Abstract：
BACKGROUND:Transcranial magnetic stimulation (TMS) can evaluate cortical excitability and integrity of motor pathways via TMS-induced responses. The responses are affected by the orientation of the stimulated neurons with respect to the direction of the TMS-induced electric field. Therefore, besides being a functional imaging tool, TMS may potentially assess the local structural properties. Yet, TMS has not been used for this purpose. NEW METHOD:A novel principle to evaluate the relation between function and structure of the motor cortex is presented. This functional anisotropy is evaluated by an anisotropy index (AI), based on motor evoked potential amplitudes induced with different TMS coil orientations, i.e. different electric field directions at a cortical target. To compare the AI with anatomical anisotropy in an explorative manner, diffusion tensor imaging-derived fractional anisotropy (FA) was estimated at different depths near the stimulation site. RESULTS:AI correlated inversely with cortical excitability through the TMS-induced electric field at motor threshold level. Further, there was a trend of negative correlation between AI and FA. COMPARISON WITH EXISTING METHODS:None of the existing methods alone can detect the relationship between direct motor cortex activation and local neuronal structure. CONCLUSIONS:The AI appears to provide information on the functional neuronal anisotropy of the motor cortex by coupling neurophysiology and neuroanatomy within the stimulated cortical region. The AI could prove useful in the evaluation of neurological disorders and traumas involving concurrent structural and functional changes in the motor cortex. Further studies on patients are needed to confirm the usability of AI.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Kallioniemi E,Könönen M,Säisänen L,Gröhn H,Julkunen Pdoi
10.1016/j.jneumeth.2015.08.028subject
Has Abstractpub_date
2015-12-30 00:00:00pages
82-90eissn
0165-0270issn
1872-678Xpii
S0165-0270(15)00319-2journal_volume
256pub_type
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