Impaired perceptual learning in a mouse model of Fragile X syndrome is mediated by parvalbumin neuron dysfunction and is reversible.

To uncover the circuit-level alterations that underlie atypical sensory processing associated with autism, we adopted a symptom-to-circuit approach in the Fmr1-knockout (Fmr1-/-) mouse model of Fragile X syndrome. Using a go/no-go task and in vivo two-photon calcium imaging, we find that impaired visual discrimination in Fmr1-/- mice correlates with marked deficits in orientation tuning of principal neurons and with a decrease in the activity of parvalbumin interneurons in primary visual cortex. Restoring visually evoked activity in parvalbumin cells in Fmr1-/- mice with a chemogenetic strategy using designer receptors exclusively activated by designer drugs was sufficient to rescue their behavioral performance. Strikingly, human subjects with Fragile X syndrome exhibit impairments in visual discrimination similar to those in Fmr1-/- mice. These results suggest that manipulating inhibition may help sensory processing in Fragile X syndrome

Hyperexcitability of the local cortical circuit in mouse models of tuberous sclerosis complex

Tuberous sclerosis complex (TSC) is a neurogenetic disorder associated with epilepsy, intellectual disabilities, and autistic behaviors. These neurological symptoms result from synaptic dysregulations, which shift a balance between excitation and inhibition. To decipher the synaptic substrate of hyperexcitability, we examined pan-neuronal Tsc1 knockout mouse and found a reduction in surface expression of a GABA receptor (GABAR) subunit but not AMPA receptor (AMPAR) subunit. Using electrophysiological recordings, we found a significant reduction in the frequency of GABAR-mediated miniature inhibitory postsynaptic currents (GABAR-mIPSCs) but not AMPAR-mediated miniature excitatory postsynaptic currents (AMPAR-mEPSCs) in layer 2/3 pyramidal neurons. To determine a subpopulation of interneurons that are especially vulnerable to the absence of TSC1 function, we also analyzed two strains of conditional knockout mice targeting two of the prominent interneuron subtypes that express parvalbumin (PV) or somatostatin (SST). Unlike pan-neuronal knockout mice, both interneuron-specific Tsc-1 knockout mice did not develop spontaneous seizures and grew into adults. Further, the properties of AMPAR-mEPSCs and GABAR-mIPSCs were normal in both Pv-Cre and Sst-Cre x Tsc1fl/fl knockout mice. These results indicate that removal of TSC1 from all neurons in a local cortical circuit results in hyperexcitability while connections between pyramidal neurons and interneurons expressing PV and SST are preserved in the layer 2/3 visual cortex. Our study suggests that another inhibitory cell type or a combination of multiple subtypes may be accountable for hyperexcitability in TSC. Keywords: Tuberous sclerosis complex; E/I balance; AMPA receptor; GABA receptor; Autism; Epilepsy; mTOR pathwa

Predicting Individuals' Learning Success from Patterns of Pre-Learning MRI Activity

Performance in most complex cognitive and psychomotor tasks improves with training, yet the extent of improvement varies among individuals. Is it possible to forecast the benefit that a person might reap from training? Several behavioral measures have been used to predict individual differences in task improvement, but their predictive power is limited. Here we show that individual differences in patterns of time-averaged T2*-weighted MRI images in the dorsal striatum recorded at the initial stage of training predict subsequent learning success in a complex video game with high accuracy. These predictions explained more than half of the variance in learning success among individuals, suggesting that individual differences in neuroanatomy or persistent physiology predict whether and to what extent people will benefit from training in a complex task. Surprisingly, predictions from white matter were highly accurate, while voxels in the gray matter of the dorsal striatum did not contain any information about future training success. Prediction accuracy was higher in the anterior than the posterior half of the dorsal striatum. The link between trainability and the time-averaged T2*-weighted signal in the dorsal striatum reaffirms the role of this part of the basal ganglia in learning and executive functions, such as task-switching and task coordination processes. The ability to predict who will benefit from training by using neuroimaging data collected in the early training phase may have far-reaching implications for the assessment of candidates for specific training programs as well as the study of populations that show deficiencies in learning new skills

A Protocol for the Secure Linking of Registries for HPV Surveillance

In order to monitor the effectiveness of HPV vaccination in Canada the linkage of multiple data registries may be required. These registries may not always be managed by the same organization and, furthermore, privacy legislation or practices may restrict any data linkages of records that can actually be done among registries. The objective of this study was to develop a secure protocol for linking data from different registries and to allow on-going monitoring of HPV vaccine effectiveness.A secure linking protocol, using commutative hash functions and secure multi-party computation techniques was developed. This protocol allows for the exact matching of records among registries and the computation of statistics on the linked data while meeting five practical requirements to ensure patient confidentiality and privacy. The statistics considered were: odds ratio and its confidence interval, chi-square test, and relative risk and its confidence interval. Additional statistics on contingency tables, such as other measures of association, can be added using the same principles presented. The computation time performance of this protocol was evaluated.The protocol has acceptable computation time and scales linearly with the size of the data set and the size of the contingency table. The worse case computation time for up to 100,000 patients returned by each query and a 16 cell contingency table is less than 4 hours for basic statistics, and the best case is under 3 hours.A computationally practical protocol for the secure linking of data from multiple registries has been demonstrated in the context of HPV vaccine initiative impact assessment. The basic protocol can be generalized to the surveillance of other conditions, diseases, or vaccination programs

Modeling the Violation of Reward Maximization and Invariance in Reinforcement Schedules

It is often assumed that animals and people adjust their behavior to maximize reward acquisition. In visually cued reinforcement schedules, monkeys make errors in trials that are not immediately rewarded, despite having to repeat error trials. Here we show that error rates are typically smaller in trials equally distant from reward but belonging to longer schedules (referred to as “schedule length effect”). This violates the principles of reward maximization and invariance and cannot be predicted by the standard methods of Reinforcement Learning, such as the method of temporal differences. We develop a heuristic model that accounts for all of the properties of the behavior in the reinforcement schedule task but whose predictions are not different from those of the standard temporal difference model in choice tasks. In the modification of temporal difference learning introduced here, the effect of schedule length emerges spontaneously from the sensitivity to the immediately preceding trial. We also introduce a policy for general Markov Decision Processes, where the decision made at each node is conditioned on the motivation to perform an instrumental action, and show that the application of our model to the reinforcement schedule task and the choice task are special cases of this general theoretical framework. Within this framework, Reinforcement Learning can approach contextual learning with the mixture of empirical findings and principled assumptions that seem to coexist in the best descriptions of animal behavior. As examples, we discuss two phenomena observed in humans that often derive from the violation of the principle of invariance: “framing,” wherein equivalent options are treated differently depending on the context in which they are presented, and the “sunk cost” effect, the greater tendency to continue an endeavor once an investment in money, effort, or time has been made. The schedule length effect might be a manifestation of these phenomena in monkeys

Surprised at All the Entropy: Hippocampal, Caudate and Midbrain Contributions to Learning from Prediction Errors

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts

Learning, Memory, and the Role of Neural Network Architecture

The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems

Different Origins of Gamma Rhythm and High-Gamma Activity in Macaque Visual Cortex

High-gamma (80–200 Hz) activity can be dissociated from gamma rhythms in the monkey cortex, and appears largely to reflect spiking activity in the vicinity of the electrode

Association of BMI, lipid-lowering medication, and age with prevalence of type 2 diabetes in adults with heterozygous familial hypercholesterolaemia: a worldwide cross-sectional study

Background: Statins are the cornerstone treatment for patients with heterozygous familial hypercholesterolaemia but research suggests it could increase the risk of type 2 diabetes in the general population. A low prevalence of type 2 diabetes was reported in some familial hypercholesterolaemia cohorts, raising the question of whether these patients are protected against type 2 diabetes. Obesity is a well known risk factor for the development of type 2 diabetes. We aimed to investigate the associations of known key determinants of type 2 diabetes with its prevalence in people with heterozygous familial hypercholesterolaemia. Methods: This worldwide cross-sectional study used individual-level data from the EAS FHSC registry and included adults older than 18 years with a clinical or genetic diagnosis of heterozygous familial hypercholesterolaemia who had data available on age, BMI, and diabetes status. Those with known or suspected homozygous familial hypercholesterolaemia and type 1 diabetes were excluded. The main outcome was prevalence of type 2 diabetes overall and by WHO region, and in relation to obesity (BMI ≥30·0 kg/m2) and lipid-lowering medication as predictors. The study population was divided into 12 risk categories based on age (tertiles), obesity, and receiving statins, and the risk of type 2 diabetes was investigated using logistic regression. Findings: Among 46 683 adults with individual-level data in the FHSC registry, 24 784 with heterozygous familial hypercholesterolaemia were included in the analysis from 44 countries. 19 818 (80%) had a genetically confirmed diagnosis of heterozygous familial hypercholesterolaemia. Type 2 diabetes prevalence in the total population was 5·7% (1415 of 24 784), with 4·1% (817 of 19 818) in the genetically diagnosed cohort. Higher prevalence of type 2 diabetes was observed in the Eastern Mediterranean (58 [29·9%] of 194), South-East Asia and Western Pacific (214 [12·0%] of 1785), and the Americas (166 [8·5%] of 1955) than in Europe (excluding the Netherlands; 527 [8·0%] of 6579). Advancing age, a higher BMI category (obesity and overweight), and use of lipid-lowering medication were associated with a higher risk of type 2 diabetes, independent of sex and LDL cholesterol. Among the 12 risk categories, the probability of developing type 2 diabetes was higher in people in the highest risk category (aged 55–98 years, with obesity, and receiving statins; OR 74·42 [95% CI 47·04–117·73]) than in those in the lowest risk category (aged 18–38 years, without obesity, and not receiving statins). Those who did not have obesity, even if they were in the upper age tertile and receiving statins, had lower risk of type 2 diabetes (OR 24·42 [15·57–38·31]). The corresponding results in the genetically diagnosed cohort were OR 65·04 (40·67–104·02) for those with obesity in the highest risk category and OR 20·07 (12·73–31·65) for those without obesity. Interpretation: Adults with heterozygous familial hypercholesterolaemia in most WHO regions have a higher type 2 diabetes prevalence than in Europe. Obesity markedly increases the risk of diabetes associated with age and use of statins in these patients. Our results suggest that heterozygous familial hypercholesterolaemia does not protect against type 2 diabetes, hence managing obesity is essential to reduce type 2 diabetes in this patient population. Funding: Pfizer, Amgen, MSD, Sanofi-Aventis, Daiichi-Sankyo, and Regeneron