Institute of Clinical Physiology (IFC), National Research Council of Italy (CNR) Pisa, Italy.
Front Neuroinform. 2013 Feb 14;7:2. doi: 10.3389/fninf.2013.00002. eCollection 2013.
Morphometric analysis of neurons and brain tissue is relevant to the study of neuron circuitry development during the first phases of brain growth or for probing the link between microstructural morphology and degenerative diseases. As neural imaging techniques become ever more sophisticated, so does the amount and complexity of data generated. The NEuronMOrphological analysis tool NEMO was purposely developed to handle and process large numbers of optical microscopy image files of neurons in culture or slices in order to automatically run batch routines, store data and apply multivariate classification and feature extraction using 3-way principal component analysis (PCA). Here we describe the software's main features, underlining the differences between NEMO and other commercial and non-commercial image processing tools, and show an example of how NEMO can be used to classify neurons from wild-type mice and from animal models of autism.
神经元和脑组织的形态计量分析与研究大脑生长早期阶段神经元回路发育有关,或者可以探究微观结构形态与退行性疾病之间的联系。随着神经影像学技术变得越来越复杂,产生的数据量和复杂性也在不断增加。NEuronMOrphological 分析工具 NEMO 是专门为处理和处理大量培养的神经元或切片的光学显微镜图像文件而开发的,以便能够自动运行批处理例程、存储数据并使用 3 路主成分分析 (PCA) 进行多元分类和特征提取。在此,我们将描述该软件的主要功能,强调 NEMO 与其他商业和非商业图像处理工具之间的区别,并举例说明如何使用 NEMO 对来自野生型小鼠和自闭症动物模型的神经元进行分类。