Abstract
The ability to measure minute structural changes in neural circuits is essential for long-term in vivo imaging studies. Here, we propose a methodology for detection and measurement of structural changes in axonal boutons imaged with time-lapse two-photon laser scanning microscopy (2PLSM). Correlative 2PLSM and 3D electron microscopy (EM) analysis, performed in mouse barrel cortex, showed that the proposed method has low fractions of false positive/negative bouton detections (2/0 out of 18), and that 2PLSM-based bouton weights are correlated with their volumes measured in EM (r = 0.93). Next, the method was applied to a set of axons imaged in quick succession to characterize measurement uncertainty. The results were used to construct a statistical model in which bouton addition, elimination, and size changes are described probabilistically, rather than being treated as deterministic events. Finally, we demonstrate that the model can be used to quantify significant structural changes in boutons in long-term imaging experiments.
Highlights
The repertoire of synaptic connectivity within neuronal networks is immensely increased through the continuous formation and elimination of synapses (Chklovskii et al, 2004; Stepanyants et al, 2002)
Detection of structural changes in boutons is hindered by various technical challenges and fundamental limitations of light microscopy
Any uncertainty that enters the analysis of long-term in vivo imaging data manifests itself as spurious structural plasticity
Summary
The repertoire of synaptic connectivity within neuronal networks is immensely increased through the continuous formation and elimination of synapses (Chklovskii et al, 2004; Stepanyants et al, 2002). In vivo imaging studies over the last 15 years have shown that synaptic structures remain dynamic throughout adulthood (Holtmaat and Svoboda, 2009; Trachtenberg et al, 2002). This structural plasticity, i.e. the appearance, disappearance, and the morphological modifications of synapses in the adult brain has been established as a fundamental underpinning of learning and experience-dependent changes in neuronal circuits (Holtmaat and Caroni, 2016; Holtmaat and Svoboda, 2009; Stepanyants and Chklovskii, 2005). Spines can be detected, most studies of structural plasticity have used manual or semi-automated tracking of these structures in time-lapse images to infer circuit changes (Holtmaat and Svoboda, 2009). A dendrite’s presynaptic apposition can be detected as an
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