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Emerging research suggests that episodic memory challenges are commonly encountered by autistic individuals; however, the specific nature of these memory challenges remains elusive. Here, we address critical gaps in the literature by examining pattern separation memory, the ability to store distinct memories of similar stimuli, and its links to the core autistic trait of repetitive, restricted interests and behaviors. Utilizing a large sample of over 120 autistic children and well-matched non-autistic peers, we found that autistic children showed significantly reduced performance on pattern separation memory. A clustering analysis identified three distinct pattern separation memory profiles in autism, each characterized by reduced or increased generalization abilities. Importantly, pattern separation memory was negatively correlated with the severity of repetitive, restricted interest and behavior symptoms in autism. These findings offer new evidence for challenges in pattern separation memory in autism and emphasize the need to consider these challenges when assessing and supporting autistic individuals in educational and clinical settings.

BackgroundIn sickle cell disease (SCD), unpredictable episodes of acute severe pain, known as vaso-occlusive crises (VOC), disrupt school, work activities and family life and ultimately lead to multiple hospitalizations. The ability to predict VOCs would allow a timely and adequate intervention. The first step towards this ultimate goal is to use patient-friendly and accessible technology to collect relevant data that helps infer a patient's pain experience during VOC. This study aims to: (1) determine the feasibility of remotely monitoring with a consumer wearable during hospitalization for VOC and up to 30 days after discharge, and (2) evaluate the accuracy of pain prediction using machine learning models based on physiological parameters measured by a consumer wearable.MethodsPatients with SCD (≥18 years) who were admitted for a vaso-occlusive crisis were enrolled at a single academic center. Participants were instructed to report daily pain scores (0–10) in a mobile app (Nanbar) and to continuously wear an Apple Watch up to 30 days after discharge. Data included heart rate (in rest, average and variability) and step count. Demographics, SCD genotype, and details of hospitalization including pain scores reported to nurses, were extracted from electronic medical records. Physiological data from the wearable were associated with pain scores to fit 3 different machine learning classification models. The performance of the machine learning models was evaluated using: accuracy, F1, root-mean-square error and area under the receiver-operating curve.ResultsBetween April and June 2022, 19 patients (74% HbSS genotype) were included in this study and followed for a median time of 28 days [IQR 22–34], yielding a dataset of 2,395 pain data points. Ten participants were enrolled while hospitalized for VOC. The metrics of the best performing model, the random forest model, were micro-averaged accuracy of 92%, micro-averaged F1-score of 0.63, root-mean-square error of 1.1, and area under the receiving operating characteristic curve of 0.9.ConclusionOur random forest model accurately predicts high pain scores during admission for VOC and after discharge. The Apple Watch was a feasible method to collect physiologic data and provided accuracy in prediction of pain scores.

Children with autism spectrum disorders (ASDs) often display atypical learning styles; however, little is known regarding learning-related brain plasticity and its relation to clinical phenotypic features. Here, we investigate cognitive learning and neural plasticity using functional brain imaging and a novel numerical problem-solving training protocol. Children with ASD showed comparable learning relative to typically developing children but were less likely to shift from rule-based to memory-based strategy. While learning gains in typically developing children were associated with greater plasticity of neural representations in the medial temporal lobe and intraparietal sulcus, learning in children with ASD was associated with more stable neural representations. Crucially, the relation between learning and plasticity of neural representations was moderated by insistence on sameness, a core phenotypic feature of ASD. Our study uncovers atypical cognitive and neural mechanisms underlying learning in children with ASD, and informs pedagogical strategies for nurturing cognitive abilities in childhood autism.

Abstract Mothers alter their speech in a stereotypical manner when addressing infants using high pitch, a wide pitch range, and distinct timbral features. Mothers reduce their vocal pitch after early childhood; however, it is not known whether mother’s voice changes through adolescence as children become increasingly independent from their parents. Here we investigate the vocal acoustics of 50 mothers of older children (ages 7–16) to determine: (1) whether pitch changes associated with child-directed speech decrease with age; (2) whether other acoustical features associated with child-directed speech change with age; and, (3) the relative contribution of acoustical features in predicting child’s age. Results reveal that mothers of older children used lower pitched voices than mothers of younger children, and mother’s voice pitch height predicted their child’s age. Crucially, these effects were present after controlling for mother’s age, accounting for aging-related pitch reductions. Brightness, a timbral feature correlated with pitch height, also showed an inverse relation with child’s age but did not improve prediction of child’s age beyond that accounted for by pitch height. Other acoustic features did not predict child age. Findings suggest that mother’s voice adapts to match their child’s developmental progression into adolescence and this adaptation is independent of mother’s age.

Collaboration plays a key role in physics and in the broader scientific enterprise. Here we develop a mathematical model for predicting new collaborations. We demonstrate that a simple ordinary differential equation model is a good fit to a data set that tracks collaborations resulting from four series of annual conferences on diverse scientific topics, 12 conferences in total over a period of five years. The model, inspired by the physics of catalysis, attempts to quantify the time-varying probability that any pair of individuals will initiate a new collaboration. It takes as input the pair's prior familiarity with one another as well as their pattern of interaction over time, and incorporates the effect of temporally decaying memory. This model accurately reproduces the collaborations formed across all first-year conferences in the four series and outperforms seven other candidate models. We also find evidence that prescribed interaction can lead to novel team formation, with observed collaboration probabilities increased by almost an order of magnitude. These results suggest that encounters among individual researchers at conferences, including encounters engineered by organizers, play an important role in shaping the future of science.

BackgroundSickle cell disease (SCD) is the most common inherited blood disorder affecting millions of people worldwide. Most patients with SCD experience repeated, unpredictable episodes of severe pain. These pain episodes are the leading cause of emergency department visits among patients with SCD and may last for several weeks. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting a patient’s pain intensity level. ObjectiveThis study aims to learn deep feature representations of subjective pain trajectories using objective physiological signals collected from electronic health records. MethodsThis study used electronic health record data collected from 496 Duke University Medical Center participants over 5 consecutive years. Each record contained measures for 6 vital signs and the patient’s self-reported pain score, with an ordinal range from 0 (no pain) to 10 (severe and unbearable pain). We also extracted 3 features related to medication: medication type, medication status (given or applied, or missed or removed or due), and total medication dosage (mg/mL). We used variational autoencoders for representation learning and designed machine learning classification algorithms to build pain prediction models. We evaluated our results using an accuracy and confusion matrix and visualized the qualitative data representations. ResultsWe designed a classification model using raw data and deep representational learning to predict subjective pain scores with average accuracies of 82.8%, 70.6%, 49.3%, and 47.4% for 2-point, 4-point, 6-point, and 11-point pain ratings, respectively. We observed that random forest classification models trained on deep represented features outperformed models trained on unrepresented data for all pain rating scales. We observed that at varying Likert scales, our models performed better when provided with medication data along with vital signs data. We visualized the data representations to understand the underlying latent representations, indicating neighboring representations for similar pain scores with a higher resolution of pain ratings. ConclusionsOur results demonstrate that medication information (the type of medication, total medication dosage, and whether the medication was given or missed) can significantly improve subjective pain prediction modeling compared with modeling with only vital signs. This study shows promise in data-driven estimated pain scores that will help clinicians with additional information about the patient’s condition, in addition to the patient’s self-reported pain scores.

The visual word form area (VWFA) is a brain region associated with written language, but it has also been linked to visuospatial attention. Here, the authors reveal distinct structural and functional circuits linking VWFA with language and attention networks, and demonstrate that these circuits separately predict language and attention abilities.

Engaging with vocal sounds is critical for children’s social-emotional learning, and children with autism spectrum disorder (ASD) often ‘tune out’ voices in their environment. Little is known regarding the neurobiological basis of voice processing and its link to social impairments in ASD. Here, we perform the first comprehensive brain network analysis of voice processing in children with ASD. We examined neural responses elicited by unfamiliar voices and mother’s voice, a biologically salient voice for social learning, and identified a striking relationship between social communication abilities in children with ASD and activation in key structures of reward and salience processing regions. Functional connectivity between voice-selective and reward regions during voice processing predicted social communication in children with ASD and distinguished them from typically developing children. Results support the Social Motivation Theory of ASD by showing reward system deficits associated with the processing of a critical social stimulus, mother’s voice, in children with ASD.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that minor issues remain unresolved (see decision letter).

Accurate modeling of opinion dynamics has the potential to help us understand polarization and what makes effective political discourse possible or impossible. Here, we use physics-based methods to model the evolution of political opinions within a continuously distributed population. We utilize a network-free system of determining political influence and a local-attraction, distal-repulsion dynamic for reaction to perceived content. Our approach allows for the incorporation of intergroup bias such that messages from trusted in-group sources enjoy greater leeway than out-group ones. We are able to extrapolate these nonlinear microscopic dynamics to macroscopic population distributions by using probabilistic functions representing biased environments. The framework we put forward can reproduce real-world political distributions and experimentally observed dynamics and is amenable to further refinement as more data becomes available.

Over the past 35 years there has been a near doubling in the worldwide prevalence of obesity. Body Mass Index (BMI) distributions in high-income societies have increasingly shifted rightwards, corresponding to increases in average BMI that are due to well-studied changes in the socioeconomic environment. However, in addition to this shift, BMI distributions have also shown marked changes in their particular shape over time, exhibiting an ongoing right-skewed broadening that is not well understood. Here, we compile and analyze the largest data set so far of year-over-year BMI changes. The data confirm that, on average, heavy individuals become lighter while light individuals become heavier year-over-year, and also show that year-over-year BMI evolution is characterized by fluctuations with a magnitude that is linearly proportional to BMI. We find that the distribution of human BMIs is intrinsically dynamic-due to the short-term variability of human weight-and its shape is determined by a balance between deterministic drift towards a natural set point and diffusion resulting from random fluctuations in, e.g., diet and physical activity. We formulate a stochastic mathematical model for BMI dynamics, deriving a theoretical shape for the BMI distribution and offering a mechanism that may explain the right-skewed broadening of BMI distributions over time. An extension of the base model investigates the hypothesis that peer-to-peer social influence plays a role in BMI dynamics. While including this effect improves the fit with the data, indicating that correlations in the behavior of individuals with similar BMI may be important for BMI dynamics, testing social transmission against other plausible unmodeled effects and interpretations remains the subject of future work. Implications of our findings on the dynamics of BMI distributions for public health interventions are discussed.

Daniel J Abrams1, Donald C Rojas1, David B Arciniegas1,21Department of Psychiatry; 2Neurobehavioral Disorders Program, Departments of Psychiatry and Neurology, University of Colorado School of Medicine, Denver, CO, USAAbstract: Considerable debate surrounds the inclusion of schizoaffective disorder in psychiatric nosology. Schizoaffective disorder may be a variant of schizophrenia in which mood symptoms are unusually prominent but not unusual in type. This condition may instead reflect a severe form of either major depressive or bipolar disorder in which episode-related psychotic symptoms fail to remit completely between mood episodes. Alternatively, schizoaffective disorder may reflect the co-occurrence of two relatively common psychiatric illnesses, schizophrenia and a mood disorder (major depressive or bipolar disorder). Each of these formulations of schizoaffective disorder presents nosological challenges because the signs and symptoms of this condition cross conventional categorical diagnostic boundaries between psychotic disorders and mood disorders. The study, evaluation, and treatment of persons presently diagnosed with schizoaffective may be more usefully informed by a dimensional approach. It is in this context that this article reviews and contrasts the categorical and dimensional approaches to its description, neurobiology, and treatment. Based on this review, an argument for the study and treatment of this condition using a dimensional approach is offered.Keywords: schizoaffective disorder, schizophrenia, bipolar disorder, manic-depressive disorder