IntroductionThe hippocampus is integral for learning and memory and is targeted by multiple diseases. Neuroimaging approaches frequently use hippocampal subfield volumes as a standard measure of neurodegeneration, thus making them an essential biomarker to study. Collectively, histologic parcellation studies contain various disagreements, discrepancies, and omissions. The present study aimed to advance the hippocampal subfield segmentation field by establishing the first histology based parcellation protocol, applied to n = 22 human hippocampal samples.MethodsThe protocol focuses on five cellular traits observed in the pyramidal layer of the human hippocampus. We coin this approach the pentad protocol. The traits were: chromophilia, neuron size, packing density, clustering, and collinearity. Subfields included were CA1, CA2, CA3, CA4, prosubiculum, subiculum, presubiculum, parasubiculum, as well as the medial (uncal) subfields Subu, CA1u, CA2u, CA3u, and CA4u. We also establish nine distinct anterior-posterior levels of the hippocampus in the coronal plane to document rostrocaudal differences.ResultsApplying the pentad protocol, we parcellated 13 subfields at nine levels in 22 samples. We found that CA1 had the smallest neurons, CA2 showed high neuronal clustering, and CA3 displayed the most collinear neurons of the CA fields. The border between presubiculum and subiculum was staircase shaped, and parasubiculum had larger neurons than presubiculum. We also demonstrate cytoarchitectural evidence that CA4 and prosubiculum exist as individual subfields.DiscussionThis protocol is comprehensive, regimented and supplies a high number of samples, hippocampal subfields, and anterior-posterior coronal levels. The pentad protocol utilizes the gold standard approach for the human hippocampus subfield parcellation.
Hippocampal subregions differ in specialization and vulnerability to cell death. Neuron death and hippocampal atrophy have been a marker for the progression of Alzheimer’s disease. Relatively few studies have examined neuronal loss in the human brain using stereology. We characterize an automated high-throughput deep learning pipeline to segment hippocampal pyramidal neurons, generate pyramidal neuron estimates within the human hippocampal subfields, and relate our results to stereology neuron counts. Based on seven cases and 168 partitions, we vet deep learning parameters to segment hippocampal pyramidal neurons from the background using the open-source CellPose algorithm, and show the automated removal of false-positive segmentations. There was no difference in Dice scores between neurons segmented by the deep learning pipeline and manual segmentations (Independent Samples t-Test: t(28) = 0.33, p = 0.742). Deep-learning neuron estimates strongly correlate with manual stereological counts per subregion (Spearman’s correlation (n = 9): r(7) = 0.97, p < 0.001), and for each partition individually (Spearman’s correlation (n = 168): r(166) = 0.90, p <0 .001). The high-throughput deep-learning pipeline provides validation to existing standards. This deep learning approach may benefit future studies in tracking baseline and resilient healthy aging to the earliest disease progression.
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