Locating primary somatosensory cortex in human brain stimulation studies: systematic review and meta-analytic evidence.
Nicholas Paul HolmesLuigi TamèPublished in: Journal of neurophysiology (2018)
Transcranial magnetic stimulation (TMS) over human primary somatosensory cortex (S1), unlike over primary motor cortex (M1), does not produce an immediate, objective output. Researchers must therefore rely on one or more indirect methods to position the TMS coil over S1. The "gold standard" method of TMS coil positioning is to use individual functional and structural magnetic resonance imaging (f/sMRI) alongside a stereotactic navigation system. In the absence of these facilities, however, one common method used to locate S1 is to find the scalp location that produces twitches in a hand muscle (e.g., the first dorsal interosseus, M1-FDI) and then move the coil posteriorly to target S1. There has been no systematic assessment of whether this commonly reported method of finding the hand area of S1 is optimal. To do this, we systematically reviewed 124 TMS studies targeting the S1 hand area and 95 fMRI studies involving passive finger and hand stimulation. Ninety-six TMS studies reported the scalp location assumed to correspond to S1-hand, which was on average 1.5-2 cm posterior to the functionally defined M1-hand area. Using our own scalp measurements combined with similar data from MRI and TMS studies of M1-hand, we provide the estimated scalp locations targeted in these TMS studies of the S1-hand. We also provide a summary of reported S1 coordinates for passive finger and hand stimulation in fMRI studies. We conclude that S1-hand is more lateral to M1-hand than assumed by the majority of TMS studies.
Keyphrases
- transcranial magnetic stimulation
- high frequency
- magnetic resonance imaging
- case control
- systematic review
- functional connectivity
- randomized controlled trial
- computed tomography
- skeletal muscle
- cancer therapy
- spinal cord
- small cell lung cancer
- spinal cord injury
- machine learning
- neuropathic pain
- meta analyses
- artificial intelligence
- pluripotent stem cells