Abstract
The objective of this study was to test, in single subjects, the hypothesis that the signs of voluntary movement-related neural activity would first appear in the prefrontal region, then move to both the medial frontal and posterior parietal regions, progress to the medial primary motor area, lateralize to the contralateral primary motor area and finally involve the cerebellum (where feedback-initiated error signals are computed). Six subjects performed voluntary finger movements while DC coupled EEG was recorded from 64 scalp electrodes. Event-related potentials (ERPs) averaged on the movements were analysed both before and after independent component analysis (ICA) combined with dipole source analysis (DSA) of the independent components. Both a simple topographic analysis of undecomposed ERPs and the ICA/DSA analysis suggested that the original hypothesis was inadequate. The major departure from its predictions was that, while activity over many brain regions did appear at the expected times, it also appeared at unexpected times. Overall, the results suggest that the neuroscientific ‘standard model’, in which neural activity occurs sequentially in a series of discrete local areas each specialized for a particular function, may reflect the true situation less well than models in which large areas of brain shift simultaneously into and out of common activity states.
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Notes
Such tasks are also shown to involve additional prefrontal areas (BA8, BA44), the anterior cingulate (BA32), Broca’s area (BA44), the parietal cortex (BA40, BA7), the superior temporal gyrus (BA 22/42) the insula, and assorted motor, somatosensory and visual areas (BA1,2,3,4,6,17,18,19). However these observations are usually downplayed over in the interests of presenting a simple story, that working memory occurs in the dorsolateral prefrontal cortex (BA46/9).
Inclusion of epochs with very long response times was tried, but trouble was experienced fitting dipoles to many of the resulting ICA components. This may have been because the assumption of stationarity underlying ICA was unacceptably violated in very long data segments, resulting in ICA components that could not be not associated with any single dipole. The 1.5 s response time cut-off was chosen as an acceptable compromise between maximization of stationarity (which would presumably be more closely approximated in shorter data segments) and truncation of the BP signal. Use of this constraint resulted in many more successful dipole fits, while preserving what was apparently the full length of the BP.
An alternative description of these operations which may be more readily acceptable to ICA afficionados is as follows. The back-projected time series of each independent component was computed by taking the outer product of the column vector of the mixing matrix with the corresponding row vector of the activation matrix. This gave a matrix time-series of rank 1 for each of the 64 independent components.
Strictly speaking, the presence of a waveform only at prefrontal leads is at best suggestive of the presence of neural activity in the prefrontal cortex.
‘Scale-free’ in this context means that the distribution of the parameter under study obeys a power law: i.e., most of the observations fall into the smallest bins of a histogram, with a few instances of very large observations. The distributions in log-log coordinates show a linear relation described as 1/fa, where the exponent, a, designates the slope. This means that there is no peak in the distribution to provide a characteristic ‘scale’.
Small-world topologies are a subset of scale-free topologies which feature dense local connections between neighboring nodes in the network, but at the same time a short path length between pairs of distant nodes due to the existence of a few long-range connections. Such topologies offer obvious possibilities with regard to global processing.
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Acknowledgments
Thanks to Professor Robert T. Knight for access to hardware, Clay Clayworth for help setting it up and Christina Karns for assistance with stimulus software. Thanks also to Associate Professor Gary Bold for support during the analysis phase of the project.
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Pockett, S., Whalen, S., McPhail, A.V.H. et al. Topography, independent component analysis and dipole source analysis of movement related potentials. Cogn Neurodyn 1, 327–340 (2007). https://doi.org/10.1007/s11571-007-9024-y
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DOI: https://doi.org/10.1007/s11571-007-9024-y