Peter A. Bandettini

This paper introduces pBOLD, a new fMRI quality metric that quantifies BOLD dominance in multi-echo scans, validates it on datasets, and shows its utility in predicting phenotypes.

This study optimizes VASO for submillimeter laminar fMRI in the human hippocampus, enabling differentiation of neural activity from vein bias in memory tasks.

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 study shows that rapid neural information can be decoded from ultra-fast fMRI signals using multivariate pattern decoders, achieving significant accuracy within 2 seconds of stimulus onset.

This paper identifies and mitigates Fuzzy Ripple artifacts in high-resolution fMRI, enabling sub-millimeter resolution and faster acquisition protocols.

This paper introduces a rapid fMRI acquisition method, T1234, that provides distortion-matched structural reference data, improving registration accuracy with functional scans.

This study shows eye metrics like pupil size and eye movements can indicate conscious awareness and neural processing in cerebral blindness, even when behavioral tests suggest otherwise.

This paper introduces rtPupilPhase, an open-source software for real-time detection of pupil size fluctuation trends across species.

The paper shows that in-scanner experiences significantly affect resting-state functional connectivity measures in fMRI data.

The paper attempts to replicate DIANA fMRI findings in anesthetized mice but fails to observe direct neuronal activity-like peaks, suggesting potential artifacts or data pre-selection issues.

The paper presents a network model that explains both concordant and discordant results in human neuroimaging studies of facial viewpoint selectivity by incorporating biological constraints and analyzing low-level stimulus biases.

The paper investigates the relationship between visual imagery vividness and afterimage perception, finding a positive correlation between imagery vividness and afterimage contrast and sharpness.

This commentary critiques Pang et al.'s 2023 study, arguing that their geometric mode approach to brain dynamics lacks specificity to brain shape and orientation, and does not sufficiently establish geometry as a more fundamental constraint than connectivity.

The paper shows that during decreased alertness, active task engagement increases frontoparietal and long-range brain synchronization, while passive listening increases thalamocortical synchronization.

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

This paper evaluates manifold learning techniques for visualizing time-varying functional connectivity in fMRI data, finding that intrinsic dimension varies and methods differ in capturing subject and task information.

This paper introduces a novel hemodynamic deconvolution algorithm for multi-echo fMRI that improves reliability and provides statistical certainty without requiring prior knowledge of experimental paradigms.

This paper identifies ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness and shows their impact on functional connectivity during sleep.

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

LayNii is a software suite designed for analyzing layer-specific functional MRI data with SNR and coverage constraints, offering tools for layerification, smoothing, and artifact mitigation.

The paper shows that movie-watching provides more accurate predictions of behavioral traits than resting-state functional connectivity.

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.

This study replicates Smith et al.'s (2015) findings on functional connectivity and subject measures using HCP datasets, showing a dominant primary mode in both smaller and larger datasets.

This paper uses TBS and resting-state fMRI to show that disrupting the right posterior superior temporal sulcus reduces functional connectivity across the face-processing network, including between non-stimulated regions.

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

This study uses sub-millimeter fMRI to show that individual fingers have multiple mirrored representations in the motor cortex based on movement types, bridging invasive and non-invasive motor organization research.

The paper uses high-resolution fMRI to show layer-specific activity in human dlPFC during working memory task epochs, with superficial layers active during delay and deeper layers during response.

The paper analyzes dynamic functional connectivity during rest and finds that distinct cognitive periods influence resting-state brain activity, which can be inferred from data without behavioral input.

This paper introduces ME-SPFM, a deconvolution algorithm for multiecho fMRI that estimates transverse relaxation changes without prior event timing knowledge, showing high concordance with standard analysis.