Publications by authors named "B J Caffo"
Article Synopsis
- This manuscript introduces a novel method for scalar-on-distribution regression, where subject-specific distributions serve as covariates to predict a single outcome, bypassing the need for prior estimation of these distributions.
- The proposed approach uses observed repeated measures directly as covariates and applies a Gaussian process prior, achieving efficient Bayesian inference without needing intermediate density estimates.
- The method shows superior performance in simulation studies compared to traditional regression that requires estimating densities first, especially when there are limited repeated measures per subject, and it also accommodates various forms of data dependencies.
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.
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.
View Article and Find Full Text PDFVast 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.
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