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

IF 4.1 Q1 CLINICAL NEUROLOGY
Brain communications Pub Date : 2024-09-03 eCollection Date: 2024-01-01 DOI:10.1093/braincomms/fcae296
Jan Oltmer, Emily M Williams, Stefan Groha, Emma W Rosenblum, Jessica Roy, Josue Llamas-Rodriguez, Valentina Perosa, Samantha N Champion, Matthew P Frosch, Jean C Augustinack
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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 pretrained 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 multicentric analyses in subregion parcellation and lays groundwork for the investigation of mental illnesses at the cellular level.

神经元共线性区分人类海马亚区:一种有效的深度学习方法。
海马体的结构多种多样。它对记忆和空间导航等认知过程做出了贡献,并且易受神经退行性疾病的影响。神经元大小和神经元共线性等细胞结构特征已被用来划分海马亚区。此外,锥体神经元方向(单个神经元的方向)和共线性(神经元如何排列)也被用作精神分裂症的疾病测量指标。然而,目前尚未对海马亚区域内锥体神经元的定向和共线性进行全面的定量研究。在本研究中,我们提出了一种高通量深度学习方法,用于自动提取海马亚区的锥体神经元方向。基于用于细胞分割的预训练 Cellpose 算法,我们测量了 168 个海马分区中的 479 873 个锥体神经元。我们校正了神经元方向估计值,以考虑海马体的曲率,并生成了适合个体间和个体内比较的共线性度量。我们的深度学习结果得到了人工定向评估的验证。这项研究提出了海马内锥体神经元共线性的量化指标。它揭示了各个海马亚区之间的显著差异(P 0.001),其中cornu ammonis 3的共线性最强,其次是cornu ammonis 2、cornu ammonis 1、medial/uncal亚区和subiculum。我们的数据确定了锥体神经元的共线性是海马亚区划分的定量参数,包括cornu ammonis 2和cornu ammonis 3的区分。 这种新颖的深度学习方法可促进亚区划分的大规模多中心分析,并为从细胞水平研究精神疾病奠定基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
7.00
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