FiberBlender: A Realistic Computer Model of Nerve Bundles for Simulating and Validating the Acquisition of Diffusion Tensor Imaging

Diffusion Tensor Imaging (DTI) is a powerful medical imaging technique that provides a unique method to investigate the structure and connectivity of neural pathways. DTI is a special magnetic resonance imaging (MRI) modality that combines the principles of magnetic resonance with molecular diffusion to trace the motion of water molecules. In the central nervous system, where nerve fibers are packed in highly-directional bundles, these molecules diffuse along the orientation of the fibers. Hence, characterizing the motion of water with DTI delivers a non-invasive in vivo technique to capture the connectivity of nerves themselves. Despite its promises and successful clinical applications for nearly thirty years, problems with validation and interpretation of measurements still persist. Most validation studies attempt to generate ground-truth data from animal models, phantoms, and computer models. This dissertation proposes a novel validation system, FiberBlender, capable of reproducing three-dimensional fiber structures and simulating the diffusion of water molecules to generate ground-truth synthetic DTI data. In particular FiberBlender contributes to: (i) creating more biologically accurate representations of fiber bundles with the inclusion of myelin and glial cells, (ii) examining the effect of demyelination and gliosis on DTI measurements, (iii) optimizing acquisition sequences, and (iv) evaluating the performance of multi-tensor models for the study of crossing fibers. FiberBlender strays away from the “one size fits all” approach taken by previous studies and uses computer algorithms in conjunction with some limited manual operations to produce brain-like geometries that take into account the random spatial location of axons and correct distributions of axon diameters, myelin to axon radius, and myelin to glia ratio. In this way no two models are the same and the system is capable of generating structures that can potentially represent any region of the brain and encompass the heterogeneity between human subjects. This feature is essential for optimization as the performance of DTI acquisition sequences may vary among subjects and the type of scanner used. In addition to better accuracy, the system offers a high degree of flexibility as the geometry can be modified to simulate events that cause drastic changes to the fiber structure. Specially, this dissertation looks at demyelination (an extensive loss of myelin volume), gliosis (a proliferation of glial cells), and axon compaction (a condensation of axons due to a loss of total brain volume) to determine their effects on the observed DTI signal. Simulation results confirm that axon compaction and partial remyelination have similar characteristics. Results also show that some standard clinically used acquisition sequences are incapable of capturing the effects of demyelination, gliosis and compaction when performing longitudinal studies. A novel sequence optimization technique based on Shannon entropy and mutual information is proposed to better capture demyelination. Optimized sequences are tested on a number of non-identical models to confirm their validity and can be used to improve the quality of DTI diagnostics. Finally this work looks at crossing fibers for the validation of multi-tensor models in their ability to characterize crossing diffusion profiles. The performance of multi-tensor models from CHARMED, Q-ball and spherical deconvolution that are widely used in both research and clinical settings are evaluated against ground-truth data generated with FiberBlender. The study is performed on a number of different crossing geometries and preliminary results show that the CHARMED model is the most comprehensive approach

A Dynamic Approach to Pose Invariant Face Identification Using Cellular Simultaneous Recurrent Networks

Face recognition is a widely covered and desirable research field that produced multiple techniques and different approaches. Most of them have severe limitations with pose variations or face rotation. The immediate goal of this thesis is to deal with pose variations by implementing a face recognition system using a Cellular Simultaneous Recurrent Network (CSRN). The CSRN is a novel bio-inspired recurrent neural network that mimics reinforcement learning in the brain. The recognition task is defined as an identification problem on image sequences. The goal is to correctly match a set of unknown pose distorted probe face sequences with a set of known gallery sequences. This system comprises of a pre-processing stage for face and feature extraction and a recognition stage to perform the identification. The face detection algorithm is based on the scale-space method combined with facial structural knowledge. These steps include extraction of key landmark points and motion unit vectors that describe movement of face sequqnces. The identification process applies Eigenface and PCA and reduces each image to a pattern vector used as input for the CSRN. In the training phase the CSRN learns the temporal information contained in image sequences. In the testing phase the network predicts the output pattern and finds similarity with a test input pattern indicating a match or mismatch.Previous applications of a CSRN system in face recognition have shown promise. The first objective of this research is to evaluate those prior implementations of CSRN-based pose invariant face recognition in video images with large scale databases. The publicly available VidTIMIT Audio-Video face dataset provides all the sequences needed for this study. The second objective is to modify a few well know standard face recognition algorithms to handle pose invariant face recognition for appropriate benchmarking with the CSRN. The final objective is to further improve CSRN face recognition by introducing motion units which can be used to capture the direction and intensity of movement of feature points in a rotating fac

A 3D model-based simulation of demyelination to understand its effects on diffusion tensor imaging

Demyelination is the progressive damage to the myelin sheath, a protective covering that surrounds a nerve\u27s axon. Due to its high sensitivity to microscopic tissue changes, diffusion tensor imaging (DTI) is a powerful means of detecting signs of demyelination and axonal injury. In this paper, we present a 3D virtual model capable of simulating the complex Brownian motion of water molecules in a bundle of myelinated axons and glial cells for the purpose of synthesizing DTI data, characterizing and verifying the impact of demyelination on DTI. Our model consists of a highly detailed and realistic 3D representation of biological fiber bundles, with a myelin sheath covering the axons and glial cells in between them. The system simulates the Brownian motion of molecules to extract diffusion data. We perform our experiment for progressive stages of demyelination and demonstrate its effect on DTI measurements

Diffusion tensor based global tractography of human brain fiber bundles

Tractography is a non-invasive process for reconstruction, modelling and visualization of neural fibers in the white matter (WM) of the human brain. It has emerged as a major breakthrough for neuroscience research due to its usefulness in clinical applications. In this research, we have investigated deterministic and probabilistic tractography approaches. We have evaluated the performance of different approaches on fiber bundle tracking using diffusion tensor imaging (DTI) data having multiple gradient directions. Experimental results show that global tractography is best for reconstruction of kissing and crossing fibers compared to deterministic tractography. We have also shown that DTI acquisition with a higher number of gradient directions provides better tracking results given limitations of acquisition and computation time

Resting state connectivity in people living with HIV before and after stopping heavy drinking

BackgroundHeavy alcohol use in people living with HIV (PLWH) has widespread negative effects on neural functioning. It remains unclear whether experimentally-induced reduction in alcohol use could reverse these effects. We sought to determine the effects of 30-days drinking cessation/reduction on resting state functional connectivity in people with and without HIV.MethodsThirty-five participants (48.6% PLWH) demonstrating heavy alcohol use attempted to stop drinking for 30 days via contingency management (CM). MRI was acquired at baseline and after thirty days, and functional connectivity across five resting-state fMRI (rsfMRI) networks was calculated with the Conn toolbox for Matlab and examined in relation to transdermal alcohol concentration (TAC) recorded by the ankle-worn secure continuous remote alcohol monitor (SCRAM) and self-reported alcohol use (timeline follow-back; TLFB). Associations between alcohol use and reduction, HIV status, functional connectivity, and change in functional connectivity across five major rsfMRI networks were determined relative to the pre- and post-CM timepoints.ResultsBaseline resting-state functional connectivity was not significantly associated with average TAC-AUC during the pre-CM period, though higher self-reported alcohol use over the preceding 30 days was significantly associated with higher baseline connectivity within the Dorsal Attention Network (DAN; p-FDR < 0.05). Baseline connectivity within the Salience network was significantly negatively related to objective drinking reduction after intervention (DAN; p-FDR < 0.05), whereas baseline connectivity within the Limbic network was positively associated with self-reported drinking reduction (p-FDR < 0.05). Change in between-networks functional connectivity after intervention was significantly positively associated with biosensor-confirmed drinking reduction such that higher reduction was associated with stronger connectivity between the limbic and fronto-parietal control networks (p-FDR < 0.05). PLWH with lower DAN connectivity at baseline demonstrated poorer alcohol reduction than those with higher DAN connectivity at baseline.DiscussionLower resting-state functional connectivity of the Salience network significantly predicted stronger drinking reduction across all participants, suggesting a potential biomarker for reduced susceptibility to the environmental and social cues that often make alcohol use reduction attempts unsuccessful. Increased between-networks connectivity was observed in participants with higher alcohol reduction after CM, suggesting a positive benefit to brain connectivity associated with reduced drinking. PLWH with lower baseline DAN connectivity may not benefit as greatly from CM for alcohol reduction

The impact of bio-inspired approaches toward the advancement of face recognition

An increased number of bio-inspired face recognition systems have emerged in recent decades owing to their intelligent problem-solving ability, flexibility, scalability, and adaptive nature. Hence, this survey aims to present a detailed overview of bio-inspired approaches pertaining to the advancement of face recognition. Based on a well-classified taxonomy, relevant bio-inspired techniques and their merits and demerits in countering potential problems vital to face recognition are analyzed. A synthesis of various approaches in terms of key governing principles and their associated performance analysis are systematically portrayed. Finally, some intuitive future directions are suggested on how bio-inspired approaches can contribute to the advancement of face biometrics in the years to come