Finger-tapping task BOLD results.

<p>(A) Robust bilateral activation was seen in the motor cortex, including the pre and postcentral gyrus, medial frontal gyrus, and cerebellum for all datasets. Increased activation was observed for the MEC data compared to E2 data and for the MECDN data compared to the MEC and E2 data. Activation was also observed in subcortical areas for the MECDN data. All maps were thresholded at p<0.005 and cluster corrected with a minimum cluster size of 182 voxels (α<0.05). (B) Average BOLD time-series extracted from a mask of voxels active for all BOLD datasets.</p

Representative PW and BOLD datasets.

<p>(A) Mean PW_ss,none (top) and PWDN_ss,art+BOLD data (bottom) images. These images were created by averaging and subtracting the label images from the control images. MB imaging allows for the collection of whole-brain images in a relatively short readout time reducing T1 effects. (B) Example individual echo, MEC, and MECDN images from a single time point from one subject. Image quality improves with echo combination.</p

Group tSNR maps.

<p>The tSNR significantly increased from the E2 to MECDN data (p<0.001). For the PW data, tSNR maps are shown for the SS data. The tSNR for the PWDN_ss,art+BOLD significantly increased compared to the PWDN_ss,art and PW_ss,none data (p<0.001).</p

Schematic showing the perfusion-weighted denoising procedure.

<p>Echoes were first despiked, volume registered, and coregistered to MNI space. Each echo was then individually low-pass filtered at f ≤ 0.09 Hz. Echoes were then combined using a T2*-weighted approach. This low-pass filtered, multi-echo combined dataset was fed into the MEICA algorithm, which extracted independent components and classified them as artifact, BOLD, or indeterminate. The BOLD and artifact components were regressed from the unfiltered first-echo data resulting in a denoised first-echo dataset. Surround subtraction and high-pass filtering followed by demodulation were performed on this data leading to denoised PW datasets. A GLM was employed on this data to determine activation.</p

MBME ASL/BOLD pulse sequence design.

<p>The sequence consists of an unbalanced pCASL labeling train, followed by a PLD, and finally an ME EPI readout. The first echo train was acquired after the acquisition of navigator echoes through the center of k-space. The phase was then rewound to the start of k-space, and the next echo train was acquired. This was repeated three times for a total of four echoes. MB imaging was implemented by replacing the single-band excitation pulse with a MB excitation pulse. Finally, blipped-CAIPI was utilized to shift the FOV of aliased slices and reduce g-factor penalties associated with MB imaging. Reprinted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190427#pone.0190427.ref031" target="_blank">31</a>] under a CC BY license, original copyright 2017.</p

Group averages for quantitative metrics.

<p>Group averages for quantitative metrics.</p

Finger-tapping task PW results.

<p>(A) For the SS results (left), bilateral activation was observed in the motor cortex for the PW_ss,none and PWDN_ss,art+BOLD data. An increased activation area was seen for the PWDN_ss,art+BOLD data compared to the PW_ss,none data. The HD data (right) showed increased activation compared to the SS data, however no differences were seen between the denoised and non-denoised data the. All maps were thresholded at p<0.005 and cluster corrected with a minimum cluster size of 131 voxels (α<0.05). (B) Average SS PW signal from one representative subject (left) and average HD PW signal from the same subject (right). All PW signal was extracted from a mask of voxels active for all PW datasets. The denoised SS time-series appear less noisy with less variance compared to the non-denoised time-series. This effect is less apparent for the HD data.</p

BOLD/CBF coupling.

<p>(A) Group averaged correlation between CBF and BOLD time-series for non-denoised (PW,E2) and denoised (PWDN,MECDN) data for SS (left) and HD data (right). In general, CBF-BOLD correlation increased with denoising. This was confirmed quantitatively (B) where the mean correlation extracted from a mask of significantly correlated voxels was highest for fully denoised datasets (PWDN,MECDN). *** = p<0.001, ** = p<0.01, * = p<0.05, Bonferroni-corrected.</p

Schematic showing the processing pipeline for the ASL and BOLD echoes.

<p>The first and second echoes were processed separately to yield the PW_none and E2 data, respectively. Echoes were combined using a T2*-weighted approach to generate the MEC dataset. This dataset was further denoised using MEICA, resulting in the MECDN dataset. No additional regression was performed in the GLM for the PW and MECDN datasets. Example activation curves and model fits are shown for the different datasets.</p

Montreal Neurological Institute coordinates of seed regions used in the functional connectivity analysis.

<p>Montreal Neurological Institute coordinates of seed regions used in the functional connectivity analysis.</p