Information capacity and robustness of encoding in the medial prefrontal cortex are modulated by the bioavailability of serotonin and the time elapsed from the cue during a reward-driven task.

Serotonin (5-HT) is a key neuromodulator of medial prefrontal cortex (mPFC) functions. Pharmacological manipulation of systemic 5-HT bioavailability alters the electrical activity of mPFC neurons. However, 5-HT modulation at the population level is not well characterized. In the present study, we made single neuron extracellular recordings in the mPFC of rats performing an operant conditioning task, and analyzed the effect of systemic administration of fluoxetine (a selective serotonin reuptake inhibitor) on the information encoded in the firing activity of the neural population. Chronic (longer than 15 days), but not acute (less than 15 days), fluoxetine administration reduced the firing rate of mPFC neurons. Moreover, fluoxetine treatment enhanced pairwise entropy but diminished noise correlation and redundancy in the information encoded, thus showing how mPFC differentially encodes information as a function of 5-HT bioavailability. Information about the occurrence of the reward-predictive stimulus was maximized during reward consumption, around 3 to 4 s after the presentation of the cue, and it was higher under chronic fluoxetine treatment. However, the encoded information was less robust to noise corruption when compared to control conditions.

Tau mis-splicing correlates with motor impairments and striatal dysfunction in a model of tauopathy.

Tauopathies are neurodegenerative diseases caused by the abnormal metabolism of the microtubule associated protein tau (MAPT), which is highly expressed in neurons and critically involved in microtubule dynamics. In the adult human brain, the alternative splicing of exon 10 in MAPT pre-mRNA produces equal amounts of protein isoforms with either three (3R) or four (4R) microtubule binding domains. Imbalance in the 3R:4R tau ratio is associated with primary tauopathies that develop atypical parkinsonism, such as progressive supranuclear palsy and corticobasal degeneration. Yet, the development of effective therapies for those pathologies is an unmet goal. Here we report motor coordination impairments in the htau mouse model of tauopathy which harbour abnormal 3R:4R tau isoforms content, and in contrast to TauKO mice, are unresponsive to l-DOPA. Preclinical-PET imaging, array tomography and electrophysiological analyses indicated the dorsal striatum as the candidate structure mediating such phenotypes. Indeed, local modulation of tau isoforms by RNA trans-splicing in the striata of adult htau mice, prevented motor coordination deficits and restored basal neuronal firing. Together, these results suggest that abnormal striatal tau isoform content might lead to parkinsonian-like phenotypes and demonstrate a proof of concept that modulation of tau mis-splicing is a plausible disease-modifying therapy for some primary tauopathies.

Probing the structure-function relationship with neural networks constructed by solving a system of linear equations.

Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully such that networks reproduce anatomical, behavioural, and neurophysiological data. These parameters are usually fitted with off-the-shelf optimization algorithms that iteratively change network parameters and simulate the network to evaluate its performance and improve fitting. Here we propose to invert the fitting process by proceeding from the network dynamics towards network parameters. Firing state transitions are chosen according to the transition graph associated with the solution of a task. Then, a system of linear equations is constructed from the network firing states and membrane potentials, in a way that guarantees the consistency of the system. This allows us to uncouple the dynamical features of the model, like its neurons firing rate and correlation, from the structural features, and the task-solving algorithm implemented by the network. We employed our method to probe the structure-function relationship in a sequence memory task. The networks obtained showed connectivity and firing statistics that recapitulated experimental observations. We argue that the proposed method is a complementary and needed alternative to the way neural networks are constructed to model brain function.

Serotonergic modulation of basolateral amygdala nucleus in the extinction of reward-driven learning: The role of 5-HT bioavailability and 5-HT1A receptor.

Serotonin (5-HT) neurotransmission has been associated with reward-related behaviour. Moreover, the serotonergic system modulates the basolateral amygdala (BLA), a structure involved in reward encoding, and reward prediction error. However, the role played by 5-HT on BLA during a reward-driven task has not been fully elucidated. In this paper, we investigated whether serotonergic modulation of the BLA is involved in reward-driven learning. To this end, we trained Long Evans rats in an operant conditioning task, and examined the effects of fluoxetine treatment (a selective serotonin reuptake inhibitor, 10 mg/kg) in combination with BLA lesions with NMDA (20 mg/mL) on extinction learning. We also investigated whether intra-BLA injection of the serotonergic 5-HT1A receptor agonist 8-OH DPAT, or antagonist WAY-100635, alters extinction performance. We found that fluoxetine treatment strongly accelerated extinction learning, while BLA lesions partially reverted this effect and slightly impaired consolidation of extinction. Stimulation and inhibition of 5-HT1A receptors in BLA induced opposite effects to those of fluoxetine, impairing or accelerating extinction performance, respectively. Our findings suggest that 5-HT modulates reward-driven learning, and 5-HT1A receptors located in the BLA are relevant for extinction.

Seizure localization using pre ictal phase-amplitude coupling in intracranial electroencephalography.

Understanding changes in brain rhythms provides useful information to predict the onset of a seizure and to localize its onset zone in epileptic patients. Brain rhythms dynamics in general, and phase-amplitude coupling in particular, are known to be drastically altered during epileptic seizures. However, the neural processes that take place before a seizure are not well understood. We analysed the phase-amplitude coupling dynamics of stereoelectroencephalography recordings (30 seizures, 5 patients) before and after seizure onset. Electrodes near the seizure onset zone showed higher phase-amplitude coupling. Immediately before the beginning of the seizure, phase-amplitude coupling dropped to values similar to the observed in electrodes far from the seizure onset zone. Thus, our results bring accurate information to detect epileptic events during pre-ictal periods and to delimit the zone of seizure onset in patients undergoing epilepsy surgery.

Putative dopamine neurons in the ventral tegmental area enhance information coding in the prefrontal cortex.

It has been proposed that neuronal populations in the prefrontal cortex (PFC) robustly encode task-relevant information through an interplay with the ventral tegmental area (VTA). Yet, the precise computation underlying such functional interaction remains elusive. Here, we conducted simultaneous recordings of single-unit activity in PFC and VTA of rats performing a GO/NoGO task. We found that mutual information between stimuli and neural activity increases in the PFC as soon as stimuli are presented. Notably, it is the activity of putative dopamine neurons in the VTA that contributes critically to enhance information coding in the PFC. The higher the activity of these VTA neurons, the better the conditioned stimuli are encoded in the PFC.

Deletion of dopamine D2 receptors from parvalbumin interneurons in mouse causes schizophrenia-like phenotypes.

Excessive dopamine neurotransmission underlies psychotic episodes as observed in patients with some types of bipolar disorder and schizophrenia. The dopaminergic hypothesis was postulated after the finding that antipsychotics were effective to halt increased dopamine tone. However, there is little evidence for dysfunction within the dopaminergic system itself. Alternatively, it has been proposed that excessive afferent activity onto ventral tegmental area dopaminergic neurons, particularly from the ventral hippocampus, increase dopamine neurotransmission, leading to psychosis. Here, we show that selective dopamine D2 receptor deletion from parvalbumin interneurons in mouse causes an impaired inhibitory activity in the ventral hippocampus and a dysregulated dopaminergic system. Conditional mutant animals show adult onset of schizophrenia-like behaviors and molecular, cellular, and physiological endophenotypes as previously described from postmortem brain studies of patients with schizophrenia. Our findings show that dopamine D2 receptor expression on parvalbumin interneurons is required to modulate and limit pyramidal neuron activity, which may prevent the dysregulation of the dopaminergic system.

A spherical treadmill system to train head-fixed adult rats.

BACKGROUND: While spherical treadmills are widely used in mouse models, there are only a few experimental setups suitable for adult rats, and none of them include head-fixation. NEW METHOD: We introduce a novel spherical treadmill apparatus for head-fixed rats that allows a wide repertory of natural responses. The rat is secured to a frame and placed on a freely rotating sphere. While being head-fixed, it can walk in any direction and perform different motor tasks. COMPARISON WITH EXISTING METHODS: Instead of being air-lifted, which is acceptable for light animals, the treadmill is sustained by three spherical bearings ensuring a smooth rotation in any direction. Movement detection is accomplished using a video camera that registers a dot pattern plotted on the sphere. RESULTS: Long Evans rats were trained to perform an auditory discrimination task in a Go/No-Go (walking/not-walking) paradigm. Animals were able to successfully discriminate between a 1 kHz and a 8 kHz auditory stimulus and execute the correct response, reaching the learning criterion (80% of correct responses) in approximately 20 training sessions. CONCLUSIONS: Our system broadens the possibilities of head-fixation experiments in adult rats making them compatible with spatial navigation on a spherical treadmill.

Efficient enhancement of information in the prefrontal cortex during the presence of reward predicting stimuli.

The prefrontal cortex (PFC) is a key brain structure for decision making, behavioural flexibility and working memory. Neurons in PFC encode relevant stimuli through changes in their firing rate, although the metabolic cost of spiking activity puts strong constrains to neural codes based on firing rate modulation. Thus, how PFC neural populations code relevant information in an efficient way is not clearly understood. To address this issue we made single unit recordings in the PFC of rats performing a GO/NOGO discrimination task and analysed how entropy between pairs of neurons changes during cue presentation. We found that entropy rises only during reward-predicting cues. Moreover, this change in entropy occurred along an increase in the efficiency of the whole process. We studied possible mechanisms behind the efficient gain in entropy by means of a two neuron leaky integrate-and-fire model, and found that a precise relationship between synaptic efficacy and firing rate is required to explain the experimentally observed results.

Exploring the limits of learning: Segregation of information integration and response selection is required for learning a serial reversal task.

Animals are proposed to learn the latent rules governing their environment in order to maximize their chances of survival. However, rules may change without notice, forcing animals to keep a memory of which one is currently at work. Rule switching can lead to situations in which the same stimulus/response pairing is positively and negatively rewarded in the long run, depending on variables that are not accessible to the animal. This fact raises questions on how neural systems are capable of reinforcement learning in environments where the reinforcement is inconsistent. Here we address this issue by asking about which aspects of connectivity, neural excitability and synaptic plasticity are key for a very general, stochastic spiking neural network model to solve a task in which rules change without being cued, taking the serial reversal task (SRT) as paradigm. Contrary to what could be expected, we found strong limitations for biologically plausible networks to solve the SRT. Especially, we proved that no network of neurons can learn a SRT if it is a single neural population that integrates stimuli information and at the same time is responsible of choosing the behavioural response. This limitation is independent of the number of neurons, neuronal dynamics or plasticity rules, and arises from the fact that plasticity is locally computed at each synapse, and that synaptic changes and neuronal activity are mutually dependent processes. We propose and characterize a spiking neural network model that solves the SRT, which relies on separating the functions of stimuli integration and response selection. The model suggests that experimental efforts to understand neural function should focus on the characterization of neural circuits according to their connectivity, neural dynamics, and the degree of modulation of synaptic plasticity with reward.