PDF(Pubmed)
Abstract：
Neurotransmitter receptor densities are relevant for understanding the molecular architecture of brain regions. Quantitative in vitro receptor autoradiography, has been introduced to map neurotransmitter receptor distributions of brain areas. However, it is very time and cost-intensive, which makes it challenging to obtain whole-brain distributions. At the same time, high-throughput light microscopy and 3D reconstructions have enabled high-resolution brain maps capturing measures of cell density across the whole human brain. Aiming to bridge gaps in receptor measurements for building detailed whole-brain atlases, we study the feasibility of predicting realistic neurotransmitter density distributions from cell-body stainings. Specifically, we utilize conditional Generative Adversarial Networks (cGANs) to predict the density distributions of the M2 receptor of acetylcholine and the kainate receptor for glutamate in the macaque monkey\'s primary visual (V1) and motor cortex (M1), based on light microscopic scans of cell-body stained sections. Our model is trained on corresponding patches from aligned consecutive sections that display cell-body and receptor distributions, ensuring a mapping between the two modalities. Evaluations of our cGANs, both qualitative and quantitative, show their capability to predict receptor densities from cell-body stained sections while maintaining cortical features such as laminar thickness and curvature. Our work underscores the feasibility of cross-modality image translation problems to address data gaps in multi-modal brain atlases.
摘要：
神经递质受体密度与理解脑区的分子结构有关。定量体外受体放射自显影,已经被引入来绘制大脑区域的神经递质受体分布。然而,这是非常耗时和成本的，这使得获得全脑分布具有挑战性。同时,高通量光学显微镜和3D重建使高分辨率的大脑图能够捕获整个人类大脑的细胞密度测量。旨在弥合受体测量中的差距，以构建详细的全脑图谱，我们研究了从细胞体染色预测现实神经递质密度分布的可行性。具体来说,我们利用条件生成对抗网络(cGANs)来预测在猕猴的初级视觉(V1)和运动皮层(M1)中乙酰胆碱的M2受体和谷氨酸的红藻氨酸受体的密度分布,基于细胞体染色切片的光学显微镜扫描。我们的模型是在显示细胞体和受体分布的对齐连续切片的相应斑块上训练的，确保两种模式之间的映射。对我们cGAN的评估，定性和定量，显示它们预测细胞体染色切片的受体密度的能力，同时保持皮层特征，如层状厚度和曲率。我们的工作强调了跨模态图像翻译问题的可行性，以解决多模态大脑地图集中的数据缺口。
