A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI.

Automated analysis of MRI data of the subregions of the hippocampus requires computational atlases built at a higher resolution than those that are typically used in current neuroimaging studies. Here we describe the construction of a statistical atlas of the hippocampal formation at the subregion level using ultra-high resolution, ex vivo MRI. Fifteen autopsy samples were scanned at 0.13 mm isotropic resolution (on average) using customized hardware. The images were manually segmented into 13 different hippocampal substructures using a protocol specifically designed for this study; precise delineations were made possible by the extraordinary resolution of the scans. In addition to the subregions, manual annotations for neighboring structures (e.g., amygdala, cortex) were obtained from a separate dataset of in vivo, T1-weighted MRI scans of the whole brain (1mm resolution). The manual labels from the in vivo and ex vivo data were combined into a single computational atlas of the hippocampal formation with a novel atlas building algorithm based on Bayesian inference. The resulting atlas can be used to automatically segment the hippocampal subregions in structural MRI images, using an algorithm that can analyze multimodal data and adapt to variations in MRI contrast due to differences in acquisition hardware or pulse sequences. The applicability of the atlas, which we are releasing as part of FreeSurfer (version 6.0), is demonstrated with experiments on three different publicly available datasets with different types of MRI contrast. The results show that the atlas and companion segmentation method: 1) can segment T1 and T2 images, as well as their combination, 2) replicate findings on mild cognitive impairment based on high-resolution T2 data, and 3) can discriminate between Alzheimer's disease subjects and elderly controls with 88% accuracy in standard resolution (1mm) T1 data, significantly outperforming the atlas in FreeSurfer version 5.3 (86% accuracy) and classification based on whole hippocampal volume (82% accuracy).

Toward a global picture of development: lessons from genome-scale analysis in Caenorhabditis elegans embryonic development.

Development is the result of complex events, including cascades of transcriptional programs and numerous molecular interactions. Traditionally, research focus has been given to the characterization of individual mutants, regulators, or interactions. With the availability of complete genome sequences and high-throughput (HT) experimental techniques, probing development on a system level has become feasible. Pioneering work initiated in invertebrate model systems such as Caenorhabditis elegans has provided first drafts of catalogs of essential components, transcriptional regulatory diagrams and molecular interaction networks underlying developmental processes. Integrating these drafts approximates a system-level picture of development and provides local models for protein/gene functions. Here we summarize the progress toward elucidating developmental processes on a system level, including the applications of genomic technologies and computational analyses. We discuss C. elegans embryonic development in case studies to illustrate how various HT approaches can be integrated and how biological insights can be gained from these approaches.