Intracranial Entrainment Reveals Statistical Learning across Levels of Abstraction.

We encounter the same people, places, and objects in predictable sequences and configurations. Humans efficiently learn these regularities via statistical learning. Importantly, statistical learning creates knowledge not only of specific regularities but also of regularities that apply more generally across related experiences (i.e., across members of a category). Prior evidence for different levels of learning comes from post-exposure behavioral tests, leaving open the question of whether more abstract regularities are detected online during initial exposure. We address this question by measuring neural entrainment in intracranial recordings. Neurosurgical patients viewed a stream of photographs with regularities at one of two levels: In the exemplar-level structured condition, the same photographs appeared repeatedly in pairs. In the category-level structured condition, the photographs were trial-unique but their categories were paired across repetitions. In a baseline random condition, the same photographs repeated but in a scrambled order. We measured entrainment at the frequency of individual photographs, which was expected in all conditions, but critically also at half that frequency-the rate at which to-be-learned pairs appeared in the two structured (but not random) conditions. Entrainment to both exemplar and category pairs emerged within minutes throughout visual cortex and in frontal and temporal regions. Many electrode contacts were sensitive to only one level of structure, but a significant number encoded both levels. These findings suggest that the brain spontaneously uncovers category-level regularities during statistical learning, providing insight into the brain's unsupervised mechanisms for building flexible and robust knowledge that generalizes across input variation and conceptual hierarchies.

Temporal Dynamics of Competition between Statistical Learning and Episodic Memory in Intracranial Recordings of Human Visual Cortex.

The function of long-term memory is not just to reminisce about the past, but also to make predictions that help us behave appropriately and efficiently in the future. This predictive function of memory provides a new perspective on the classic question from memory research of why we remember some things but not others. If prediction is a key outcome of memory, then the extent to which an item generates a prediction signifies that this information already exists in memory and need not be encoded. We tested this principle using human intracranial EEG as a time-resolved method to quantify prediction in visual cortex during a statistical learning task and link the strength of these predictions to subsequent episodic memory behavior. Epilepsy patients of both sexes viewed rapid streams of scenes, some of which contained regularities that allowed the category of the next scene to be predicted. We verified that statistical learning occurred using neural frequency tagging and measured category prediction with multivariate pattern analysis. Although neural prediction was robust overall, this was driven entirely by predictive items that were subsequently forgotten. Such interference provides a mechanism by which prediction can regulate memory formation to prioritize encoding of information that could help learn new predictive relationships.SIGNIFICANCE STATEMENT When faced with a new experience, we are rarely at a loss for what to do. Rather, because many aspects of the world are stable over time, we rely on past experiences to generate expectations that guide behavior. Here we show that these expectations during a new experience come at the expense of memory for that experience. From intracranial recordings of visual cortex, we decoded what humans expected to see next in a series of photographs based on patterns of neural activity. Photographs that generated strong neural expectations were more likely to be forgotten in a later behavioral memory test. Prioritizing the storage of experiences that currently lead to weak expectations could help improve these expectations in future encounters.

Remembering the pattern: A longitudinal case study on statistical learning in spatial navigation and memory consolidation.

Distinct brain systems are thought to support statistical learning over different timescales. Regularities encountered during online perceptual experience can be acquired rapidly by the hippocampus. Further processing during offline consolidation can establish these regularities gradually in cortical regions, including the medial prefrontal cortex (mPFC). These mechanisms of statistical learning may be critical during spatial navigation, for which knowledge of the structure of an environment can facilitate future behavior. Rapid acquisition and prolonged retention of regularities have been investigated in isolation, but how they interact in the context of spatial navigation is unknown. We had the rare opportunity to study the brain systems underlying both rapid and gradual timescales of statistical learning using intracranial electroencephalography (iEEG) longitudinally in the same patient over a period of three weeks. As hypothesized, spatial patterns were represented in the hippocampus but not mPFC for up to one week after statistical learning and then represented in the mPFC but not hippocampus two and three weeks after statistical learning. Taken together, these findings suggest that the hippocampus may contribute to the initial extraction of regularities prior to cortical consolidation.

Development of a TaqMan® Allelic Discrimination qPCR Assay for Rapid Detection of Equine CXCL16 Allelic Variants Associated With the Establishment of Long-Term Equine Arteritis Virus Carrier State in Stallions.

Equine arteritis virus (EAV) is the causative agent of equine viral arteritis (EVA), a respiratory, systemic, and reproductive disease of equids. Following natural infection, up to 70% of the infected stallions can remain persistently infected over 1 year (long-term persistent infection [LTPI]) and shed EAV in their semen. Thus, the LTP-infected stallions play a pivotal role in maintaining and perpetuating EAV in the equine population. Previous studies identified equine C-X-C motif chemokine ligand 16 (CXCL16) as a critical host cell factor determining LTPI in the stallion's reproductive tract. Two alleles (CXCL16 S and CXCL16 r ) were identified in the equine population and correlated with the susceptibility or resistance of a CD3+ T cell subpopulation in peripheral blood to in vitro EAV infection, respectively. Interestingly, CXCL16 S has been linked to the establishment of LTPI in stallions, and thus, genotyping stallions based on CXCL16 S/r would allow identification of those at the highest risk of establishing LTPI. Thus, we developed a TaqMan® allelic discrimination qPCR assay for the genotyping of the equine CXCL16 gene based on the identification of a single nucleotide polymorphism in position 1,073 based on NCBI gene ID: 100061442 (or position 527 based on Ensembl: ENSECAG00000018406.2) located in exon 2. One hundred and sixty horses from four breeds were screened for the CD3+ T cell susceptibility phenotype to EAV infection by flow cytometry and subsequently sequenced to determine CXCL16 allelic composition. Genotyping by Sanger sequencing determined that all horses with the resistant CD3+ T cell phenotype were homozygous for CXCL16 r while horses with the susceptible CD3+ T cell phenotype carried at least one CXCL16 S allele or homozygous for CXCL16 S . In addition, genotypification with the TaqMan® allelic discrimination qPCR assay showed perfect agreement with Sanger sequencing and flow cytometric analysis. In conclusion, the new TaqMan® allelic discrimination genotyping qPCR assay can be used to screen prepubertal colts for the presence of the CXCL16 genotype. It is highly recommended that colts that carry the susceptible genotype (CXCL16  S/S or CXCL16 S/r ) are vaccinated against EAV after 6 months of age to prevent the establishment of LTPI carriers following possible natural infection with EAV.

Spatial gist extraction during human memory consolidation.

Theories of memory consolidation suggest that initially rich, vivid memories become more gist-like over time. However, it is unclear whether gist-like representations reflect a loss of detail through degradation or the blending of experiences into statistical averages, and whether the strength of these representations increases, decreases, or remains stable over time. We report three behavioral experiments that address these questions by examining distributional learning during spatial navigation. In Experiment 1, human subjects navigated a virtual maze to find hidden objects with locations varying according to spatial distributions. After 15 minutes, 1 day, 7 days, or 28 days, we tested their navigation performance and explicit memory. In Experiment 2, we created spatial distributions with no object at their mean locations, thereby disentangling learned object exemplars from statistical averages. In Experiment 3, we created only a single, bimodal distribution to avoid possible confusion between distributions and administered tests after 15 minutes or 28 days. Across all experiments, and for both navigation and explicit tests, representations of the spatial distributions were present soon after exposure, but then receded over time. These findings suggest gist-like representations do not improve over time, helping to clarify the temporal dynamics of consolidation in human learning and memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Finding the Pattern: On-Line Extraction of Spatial Structure During Virtual Navigation.

While navigating the world, we pick up on patterns of where things tend to appear. According to theories of memory and studies of animal behavior, knowledge of these patterns emerges gradually over days or weeks via consolidation of individual navigation episodes. Here, we discovered that navigation patterns can also be extracted on-line, prior to the opportunity for off-line consolidation, as a result of rapid statistical learning. Thirty human participants navigated a virtual water maze in which platform locations were drawn from a spatial distribution. Within a single session, participants increasingly navigated through the mean of the distribution. This behavior was better simulated by random walks from a model that had only an explicit representation of the current mean, compared with a model that had only memory for the individual platform locations. These results suggest that participants rapidly summarized the underlying spatial distribution and used this statistical knowledge to guide future navigation.

The prevalence and importance of statistical learning in human cognition and behavior.

Statistical learning, the ability to extract regularities from the environment over time, has become a topic of burgeoning interest. Its influence on behavior, spanning infancy to adulthood, has been demonstrated across a range of tasks, both those labeled as tests of statistical learning and those from other learning domains that predated statistical learning research or that are not typically considered in the context of that literature. Given this pervasive role in human cognition, statistical learning has the potential to reconcile seemingly distinct learning phenomena and may be an under-appreciated but important contributor to a wide range of human behaviors that are studied as unrelated processes, such as episodic memory and spatial navigation.

The Role of Frontostriatal Systems in Instructed Reinforcement Learning: Evidence From Genetic and Experimentally-Induced Variation.

Instructions have a powerful effect on learning and decision-making, biasing choice even in the face of disconfirming feedback. Detrimental biasing effects have been reported in a number of studies in which instruction was given prior to trial-and-error learning. Previous work has attributed individual differences in instructional bias to variations in prefrontal and striatal dopaminergic genes, suggesting a role for prefrontally-mediated cognitive control processes in biasing learning. The current study replicates and extends these findings. Human subjects performed a probabilistic reinforcement learning task after receiving inaccurate instructions about the quality of one of the options. In order to establish a causal relationship between prefrontal cortical mechanisms and instructional bias, we applied transcranial direct current stimulation over dorsolateral prefrontal cortex (anodal, cathodal, or sham) while subjects performed the task. We additionally genotyped subjects for the COMT Val158Met genetic polymorphism, which influences the breakdown of prefrontal dopamine, and for the DAT1/SLC6A3 variable number tandem repeat, which affects expression of striatal dopamine transporter. We replicated the finding that the COMT Met allele is associated with increased instructional bias and further demonstrated that variation in DAT1 has similar effects to variation in COMT, with 9-repeat carriers demonstrating increased bias relative to 10-repeat homozygotes. Consistent with increased top-down regulation of reinforcement learning, anodal subjects demonstrated greater bias relative to sham, though this effect was present only early in training. In contrast, there was no effect of cathodal stimulation. Finally, we fit computational models to subjects' data to better characterize the mechanisms underlying instruction bias. A novel choice bias model, in which instructions influence decision-making rather than learning, was found to best account for subjects' behavior. Overall, these data provide further evidence for the role of frontostriatal interactions in biasing instructed reinforcement learning, which adds to the growing literature documenting both costs and benefits of cognitive control.

Genetic diversity in the modern horse illustrated from genome-wide SNP data.

Horses were domesticated from the Eurasian steppes 5,000-6,000 years ago. Since then, the use of horses for transportation, warfare, and agriculture, as well as selection for desired traits and fitness, has resulted in diverse populations distributed across the world, many of which have become or are in the process of becoming formally organized into closed, breeding populations (breeds). This report describes the use of a genome-wide set of autosomal SNPs and 814 horses from 36 breeds to provide the first detailed description of equine breed diversity. F(ST) calculations, parsimony, and distance analysis demonstrated relationships among the breeds that largely reflect geographic origins and known breed histories. Low levels of population divergence were observed between breeds that are relatively early on in the process of breed development, and between those with high levels of within-breed diversity, whether due to large population size, ongoing outcrossing, or large within-breed phenotypic diversity. Populations with low within-breed diversity included those which have experienced population bottlenecks, have been under intense selective pressure, or are closed populations with long breed histories. These results provide new insights into the relationships among and the diversity within breeds of horses. In addition these results will facilitate future genome-wide association studies and investigations into genomic targets of selection.

Genome-wide analysis reveals selection for important traits in domestic horse breeds.

Intense selective pressures applied over short evolutionary time have resulted in homogeneity within, but substantial variation among, horse breeds. Utilizing this population structure, 744 individuals from 33 breeds, and a 54,000 SNP genotyping array, breed-specific targets of selection were identified using an F(ST)-based statistic calculated in 500-kb windows across the genome. A 5.5-Mb region of ECA18, in which the myostatin (MSTN) gene was centered, contained the highest signature of selection in both the Paint and Quarter Horse. Gene sequencing and histological analysis of gluteal muscle biopsies showed a promoter variant and intronic SNP of MSTN were each significantly associated with higher Type 2B and lower Type 1 muscle fiber proportions in the Quarter Horse, demonstrating a functional consequence of selection at this locus. Signatures of selection on ECA23 in all gaited breeds in the sample led to the identification of a shared, 186-kb haplotype including two doublesex related mab transcription factor genes (DMRT2 and 3). The recent identification of a DMRT3 mutation within this haplotype, which appears necessary for the ability to perform alternative gaits, provides further evidence for selection at this locus. Finally, putative loci for the determination of size were identified in the draft breeds and the Miniature horse on ECA11, as well as when signatures of selection surrounding candidate genes at other loci were examined. This work provides further evidence of the importance of MSTN in racing breeds, provides strong evidence for selection upon gait and size, and illustrates the potential for population-based techniques to find genomic regions driving important phenotypes in the modern horse.

Mutation in HSF4 associated with early but not late-onset hereditary cataract in the Boston Terrier.

Primary hereditary cataract (HC) is one of the most common disorders in purebred dogs and is a leading cause of blindness. Boston Terriers suffer from 2 distinct forms of HC which occur at different ages and which are different in their appearance and progression. Early-onset hereditary cataract (EHC) affects dogs within the first few months of life, is always progressive and bilateral, and results in total blindness, whereas late-onset hereditary cataract (LHC) in general affects dogs over the age of 3 and is more variable in its clinical phenotype, age of onset, progression, and the degree to which vision is impaired. A mutation in HSF4 has recently been reported in a small number of Boston Terriers affected with EHC. In this study, we analyzed 22 additional Boston Terriers affected with early-onset cataract to confirm that the HSF4 mutation is causative for this form of cataract in this breed. In addition, we analyzed 40 Boston Terriers that were either clinically clear or affected with LHC for the presence or absence of the HSF4 mutation. By also sequencing HSF4 in dogs affected with LHC, we conclude that HSF4 is not associated with the development of the late-onset form of cataract and that the 2 forms of cataract in this breed are therefore genetically discrete conditions.

Reduction in weight and cardiovascular disease risk factors in individuals with type 2 diabetes: one-year results of the look AHEAD trial.

OBJECTIVE: The effectiveness of intentional weight loss in reducing cardiovascular disease (CVD) events in type 2 diabetes is unknown. This report describes 1-year changes in CVD risk factors in a trial designed to examine the long-term effects of an intensive lifestyle intervention on the incidence of major CVD events. RESEARCH DESIGN AND METHODS: This study consisted of a multicentered, randomized, controlled trial of 5,145 individuals with type 2 diabetes, aged 45-74 years, with BMI >25 kg/m2 (>27 kg/m2 if taking insulin). An intensive lifestyle intervention (ILI) involving group and individual meetings to achieve and maintain weight loss through decreased caloric intake and increased physical activity was compared with a diabetes support and education (DSE) condition. RESULTS: Participants assigned to ILI lost an average 8.6% of their initial weight vs. 0.7% in DSE group (P < 0.001). Mean fitness increased in ILI by 20.9 vs. 5.8% in DSE (P < 0.001). A greater proportion of ILI participants had reductions in diabetes, hypertension, and lipid-lowering medicines. Mean A1C dropped from 7.3 to 6.6% in ILI (P < 0.001) vs. from 7.3 to 7.2% in DSE. Systolic and diastolic pressure, triglycerides, HDL cholesterol, and urine albumin-to-creatinine ratio improved significantly more in ILI than DSE participants (all P < 0.01). CONCLUSIONS: At 1 year, ILI resulted in clinically significant weight loss in people with type 2 diabetes. This was associated with improved diabetes control and CVD risk factors and reduced medicine use in ILI versus DSE. Continued intervention and follow-up will determine whether these changes are maintained and will reduce CVD risk.