Monica D. Rosenberg

The paper investigates how individual differences in sustained attention affect neural semantic encoding of narratives using fMRI and LLM embeddings, finding that attention shifts representations toward gist-level formats.

The paper shows that weakened neural representations during poor sustained attention predict behavioral failures and suggest a disengagement mechanism in attentional lapses.

The paper shows that neural patterns reconfigure with increasing memory load, revealing a low-dimensional trajectory linked to memory performance.

The paper examines how individual alcohol drinking motives influence neural and behavioral craving responses to naturalistic alcohol-drinking videos.

The paper investigates how working memory capacity influences voluntary task switching, finding that lower capacity individuals are more likely to switch after attentional lapses, while higher capacity individuals switch from focused states.

The paper develops a connectome-based neural reference space for arousal by showing dynamic connectivity captures shared dynamics across affective, autonomic, and wakefulness arousal measures.

The paper investigates how neural patterns representing causally related past events are reinstated and updated in the brain during moments of narrative insight.

The paper investigates how the brain updates social impressions during movie watching, finding that neural synchrony and insight moments drive impression changes.

ReMind estimates mind-wandering onset during reading by combining retrospective self-reports with eye-tracking, showing improved detection using onset-aligned windows.

This study finds that high levels of PM2.5 and O3 air pollutants are associated with delayed neurocognitive maturation in early adolescents.

BIBSNet is a deep learning model for infant brain segmentation in MRI scans that outperforms JLF in accuracy and speed, with compatible outputs for further analysis.

The paper models neural dynamics on an attractor landscape to show how attentional states and contexts alter the geometry of brain activity trajectories.

The paper identifies a unique neural signature of long-term memory encoding using EEG inter-electrode correlation, showing it predicts individual differences in LTM encoding independent of VWM performance.

This study investigates the relationship between sustained attention and cognitive processes, finding that individual differences in sustained attention are more closely linked to long-term memory than to attentional control and working memory.

This study shows that time-resolved functional brain networks capture both objective and subjective attentional fluctuations across tasks and movies.

This study examines how a single dose of methamphetamine affects brain functional connectivity related to sustained attention and arousal in healthy adults.

This study identifies EEG signatures of attentional lapses distinct from selective attention failures using machine learning and reaction-time analysis.

This study shows that ongoing thoughts during rest are reflected in brain connectivity and predict individual behavioral differences.

This paper analyzes fMRI neurofeedback data to identify brain regions and networks involved in feedback processing and regulation recalibration during training.

The paper assesses neurocognitive maturation in early adolescence by comparing resting-state functional connectivity to adult and infant networks, finding it predicts cognitive performance and development.

This study compares objective and subjective measures of attentional fluctuations during sustained attention tasks, finding they capture distinct patterns.

The paper identifies a brain network model that predicts surprise across different contexts using fMRI data.

The paper shows that functional brain networks predicting sustained attention generalize across perceptual modalities like vision and audition.

The paper introduces Baby Open Brains (BOBs), an open-source repository of manually curated infant brain segmentations to improve reproducibility in neuroimaging research.

The paper examines brain-behavior relationships using fMRI data and finds that neural similarity does not always correlate with behavioral similarity across different domains.

This study examines reliability, heritability, and behavioral relevance of resting-state functional connectivity at different spatial scales in children, finding that fine-scale connectivity is more reliable but less heritable than coarse-scale connectivity.

The paper identifies a generalizable arousal representation from dynamic functional connectivity across different individuals and movie-watching contexts, while finding no evidence of a generalizable valence representation.

The paper uses edge-based general linear models to analyze fMRI data and identifies high-frequency network fluctuations related to attention.

The paper shows that large-scale neural dynamics in a shared low-dimensional state space reflect cognitive and attentional states during tasks and naturalistic conditions.