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in figure 2b were processed with IMARIS. Measure-ment data of each spine from the software were exported into a spreadsheet file. Using the spine length, head diameter, and neck diameter data on the spreadsheet file, each neuron was redrawn into three dimensional models on the graphic software Blender as seen in figure 2c and more closely in figure 2d. The measurement data were also used to classify each spine. Spines were classified using its spine length, head radius, and neck radius according to the algorithm seen in figure 411).We are currently working on this novel method to overcome the shortcomings of previous methods to analyze spine morphology. Compared with the reported techniques using electron microscopy, this method allows for quick analysis of thousands of spines at once. This method opens the door for large scale analysis of spine morphology on multiple neurons. The approach developed in the present study allows rapid extraction of abnormalities in spine morphology in neurological diseases and will contribute to the further understanding of the pathogenesis. AcknowledgementsThis work was performed at the Institute for Diseases of Old Age under Juntendo University Graduate School of Medicine. I wish to thank Eri Arikawa-Hirasawa and Aurelien Kerever for their advice on the experimental design. I also would like to thank Fumihito Saitow and Hidenori Suzuki 332from Nippon Medical College for injecting the Biocytin in the pyramidal neurons on the sample slices. 1) Qiao H, Li MX, Xu C, et al: Dendritic Spines in Depres-sion: What We Learned from Animal Models. Neural Plast, 2016; 2016: 8056370. 2) Hering H, Sheng M: Dendritic spines: structure, dynamics and regulation. Nat Rev Neurosci, 2001; 2: 880-888. 3) Phillips M, Pozzo-Miller L: Dendritic spine dysgenesis in autism related disorders. Neurosci Lett, 2015; 601: 30-40. 4) Marin-Padilla M: Pyramidal cell abnormalities in the motor cortex of a child with Down’s syndrome. A Golgi study. J Comp Neurol, 1976; 167: 63-81. 5) Takashima S, Iida K, Mito T, et al: Dendritic and histo-chemical development and ageing in patients with Down’s syndrome. J Intellect Disabil Res, 1994; 38: 265-273. 6) Tang G, Gudsnuk K, Kuo S, et al: Loss of mTOR-de-pendent macroautophagy causes autistic-like synaptic pruning deficits. Neuron, 2014; 83: 1131-1143. 7) Kubota Y, Sohn J, Kawaguchi Y: Large volume elec-tron microscopy and neural microcircuit analysis. Front Neural Circuits, 2018; 12: 98. 8) Kashiwagi Y, Higashi T, Obashi K, et al: Computational geometry analysis of dendritic spines by structured illumination microscopy. Nat Commun, 2019; 10: 1285. 9) Susaki EA, Tainaka K, Perrin D, et al: Advanced CUBIC protocols for whole-brain and whole-body clearing and imaging. Nat Protoc, 2015; 10: 1709-1727.10) Hama H, Hioki H, Namiki K, et al: ScaleS: an optical clearing palette for biological imaging. Nat Neurosci, 2015; 18: 1518-1529.11) Rodriguez A, Ehlenberger B, Dickstein D, et al: Auto-mated three-dimensional detection and shape classifi-cation of dendritic spines from fluorescence micros-copy images. PLos One, 2008; 3: e1997.　References