Figure 4
From: Life-long brain compensatory responses to galactic cosmic radiation exposure

Enhancements in Schaffer collateral-CA1 LTP and spatial learning 12 months after exposure to 56Fe particle radiation (a, b) Time course and magnitude (inset bar graph) of LTP in slices from mice exposed to 10 cGy, 50 cGy, or 100 cGy radiation, compared to sham-irradiated controls (0 cGy). After a 15 min baseline, LTP was elicited by two high-frequency TBS stimulus trains (arrows), and magnitude of LTP between 35 and 40 min post TBS (perforated box) was compared across doses in (a) male mice, One-way RM ANOVA, F(1.73,17.31) = 3137.0, P < 0.001, Tukey’s post hoc test: 0 cGy vs. 10 cGy, P < 0.0001; 0 cGy vs. 50 cGy, P < 0.0001; 0 cGy vs. 100 cGy, P < 0.0001, 10 cGy vs. 50 cGy, P < 0.0001, 10 cGy vs. 100 cGy, P < 0.0001, 50 cGy vs. 100 cGy, P < 0.0001 and (b) female mice, One-way RM ANOVA, F(2.48,24.77) = 449.1, P < 0.0001, Tukey’s post hoc test: 0 cGy vs. 10 cGy, P < 0.0001; 0 cGy vs. 50 cGy, P < 0.0001; 0 cGy vs. 100 cGy, P < 0.0001, 10 cGy vs. 50 cGy, P < 0.0001, 10 cGy vs. 100 cGy, P < 0.0001, 50 cGy vs. 100 cGy, P < 0.0001. *P < 0.05 compared to 0 cGy, n = 12–16 slices per dose per sex. (c, d) Learning curves, including pretraining, training days 1–3, and conflict training days 1–2, are shown for (c) male mice Two-way RM ANOVA, dose: F(3,16) = 2.78, P = 0.08; trial: F(1.933,30.93) = 54.98, P < 0.0001; interaction: F(15,80) = 1.31, P = 0.21, Tukey’s post hoc test: Training Day 1, 0 cGy vs. 50 cGy, P = 0.008, 0 cGy vs. 100 cGy, P = 0.045, Training Day 2, 0 cGy vs. 50 cGy, P = 0.019 and (d) female mice Two-way RM ANOVA, dose: F(3,16) = 2.57, P = 0.09; trial: F(2.445,39.12) = 47.93, P < 0.0001; interaction: F(15,80) = 1.23, P = 0.266, Tukey’s post hoc test: Training Day 2, 0 cGy vs. 50 cGy, P = 0.021, Training Day 3, 0 cGy vs. 50 cGy, P = 0.044, Conflict Day 2, 0 cGy vs. 100 cGy, P = 0.038, as a function of normalized number of entries into the stationary shock zone (Errors). *P < 0.05, n = 5 mice per dose per sex. Each point represents mean errors normalized to pre-training entries ± SEM.