Temporal context memory is a type of episodic memory that refers to memory for the timing of events. Temporal context includes environmental cues that provide information about the time point at which an event happened. The purpose of the present studies is to investigate the brain mechanisms underlying temporal context processing by using both fMRI and ERP techniques. The fMRI study investigated whether hippocampal representations in CA1 and DG/CA3 subfields were sensitive to the flow of physical time, and if so, whether the number of events that occur during a time period influences the temporal representation of a target event. Results showed that both CA1 and DG/CA3 were sensitive to the flow of physical time, which was indicated by higher representational similarity between two pictures that occurred closer in time than those that occurred more distant in time. However, the variety of preceding events did not influence temporal representation, which was demonstrated by the lack of a significant representational similarity difference between two pictures that were interleaved with variable events as opposed to similar events. The ERP study compared the ERP correlates of temporal to spatial context. Results showed that temporal and spatial contexts had overlapping ERP effects except that the ERP effects of temporal context were more frontally distributed than spatial context. Both the fMRI and ERP studies indicate that temporal context is associated with similar neural correlates to other types of context in episodic memory. / Ph. D.

Indiana University-Purdue University Indianapolis (IUPUI) / Object segmentation, quantification, and shape modeling are important areas inmedical image processing. By combining these techniques, researchers can find valuableways to extract and represent details on user-desired structures, which can functionas the base for subsequent analyses such as feature classification, regression, and prediction. This thesis presents a new framework for building a three-dimensional (3D) hippocampal atlas model with subfield information mapped onto its surface, with which hippocampal surface registration can be done, and the comparison and analysis can be facilitated and easily visualized. This framework combines three powerful tools for automatic subcortical segmentation and 3D surface modeling. Freesurfer and Functional magnetic resonance imaging of the brain's Integrated Registration and Segmentation Tool (FIRST) are employed for hippocampal segmentation and quantification, while SPherical HARMonics (SPHARM) is employed for parametric surface modeling. This pipeline is shown to be effective in creating a hippocampal surface atlas using the Alzheimer's Disease Neuroimaging Initiative Grand Opportunity and phase 2 (ADNI GO/2) dataset. Intra-class Correlation Coefficients (ICCs) are calculated for evaluating the reliability of the extracted hippocampal subfields. The complex folding anatomy of the hippocampus offers many analytical challenges, especially when informative hippocampal subfields are usually ignored in detailed morphometric studies. Thus, current research results are inadequate to accurately characterize hippocampal morphometry and effectively identify hippocampal structural changes related to different conditions. To address this challenge, one contribution of this study is to model the hippocampal surface using a parametric spherical harmonic model, which is a Fourier descriptor for general a 3D surface. The second contribution of this study is to extend hippocampal studies by incorporating valuable hippocampal subfield information. Based on the subfield distributions, a surface atlas is created for both left and right hippocampi. The third contribution is achieved by calculating Fourier coefficients in the parametric space. Based on the coefficient values and user-desired degrees, a pair of averaged hippocampal surface atlas models can be reconstructed. These contributions lay a solid foundation to facilitate a more accurate, subfield-guided morphometric analysis of the hippocampus and have the potential to reveal subtle hippocampal structural damage associated.