Simulating transcranial magnetic stimulation during PET with a large-scale neural network model of the prefrontal cortex and the visual system.
Transcranial magnetic stimulation (TMS) exerts both excitatory and inhibitory effects on the stimulated neural tissue, although little is known about the neurobiological mechanisms by which it influences neuronal function. TMS has been used in conjunction with PET to examine interregional connectivity of human cerebral cortex. To help understand how TMS affects neuronal function, and how these effects are manifested during functional brain imaging, we simulated the effects of TMS on a large-scale neurobiologically realistic computational model consisting of multiple, interconnected regions that performs a visual delayed-match-to-sample task. The simulated electrical activities in each region of the model are similar to those found in single-cell monkey data, and the simulated integrated summed synaptic activities match regional cerebral blood flow (rCBF) data obtained in human PET studies. In the present simulations, the excitatory and inhibitory effects of TMS on both locally stimulated and distal sites were studied using simulated behavioral measures and simulated PET rCBF results. The application of TMS to either excitatory or inhibitory units of the model, or both, resulted in an increased number of errors in the task performed by the model. In experimental studies, both increases and decreases in rCBF following TMS have been observed. In the model, increasing TMS intensity caused an increase in rCBF when TMS exerted a predominantly excitatory effect, whereas decreased rCBF following TMS occurred if TMS exerted a predominantly inhibitory effect. We also found that regions both directly and indirectly connected to the stimulating site were affected by TMS.
Husain
F T
FT
Language Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland 20892, USA. husainf@nidcd.nih.gov
Nandipati
G
G
Braun
A R
AR
Cohen
L G
LG
Tagamets
M-A
MA
Horwitz
B
B
D001931:Brain Mapping; D004193:Discrimination Learning / Q000502:physiology*; D004574:Electromagnetic Fields; D006801:Humans; D009433:Neural Inhibition / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D009778:Occipital Lobe / Q000000981:diagnostic imaging / Q000502:physiology; D010364:Pattern Recognition, Visual / Q000502:physiology*; D017397:Prefrontal Cortex / Q000000981:diagnostic imaging* / Q000502:physiology; D011930:Reaction Time / Q000502:physiology; D012039:Regional Blood Flow / Q000502:physiology; D009435:Synaptic Transmission / Q000502:physiology; D013702:Temporal Lobe / Q000000981:diagnostic imaging / Q000502:physiology; D014055:Tomography, Emission-Computed; D014793:Visual Cortex / Q000000981:diagnostic imaging* / Q000502:physiology; D014795:Visual Pathways / Q000502:physiology