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. 2017 Sep 1;27(9):4492-4502.
doi: 10.1093/cercor/bhw253.

Data Quality Influences Observed Links Between Functional Connectivity and Behavior

Joshua S Siegel  1 , Anish Mitra  2 , Timothy O Laumann  1 , Benjamin A Seitzman  1 , Marcus Raichle  1   2 , Maurizio Corbetta  1   2   3   4 , Abraham Z Snyder  1   2
Affiliations

Data Quality Influences Observed Links Between Functional Connectivity and Behavior

Joshua S Siegel et al. Cereb Cortex. .

Abstract

A growing field of research explores links between behavioral measures and functional connectivity (FC) assessed using resting-state functional magnetic resonance imaging. Recent studies suggest that measurement of these relationships may be corrupted by head motion artifact. Using data from the Human Connectome Project (HCP), we find that a surprising number of behavioral, demographic, and physiological measures (23 of 122), including fluid intelligence, reading ability, weight, and psychiatric diagnostic scales, correlate with head motion. We demonstrate that "trait" (across-subject) and "state" (across-day, within-subject) effects of motion on FC are remarkably similar in HCP data, suggesting that state effects of motion could potentially mimic trait correlates of behavior. Thus, head motion is a likely source of systematic errors (bias) in the measurement of FC:behavior relationships. Next, we show that data cleaning strategies reduce the influence of head motion and substantially alter previously reported FC:behavior relationship. Our results suggest that spurious relationships mediated by head motion may be widespread in studies linking FC to behavior.

Keywords: functional connectivity, head motion, IQ, movement, resting state.

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Figures

Figure 1.
Figure 1.
Data processing schematic. Boxes with black text indicate processing steps that occurred prior to accessing data. Surface projected and FIX-ICA pipelineR-fMRI data (e.g., rfMRI_REST1_LR_hp2000_clean.dtseries.nii) was accessed from https://db.humanconnectome.org. Italic text indicates processing steps implemented prior to access of R-fMRI data. Type B processing included a number of additional data cleaning steps implemented after parcel time series were extracted, demeaned, and detrended.
Figure 2.
Figure 2.
Head motion exerts a stereotyped influence on FC. (a) Full matrix of intersubject FC:FD correlation coefficients, measured with Type A processed FC data. (b) Full matrix of intrasubject (between day) FC:FD correlation coefficients. The spatial correlation between intrasubject and intersubject measures was r = 0.64. In (c) the top positive and negative intrasubject FC:FD weights are projected to parcels on the brain surface. The value for each parcel in the positive projection is the mean of the top quartile of values from all of its connections. The negative projection is the bottom quartile. RSN abbreviations: VIS = Visual network (38 ROIs); PO = parieto-occipital (7 ROIs); SMD = dorsal somato-motor (37 ROIs); SMV = ventral somato-motor (8 ROIs); AUD = auditory (23 ROIs); CON = cingulo-opercular (39 ROIs); VAN = ventral attention (23 ROIs); SAL = salience (4 ROIs); CP = Cingulo-parietal (5 ROIs); DAN = dorsal attention network (32 ROIs); FPN = frontoparietal control network (24 ROIs); DMN = default mode network (40 ROIs); NON = no assigned network (44 ROIs).
Figure 3.
Figure 3.
Motion influences FC:IQ topography. This influence decreases with Type B FC processing. Each scatter plot shows FC:FD (intrasubject) weights versus FC:behavior (intersubject) correlations. Each dot represents one of the 52 326 FC edges (324-choose-2). A larger correlation suggests greater head motion influence. Pperm denotes the P-value generated by permutations testing as described in the methods–motion influence on FC:behavior section. (a) Left: map of connection strength increases associated with IQ after Type A preprocessing (projection of top matrix weights). Right: scatter plot showing motion influence on Type A FC:IQ relationships. FC:IQ and FC:FD are correlated at r = −0.31, r2 = 0.10, Pperm = 0.008. (b) Left: map of connection strength increases consistently associated with 19 motion-correlated behavioral measures in Table 1 (excluding 4 physiological measures, flipping negatively correlated measures). Weights are averaged across measures and then projected. Right: scatter plot showing motion influence on average FC:behavior relationships. FC:behavior and FC:FD are correlated r = 0.50, r2 = 0.25, Pperm < 0.0001. Panels (c) and (d) were generated with identical methods to (a) and (b), but with Type B FC data instead of Type A.
Figure 4.
Figure 4.
Comparing global FC-behavior relationships. Each blue dot represents the proportion of edges with FC:behavior correlation P < 0.05 for one 100-subject subgroup. The solid and dashed red lines represent mean and 95% bounds across subgroups, respectively. (a) For Type A subgroups, 11.4 ± 11.9% (mean ± SD) of edges correlated with IQ at P < 0.05 (uncorrected). (c) For Type B subgroups, 5.8 ± 1.2% of edges correlated with IQ at P < 0.05. (b/d) The same procedure is applied to all 19 motion-correlated measures. On average, for Type A and Type B subgroups, 7.9 ± 10.0 and 5.6 ± 1.5% of edges, respectively, correlated with behavior at P < 0.05.
Figure 5.
Figure 5.
Type B processing reduced the influence of head motion across many behaviors. Each scatter plot shows FC:FD (intrasubject) weights versus FC:behavior (intersubject) correlations. A larger correlation suggests greater head motion influence (correlation coefficients greater than r = 0.272 are significant at P > 0.05 based on permutations). The left column is generated with Type A processing, the right column is generated with Type B processing. (a) The influence of head motion on FC:IQ relationships. r-Values are given above each plot. (b) The influence of head motion on FC:behavior relationships for 4 measures showing apparent influence of head motion but not included in Table 1. Pearson correlations are shown in red above each plot. (c) Across all 122 subject measures, the influence of head motion drops toward zero with additional cleaning steps included in Type B processing. IQ is shown in magenta and the 4 measures in Panel b are shown in red.
Figure 6.
Figure 6.
Motion influence on FC:IQ across FC processing regimes. The approach to estimate motion influence in Figure 3 was applied following several FC processing strategies. Bars indicate mean motion influence for the 19 behavior measures in table 1, error bars indicate standard error of the mean. Asterisks and horizontal bars above the indicate P < 0.05 following 8 two-tailed paired t-tests comparing Type B processing with all other processing regimes, Bonferroni corrected for multiple comparisons. “MPP+”: HCP minimal preprocessing plus motion regression (24 regressors), demeaning, detrending, and variance normalization; “Type A–36 subs”: 36 high-motion subjects were removed (matching those removed in Type A + scrub); Finn et al.: minimal preprocessing (excluding FIX), with regression of 24 motion parameters as well as mean gray, white, and ventricle time courses, and bandpass filtering (as used in Finn et al. 2015 prediction of IQ). CompCor + scrub: similar to Type B except CompCor regressors (10 white matter and ventricle PCA-derived regressors) were included and the gray matter regressor was excluded; Type A + partial correlation: “Permutation”: the mean and SD of motion influence for 1000 random permutations of Type A processed FC data.
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