Automated image analysis was performed with Neurolucida 360 (2.70.1, MBF Biosciences, Williston, Vermont) based on previously described methods (79). Deconvolved image stacks were imported into Neurolucida 360, and the full dendrite length was traced with semiautomatic directional kernel algorithm. The experimenter manually confirmed that all assigned points matched dendrite diameter and position in X, Y, and Z planes and adjusted each reconstruction if necessary. For wide-field microscopy, dendritic spine reconstruction was performed automatically using a voxel-clustering algorithm and the following parameters: outer range, 10.0 μm; minimum height, 0.5 μm; detector sensitivity, 100%; and minimum count, 8 voxels. For confocal microscopy, dendritic spine reconstruction was performed automatically using a voxel-clustering algorithm and the following parameters: outer range, 5.0 μm; minimum height, 0.3 μm; detector sensitivity, 80%; and minimum count, 8 voxels. Next, the experimenter manually verified that the classifier correctly identified all protrusions. When necessary, the experimenter added any protrusions semiautomatically by increasing detector sensitivity. The morphology and backbone points of each spine were verified to ensure a representative spine shape, and merge and slice tools were used to correct inconsistencies. Each dendritic protrusion was automatically classified as a dendritic filopodium, thin spine, stubby spine, or mushroom spine based on previously described morphological measurements (78). Reconstructions were collected in Neurolucida Explorer (2.70.1, MBF Biosciences, Williston, VT, USA) for branched structure analysis and then exported to Microsoft Excel (Redmond, WA, USA). Spine density was calculated as the number of spines per 10 μm of dendrite length.

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