Neurons and the synaptic basis of the fMRI signal associated with cognitive 
flexibility

Stemme, Anja; Deco, Gustavo; Busch, Astrid; Schneider, Werner X.

doi:10.1016/j.neuroimage.2005.01.044

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

Neurons and the synaptic basis of the fMRI signal associated with cognitive flexibility

MPS-Authors

Stemme, Anja
Max Planck Society;

Deco, Gustavo
Max Planck Society;

Busch, Astrid
Max Planck Society;

/persons/resource/persons22381

Schneider, Werner X.
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Stemme, A., Deco, G., Busch, A., & Schneider, W. X. (2005). Neurons and the synaptic basis of the fMRI signal associated with cognitive flexibility. NeuroImage, 26(2), 454-470. doi:10.1016/j.neuroimage.2005.01.044.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0011-2C2A-9

Abstract

The Wisconsin Card Sorting Test (WCST) is well known to test cognitive flexibility in terms of set-shifting capabilities. Many fMRI studies with behaving monkeys as well as human subjects have shown transient neural activity in the Prefrontal Cortex (PFC), as indicated by an increase in the fMRI signal, following a rule change in the WCST or when using a WCST-like paradigm. We present a computational model, covering a limited number of PFC neurons and using precise biophysical descriptions, which is able to simulate WCS-like tests. Further, the detailed neuronal representation of the model allows us to calculate the resulting fMRI signal. Thus, we are able to analyze the adequacy of the model and its structure by comparing the calculated fMRI signal with the experimental data which in turn provides promising insights into the neural base of the increase in the fMRI signal.