University of Macau / Faculty of Health Sciences

This thesis aimed to investigate the feasibility of two diffusion imaging techniques, Diffusion Tensor Imaging (DTI) and Diffusion Kurtosis Imaging (DKI), on detecting subtle physiological or pathological microstructural changes in normal-appearing neural tissues of human central nervous system. 　　　At first, ten patients with neuromyelitis optica (NMO) and twelve age- and gender-matched healthy subjects were recruited. DTI-derived indices including fractional anisotropy (FA), mean diffusivity (MD), axial and radial diffusivities were quantified in the lateral and dorsal columns of cervical spinal cord. Based on the regions of interest (ROIs) measurement, NMO patients showed reduced FA, increased MD and radial diffusivity compared to control subjects, while axial diffusivity did not show any significant difference. The three former DTI metrics also showed significant correlations with disability scores, and especially FA was found to be sensitive to mild NMO. Our results show that DTI-derived indices can quantitatively assess the white matter (WM) abnormalities with seemingly normal appearance in conventional MRI, and are associated with the level of clinical disability, suggesting that DTI may have great potential as a useful diagnostic tool in the clinical setting. 　　　DKI is an extension of conventional DTI to probe the non-Gaussian diffusion property in biological tissues. Besides the four conventional DTI-derived metrics, DKI also provide three additional kurtosis metrics (mean kurtosis (MK), axial and radial kurtosis). In the second study, ROI-based analysis was used to characterize age-related microstructural changes in WM, cortical and subcortical gray matter (GM) of 27 healthy adults (21~59 yrs). Though the volumes of GM and WM were still preserved, DTI-derived metrics can detect the subtle changes in WM and GM. Meanwhile, MK and radial kurtosis significantly increased in both caudate nucleus and putamen while Thalamus showed little aging effect in the diffusivity and kurtosis metrics but significantly decreased only in FA. Our results demonstrated that DKI is sensitive to detect the age-related alterations in neural microstructures at the stage of early aging. 　　　In addition, DKI has been applied to detect the pathological changes in the normal-appearing neural tissues of 18 patients with multiple sclerosis (MS), compared to 22 healthy controls. Diffuse WM abnormalities have been observed extensively in the brain, revealed by DKI-derived metrics. Though the volumetric and voxel-wise analysis revealed no significant changes in the volume of cortical GM, decreased FA and kurtoses with increased diffusivities in MS group were sensitive to disclose the subtle alterations in global and regional cortical GM tissues. Significant correlations have been found between FA in the global, frontal and temporal cortical GM in relapsing-remitting MS patients and their disability scores, suggesting FA as an important biomarker to monitor the disease progress in cortical GM. Moreover, elevated kurtosis indices in MS patients did not correlate with diffusivities in caudate nucleus, putamen and thalamus, suggesting these metrics may be vulnerable to different pathologic aspects of the disease. 　　　In conclusion, DKI is sensitive to neural alterations during normal aging and in MS pathologies, and can provide complementary information to conventional MRI and DTI. / published_or_final_version / Diagnostic Radiology / Doctoral / Doctor of Philosophy

In 2023, breakthroughs achieved by large language models like ChatGPT have been transformative, revealing the hidden structures within natural language. This has enabled these models to reason and perform tasks with intelligence previously unattainable, a feat made possible by analyzing vast datasets. However, the domain of medical imaging—characterized by the high costs and intensive labor of data acquisition, along with the scarcity of data from pathological sources—presents unique challenges. Neuroimaging data, for instance, is marked by its high dimensionality, limited sample sizes, complex hierarchical and temporal structures, significant noise, and contextual variability. These obstacles are especially prevalent in methodologies like functional Magnetic Resonance Imaging (fMRI) and computer vision applications, where datasets are naturally sparse. Developing sophisticated methods to overcome these challenges is essential for maximizing the utility of imaging technologies and enhancing our understanding of neurological functions. Such advancements are critical for the creation of innovative diagnostic tools and therapeutic approaches for neurological and psychiatric conditions. The data from current set of non-invasive neuroimaging modalities is most often analyzed using classical statistical and machine learning methods. In this work we show that widely used machine learning methods for neural imaging data can be unified under a Bayesian perspective. We use this unifying view of probabilistic modeling techniques to further develop models and statistical inference methods to address the aforementioned challenges by leveraging substantial research developments in artificial intelligence i.e. deep learning, and probabilistic modeling methods over the last decade. In this work, we broaden the family of probabilistic models to encompass various prior structures,including discrete, hierarchical, and temporal elements. We derive efficient inference models using principled Bayesian inference and modern stochastic optimization and empirically demonstrate how the representational capacity of neural networks can be combined with principled probabilistic generative models to achieve state-of-the-art results on neuroimaging and computer vision datasets. The methods we develop are applicable to a diverse range of datasets beyond neuroimaging; for instance, we apply these probabilistic inference principles to improve movie and song recommendations, enhance object detection in computer vision models, and perform neural architecture search.

Neurological disorders present a significant challenge in global health. With the increasing availability of imaging datasets and the development of precise machine learning models, early and accurate diagnosis of neurological conditions is a promising and active area of research. However, several characteristic factors in neurology domains, such as heterogeneous imaging, inaccurate labels, or limited data, act as bottlenecks in using deep learning on clinical neuroimaging. Given these circumstances, this dissertation attempts to provide a guideline, proposing several methods and showcasing successful implementations in broad neurological conditions, including epilepsy and neurodegeneration. Methodologically, a particular focus is on comparing a two-dimensional approach as opposed to three-dimensional neural networks. In most clinical domains of neurological disorders, data are scarce and signals are weak, discouraging the use of 3D representation of raw scan data. This dissertation first demonstrates competitive performances with 2D models in tuber segmentation and AD comorbidity detection. Second, the potentials of ensemble learning are explored, further justifying the use of 2D models in the identification of neurodegeneration. Lastly, CleanNeuro is introduced in the context of 2D classification, a novel algorithm for denoising the datasets prior to training. CleanNeuro, on top of 2D classification and ensemble learning, demonstrates the feasibility of accurately classifying patients with comorbid AD and cerebral amyloid angiopathy from AD controls. Methods presented in this dissertation may serve as exemplars in the study of neurological disorders using deep learning and clinical neuroimaging. Clinically, this dissertation contributes to improving automated diagnosis and identification of regional vulnerabilities of several neurological disorders on clinical neuroimaging using deep learning. First, the classification of patients with Alzheimer’s disease from cognitively normal group demonstrates the potentials of using positron emission tomography with tau tracers as a competitive biomarker for precision medicine. Second, the segmentation of tubers in patients with tuberous sclerosis complex proves a successful 2D modeling approach in quantifying neurological burden of a rare yet deadly disease. Third, the detection of comorbid pathologies from patients with Alzheimer’s disease is analyzed and discussed in depth. Based on prior findings that comorbidities of Alzheimer’s disease affect the brain structure in a distinctive pattern, this dissertation proves for the first time the effectiveness of using deep learning on the accurate identification of comorbid pathology in vivo. Leveraging postmortem neuropathology as ground truth labels on top of the proposed methods records competitive performances in comorbidity prediction. Notably, this dissertation discovers that structural magnetic resonance imaging is a reliable biomarker in differentiating the comorbid cereberal amyloid angiopathy from Alzheimer’s disease patients. The dissertation discusses experimental findings on a wide range of neurological disorders, including tuberous sclerosis complex, dementia, and epilepsy. These results contribute to better decision-making on building neural network models for understanding and managing neurological diseases. With the thorough exploration, the dissertation may provide valuable insights that can push forward research in clinical neurology.