Brian S. Caffo

Neuromodulation in neural organoids with shell MEAs

The paper demonstrates neuromodulation in neural organoids using 3D shell MEAs, showing increased neuron firing rates with specific stimulation ranges.

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

Proust detects spatial domains using contrastive self-supervised learning on multi-omics data, showing improved accuracy across benchmark datasets.

When no answer is better than a wrong answer: a causal perspective on batch effects

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.

Image Processing in the Acute to Chronic Pain Signatures (A2CPS) Project

The paper describes the neuroimaging component of the A2CPS project, detailing data collection, processing, and initial findings linking brain measures to clinical variables.

A machine learning based approach towards high-dimensional mediation analysis

This paper introduces a machine learning method for high-dimensional mediation analysis and applies it to fMRI data to identify brain patterns mediating relationships between stimuli and behavioral outcomes.

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

The paper analyzes the effect of transcranial direct-current stimulation on functional connectivity using distributional analysis of correlation densities in PPA patients.

Structured Joint Decomposition (SJD) identifies shared dynamics across collections of biologically related multi-omics data matrices

SJD identifies shared molecular variation across multi-omics datasets by decomposing matrices with hierarchical structure, applied to neurogenesis RNA-seq data.

Two-stage Linked Component Analysis for Joint Decomposition of Multiple Biologically Related Data Sets

The paper introduces 2s-LCA, a method for joint analysis of multiple biological data sets, and applies it to human brain development data to identify shared gene expression patterns.

A low-resource reliable pipeline to democratize multi-modal connectome estimation and analysis

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

Methodological considerations in analyzing synchronization of resting-state brain networks with the intrinsic electrical rhythm of the stomach: Advantages of weighted phase-locking

The paper reanalyzes brain network synchronization with gastric rhythms using amplitude-weighted phase-locking value (awPLV), finding more synchronized networks than previously identified with PLV.

Beyond massive univariate tests: Covariance regression reveals complex patterns of functional connectivity related to attention-deficit/hyperactivity disorder, age, sex, and response control

The paper uses a novel covariance regression approach to identify functional brain networks associated with ADHD, age, sex, and response control in children.

On statistical tests of functional connectome fingerprinting

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.

Phase-locking of resting-state brain networks with the gastric basal electrical rhythm

The paper investigates phase-locking between resting-state brain networks and the gastric basal electrical rhythm using rsfMRI and EGG data.

Semiparametric Partial Common Principal Component Analysis for Covariance Matrices

This paper develops a semiparametric method for estimating shared eigenvectors in multiple covariance matrices and applies it to identify brain networks in fMRI data.

A Whole-Brain Regression Method to Identify Individual and Group Variations in Functional Connectivity

This paper introduces a whole-brain regression method to identify individual and group variations in functional connectivity by linking covariates to matrix variations using group ICA and covariate assisted principal regression.

Covariate Assisted Principal Regression for Covariance Matrix Outcomes

This paper introduces a method to regress covariance matrices on covariates and applies it to identify brain network changes related to age and sex.

Improved state change estimation in dynamic functionalconnectivity using hidden semi-Markov models

This paper improves state change estimation in dynamic functional connectivity using hidden semi-Markov models, showing better sojourn time estimation compared to hidden Markov models.

Modular preprocessing pipelines can reintroduce artifacts into fMRI data

This paper shows that modular fMRI preprocessing pipelines can reintroduce artifacts by sequentially projecting data into non-orthogonal subspaces, and suggests remedies like combining steps into a single filter or orthogonalizing them.

A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability

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