Introduction:
The human motor system has for many years been subject to extensive studies. Its function
and properties became a matter of intensive debate after Penfield first disclosed
several motor areas with direct cortical stimulation (Penfield, 1952). Ablation studies
in animals and lesion studies in humans then provided a better, yet incomplete, understanding
of the function of the respective areas (Freund, 1985). Especially imaging studies
have highlighted the importance of non-primary motor areas (NPMA) in motor learning
and recovery from cortical or subcortical lesions of the motor system (Chen, Cohen
Hallet, 2002). A recent study confirmed that navigated transcranial magnetic stimulation
(nTMS) can locate direct corticofugal projections originating in NPMAs (Teitti, 2008).
This offers the interesting perspective to directly investigate NPMAs neural circuitry
under physiological as well as pathological conditions.
The aim of this study was to develope a reliable and accurate nTMS-mapping algorithm
for NPMAs. We argue that a reliable algorithm should consider (i) remote electric
field effects and volume conduction by nTMS, e.g. stimulating the NPMA does not induce
distant effects in M1, (ii) motor evoked potential (MEP) latencies after NPMA stimulation
should not be longer than after M1 stimulation, (iii) the maps should not be confounded
by multiple local maxima in one representation and (iv) should differentiate primary
and secondary motor representations. To test accuracy we compared the M1 hotspot with
data from intraoperative stimulation and the NPMA results with results described in
functional imaging.
Methods:
We mapped the motor system in 18 healthy subjects with nTMS. First, the precentral
gyrus was mapped and the M1 first dorsal interosseus muscle (FDI) hotspot defined.
A stimulator intensity to produce a 500µV mean MEP was subsequently used to map the
middle and superior frontal gyrus. The remote electric field intensity over the M1
hotspot was kept at subthreshold levels and the highest response considered the NPMA
hotspot. Electric field location, direction and intensity of applied stimuli and MEP
responses during mapping were saved and subsequently analysed offline using custom
software in the MATLAB environment. We applied Gaussian smoothing and Euclidean cluster-analysis
to account for multiple local maxima and differentiate primary and second motor representations.
Results:
A NPMA motor cluster could be found anterior and medial to the M1 cluster in all subjects.
The M1 and NPMA hotspot orientation differed significantly. Suprathreshold nTMS over
the NPMA hotspot did not elicit MEP responses in M1at the respective estimated stimulation
strength. MEP latencies did not differ for all recorded finger, forearm and arm muscles.
Clustering analysis of mapping data revealed either single or multiple local maxima.
Conclusion:
We have confirmed that identifying direct corticofugal responses by nTMS of NPMAs
is feasible. The NPMA hotspot location suits data from imaging studies of the dorsal
premotor cortex (dPMC) (Fink, 1997). The dPMC also possesses direct corticofugal projections
(Dum, 1991; Freund, 1985). Corticocortical dPMC-M1 connections unlikely account for
MEP responses from dPMC as latencies are not longer than those of direct M1 corticospinal
projections.
Remote electric field effects do improbably confound the dPMC responses as their intensity
exerted over M1 is subthreshold and the substantially different electric field orientation
additionally weakens remote effects (Tranchina, 1986; Werhahn, 1994).
Corticofugal dPMC projections can be examined non-invasively with high temporal and
spatial resolution by nTMS. Future studies should address their properties under physiological
(e.g.motor learning) and pathological (e.g. motor recovery) conditions.
