David F. Montez

This study introduces MEDIC, a framewise distortion correction method for multi-echo fMRI that improves alignment and reduces motion impact without requiring field maps.

The paper introduces SHAMAN, a method to assess motion impact on brain-behavior associations in fMRI studies, finding significant motion overestimation and underestimation in various traits.

This study investigates disuse-driven plasticity in the human thalamus and putamen using fMRI, finding strengthened functional connectivity and signal pulses in these regions during limb immobilization.

This paper introduces Synth, a field map-less distortion correction method for fMRI data, showing comparable or better performance than field map-based approaches in pediatric and adult datasets.

This study shows that dopamine release in the ventral striatum is linked to reward learning, not just reward processing, using combined PET and fMRI in humans.

The paper reveals that motor cortex contains inter-effector regions connected to the Cingulo-opercular network, alternating with effector-specific areas, suggesting a dual system for motor control and whole-body coordination.

This study shows that real-time motion monitoring with FIRMM software improves the quality and usability of fMRI data in infants by reducing motion artifacts.

The paper identifies distinct functional connectivity patterns in the hippocampus, showing anterior regions connect to the default mode network and posterior regions connect to the parietal memory network, supporting different cognitive processes.

This study identifies individual-specific discrete subnetworks of human cortico-striatal connectivity, revealing a stepped rostral-caudal gradient including previously unknown language network connections.

The paper shows that disused motor regions become more connected to the cingulo-opercular network during limb constraint, mediated by spontaneous activity pulses.

This study evaluates diffusion imaging methods for individual-specific precision, finding that BaMM reduces overfitting and achieves better crossing-fiber accuracy with more data.

Brain-behaviour correlations in ABCD are tiny (median |r|=0.01), so samples of typical size (N=25) give inflated, irreproducible results; estimates only stabilise around N⪆2,000.