Autism Symptom Presentation and Hierarchical Models of Intelligence.

Background: There is a substantial history studying the relationship between general intelligence and the core symptoms of autism. However, a gap in knowledge is how dimensional autism symptomatology associates with different components of clinically-relevant hierarchical models of intelligence.

Method: We examined correlations between autism diagnostic symptom magnitude (Autism Diagnostic Observational Schedule; ADOS) and a hierarchical statistical model of intelligence.

Baseline functional connectivity predicts who will benefit from neuromodulation: evidence from primary progressive aphasia.

Background: Identifying the characteristics of individuals who demonstrate response to an intervention allows us to predict who is most likely to benefit from certain interventions. Prediction is challenging in rare and heterogeneous diseases, such as primary progressive aphasia (PPA), that have varying clinical manifestations. We aimed to determine the characteristics of those who will benefit most from transcranial direct current stimulation (tDCS) of the left inferior frontal gyrus (IFG) using a novel heterogeneity and group identification analysis.

A Curated Compendium of Transcriptomic Data for the Exploration of Neocortical Development.

Vast quantities of multi-omic data have been produced to characterize the development and diversity of cell types in the cerebral cortex of humans and other mammals. To more fully harness the collective discovery potential of these data, we have assembled gene-level transcriptomic data from 188 published studies of neocortical development, including the transcriptomes of ~30 million single-cells, extensive spatial transcriptomic experiments and RNA sequencing of sorted cells and bulk tissues: nemoanalytics.org/landing/neocortex.

Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies.

Recent advances in spatially-resolved single-omics and multi-omics technologies have led to the emergence of computational tools to detect or predict spatial domains. Additionally, histological images and immunofluorescence (IF) staining of proteins and cell types provide multiple perspectives and a more complete understanding of tissue architecture. Here, we introduce Proust, a scalable tool to predict discrete domains using spatial multi-omics data by combining the low-dimensional representation of biological profiles based on graph-based contrastive self-supervised learning.

Regression models for partially localized fMRI connectivity analyses.

Background: Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is an assumption that brain regions are functionally aligned across subjects; however, it is known that this functional alignment assumption is often violated.

Longitudinal trajectories of glycemic control among U.S. Adults with newly diagnosed diabetes.

Aims: To identify longitudinal trajectories of glycemic control among adults with newly diagnosed diabetes, overall and by diabetes type.

Methods: We analyzed claims data from OptumLabs® Data Warehouse for 119,952 adults newly diagnosed diabetes between 2005 and 2018. We applied a novel Mixed Effects Machine Learning model to identify longitudinal trajectories of hemoglobin A (HbA) over 3 years of follow-up and used multinomial regression to characterize factors associated with each trajectory.

Timing matters: The contribution of running during different periods of the light/dark cycle to susceptibility to activity-based anorexia in rats.

Individuals with anorexia nervosa (AN) exhibit dangerous weight loss due to restricted eating and hyperactivity. Those with AN are predominantly women and most cases have an age of onset during adolescence. Activity-based anorexia (ABA) is a rodent behavioral paradigm that recapitulates many of the features of AN including restricted food intake and hyperactivity, resulting in precipitous weight loss.

A machine learning-based choledocholithiasis prediction tool to improve ERCP decision making: a proof-of-concept study.

Background: Previous studies demonstrated limited accuracy of existing guidelines for predicting choledocholithiasis, leading to overutilization of endoscopic retrograde cholangiopancreatography (ERCP). More accurate stratification may improve patient selection for ERCP and allow use of lower-risk modalities.

Methods: A machine learning model was developed using patient information from two published cohort studies that evaluated performance of guidelines in predicting choledocholithiasis.

A Multi-Trait Multi-Method Examination of Psychometric Instrument Performance in Autism Spectrum Disorder.

Anecdotal evidence has suggested that rater-based measures (e.g., parent report) may have strong across-trait/within-individual covariance that detracts from trait-specific measurement precision; rater measurement-related bias may help explain poor correlation within Autism Spectrum Disorder (ASD) samples between rater-based and performance-based measures of the same trait.

Template independent component analysis with spatial priors for accurate subject-level brain network estimation and inference.

Independent component analysis is commonly applied to functional magnetic resonance imaging (fMRI) data to extract independent components (ICs) representing functional brain networks. While ICA produces reliable group-level estimates, single-subject ICA often produces noisy results. Template ICA is a hierarchical ICA model using empirical population priors to produce more reliable subject-level estimates.

Predicting chronic postsurgical pain: current evidence and a novel program to develop predictive biomarker signatures.

Chronic pain affects more than 50 million Americans. Treatments remain inadequate, in large part, because the pathophysiological mechanisms underlying the development of chronic pain remain poorly understood. Pain biomarkers could potentially identify and measure biological pathways and phenotypical expressions that are altered by pain, provide insight into biological treatment targets, and help identify at-risk patients who might benefit from early intervention.

Specificity in Generalization Effects of Transcranial Direct Current Stimulation Over the Left Inferior Frontal Gyrus in Primary Progressive Aphasia.

Objectives: Generalization (or near-transfer) effects of an intervention to tasks not explicitly trained are the most desirable intervention outcomes. However, they are rarely reported and even more rarely explained. One hypothesis for generalization effects is that the tasks improved share the same brain function/computation with the intervention task.

Regression models for partially localized fMRI connectivity analyses.

Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. This can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is the assumption of complete localization (or spatial alignment) of brain regions across subjects.

Autism and Hierarchical Models of Intelligence.

Background: The Wechsler Intelligence Scale for Children (WISC) employs a hierarchical model of general intelligence in which index scores separate out different clinically-relevant aspects of intelligence; the test is designed such that index scores are statistically independent from one another within the normative sample. Whether or not the existing index scores meet the desired psychometric property of being statistically independent within autistic samples is unknown.

Method: We conducted a factor analysis on WISC fifth edition (WISC-V) (N = 83) and WISC fourth edition (WISC-IV) (N = 131) subtest data in children with autism.

Rethinking recovery in adolescent concussions: Network-level functional connectivity alterations associated with motor deficits.

Adolescents who are clinically recovered from concussion continue to show subtle motor impairment on neurophysiological and behavioral measures. However, there is limited information on brain-behavior relationships of persistent motor impairment following clinical recovery from concussion. We examined the relationship between subtle motor performance and functional connectivity of the brain in adolescents with a history of concussion, status post-symptom resolution, and subjective return to baseline.

Evaluating causal psychological models: A study of language theories of autism using a large sample.

We used a large convenience sample ( = 22,223) from the Simons Powering Autism Research (SPARK) dataset to evaluate causal, explanatory theories of core autism symptoms. In particular, the data-items collected supported the testing of theories that posited altered language abilities as cause of social withdrawal, as well as alternative theories that competed with these language theories. Our results using this large dataset converge with the evolution of the field in the decades since these theories were first proposed, namely supporting primary social withdrawal (in some cases of autism) as a cause of altered language development, rather than vice versa.

Corrigendum: Editorial: Explainable artificial intelligence models and methods in finance and healthcare.

[This corrects the article DOI: 10.3389/frai.2022.

Identification of the Language Network from Resting-State fMRI in Patients with Brain Tumors: How Accurate Are Experts?

Background And Purpose: Resting-state fMRI helps identify neural networks in presurgical patients who may be limited in their ability to undergo task-fMRI. The purpose of this study was to determine the accuracy of identifying the language network from resting-state-fMRI independent component analysis (ICA) maps.

Materials And Methods: Through retrospective analysis, patients who underwent both resting-state-fMRI and task-fMRI were compared by identifying the language network from the resting-state-fMRI data by 3 reviewers.

A machine learning based approach towards high-dimensional mediation analysis.

Mediation analysis is used to investigate the role of intermediate variables (mediators) that lie in the path between an exposure and an outcome variable. While significant research has focused on developing methods for assessing the influence of mediators on the exposure-outcome relationship, current approaches do not easily extend to settings where the mediator is high-dimensional. These situations are becoming increasingly common with the rapid increase of new applications measuring massive numbers of variables, including brain imaging, genomics, and metabolomics.

Longitudinal regression of covariance matrix outcomes.

In this study, a longitudinal regression model for covariance matrix outcomes is introduced. The proposal considers a multilevel generalized linear model for regressing covariance matrices on (time-varying) predictors. This model simultaneously identifies covariate-associated components from covariance matrices, estimates regression coefficients, and captures the within-subject variation in the covariance matrices.

Differences in functional connectivity distribution after transcranial direct-current stimulation: A connectivity density point of view.

In this manuscript, we consider the problem of relating functional connectivity measurements viewed as statistical distributions to outcomes. We demonstrate the utility of using the distribution of connectivity on a study of resting-state functional magnetic resonance imaging association with an intervention. The method uses the estimated density of connectivity between nodes of interest as a functional covariate.