Martin A. Lindquist

The paper introduces a Bayesian beta-binomial mixed-effects model to analyze zero- and one-inflated patient-reported pain scores, showing improved fit and accuracy compared to existing methods.

This study uses finite impulse response modeling to reveal distinct temporal dynamics of amygdala subregions in response to negative affect, showing prolonged activation in centromedial regions.

The paper examines how motion-based exclusion in fMRI disproportionately affects certain demographic groups and alters study outcomes.

The paper shows that higher-intensity sensory stimulation expands neural activation topography across multiple modalities, challenging the assumption of fixed neural populations.

The paper introduces MM-ComBat and MM-CovBat, multivariate frameworks to harmonize multi-metric neuroimaging data by jointly addressing batch effects across metrics and features.

The paper shows that brain representations converge across individuals despite diverse topographic layouts, with topographic differences linked to architectural constraints and developmental factors.

The paper describes the Common Fund Data Ecosystem (CFDE), which integrates NIH Common Fund data resources to enable discovery, analysis, and collaboration across heterogeneous datasets.

The paper examines the neural correlates of social interaction perception and theory of mind using fMRI, finding overlapping and distinct brain regions involved in these processes.

The paper investigates the association between brain image-derived phenotypes and physical activity using UK Biobank data, finding a strong link between PA and functional connectivity with moderate predictive value for cardiometabolic diseases.

The paper identifies dynamic resting-state brain network markers associated with disruptive behavior problems in children using fMRI data from the ABCD Study.

The paper evaluates the reliability of small-sample task-based fMRI studies using two datasets, showing that sample size requirements vary by analysis type and effect size.

The paper presents a large-scale multimodal fMRI dataset combining naturalistic stimuli with diverse cognitive tasks to enable comprehensive cognitive neuroscience research.

The paper tests how expectations influence perception of pain and visual contrast, finding modality-specific effects and higher-level neural mechanisms.

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

The paper compares automated subcortical volume estimation methods, finding poor consistency in amygdala volume estimates between FSL and FreeSurfer, which can lead to conflicting results.

This study examines how connectivity hyperalignment (CHA) affects brain network properties across spatial scales, showing improved inter-subject similarity while preserving individual-specific features.

This study shows placebo treatment reduces pain-related brain activity in affective and cognitive systems but not in nociceptive regions, using fMRI data from 392 participants.

The paper identifies pathological claustrum activity and its influence on cognitive network processing in chronic pain patients.

This paper introduces regression models for fMRI connectivity analysis that account for covariates like network labels and regional characteristics, showing they explain more variation than geographic or homotopic distances.

This paper shows that sliding window analysis on fMRI data can reintroduce non-neuronal signal, questioning the neuronal origin of brain states identified through this method.

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.

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

The paper identifies three dimensions linking migraine symptoms to brain functional connectivity using CCA and RSFC, revealing novel network targets and patient subtypes.

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.

This study examines how neighborhood socioeconomic status affects executive system activation during a working memory task in youth and identifies mediating brain activity patterns.

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

The paper identifies brain networks mediating stimulus-pain relationships using high-dimensional mediation analysis in fMRI data from 284 humans.

This study shows that fatigue impairs motor skill learning even when task execution is not affected, suggesting training beyond fatigue may hinder long-term skill acquisition.

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.