Experimental Design.

<p>The participants performed four different types of tapping sequences, which were selected to increase demand on bimanual coordination gradually: 1) <i>unimanual</i>: moving only the right index finger, 2) <i>bimanual synchronous</i>: moving both index fingers in synchrony, 3) <i>bimanual alternating</i>: moving both index fingers at the same pace in an alternating fashion, and 4) <i>bimanual unbalanced</i>: moving the left index finger in synchrony with the right index finger, but at half of the pace. Each of these tapping sequences was performed at four different tapping speeds for the right index finger (1, 2, 3, and 4 Hz), which resulted in 16 different experimental conditions. Task difficulty increased along both manipulated task dimensions.</p

Behavioral results.

<p>The behavioral results showed a significant linear increase of tapping speed consistent with the experimental manipulation for all participants. The actual tapping speed of the right index finger from all individuals (S01-05, mean ± individual SE), as well as the average (AVG) is plotted dependent on the required speed in panel <b>A</b>. Second, there was a significant linear increase of error rate with increasing demand on bimanual coordination (from left to right) in all participants. In panel <b>B</b> the individual error rate (S01-05, mean ± individual SE), and average (AVG) is plotted for the four performed tapping sequences.</p

Sensitivity and specificity in detecting bimanual tapping.

<p>The sensitivity and specificity in detecting if a task was uni- or bimanually performed was computed using a simple threshold approach. The results for the block-wise activation-level measures from right M1 (panel <b>A</b>), and the 26-s full task correlations (panel <b>B</b>), and the 12-s steady-state task correlations (panel <b>C</b>) are presented for two participants. Each dot represents one block. Significant results are marked with an asterisk. Activation level based and overall task connectivity measures both performed well in making this binary decision, while steady-state connectivity measures performed more poorly, but still above chance level (50%) in three of five participants (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085929#pone-0085929-t002" target="_blank">table 2</a>).</p

Correlation with finger tapping speed.

<p>The criterion validity for detecting performed tapping speed was calculated by correlating the block-wise brain measures with the block-wise finger tapping speed. These brain-behavior correlations are presented for all participants for each of the three different bimanual tapping tasks separately as well as averaged (bold with asterisk = significant results).</p

Correlation with finger tapping speed.

<p>The criterion validity for detecting performed tapping speed was calculated by correlating the block-wise brain measures with the block-wise behavioral performance measures. The results from one representative participant are depicted for <i>bimanual synchronous</i> tapping (upper row) and <i>bimanual unbalanced</i> tapping (lower row). The correlation between finger tapping speed and the block-wise activation-level measures from left M1 (panel <b>A</b>), the 26-s full task block-wise correlations (panel <b>B</b>), and the 12-s steady-state task correlations (panel <b>C</b>) are shown. Each dot represents one block, with the regression line indicating the average strength of the brain-behavior correlation. Significant results are marked with an asterisk. The steady-state connectivity measures were modulated by finger tapping speed during the most difficult <i>unbalanced</i> tapping task, but not during the easier <i>synchronous</i> tapping task. The same effect is visible but less pronounced for the overall task connectivity measures, and much weaker for the activation-level based measures. The connectivity measures thus indicate overall task difficulty best, showing the strongest increase from low to high overall task difficulty.</p

Within-participant region-of-interest results.

<p>The individually selected regions of interest in the left and right primary motor cortices (M1) of the five participants are projected onto an average of all participants’ anatomical brain images in panel <b>A</b> (z = 51, Talairach space), and onto the individual anatomical brain images in panel <b>B</b>. In Panel <b>C</b> the BOLD responses from left M1 (averaged across all tasks) are depicted for all participants (mean ± individual SE). The time windows used to compute the block-wise correlations are superimposed on the BOLD responses. Panel <b>D</b> displays the average activation level (group mean ± group SE) during each of the sixteen experimental conditions (four different tapping sequences performed at four different speeds) in right and left M1 (group mean ± group SE), while panel E shows the results (group mean ± group SE) from the correlation analysis of the same regions of interest. From unimanual to bimanual finger tapping the average activation level increased, as expected, in the right, but not left primary motor cortex (left M1: <i>unimanual</i> 1.4%, <i>synchronous</i> 1.3%, <i>alternating</i> 1.2%, <i>unbalanced</i> 1.2%; right M1: <i>unimanual</i> −0.2%, <i>synchronous</i> 1.3%, <i>alternating</i> 1.2%, <i>unbalanced</i> 0.9%). This effect was reflected in the steady-state task and overall task connectivity (26-s full task window: <i>unimanual</i>: 0.02, <i>synchronous</i> 0.75, <i>alternating</i> 0.73, <i>unbalanced</i> 0.73; 12-s steady-state task window: <i>unimanual</i>: 0.24, <i>synchronous</i> 0.42 <i>alternating</i> 0.47, <i>unbalanced</i> 0.47,), but not visible during rest connectivity (<i>unimanual</i> 0.40, <i>synchronous</i> 0.49 <i>alternating</i> 0.48, <i>unbalanced</i> 0.48). Additionally, all task derived measures were modulated by finger tapping speed. For the activation level derived measures, this effect was most pronounced when the performed tapping sequence was easy. During steady-state connectivity the modulation by finger tapping was strongest during <i>unimanual</i>, <i>alternating</i> and <i>unbalanced</i> tapping, and for the overall task connectivity the modulation by finger tapping speed was most pronounced as tapping sequences became most difficult (<i>alternating</i> and <i>unbalanced</i> tapping).</p

Sensitivity and specificity in detecting bimanual tapping.

<p>The sensitivity and specificity (sensitivity/specificity) in detecting if a task was performed with only the right index finger (<i>unimanual</i>), or with both index fingers (<i>bimanual</i>) was computed for all participants using a simple threshold approach (bold with asterisk = significant results).</p

Group-level functional network results.

<p>The task-dependent modulation of the group-level overall task connectivity (26-s full task window, depicted on the left) and the steady-state task connectivity (12-s steady-state task window, shown on the right) are visualized schematically for the investigated functional network (M1 = primary motor cortex, dPMC = dorsal premotor cortex, SMA = supplementary motor area, V5 = visual motion area). The upper row shows the significant difference in functional connectivity between unimanual and bimanual tapping. The second row depicts the significant linear increase of functional connection with increasing demand on bimanual coordination during bimanual tapping. The third row shows how connectivity significantly increased with increasing tapping speed. The bottom row depicts the significant interaction effects between demand on bimanual coordination and tapping speed. While the effects were weaker during steady-state in comparison to overall task connectivity, the task-dependent modulations were qualitatively very similar independent of the time window used. Only one connection, the connection between the two primary motor cortices, showed all effects independent of the time window used. This connection also showed the highest average correlation in the functional network (the thickness of the depicted connections equals the average correlation across all experimental conditions).</p

Cortical depth sampling procedure depicted in one exemplary subject for the analysis of cortical depth responses in area V1.

<p>The upper right corner illustrates our approach on a whole hemisphere, all analysis were performed using the local approach demonstrated to the right. A region of interest is defined based on the activation (F-map) as obtained by the analysis of the functional data (left columns, GE and 3D GRASE). Anatomical data is segmented and a measure of cortical depth obtained. Gridlines (color coded according to their relative depth) are constructed at relative depths (right column). Full grids, orthogonal gradient lines relative to the middle layer as well as the upper and lower bounds can be seen in the right column.</p

Single subjects' results representing the cortical depth dependent percentage of voxels tuned to each axis of motion in area hMT for both 3D GRASE (top) and GE-EPI (bottom).

<p>Colors represent relative cortical depths.</p