Neuron collinearity differentiates human hippocampal subregions: a validated deep learning approach

Abstract

Abstract The hippocampus is heterogeneous in its architecture. It contributes to cognitive processes such as memory and spatial navigation, and is susceptible to neurodegenerative disease. Cytoarchitectural features such as neuron size and neuronal collinearity have been used to parcellate the hippocampal subregions. Moreover, pyramidal neuron orientation (orientation of one individual neuron) and collinearity (how neurons align) have been investigated as a measure of disease in schizophrenia. However, a comprehensive quantitative study of pyramidal neuron orientation and collinearity within the hippocampal subregions has not yet been conducted. In this study, we present a high-throughput deep learning approach for the automated extraction of pyramidal neuron orientation in the hippocampal subregions. Based on the pre-trained Cellpose algorithm for cellular segmentation, we measured 479,873 pyramidal neurons in 168 hippocampal partitions. We corrected the neuron orientation estimates to account for the curvature of the hippocampus and generated collinearity measures suitable for inter- and intra-individual comparisons. Our deep learning results were validated with manual orientation assessment. This study presents a quantitative metric of pyramidal neuron collinearity within the hippocampus. It reveals significant differences among the individual hippocampal subregions (p < 0.001), with Cornu ammonis 3 being the most collinear, followed by Cornu ammonis 2, Cornu ammonis 1, the medial/uncal subregions, and Subiculum. Our data establishes pyramidal neuron collinearity as a quantitative parameter for hippocampal subregion segmentation, including the differentiation of Cornu ammonis 2 and Cornu ammonis 3. This novel deep learning approach could facilitate large-scale multi-centric analyses in subregion parcellation and lays groundwork for the investigation of mental illnesses at the cellular level.