Joshua T Vogelstein

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.

The paper develops a statistic called XORness to measure functional complexity in nanoscale connectomes and shows it is high in both real and generated connectomes with similar network statistics.

The paper evaluates the impact of different preprocessing pipelines on functional MRI reproducibility and shows moderate inter-pipeline agreement, affecting brainwide association study consistency.

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 maps the synaptic-resolution connectome of a Drosophila larva brain, identifying neuron types, circuit motifs, and structural features.

This paper introduces a modified graph matching algorithm for better pairing of bilaterally homologous neurons in connectomes by leveraging contralateral connections.

Simulations and neuroimaging analyses argue that unreliable clinical and cognitive phenotypes, not only noisy fMRI, cap biomarker discovery; aggregating across raters or repeated measures beats enlarging samples.

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.

The paper develops tools to track synaptic plasticity in vivo by imaging endogenous AMPA receptors and generates a detailed spatiotemporal map of synaptic changes during behavior.

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.

This paper introduces a method to classify neurons based on their connectivity using a stochastic block model and spectral embedding, achieving accurate clustering even with noise and edge swaps.

The paper introduces an optimized tissue clearing protocol for rat brains and develops an open-source platform for high-throughput analysis.

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 investigates statistical tests for functional connectome fingerprinting, showing a Poisson(1) approximation applies and highlighting sensitivity issues in permutation tests using fMRI datasets.

The paper examines how concatenating fMRI data affects the reliability of functional connectomics, finding that concatenating multiple scans under the same state improves reliability compared to a single long scan.

This paper introduces a function-based method to align cortical organization across species, revealing evolutionary changes in brain regions associated with human cognition.

This paper evaluates the reliability, reproducibility, and predictive validity of functional connectivity gradients in fMRI data across different analytical settings.

The paper compares linear and nonlinear model performance on brain imaging data and standard datasets, finding linear models outperform nonlinear ones in brain scans despite larger sample sizes.

This paper explains decision forests and deep networks as partition and vote schemes, linking them to statistical pattern recognition and brain functioning.

This paper shows that bagging improves reproducibility and test-retest reliability of functional brain parcellations across various conditions and scan times.

This study examines the heritability of mouse brain connectomes using high-resolution MRI and finds high heritability in brain volumes and diffusion metrics.

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

This paper demonstrates that adding thermal sensors to wrist-worn devices improves position tracking accuracy for head locations, with potential applications in monitoring body-focused repetitive behaviors.

The paper develops a biophysical model of GCaMP6s calcium binding kinetics to accurately infer action potentials from fluorescence data in vivo.

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