Four sample dPLACE+DORK+DMA displacement maps for a representative subject at different time points during task-based fMRI (scan f).

<p>The static displacement map generated by sPLACE is shown for comparison.</p

tSD maps of three different slices for at rest scans (scan e) of a representative subject after: 1) no further processing, 2) sPLACE, 3) dPLACE, and 6) DORK and dPLACE with DMA.

<p>Maps of the percent signal change achieved by the combined approach 6) compared to no further processing 1) are shown at the far right.</p

Activation brain maps for a representative subject after no further processing (baseline), sPLACE and DORK+dPLACE+DMA on two brain slices.

<p>Activation brain maps for a representative subject after no further processing (baseline), sPLACE and DORK+dPLACE+DMA on two brain slices.</p

Phantom experiment results: Temporal standard deviation (tSD) maps in the absence (first row) and presence (second row) of simulated respiration after: 1) no further processing, 2) sPLACE, 3) dPLACE, 4) dPLACE with DMA, 5) DORK and dPLACE, and 6) DORK and dPLACE with DMA.

<p>Phantom experiment results: Temporal standard deviation (tSD) maps in the absence (first row) and presence (second row) of simulated respiration after: 1) no further processing, 2) sPLACE, 3) dPLACE, 4) dPLACE with DMA, 5) DORK and dPLACE, and 6) DORK and dPLACE with DMA.</p

Group mean, standard deviation, and range of p-p head motion for time series data collection for subjects (a) at rest and (b) during task-based fMRI. ΔSI, ΔRL, and ΔAP denote displacements in the superior-inferior, right-left, and anterior-posterior directions, respectively; roll, pitch and yaw denote the angular rotations about the SI, RL and AP axes, respectively.

<p>Group mean, standard deviation, and range of p-p head motion for time series data collection for subjects (a) at rest and (b) during task-based fMRI. ΔSI, ΔRL, and ΔAP denote displacements in the superior-inferior, right-left, and anterior-posterior directions, respectively; roll, pitch and yaw denote the angular rotations about the SI, RL and AP axes, respectively.</p

Summary of all imaging runs.

<p>Summary of all imaging runs.</p

Functional MRI of working memory and selective attention in vibrotactile frequency discrimination-0

<p><b>Copyright information:</b></p><p>Taken from "Functional MRI of working memory and selective attention in vibrotactile frequency discrimination"</p><p>http://www.biomedcentral.com/1471-2202/8/48</p><p>BMC Neuroscience 2007;8():48-48.</p><p>Published online 4 Jul 2007</p><p>PMCID:PMC1925104.</p><p></p>er rested on a two-button response pad. The arms were extended during the measurement. Pressure points were avoided using foam pads

Functional MRI of working memory and selective attention in vibrotactile frequency discrimination-2

<p><b>Copyright information:</b></p><p>Taken from "Functional MRI of working memory and selective attention in vibrotactile frequency discrimination"</p><p>http://www.biomedcentral.com/1471-2202/8/48</p><p>BMC Neuroscience 2007;8():48-48.</p><p>Published online 4 Jul 2007</p><p>PMCID:PMC1925104.</p><p></p> conditions with and without distractor. Areas with significantly stronger activation following the probe with simultaneous distractor compared to frequency discrimination without distractor are colour-coded in yellow and red, areas with less activation are coded in blue (clustered activation images with an overall corrected p < 0.05). Processing of the probe with distractor was associated with increased activity in the right middle temporal gyrus (1), the right anterior insula (2), the left precuneus (3) and the bilateral posterior parietal cortex (4, 5). Deactivation was seen in the right posterior cingulate gyrus (6), the left medial frontal gyrus (7) and the left precentral gyrus (8). Brain images are shown in radiological convention (the right hemisphere is seen on the left side of the image)

Comparing performance of different subspace estimation methods, for Strong-Contrast data.

<p>For <i>strong</i> contrast (Task vs. Control), model performance is shown for different subspace estimation methods, relative to full-dimensionality data (i.e. retaining all PCs). The median, [minimum, maximum] changes are shown for prediction (<b>Δ</b><i>P</i>), reproducibility (<b>Δ</b><i>R</i>) and distance <b>Δ</b><i>D</i> from (<i>P</i> = 1,<i>R</i> = 1), over all single-subject results. Significance is given by Wilcoxon tests, with * indicating significant improvement. We show results for combinations of <b>ICA</b> = MELODIC subspace estimation, <b>PCA_split</b> = optimized PC subspace on each data split-half, and <b>PCA_full</b> = retaining 35% of PCs from the full data matrix. Note that (<b>PCA_full</b>+<b>PCA_split</b>) is the subspace selection method used for the rest of the manuscript. Results are shown for optimal fixed preprocessing: motion correction and 2^nd-order detrending.</p

Effects of individual subject optimization on model performance.

<p>Prediction and reproducibility are plotted for the optimal fixed pipeline (red) and individually optimized pipeline (blue) of each subject, connected by a solid line. Performance metrics are plotted for (A) <i>strong</i> (Task vs. Control) and (B) <i>weak</i> (TaskB vs. TaskA) task contrasts. For <i>strong</i> and <i>weak</i> contrasts, optimal fixed pipelines are {ICA_M,MC,DET2} and {ICA_M,DET4}, respectively. Subjects with no change in pipeline are coloured in grey.</p