<i>CACNA1C</i> gene regulates behavioral strategies in operant rule learning

<div><p>Behavioral experiments are usually designed to tap into a specific cognitive function, but animals may solve a given task through a variety of different and individual behavioral strategies, some of them not foreseen by the experimenter. Animal learning may therefore be seen more as the process of selecting among, and adapting, potential behavioral policies, rather than mere strengthening of associative links. Calcium influx through high-voltage-gated Ca²⁺ channels is central to synaptic plasticity, and altered expression of Ca_v1.2 channels and the <i>CACNA1C</i> gene have been associated with severe learning deficits and psychiatric disorders. Given this, we were interested in how specifically a selective functional ablation of the <i>Cacna1c</i> gene would modulate the learning process. Using a detailed, individual-level analysis of learning on an operant cue discrimination task in terms of behavioral strategies, combined with Bayesian selection among computational models estimated from the empirical data, we show that a <i>Cacna1c</i> knockout does not impair learning in general but has a much more specific effect: the majority of <i>Cacna1c</i> knockout mice still managed to increase reward feedback across trials but did so by adapting an outcome-based strategy, while the majority of matched controls adopted the experimentally intended cue-association rule. Our results thus point to a quite specific role of a single gene in learning and highlight that much more mechanistic insight could be gained by examining response patterns in terms of a larger repertoire of potential behavioral strategies. The results may also have clinical implications for treating psychiatric disorders.</p></div

Task performance and reward feedback according to outcome-based rules.

<p>A) Relative frequency of win-shift (magenta) and lose-shift (cyan) responses per day for Ca_v1.2^NesCre (left) and Ca_v1.2^fl/fl (right) animals (error shadings = SEM). Different gray shadings indicate the three task phases. Note that win- and lose-stay frequencies are symmetric to win- and lose-shift frequencies about the 0.5-axis and are thus not shown. B) Relative frequency with which outcome-rule-consistent responses led to reward for Ca_v1.2^NesCre (left) and Ca_v1.2^fl/fl (right) animals. Data available at <a href="https://github.com/GKoppe/BehavioralData_Ana" target="_blank">https://github.com/GKoppe/BehavioralData_Ana</a>.</p

Two-choice operant cue learning task.

<p>A square-shaped cue appears in either the top or bottom position of a central field, indicating a correct left/right side response. A “stay trial,” as defined here, requires a response to the same side as on the previous trial, while a “shift trial” requires a response to the opposite side (given a correct previous response). The figure displays the different trial types added in successive task phases (labeled I, II, and III). In phase I, only high-contrast (HC) cues were presented. In phases II and III, low-contrast (LC) cues were added. Phase III further included completely ambiguous trials for which both response options were rewarded.</p

Outcome-rule-based bootstrap distributions for shift trials.

<p>A) Actual performance (blue curves) and bootstrapped performance distributions (gray-shaded: 90% CI, black: mean) generated from the purely outcome-based response behavior that is most consistent with the animal’s actual behavior, i.e., with day-specific outcome-rule choice probabilities inferred from the animal’s actual distribution of outcome-rule-consistent responses. Curves and corresponding bootstrap distributions are shown for all Ca_v1.2^fl/fl animals. B) Same for Ca_v1.2^NesCre animals. Note that while most Ca_v1.2^fl/fl mice escape the bootstrap distributions as the task progresses (later in phase I or in phase II/III), most Ca_v1.2^NesCre animals remain within the bootstrap 90%-confidence bounds. Data available at <a href="https://github.com/GKoppe/BehavioralData_Ana" target="_blank">https://github.com/GKoppe/BehavioralData_Ana</a>.</p