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OF THE THESISLongitudinal Imaging of Cortical and Sub-cortical Inputs to Primary Motor Cortex During Motor Learning byYuxin HuMaster of Science in BiologyUniversity of California San Diego, 2021Professor Takaki Komiyama, Chair The primary motor cortex (M1) is critical for motor learning and performance, and it is heavily innervated by inputs from the contralateral M1 (cM1), the primary somatosensory cortex (S1), and the motor thalamus. M1 integrates these inputs and sends out divergent outputs to different brain regions. To decipher how this relay system functions, we investigated the major M1 inputs to find whether there is a unique activity pattern for each input and whether there is a difference in their functional roles. Here, we expressed a calcium indicator in the above brain regions and used two-photon calcium imaging to study the correlation between movement and axonal bouton activity at M1 during motor learning. We also selectively suppressed the neurotransmitter release at synaptic terminals to investigate the effect of inhibiting of M1 inputs on behavior. We found these three types of inputs appear to be different from each other in terms of peak time, consistency of a single axonal bouton’s activity pattern across sessions, and correlation in activity between all axonal boutons from a single input. Therefore, we proposed the activity patterns for cM1, S1, and thalamocortical inputs innervating M1 are different, and the different functional roles of each brain region might account for the variabilities in activity patterns.

OF THE THESISLongitudinal Imaging of Cortical and Sub-cortical Inputs to Primary Motor Cortex During Motor Learning byYuxin HuMaster of Science in BiologyUniversity of California San Diego, 2021Professor Takaki Komiyama, Chair The primary motor cortex (M1) is critical for motor learning and performance, and it is heavily innervated by inputs from the contralateral M1 (cM1), the primary somatosensory cortex (S1), and the motor thalamus. M1 integrates these inputs and sends out divergent outputs to different brain regions. To decipher how this relay system functions, we investigated the major M1 inputs to find whether there is a unique activity pattern for each input and whether there is a difference in their functional roles. Here, we expressed a calcium indicator in the above brain regions and used two-photon calcium imaging to study the correlation between movement and axonal bouton activity at M1 during motor learning. We also selectively suppressed the neurotransmitter release at synaptic terminals to investigate the effect of inhibiting of M1 inputs on behavior. We found these three types of inputs appear to be different from each other in terms of peak time, consistency of a single axonal bouton’s activity pattern across sessions, and correlation in activity between all axonal boutons from a single input. Therefore, we proposed the activity patterns for cM1, S1, and thalamocortical inputs innervating M1 are different, and the different functional roles of each brain region might account for the variabilities in activity patterns.

High-resolution diffusion tensor imaging (DTI) is beneficial for probing tissue microstructure in fine neuroanatomical structures, but long scan times and limited signal-to-noise ratio pose significant barriers to acquiring DTI at sub-millimeter resolution. To address this challenge, we propose a deep learning-based super-resolution method entitled "SRDTI" to synthesize high-resolution diffusion-weighted images (DWIs) from low-resolution DWIs. SRDTI employs a deep convolutional neural network (CNN), residual learning and multi-contrast imaging, and generates high-quality results with rich textural details and microstructural information, which are more similar to high-resolution ground truth than those from trilinear and cubic spline interpolation.

Aerial image categorization plays an indispensable role in remote sensing and artificial intelligence. In this paper, we propose a new aerial image categorization framework, focusing on organizing the local patches of each aerial image into multiple discriminative subgraphs. The subgraphs reflect both the geometric property and the color distribution of an aerial image. First, each aerial image is decomposed into a collection of regions in terms of their color intensities. Thereby region connected graph (RCG), which models the connection between the spatial neighboring regions, is constructed to encode the spatial context of an aerial image. Second, a subgraph mining technique is adopted to discover the frequent structures in the RCGs constructed from the training aerial images. Thereafter, a set of refined structures are selected among the frequent ones toward being highly discriminative and low redundant. Lastly, given a new aerial image, its sub-RCGs corresponding to the refined structures are extracted. They are further quantized into a discriminative vector for SVM classification. Thorough experimental results validate the effectiveness of the proposed method. In addition, the visualized mined subgraphs show that the discriminative topologies of each aerial image are discovered.