Choong‐Wan Woo

This study introduces a neuroimaging framework to discover state-dependent signal flow hierarchy in the human cerebral cortex using integrated effective connectivity and signal flow mapping.

The paper identifies parallel processing chains in association cortex using multiple functional mapping modalities, showing they are experience-driven and organized across different cytoarchitectures.

The paper identifies brain mediators involved in integrating predictive cues with noxious inputs to modulate pain over time using temporal mediation analysis.

This study evaluates how different modeling options affect neuroimaging-based pain models' performance using a benchmarking analysis and literature survey.

The paper reanalyzes pain prediction using sociodemographic, psychological, and fMRI data, finding that combining multiple measures can predict interindividual pain intensity differences.

This study uses fMRI to identify brain regions and networks associated with affective valence and intensity during sustained pleasure and pain, showing distinct neural coding for each.

This paper introduces fcANNs, a model of brain dynamics based on functional connectivity, showing it can reconstruct and predict brain activity in rest, tasks, and disease states.

This study uses real-time fMRI decoding to modulate pain-related brain patterns and shows that participants can down-regulate pain signatures, correlating with changes in perceived pain intensity.

This study develops predictive models of spontaneous thought using body maps and validates them across datasets, showing the role of body regions in valence and self-relevance.

This study decodes self-relevance and valence of spontaneous thought from fMRI signals using personal narratives and resting-state data.

This study examines how functional brain networks reconfigure during sustained pain using machine learning to predict pain experience changes.

This study uses fMRI to examine brain dynamics of self-generated concepts during spontaneous thought and links them to individual differences in negative affectivity.

The paper evaluates the effect size and test-retest reliability of the Neurologic Pain Signature (NPS) across multiple studies, finding large within-person effect sizes and reliable performance with sufficient trial numbers.

This study investigates the spatial scale of acute pain representation using fMRI data from 289 participants, finding that distributed, multi-system models better capture pain intensity than single regions or networks.

This study uses machine learning to identify physiological markers of pain and shows cognitive self-regulation affects both pain ratings and autonomic responses.

The paper identifies issues with false-positive findings in neuroimaging by showing how flexible region-level hypothesis testing leads to ambiguous replication claims.