Exploring the Neural Basis of Real-Life Joint Action: Measuring Brain Activation during Joint Table Setting with Functional Near-Infrared Spectroscopy

A new class of visuomotor neuron has been recently discovered in the monkey's premotor cortex: mirror neurons. These neurons respond both when a particular action is performed by the recorded monkey and when the same action, performed by another individual, is observed. Mirror neurons appear to form a cortical system matching observation and execution of goal-related motor actions. Experimental evidence suggests that a similar matching system also exists in humans. What might be the functional role of this matching system? One possible function is to enable an organism to detect certain mental states of observed conspecifics. This function might be part of, or a precursor to, a more general mind-reading ability. Two different accounts of mind-reading have been suggested. According to `theory theory', mental states are represented as inferred posits of a naive theory. According to `simulation theory', other people's mental states are represented by adopting their perspective: by tracking or matching their states with resonant states of one's own. The activity of mirror neurons, and the fact that observers undergo motor facilitation in the same muscular groups as those utilized by target agents, are findings that accord well with simulation theory but would not be predicted by theory theory.

In both diagnostic and research applications, the interpretation of MR images of the human brain is facilitated when different data sets can be compared by visual inspection of equivalent anatomical planes. Quantitative analysis with predefined atlas templates often requires the initial alignment of atlas and image planes. Unfortunately, the axial planes acquired during separate scanning sessions are often different in their relative position and orientation, and these slices are not coplanar with those in the atlas. We have developed a completely automatic method to register a given volumetric data set with Talairach stereotaxic coordinate system. The registration method is based on multi-scale, three-dimensional (3D) cross-correlation with an average (n > 300) MR brain image volume aligned with the Talariach stereotaxic space. Once the data set is re-sampled by the transformation recovered by the algorithm, atlas slices can be directly superimposed on the corresponding slices of the re-sampled volume. the use of such a standardized space also allows the direct comparison, voxel to voxel, of two or more data sets brought into stereotaxic space. With use of a two-tailed Student t test for paired samples, there was no significant difference in the transformation parameters recovered by the automatic algorithm when compared with two manual landmark-based methods (p > 0.1 for all parameters except y-scale, where p > 0.05). Using root-mean-square difference between normalized voxel intensities as an unbiased measure of registration, we show that when estimated and averaged over 60 volumetric MR images in standard space, this measure was 30% lower for the automatic technique than the manual method, indicating better registrations. Likewise, the automatic method showed a 57% reduction in standard deviation, implying a more stable technique. The algorithm is able to recover the transformation even when data are missing from the top or bottom of the volume. We present a fully automatic registration method to map volumetric data into stereotaxic space that yields results comparable with those of manually based techniques. The method requires no manual identification of points or contours and therefore does not suffer the drawbacks involved in user intervention such as reproducibility and interobserver variability.

The recent advent of multichannel near-infrared spectroscopy (NIRS) has expanded its technical potential for human brain mapping. However, NIRS measurement has a technical drawback in that it measures cortical activities from the head surface without anatomical information of the object to be measured. This problem is also found in transcranial magnetic stimulation (TMS) that transcranially activates or inactivates the cortical surface. To overcome this drawback, we examined cranio-cerebral correlation using magnetic resonance imaging (MRI) via the guidance of the international 10-20 system for electrode placement, which had originally been developed for electroencephalography. We projected the 10-20 standard cranial positions over the cerebral cortical surface. After examining the cranio-cerebral correspondence for 17 healthy adults, we normalized the 10-20 cortical projection points of the subjects to the standard Montreal Neurological Institute (MNI) and Talairach stereotactic coordinates and obtained their probabilistic distributions. We also expressed the anatomical structures for the 10-20 cortical projection points probabilistically. Next, we examined the distance between the cortical surface and the head surface along the scalp and created a cortical surface depth map. We found that the locations of 10-20 cortical projection points in the standard MNI or Talairach space could be estimated with an average standard deviation of 8 mm. This study provided an initial step toward establishing a three-dimensional probabilistic anatomical platform that enables intra- and intermodal comparisons of NIRS and TMS brain imaging data.