Abstract
Transcranial magnetic stimulation (TMS) is often used to link behaviour to anatomy by targeting a brain area during an associated task. Decreases in performance on that task are often explained as a suppression of stimulus-driven signals, but could also be explained by increases in neural noise. This study used a 2IFC double-pass contrast discrimination paradigm (Burgess & Colborne, 1988, J Opt Soc Am A, 5:617-627) to distinguish between these two possibilities in four types of TMS: online single-pulse (spTMS), online three-pulse repetitive (rTMS), offline continuous (cTBS) and intermittent theta burst stimulation (iTBS). Using standard stimulation protocols with a Magstim Super Rapid2, online TMS was applied to early visual cortex 50ms after onset of each stimulus in each interval, and offline TBS was applied before the start of the task. On each trial (200 total) two grating stimuli of random contrast were presented peripherally (position determined by phosphene localization). Half of the trials contained a 4% contrast increment in one of the intervals. The exact same trial sequence was then repeated with randomized interval order (second pass). A decrease in accuracy in the 4% target condition would indicate signal suppression whereas a reduction in consistency of responses between the two passes would indicate an increase in neural noise. Mean accuracy and consistency scores were bootstrapped within participants. It was found that spTMS reduced accuracy whereas rTMS decreased consistency. This implies that spTMS decreases signal strength whilst rTMS increases neural noise without affecting the stimulus-driven signal. Offline stimulation (cTBS, iTBS) did not affect accuracy or consistency. This is the first study to compare several types of TMS using a single paradigm that can dissociate noise from suppression. These findings can explain inconsistencies in results between previous studies using different TMS protocols and so comparisons across protocols should be made with caution.
Meeting abstract presented at VSS 2017