Daniel J. Mitchell

This study investigates age-related changes in visual short-term memory using fMRI decoding, finding that visual cortex representations weaken with age and load, while frontoparietal regions compensate by strengthening during memory maintenance.

This study compares cognitive control networks in humans and macaques using fMRI, finding similarities in the MD network but differences in lateral frontal cortex activation patterns.

The paper examines bidirectional information convergence between dorsal and ventral LPFC during attentional selection, showing it is specific to the choice phase of a task.

The paper identifies subgroups with distinct brain activation patterns during a visual short-term memory task across the adult lifespan, showing differences in brain activity-performance associations and white matter integrity.

This study shows how feedback reshapes neural representations in vlPFC through oscillatory dynamics, making them closer to future retrieval states.

The paper shows that stimulus effects dominate representational variance in visual cortex, while task effects dominate in prefrontal and parietal cortex, suggesting distinct optimization goals for sensory vs. higher-order brain regions.

This study examines how goal-subgoal problem solving is encoded in a distributed network of frontal lobe regions in monkeys, showing specialized and shared representations across regions.

This study examines how default mode network subnetworks and multiple-demand regions respond to transitions of person, place, and time using functional MRI data.

The paper examines brain activity and cognitive performance in older adults during a problem-solving task, finding increased cuneal cortex activity with age and performance, suggesting functional compensation for fluid intelligence.

This study shows that Default Mode Network (DMN) activation during task switches depends on the hierarchical structure of task groups rather than the number of tasks.

The paper replicates increased default mode network (DMN) activity during external task switches and shows no enhanced processing of the surrounding scene.

The paper examines how age affects load-modulated functional connectivity during visual short-term memory tasks across a lifespan cohort.

This paper investigates how age-related changes in frontoparietal brain activity mediate declines in fluid intelligence and identifies physical activity variety as a potential modifiable factor.

This study investigates the causal roles of domain-general and language-selective brain networks in speech comprehension challenges using lesion data and behavioral tasks.

This study examines how focal brain lesions affect performance on naturalistic tasks and finds that deficits are not explained by fluid intelligence or specific network damage but are instead linked to diverse lesions.

The paper examines how the multiple-demand and default mode networks represent multi-step tasks, finding dissociable temporal dynamics and representational content for task identity and step-level information.

The paper examines DMN subsystem activity during various cognitive tasks, finding partial functional separation and integration across subsystems.

This study uses MEG/EEG to track and compare multiple component processes of selective attention over time, identifying five distinct processing operations with different time-courses.

This study examines how functional connectivity changes in macaque monkeys after cortical lesions and how these changes correlate with behavioral recovery.

The paper investigates the role of the default mode network (DMN) in cognitive transitions, finding that it shows both decreases and increases during task switches and rests, suggesting it encodes context beyond internal self-referential processes.

This study examines how the multiple-demand network responds to varying task difficulty in simple sensory discriminations, finding little effect of sensory manipulations but increased activity with rule complexity.

This study shows that visual short-term memory for complex recognisable objects is more precise than for unrecognisable ones, without involving additional brain regions.