Brian S. Caffo’s research while affiliated with Johns Hopkins University and other places

Imitation plays a critical role in enhancing social reciprocity and social/non‐social skill learning. Accordingly, impaired imitation may have downstream implications on skill acquisition in autism. Social, motor, representational, and executive processes contribute to imitation performance, but it is unknown the degree to which differences in these domains contribute to imitation differences in autism. In the present study, we evaluated the role of various psychological mechanisms of autism‐related imitation differences using mediation models. We assessed autistic and non‐autistic 7–12‐year‐old children ( n = 708) with FSIQ ≥ 80, using a wide battery of performance‐based and parent‐report tests that measured meaningful and non‐meaningful gesture imitation performance, motor execution, action representation, social motivation, and executive function processes. Multiple marginal mediation analyses revealed that motor execution tests most strongly mediated imitation deficits in autism, though effects from social motivation, action representation, and executive function also partially mediated the relationship between autism diagnosis and imitation performance. Using cross‐validated regression models, the domains tested here accounted for 39% of the variation in imitation performance. Results are contextualized across a broad range of experimental and observational studies with respect to the prompted imitation task utilized here. Future research will require longitudinal data, particularly from earlier stages of development.

Brain atrophy over time, as measured by magnetic resonance imaging (MRI), has been shown to predict subsequent cognitive impairment among individuals who were cognitively normal when first evaluated, indicating that subtle brain atrophy associated with Alzheimer's disease (AD) may begin years before clinical symptoms appear. Traditionally, atrophy has been quantified by differences in brain volume or thickness over a specified timeframe. Research indicates that the rate of atrophy varies across different brain regions, which themselves exhibit complex spatial and hierarchical organizations. These characteristics collectively emphasize the need for diverse summary measures that can effectively capture the multidimensional nature of degeneration. In this study, we explore the use of distance measurements to quantify brain volumetric changes using processed MRI data from the Preclinical Alzheimer's Disease Consortium (PAC). We conducted a series of analyses to predict future diagnostic status by modeling MRI trajectories for participants who were cognitively normal at baseline and either remained cognitively normal or progressed to mild cognitive impairment (MCI) over time, with adjustments for age, sex, education, and APOE genotype. We consider multiple distance measures and brain regions through a two‐step approach. First, we build base models by fitting individual mixed‐effect models for each distance metric and brain region pairing, using follow‐up diagnosis (normal vs. MCI) as the outcome and volumetric changes from the baseline, as summarized by a given distance measure, as predictors. The second step aggregates these individual region‐distance base models to derive an overall estimate of diagnostic status. Our analyses showed that the distance measures approach consistently outperformed the traditional direct volumetric approach in terms of predictive accuracy, both in individual base models and the aggregated models. This work highlights the potential advantage of using distance measures over the traditional direct volumetric approach to capture the multidimensional aspects of atrophy in the development of AD and related disorders.

Neural organoids (NOs) have emerged as important tissue engineering models for brain sciences and biocomputing. Establishing reliable relationships between stimulation and recording traces of electrical activity is essential to monitor the functionality of NOs, especially as it relates to realizing biocomputing paradigms such as reinforcement learning or stimulus discrimination. While researchers have demonstrated neuromodulation in NOs, they have primarily used 2D microelectrode arrays (MEAs) with limited access to the entire 3D contour of the NOs. Here, we report neuromodulation using tiny mimics of macroscale EEG caps or shell MEAs. Specifically, we observe that stimulating current within a specific range (20 to 30 microA) induced a statistically significant increase in neuron firing rate when comparing the activity five seconds before and after stimulation. We observed neuromodulatory behavior using both three- and 16-electrode shells and could generate 3D spatiotemporal maps of neuromodulatory activity around the surface of the NO. Our studies demonstrate a methodology for investigating 3D spatiotemporal neuromodulation in organoids of broad relevance to biomedical engineering and biocomputing.

Imitation plays a critical role in enhancing social reciprocity and social/non-social skill learning. Accordingly, impaired imitation may have downstream implications on skill acquisition in autism. Social, motor, representational, and executive processes contribute to imitation performance, but it is unknown the degree to which differences in these domains contribute to imitation differences in autism. In the present study, we evaluated the role of various psychological mechanisms of autism-related imitation differences using mediation models. We assessed autistic and non-autistic 7–12-year-old children (n = 708) with FSIQ ≥ 80, using a wide battery of performance-based and parent-report tests that measured gesture imitation performance, motor execution, action representation, social motivation, and executive function processes. Multiple marginal mediation analyses revealed that motor execution tests most strongly mediated imitation deficits in autism, though effects from social motivation, action representation and executive function also partially mediated the relationship between autism diagnosis and imitation performance. Using cross-validated regression models, the domains tested here accounted for 39% of the variation in imitation performance. Results are contextualized across a broad range of experimental and observational studies with respect to the prompted imitation task utilized here. Future research will require longitudinal data, particularly from earlier stages of development.

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. We examined correlations between autism diagnostic symptom magnitude (Autism Diagnostic Observational Schedule; ADOS) and a hierarchical statistical model of intelligence. One autistic cohort was tested on the fourth edition of Wechsler Intelligence Scale for Children (WISC-IV; N = 131), and another on the fifth edition (WISC-V; N = 83). We anticipated a convergent pattern of results between cohorts. On WISC-IV, ADOS scores were correlated significantly with g and three out of four intermediate factor scores, which was a broader pattern of correlations than anticipated from the literature. In the WISC-V cohort, only one intermediate factor correlated significantly with the ADOS; correlations with g and the other intermediate factors were less statistically certain. ADOS-factor correlations were larger in the WISC-IV than WISC-V cohort; this difference was significant at the 90% level. WISC-IV shows dimensional relationships with ADOS at multiple points in the hierarchical model of intelligence. Moreover, the current results provide evidence that relationship between core autism symptomatology and the construct of general intelligence may depend on how intelligence is measured. Known cohort effects in the relationship between categorical autism diagnosis and general intelligence have previously been attributed to changes in autism diagnostic practices. To our knowledge, this is the first evidence that differing versions of IQ tests may be implicated.

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. We used a multi-trait, multi-method approach to examine method-associated bias within an ASD sample (n = 83). We examined performance/rater-instrument pairs for attention, inhibition, working memory, motor coordination, and core ASD features. Rater-based scores showed an overall greater methodology bias (57% of variance in score explained by method), while performance-based scores showed a weaker methodology bias (22%). The degree of inter-individual variance explained by method alone substantiates an anecdotal concern associated with the use of rater measures in ASD.

Background: Prior studies have demonstrated that existing guidelines to predict choledocholithiasis have limited accuracy, leading to overutilization of ERCP. Improved stratification may allow for appropriate patient selection for ERCP and the use of lower-risk modalities (i.e. EUS and MRCP). Methods: A machine learning model was developed using patient information from two published cohort studies originally used to evaluate performance of published guidelines in predicting choledocholithiasis. Prediction models were developed using the gradient boosting (GBM) machine learning method. GBM performance was evaluated using 10-fold cross-validation and area under the receiver operating curve (AUC). Important predictors of choledocholithiasis were identified based on relative importance in GBM. Results: 1,378 patients (mean age 43.3 years; 55.5% females) were included in the GBM model and 59.4% had choledocholithiasis. Eight variables were identified as predictors of choledocholithiasis. The GBM model was evaluated with 10-fold cross-validation and had an accuracy of 71.5±2.5% (AUC 0.79±0.06) and performed better than the 2019 ASGE guidelines (accuracy 62.4±2.6%, AUC 0.63±0.03) and the ESGE guidelines (accuracy 62.8±2.6%, AUC 0.67±0.02). The GBM model correctly categorized 22% of patients directed to unnecessary ERCP by the ASGE guidelines and appropriately recommended 48% of ERCPs incorrectly rejected by the ESGE guidelines as the next step in the management. Conclusions: A machine learning-based tool was created that provides a real-time, personalized, objective probability of choledocholithiasis and ERCP recommendations. This more accurately directs ERCP use than the existing ASGE and ESGE guidelines and has the potential to reduce morbidity and healthcare costs associated with ERCP or missed choledocholithiasis.