Paul A. Taylor

This paper examines limitations in interpreting resting-state fMRI correlations, distinguishing measurement distortion from causal non-identifiability and their implications for biological interpretation.

This study investigates the neural mechanisms of afterimages using fMRI, finding that they engage widespread brain activity and specific cortical layers, challenging previous views on their origins.

This paper reassesses the measurement error assumption in twin studies and introduces a hierarchical model to improve heritability estimation accuracy.

The paper uses MRI similarity network analysis to study how early life stress affects cortical network development in rats, finding altered inter-areal similarity in key brain regions.

The paper highlights pitfalls in covariate selection and advocates for principled approaches to improve statistical validity and reproducibility in data analysis.

This paper identifies novel neural markers of sleep depth using auditory arousal threshold instead of conventional sleep stages, revealing unique brain network correlations.

The paper introduces FMRI quality control tools in AFNI, providing systematic, in-depth, and interactive QC features for preprocessing pipelines.

This paper improves fMRI analysis by capturing hemodynamic profiles instead of relying on a canonical HRF, showing better sensitivity and cross-study reproducibility.

The paper argues against hiding subthreshold results in neuroimaging studies and proposes a 'highlighting' approach to improve reproducibility and understanding.

The paper proposes using the second derivative (DoG) for edge detection in MRI coregistration, improving alignment quality by generating closed surfaces and reducing intensity dependency.

The paper identifies information loss sources in neuroimaging and proposes a Bayesian framework to improve inference and reproducibility.

The paper examines how balancing trial and subject sample sizes affects statistical efficiency in neuroimaging, finding trial sample size has a comparable impact to subject sample size.

This study investigates how rTMS-induced changes in hippocampal functional connectivity and memory are linked to white matter pathways from the inferior parietal cortex.

The paper introduces NMT v2 and the CHARM atlas for macaque neuroimaging, integrated into AFNI for fMRI analysis.

This study uses fMRI to examine time-dependent brain activity during haptic 3D curve and roughness estimation, finding differential activation patterns in sensorimotor and higher-level regions.

The paper introduces PRIME-RE, a collaborative platform for sharing NHP neuroimaging resources, tools, and data.

The paper investigates how inhibitory TMS over the right PPC affects visuospatial attention networks and finds that callosal anisotropy correlates with changes in resting-state functional connectivity.

This paper introduces a Bayesian multilevel model framework to account for cross-trial variability in fMRI data, showing improved statistical sensitivity and accuracy compared to traditional methods.

The paper develops and validates age-specific Indian brain templates and atlases for spatial normalization of MRI data across ages 6-60 years.

This paper introduces a Bayesian multilevel framework for inter-subject correlation analysis in naturalistic scanning data, improving inference efficiency and reproducibility.

The paper proposes a Bayesian multilevel approach to address multiple testing in neuroimaging by embracing multiplicity through global calibration, aiming to improve model efficiency and reproducibility.

The paper introduces a Bayesian multilevel modeling approach for neuroimaging connectomics that integrates region, region pairs, and subject analyses to improve statistical inference and avoid multiple testing issues.

This paper introduces Bayesian multilevel modeling to address inefficiency and over-penalization in neuroimaging by improving error control and inference efficiency.

The paper shows that using two one-sided t-tests without correction doubles the false positive rate compared to a single two-sided test.

The paper comments on and corrects a previous study comparing FMRI analysis software, focusing on AFNI's processing pipeline and its implementation.

The paper improves ICC modeling for neuroimaging by introducing four new approaches and recommends their use based on data characteristics.