Jörn Diedrichsen

The paper investigates how the brain encodes bimanual movement directions using fMRI, finding distinct neural representations for unimanual and bimanual movements.

This study shows early cerebellar morphology drives neocortical maturation in a regionally specific manner across humans and mice.

This study shows that individualized parcellation of the human prefrontal cortex reveals sharp functional boundaries not visible in group atlases, suggesting a mosaic of fine-grained subdivisions within large-scale gradients.

This study investigates neural representations of speech production in both cortical and cerebellar regions using fMRI and finds that cerebellar representations align more closely with parietal operculum than primary motor cortex.

The paper investigates how probabilistic predictions influence feedback responses in the human brain using fMRI, showing expected sensory signals and prediction error signals in motor and somatosensory cortices.

This paper introduces mRBM-HBP, a Bayesian framework that integrates resting-state and task fMRI to improve cortical parcellation accuracy and individual specificity.

The paper shows that reading ability in deaf and hearing adults is linked to shared neural representations of abstract phonology from visual speech and other language forms in the superior and middle temporal cortex.

The paper investigates whether increased reaction time for longer movement sequences reflects sequence preparation or first-target ambiguity, finding that RT increases are fully explained by ambiguity, not sequence preparation.

This paper develops voxelwise normative models of brain morphometry across the lifespan and applies them to study brain development after preterm birth and neurodegenerative disorders.

This paper develops a method to test hypotheses about the correlation between brain activation patterns in fMRI studies, showing that a subject-wise bootstrap on the maximum-likelihood group estimate performs best.

This paper introduces multi-task batteries for functional brain mapping, showing they improve localization and parcellation compared to single-contrast approaches.

This study shows that multi-task fMRI better predicts brain functional organization than resting-state fMRI across diverse tasks and datasets.

The paper investigates how sensory cues and memory influence sequential movement production, showing that integration of both sources changes with learning and predictability.

SUITPy improves cerebellar imaging analysis by isolating the cerebellum with a U-Net model, enhancing alignment and reducing contamination from surrounding cortical structures.

This study investigates the spatiotemporal dynamics of cross-modal memory recall and imagery using fMRI, finding a reversed hierarchy in neural activity during visual and auditory recall.

This study shows that sensory expectations influence motor preparation and corrective responses in humans and monkeys, with neural activity scaling with perturbation probabilities.

The paper shows that micro-offline gains in motor skill learning are short-lived performance benefits driven by motor pre-planning, not replay-mediated offline learning.

This study maps cerebellar volumetric growth from infancy to young adulthood and links it to cerebral development and socio-linguistic behaviors.

This study investigates neural population dynamics in motor cortex during reaching, revealing a compositional structure that enables posture-dependent control.

The paper investigates how repetition of movement sequences affects performance, finding that memory traces are activated when the last 2-3 movements match the current execution.

This study disentangles the mechanisms of motor sequence learning by distinguishing between anticipation of next actions and optimization of movement trajectories using a continuous reaching task.

This paper introduces a new paradigm to study muscle activity pattern learning, finding that difficulty is linked to muscle activity similarity to everyday use and activity size.

The paper introduces an open-source Python toolbox for representational similarity analysis, providing tools to evaluate neural representations and models.

This paper introduces a hierarchical Bayesian framework that fuses task-based and resting-state neuro-imaging datasets to create a population-based atlas of the human cerebellum, achieving better individual parcellations with limited data.

The paper investigates how cortical areas contribute to pre-planning and online planning of movement sequences using fMRI in humans.

This paper presents a hierarchical functional atlas of the human cerebellum integrating multiple datasets, improving individual precision mapping and enabling analysis at different levels of granularity.

The paper investigates how the brain plans multiple future arm movements during sequential actions, finding that planning processes interact and influence each other.

The paper identifies structural covariation patterns between cerebellum subregions and cerebral cortex in a large cohort, revealing dissociations and novel associations with phenotypes.

This study examines how neural activity in the primate lateral prefrontal cortex is organized during different cognitive tasks, finding that task-related activity has a consistent spatial scaffold with task-specific reconfiguration.

The paper investigates neural mechanisms of online action preparation during sequential movements, finding increased activation in parietal and visual areas for overlapping actions, while premotor cortex remains unaffected.