![]() Examination of this structure with non-invasive neuroimaging, such as MRI, provides great promise for furthering our understanding, diagnosis, and subtyping of these diseases and cognitive processes in the hippocampus and its component subfields ( Dill et al., 2015). ![]() This highly plastic grey matter (GM) structure is also critical in the fast formation of episodic and spatial memories (e.g. Most neurological or psychiatric diseases with widespread effects on the brain show strong and early impact on the hippocampus (e.g. In this paper, we describe the power of HippUnfold in feature extraction, and highlight its unique value compared to several extant hippocampal subfield analysis methods. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with possible extension to microscopic resolution. HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints to generate uniquely folded surfaces to fit a given subject’s hippocampal conformation. It is critical for refining current neuroimaging analyses at a meso- as well as micro-scale. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or its subfields. Such tailoring is critical for inter-individual alignment, with topology serving as the basis for homology. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing individual-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals.
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