions of this dissertation are (1) to show that our GSBSS approach improves the statistical power for performing group studies in neuroimaging compared to that of traditional registration methods, (2) to address source of bias and variance in group studies by correcting for inter-scanner variability effects of diffusion microstructure features and constructing unbiased feature based cortical surface template, (3) to apply deep learning techniques on cortical surfaces for improved sulcal curve labeling on large datasets

Improving human cortical sulcal curve labeling in large scale cross-sectional MRI using deep neural networks

Background: : Human cortical primary sulci are relatively stable landmarks and commonly observed across the population. Despite their stability, the primary sulci exhibit phenotypic variability. New Method: : We propose a fully automated pipeline that integrates both sulcal curve extraction and labeling. In this study, we use a large normal control population (n = 1424) to train neural networks for accurately labeling the primary sulci. Briefly, we use sulcal curve distance map, surface parcellation, mean curvature and spectral features to delineate their sulcal labels. We evaluate the proposed method with 8 primary sulcal curves in the left and right hemispheres compared to an established multi-atlas curve labeling method. Results: : Sulcal labels by the proposed method reasonably well agree with manual labeling. The proposed method outperforms the existing multi-atlas curve labeling method. Comparison with Existing Method: : Significantly improved sulcal labeling results are achieved with over 12.5 and 20.6 percent improvement on labeling accuracy in the left and right hemispheres, respectively compared to that of a multi-atlas curve labeling method in eight curves (p << 0.001, two-sample t-test). Conclusion: : The proposed method offers a computationally efficient and robust labeling of major sulci

Determination of Gyro Frequency from Critical Frequency Measurements Over Parit Raja, Malaysia

The critical frequencies of the ionosphere are observed from ionograms to determine the gyro frequencies. The ionograms are obtained from the Parit Raja station using an ionosonde at latitude 1° 52' N and longitude 103° 48' E which is close to the geomagnetic equator at geographic latitude 5° N. Preliminary results show that the gyro frequencies obtained are between 0.2 MHz and 1.4 MHz

Registration-based image enhancement improves multi-atlas segmentation of the thalamic nuclei and hippocampal subfields

Magnetic resonance imaging (MRI) is an important tool for analysis of deep brain grey matter structures. However, analysis of these structures is limited due to low intensity contrast typically found in whole brain imaging protocols. Herein, we propose a big data registration-enhancement (BDRE) technique to augment the contrast of deep brain structures using an efficient large-scale non-rigid registration strategy. Direct validation is problematic given a lack of ground truth data. Rather, we validate the usefulness and impact of BDRE for multi-atlas (MA) segmentation on two sets of structures of clinical interest: the thalamic nuclei and hippocampal subfields. The experimental design compares algorithms using T1-weighted 3T MRI for both structures (and additional 7 T MRI for the thalamic nuclei) with an algorithm using BDRE. As baseline comparisons, a recent denoising (DN) technique and a super-resolution (SR) method are used to preprocess the original 3 T MRI. The performance of each MA segmentation is evaluated by the Dice similarity coefficient (DSC). BDRE significantly improves mean segmentation accuracy over all methods tested for both thalamic nuclei (3 T imaging: 9.1%; 7 T imaging: 15.6%; ON: 6.9%; SR: 16.2%) and hippocampal subfields (3 T T1 only: 8.7%; DN: 8.4%; SR: 8.6%). We also present DSC performance for each thalamic nucleus and hippocampal subfield and show that BDRE can help MA segmentation for individual thalamic nuclei and hippocampal subfields. This work will enable large-scale analysis of clinically relevant deep brain structures from commonly acquired T1 images