Head motion during scanning is a difficult problem in MRI, and those working with patient populations can lose large amounts of data to motion corruption. Though multiple motion metrics have been proposed, traditional volumetric motion metrics correlate poorly with signal corruption in the data [1]. By accounting for slice-wise effects within each acquired volume, SLice-Oriented MOtion COrrection (SLOMOCO) attempts to identify intra-volume as well as volumetric motion corruption [2]. In this work, we compare the motion metric from SLOMOCO to a traditional volumetric motion metric.455 subjects were scanned at 3T in a 12-ch receive head coil. Resting state functional connectivity (rs-fMRI) parameters: 2x2x4mm voxels, 1954 Hz/pix BW, 31 axial slices, TR/TE/FA=2800/29/80. Scans were corrected for physiological noise [3] and motion using both AFNI 3dvolreg [4] and SLOMOCO. All scans were visually inspected for motion, which included assessment of multiple whole-brain correlation maps for patterns typically related to motion (i.e. slice artifact, strong ventricular correlation, rings of correlation around the edges of the brain), and the specificity of multiple networks (default mode, motor, frontal-parietal, visual). Each scan was rated on a five point scale: 1. Major artifacts, no networks visible 2. Major artifacts, some networks may be visible but are overwhelmed by many unrelated regions or other strong artifacts 3. Most networks are visible but may also show many unrelated regions and/or have some weaker artifacts 4. Networks are clear though there may be weak artifacts 5. Networks are clear with few unrelated correlations, no visible artifacts. Using the x, y, and z rotation and translation parameters output by each motion correction method, mean and maximum displacement was calculated for each scan according to Jiang et al [5]. See Beall & Lowe, 2014 for technical details regarding SLOMOCO motion correction methodology.Figure 1 shows the relationship between motion metrics derived from SLOMOCO and 3dvolreg. One-way ANOVAs using visual rating as group were used to compare the two metrics. For the 3dvolreg-derived measures, an ANOVA showed a significantly higher maximum in group 2 compared to groups 3, 4, and 5 (F=13.3, p=3.0x10-10). Mean displacement was higher in group 2 compared to groups 3 and 5 (F=4.79, p=0.0009). SLOMOCO-derived measures showed higher maximum in groups 1 and 2 than in groups 3, 4, and 5 (F=28.44, p=4.4x10-21), and mean displacement was higher in group 1 than in all other groups and in group 2 than in group 3, 4, and 5 (F=36.3, p=2.6x10-26). Figure 2 shows motion in each group. Note the much higher standard deviation in the 3dvolreg-derived metric.Motion metrics derived from SLOMOCO show a stronger relationship to visual ratings of quality in a large dataset. For researchers using rs-fMRI, a single motion metric for each scan is a useful way to determine which datasets are too corrupted to be included in analysis. A metric that is more specific can lead to increased sample size and a reduction of noise from motion corruption. Future work will include comparisons to other motion metrics and refinement of motion cutoffs for corrupted scans.