Alzheimer's disease (AD), a neurodegenerative disease, is identified as the most common cause of dementia (Goedert and Spillantini, 2006). Typical symptoms of AD in neuropathology are closely associated with changes in synapses and neurons (Serrano-Pozo et al., 2011). The prefrontal cortex (PFC) plays a crucial role in executive function, controlling the highest level of cognitive and emotional processes, and is also vulnerable to neurodegeneration in AD (Salat et al., 2001). While synaptic degeneration is believed to underlie the progressive decline of cognition in AD, specific changes in synaptic structures relevant to AD remain elusive due to a shortage of quantitative tools. Synaptic dysfunction, while key to AD pathophysiology, is difficult to monitor and study in human AD patients. The existing technologies, such as cerebrospinal fluid or blood biomarkers, magnetic resonance imaging, and positron emission tomography, can only indirectly infer synaptic changes in the brain. Thus, it is critical to have an animal model that resembles closely human AD. The newly developed AD rat model, the amyloid precursor protein (App) knock-in rat line harboring Swedish–Beyreuther/Iberian−Arctic mutations (homozygous App rat), offers a great opportunity (Pang et al., 2021). Electron microscopy (EM) has been the method of choice to study the ultrastructure of synapses. While allowing magnification of images by a million times, this conventional EM provides snapshots, rather than quantitative measures, of ultrastructural details of synapses. With the development of automated tape-collecting ultramicrotome (ATUM; Baena et al., 2019) and artificial intelligence (AI)-assisted 3D reconstruction of scanning electron microscopy (SEM) images (Motta et al., 2019), it is now possible to quantitatively characterize morphological features of synapses and dendrites in detail.