Eric Bridgeford

This paper develops a framework combining Oblique Random Forests and NEOFIT for sex classification in brain imaging across species, achieving high accuracy and interpretable feature importance.

This study identifies network biomarkers of Alzheimer's disease risk in mouse models by analyzing joint volume and texture covariance patterns influenced by genetic, dietary, and immune factors.

This paper introduces causal methods to detect batch effects in multi-site data, showing they provide more accurate or 'no answer' results compared to non-causal methods.

This study investigates the heritability of human brain connectomes using a novel method, finding that heritability is insignificant after accounting for network variability.

The paper uses generative network models to analyze bilateral symmetry in a Drosophila larval brain connectome, identifying significant differences in connection probabilities and proposing adjusted definitions of symmetry.

The paper introduces a framework to quantify and optimize measurement reliability for studying individual differences in neuroscience and clinical research.

This paper introduces a low-resource pipeline for estimating connectomes from MRI data, demonstrating its reliability across diverse datasets without manual intervention.

This paper introduces a new statistic called discriminability to improve replicability by distinguishing accidental from systematic deviations in data, showing its effectiveness in both simulated and real datasets.

The paper standardizes human brain parcellations by curating and organizing multiple atlases into a single open-source repository with standardized metadata and transformation functions.

The paper introduces a formal statistical framework for connectal coding to link cognitive phenotypes to individual histories across different experimental modalities and phylogeny.

The paper develops a pipeline to estimate connectomes from neuroimaging data, showing high consistency within individuals but significant variability across studies and sites.