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. 2019 Dec 9;9(1):18651.
doi: 10.1038/s41598-019-55095-w.

Chronic Voluntary Alcohol Consumption Causes Persistent Cognitive Deficits and Cortical Cell Loss in a Rodent Model

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Free PMC article

Chronic Voluntary Alcohol Consumption Causes Persistent Cognitive Deficits and Cortical Cell Loss in a Rodent Model

Annai J Charlton et al. Sci Rep. .
Free PMC article

Abstract

Chronic alcohol use is associated with cognitive decline that impedes behavioral change during rehabilitation. Despite this, addiction therapy does not address cognitive deficits, and there is poor understanding regarding the mechanisms that underlie this decline. We established a rodent model of chronic voluntary alcohol use to measure ensuing cognitive effects and underlying pathology. Rats had intermittent access to alcohol or an isocaloric solution in their home cage under voluntary 2-bottle choice conditions. In Experiments 1 and 2 cognition was assessed using operant touchscreen chambers. We examined performance in a visual discrimination and reversal task (Experiment 1), and a 5-choice serial reaction time task (Experiment 2). For Experiment 3, rats were perfused immediately after cessation of alcohol access period, and volume, cell density and microglial populations were assessed in the prefrontal cortex and striatum. Volume was assessed using the Cavalieri probe, while cell and microglial counts were estimated using unbiased stereology with an optical fractionator. Alcohol-exposed and control rats showed comparable acquisition of pairwise discrimination; however, performance was impaired when contingencies were reversed indicating reduced behavioral flexibility. When tested in a 5-choice serial reaction time task alcohol-exposed rats showed increased compulsivity and increased attentional bias towards a reward associated cue. Consistent with these changes, we observed decreased cell density in the prefrontal cortex. These findings confirm a detrimental effect of chronic alcohol and establish a model of alcohol-induced cognitive decline following long-term voluntary intake that may be used for future intervention studies.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Basic timeline for all experiments. (A) Touchscreen chamber with discrimination and reversal task (B) and 5-choice serial reaction time task (C).
Figure 2
Figure 2
Responding in the reversed visual discrimination task. (A) Performance in the first session where contingencies were reversed. Alcohol exposure rats performed fewer trials, with a fewer correct, and had a greater tendency to perseverate on the incorrect choice. (B) Number of sessions required to reach mid-criterion (first session where they achieved ≥50% correct). Rats that had previously been consuming alcohol required a greater number of sessions to reach mid-reversal criterion. (C) Performance in the first session of mid-reversal (first session after achieving ≥50% correct). There were no significant differences between groups during this session. (D) Number of trials required to reach criterion for reversal (≥80%). There were no significant between group differences in the number of trials required to reach this criterion. *Difference between groups, p <  0.05. Individual data points are shown, as well as mean ± SEM, group sizes: Maltodextrin n = 8, Alcohol, n = 10.
Figure 3
Figure 3
Responding over the first 8 sessions of the reversal period. Rats in the alcohol group completed fewer trials over this period than those in the maltodextrin group, with a less proportion correct. Perseverative index was calculated by dividing the number of correction trials by the total number of trials (trials + correction trials). Alcohol rats showed a greater perseverative index than Maltodextrin rats. *p < 0.05, error bars represent SEM Maltodextrin n = 8, Alcohol, n = 10.
Figure 4
Figure 4
Responding over the training period for the 5-choice serial reaction time task. (A) A schematic of the steps during each trial, showing variable factors in boxes with dotted outlined. Thus, during training there was variable (decreasing) stimulus durations. This schematic also shows variable factors for probe tasks: for Delay probe, variable delays between trial initiation and stimulus onset; for Distractor probe, a novel sound (0.5 s pulse of white noise) played between trial initiation and stimulus onset, with variable delays between sound and stimulus onset; for Detection probe there was variable contrast for the stimulus. Across training, stimulus length decreased in phases, and rats had to reach a criterion 60 trials with ≥80% correct and ≤20% omissions in order to progress to the next decrease (phase). For each phase across training, we have shown (B) number of sessions required to reach to criterion, as well as (C) accuracy (percent correct) (D) mean number of omission trials, (E) mean number of premature responses and (F) mean number of preservative responses per session. A significant interaction between group and phase for premature responses (E) (p<0.05) showed that alcohol-exposed rats initially performed a greater number over premature responses than maltodextrin rats, but this difference dissipated as the training progressed. Individual data points are shown, as well as mean ± SEM, n = 8/group.
Figure 5
Figure 5
Regression of GLAM models, showing the likelihood of performing certain types of responses at each trial. Effect sizes and 95% confidence intervals where a significant treatment effect (difference between groups) was found are shown. When controlling for all other factors, rats that had been consuming alcohol were (A) less likely to perform a correct response across training, (B) more likely to perform perseverative responses in the Delay probe, (C) more likely to respond (less likely to have an omission response, and (D) more likely to perform a perseverative response in the Distractor probe. N = 8/group.
Figure 6
Figure 6
Responding on probe tests. Accuracy (i.e. correct if responded), omissions, and perseverative touches are shown for the Delay probe (A–C), the Distractor probe (D–F), and the Detection probe (G–I). Individual data points as well as mean ± SEM are shown, n = 8 per group.
Figure 7
Figure 7
Stereology (A) Coronal sections counted across the PFC (adapted from Paxinos and Watson, 2007), and representative micrograph taken using the 5 x objective, with representative outlines for the OFC, mPFC, motor cortex and sensory cortex delineated. (B) Coronal sections counted across the striatum (adapted from Paxinos and Watson, 2007), and representative micrograph taken using the 5 x objective, with representative outlines for the OFC, mPFC, motor cortex and sensory cortex delineated. (C) Representative micrograph with red arrow marking examples of Cresyl-violet positive staining in the left panel (note that not all cells counted are marked with the arrows, for clarity), and yellow arrows marking Iba1 positive staining in the right panel, scale bar = 10 µm.
Figure 8
Figure 8
Cell Density in the Prefrontal Cortex and Ventral Striatum. (A) Cell density was significantly decreased in the OFC of Alcohol group rats compared to Naïve but not Maltodextrin. (B) Cell density was decreased in the mPFC of the Alcohol group compared to Naïve and Maltodextrin. (C) Cell density was decreased in the motor cortex of Alcohol group compared to Maltodextrin, but not Naive rats. (D) Cell density significantly decreased in the sensory cortex of Alcohol group rats compared to Naïve but not Maltodextrin. In the striatum, there were no difference between groups within either the dorsal (E) or ventral (F) regions. *p < 0.05, **p < 0.01, all error bars indicate SEM. n=6 per group.
Figure 9
Figure 9
Microglial coverage in the Prefrontal Cortex (PFC) and Striatum. Coverage was determined by the proportion of total area imaged that was covered by Iba1 staining (brown DAB). There were no differences found in either the PFC (A) or the Striatum (B), and representative staining (C) confirms that there were no visible differences in morphology between groups. All error bars indicate SEM, n = 6 per group.

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