Skip to content

Theodoros Karapanagiotidis

3 papers in the library · 503 citations · publishing 2018-2021

Papers

Default mode network can support the level of detail in experience during active task states

Proceedings of the National Academy of Sciences August 27, 2018 Mladen Sormaz, Charlotte Murphy, Hao-ting Wang et al. 291 citations

The default mode network (DMN), a set of brain regions traditionally linked to off-task or mind-wandering states, actually contributes to detailed, task-relevant cognition during active tasks. Using fMRI, participants performed working-memory tasks while reporting their thoughts. Patterns of neural activity showed that distinctions between on- and off-task thought involved regions near sensory and motor cortex, not the DMN. However, the level of detail in ongoing thought corresponded to activity patterns within the DMN during memory maintenance. These findings indicate the DMN supports detailed cognition under active task conditions, challenging the view that it is solely task-negative.

The neural correlates of ongoing conscious thought

iScience February 2, 2021 Jonathan Smallwood, Adam Turnbull, Hao-ting Wang et al. 130 citations

The landscape of ongoing thought is heterogeneous and shaped by both personal traits and environmental context. Recent work shows that attention and control systems organize experience in response to changing demands, while the default mode network contributes not only to task-negative or episodic content but also to the vividness of experience in both task contexts and spontaneous self-generated states. Multiple neural systems reflect the landscape of ongoing thought, and it is important to distinguish processes that shape how experience unfolds from those that regulate it.

The psychological correlates of distinct neural states occurring during wakeful rest

Scientific Reports December 3, 2020 Theodoros Karapanagiotidis, Diego Vidaurre, Andrew J. Quinn et al. 82 citations

When people are not engaged in an explicit task, they experience a variety of self-generated thoughts, such as planning or reminiscing. Using machine learning to analyze brain activity from resting-state fMRI scans, researchers identified distinct neural states that recur over time. Two of these states predicted different patterns of thinking. One neural state, resembling activity seen during demanding tasks, was linked to problem-solving about the future. Another state, associated with less demanding conditions, was tied to intrusive thoughts about the past. These two states fell at opposite ends of a brain hierarchy related to cognitive demand. The findings show that tracking moment-to-moment changes in brain function can help classify self-generated mental states and that these states align with the brain's response to cognitive tasks.