Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Canada; Western Institute for Neuroscience, The University of Western Ontario, London, Canada
Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Canada
Jonathan C Lau
Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Canada; Department of Clinical Neurological Sciences, Division of Neurosurgery, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Canada; School of Biomedical Engineering, The University of Western Ontario, London, Canada
Western Institute for Neuroscience, The University of Western Ontario, London, Canada; Department of Psychology, Faculty of Social Science, The University of Western Ontario, London, Canada
Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Canada; Western Institute for Neuroscience, The University of Western Ontario, London, Canada; School of Biomedical Engineering, The University of Western Ontario, London, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Canada
Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals. 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. Such tailoring is critical for inter-individual alignment, with topology serving as the basis for homology. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or its subfields. It is critical for refining current neuroimaging analyses at a meso- as well as micro-scale. 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 designed to work with commonly employed sub-millimetric MRI acquisitions, with possible extension to microscopic resolution. 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.
