Table 4.

Parameter estimates for the LME models used to assess changes in vesicles

		Estimate	SE
LME model: log(total + 1)
Random effects	SE
Dendrite (Intercept)	0.0839
Mouse (Intercept)	0.2772
Residual	0.5500
Fixed effects	Level
Intercept		5.1680	0.1807
Condition	EW (reference)	0	0
	SW	−0.7498	0.2397
	S	−0.2606	0.2227
ASI	Continuous (linear)	0.5574	0.0313
Condition × ASI	EW (reference)	0	0
	SW	−0.0990	0.0418
	S	−0.0616	0.0392
LME model: log(near + 1)
Random effects	SE
Dendrite (Intercept)	0.0460
Mouse (Intercept)	0.2474
Residual	0.5029
Fixed effects	Level
Intercept		4.7404	0.1610
Condition	EW (reference)	0	0
	SW	−0.7737	0.2135
	S	−0.1821	0.1984
ASI	Continuous (linear)	0.8070	0.0286
Condition × ASI	EW (reference)	0	0
	SW	−0.1545	0.0382
	S	−0.0954	0.0357
LME model: sqrt(near/total)
Random effects	SE
Mouse (Intercept)	0.0554
Mouse (ASI)	0.0152
Residual	0.1440
Fixed effects	Level
Intercept		0.7766	0.0223
Condition	EW (reference)	0	0
	SW	0.0363	0.0183
	S	0.0509	0.0170
ASI	Continuous (log)	0.0742	0.0183

A log transformation was applied to the number of total and near vesicles to give the residuals an approximate Gaussian distribution. Because some synapses had zero vesicles, we added one to the total/near value before applying the transformation to ensure the log was well defined. For both total and near vesicles, a Q–Q plot showed mild violations of the normality assumption in the lower tail due to boundary effects (boundary at 0). A square-root transformation was applied to the ratio of near to total vesicles to give the residuals an approximate Gaussian distribution. The Q–Q plot showed mild violations of the normality assumption because of boundary effects at both ends of the range (boundaries at 0 and 1).