Research Article: Plasticity in the Rat Prefrontal Cortex: Linking Gene Expression and an Operant Learning with a Computational Theory

Date Published: January 11, 2010

Publisher: Public Library of Science

Author(s): Maximiliano Rapanelli, Sergio Eduardo Lew, Luciana Romina Frick, Bonifacio Silvano Zanutto, David Finkelstein.

Abstract: The plasticity in the medial Prefrontal Cortex (mPFC) of rodents or lateral prefrontal cortex in non human primates (lPFC), plays a key role neural circuits involved in learning and memory. Several genes, like brain-derived neurotrophic factor (BDNF), cAMP response element binding (CREB), Synapsin I, Calcium/calmodulin-dependent protein kinase II (CamKII), activity-regulated cytoskeleton-associated protein (Arc), c-jun and c-fos have been related to plasticity processes. We analysed differential expression of related plasticity genes and immediate early genes in the mPFC of rats during learning an operant conditioning task. Incompletely and completely trained animals were studied because of the distinct events predicted by our computational model at different learning stages. During learning an operant conditioning task, we measured changes in the mRNA levels by Real-Time RT-PCR during learning; expression of these markers associated to plasticity was incremented while learning and such increments began to decline when the task was learned. The plasticity changes in the lPFC during learning predicted by the model matched up with those of the representative gene BDNF. Herein, we showed for the first time that plasticity in the mPFC in rats during learning of an operant conditioning is higher while learning than when the task is learned, using an integrative approach of a computational model and gene expression.

Partial Text: Computational theories have been widely used in order to study the emergent properties of neural circuits [1]–[2]. In this sense, several models have been designed to describe the neuronal mechanisms underlying visual tasks, feeding behavior, reward prediction and operant conditioning, among others, integrating different brain areas [3]–[6]. In a previous work, we proposed a computational theory to simulate learning of several tasks [7]. Given that the lateral Prefrontal Cortex (lPFC) in primates or medial Prefrontal Cortex (mPFC) in rodents, is involved in cognitive processes such as goal-directed behavior, working memory, executive control and reward information [8]–[9]. The lPFC is a key element in complex behaviors, as for example, perceptual categorization and matching to sample. For this reason, we included the lPFC to improving the model for other tasks [7]. One of the predictions of this model is that neural plasticity activity is higher in the lPFC while animals are actually learning an operant conditioning task rather than after it has been learned. In our model, synaptic plasticity modifications are calculated as hebbian and anti-hebbian law, simulating long term potentiation (LTP) and long term depression (LTD), respectively. Therefore, this model is a behavioral and neurophysiological plausible neural network representation; however, it has not been yet confirmed by biological evidence. Knowledge of the molecular mechanisms underlying task learning would be useful to verify and fit plasticity computations in the model. An accepted approach to indirectly determine synaptic plasticity in vivo is to measure transcriptional fluctuations of genes whose expression is deeply associated to synaptic plasticity. Neural plasticity is required for circuit formation, depends on bi-directional communication between pre and post synaptic neurons, dendrite and axonal branching and remodelling, among others [10]. There are several genes associated with plasticity, among which the most important are brain derived neurotrophic factor (BDNF), cAMP Response Element Binding Protein (CREB), Synapsin I, Calcium/Calmodulin protein kinase II (CamKII), activity-regulated cytoskeleton-associated protein (Arc), c-fos and c-jun. BDNF is the main protein in the brain involved in the activity-dependent neuronal plasticity, synaptic transmission and growth of dendrites and axons [11]. Moreover, regulation of BDNF secretion is related to LTP and LTD [12]. The transcription factor CREB is another crucial mediator of these processes that acts by regulating transcription of effector genes, including BDNF [13]. Synapsin I, a major component of synaptic vesicles, is known to be up-regulated by LTP in the dentate gyrus [14], and its transcription, which can be regulated by BDNF, is also associated to different degrees of learning [15]–[16]. In addition, CamKII plays a key role in neurotransmission, gene expression and plasticity [17]. The transcripts of CamKII isoforms are tightly influenced by LTP in the rat cortex [18] and it was observed that gene transcription and availability are regulated by BDNF [19]. Besides, immediate early genes (IEGs), like c-fos, c-jun and Arc, have been proposed as markers of neuronal activation [20], which are also regulated by BDNF [13], [21], [22]. It has been found that Arc expression is involved in spatial learning, exploration learning and selective reactivation of networks [23]. The AP-1 subunits c-fos and c-jun are closely related to learning processes, plasticity and neuronal activation in rat cortex and hippocampus [24]–[28].

In a first approach run in vivo, we showed that mRNA gene expression related to plasticity is differentially modified during the course of learning of an operant conditioning task. At a first stage, all genes studied herein are up-regulated in the mPFC of animals that belong to 50%CR. Instead, in animals from 100%CR, these increments are lower.



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