Ogut Eren
Faculty of Medicine, Department of Anatomy, Istanbul Medeniyet University, Istanbul, 34700, Türkiye.
Neuroinformatics. 2025 Aug 7;23(3):41. doi: 10.1007/s12021-025-09743-4.
The amygdala plays a central role in emotion, memory, and decision-making and comprises approximately 13 distinct nuclei with connectivity. Despite its functional importance, high-resolution subnuclear mapping is challenging. This study aimed to construct a 3D model of the anatomical location of the amygdala in the brain and a functional dynamic model of the amygdala, integrating deep learning and elastic shape metrics. We used multimodal datasets from the Julich-Brain Atlas, BigBrain Project, and FreeSurfer, which were aligned with the Montreal Neurological Institute (MNI) and Colin 27 spaces. Subnuclei segmentation was performed using a Bayesian Fully Convolutional Network (FCN), and geometric morphometrics were analyzed using elastic shape analysis on the unit sphere. Functional dynamics were simulated using a MATLAB-based model of the amygdala incorporating theta (4-8 Hz) and gamma (30-40 Hz) oscillations with spike-timing-dependent plasticity (STDP). The mean MNI coordinates of the left and right amygdalae were (-20, -4, -15) and (22, -2, -15), respectively, with an inter-amygdalar distance of 42.48 mm. The Dice Similarity Coefficients (DSCs) for FCN-based subnuclear segmentation were as follows: basolateral amygdala (BLA) nucleus = 0.89 ± 0.03, centromedial nucleus = 0.83 ± 0.04, and cortical nucleus = 0.81 ± 0.05. Principal component analysis of elastic shape metrics revealed post-traumatic stress disorder (PTSD)-related morphological deviations, with the first principal component (PC1) accounting for 38% of the variance (p < 0.01). Oscillatory simulations captured the BLA rhythm dynamics and STDP-induced synaptic changes. This study presents a comprehensive 3D model of the human amygdala that bridges anatomical accuracy with computational modeling. Unlike prior models that focus solely on structural or functional domains, our approach integrates subnuclear segmentation, morphometrics, and real-time functional simulation. This study introduces a fully integrated anatomical-functional 3D model of the human amygdala, providing a translational platform for neuromodulation targeting, psychiatric diagnostics, and computational neuroengineering applications.
杏仁核在情绪、记忆和决策中起着核心作用，由大约13个具有连接性的不同核团组成。尽管其功能重要，但高分辨率亚核映射具有挑战性。本研究旨在构建大脑中杏仁核解剖位置的三维模型以及杏仁核的功能动态模型，整合深度学习和弹性形状度量。我们使用了来自于朱利希脑图谱、大脑计划和FreeSurfer的多模态数据集，这些数据集与蒙特利尔神经学研究所（MNI）和科林27空间对齐。使用贝叶斯全卷积网络（FCN）进行亚核分割，并在单位球面上使用弹性形状分析来分析几何形态计量学。使用基于MATLAB的杏仁核模型模拟功能动力学，该模型结合了theta（4 - 8Hz）和gamma（30 - 40Hz）振荡以及依赖于尖峰时间的可塑性（STDP）。左右杏仁核的平均MNI坐标分别为（-20，-4，-15）和（22，-2，-15），杏仁核间距离为42.48mm。基于FCN的亚核分割的骰子相似系数（DSC）如下：基底外侧杏仁核（BLA）核 = 0.89±0.03，中央内侧核 = 0.83±0.04，皮质核 = 0.81±0.05。弹性形状度量的主成分分析揭示了与创伤后应激障碍（PTSD）相关的形态偏差，第一主成分（PC1）占方差的38%（p < 0.01）。振荡模拟捕捉到了BLA节律动力学和STDP诱导的突触变化。本研究提出了一个全面的人类杏仁核三维模型，该模型将解剖学准确性与计算建模联系起来。与先前仅关注结构或功能领域的模型不同，我们的方法整合了亚核分割、形态计量学和实时功能模拟。本研究引入了一个完全集成的人类杏仁核解剖 - 功能三维模型，为神经调节靶向、精神诊断和计算神经工程应用提供了一个转化平台。
