Using Coherence to Measure Regional Homogeneity of Resting-State fMRI Signal

In this study, we applied coherence to voxel-wise measurement of regional homogeneity of resting-state functional magnetic resonance imaging (RS-fMRI) signal. We compared the current method, regional homogeneity based on coherence (Cohe-ReHo), with previously proposed method, ReHo based on Kendall's coefficient of concordance (KCC-ReHo), in terms of correlation and paired t-test in a large sample of healthy participants. We found the two measurements differed mainly in some brain regions where physiological noise is dominant. We also compared the sensitivity of these methods in detecting difference between resting-state conditions [eyes open (EO) vs. eyes closed (EC)] and in detecting abnormal local synchronization between two groups [attention deficit hyperactivity disorder (ADHD) patients vs. normal controls]. Our results indicated that Cohe-ReHo is more sensitive than KCC-ReHo to the difference between two conditions (EO vs. EC) as well as that between ADHD and normal controls. These preliminary results suggest that Cohe-ReHo is superior to KCC-ReHo. A possible reason is that coherence is not susceptible to random noise induced by phase delay among the time courses to be measured. However, further investigation is still needed to elucidate the sensitivity and specificity of these methods

White matter microstructure correlates with autism trait severity in a combined clinical–control sample of high-functioning adults

AbstractDiffusion tensor imaging (DTI) studies have demonstrated white matter (WM) abnormalities in tracts involved in emotion processing in autism spectrum disorder (ASD), but little is known regarding the nature and distribution of WM anomalies in relation to ASD trait severity in adults. Increasing evidence suggests that ASD occurs at the extreme of a distribution of social abilities. We aimed to examine WM microstructure as a potential marker for ASD symptom severity in a combined clinical–neurotypical population. SIENAX was used to estimate whole brain volume. Tract-based spatial statistics (TBSS) was used to provide a voxel-wise comparison of WM microstructure in 50 high-functioning young adults: 25 ASD and 25 neurotypical. The severity of ASD traits was measured by autism quotient (AQ); we examined regressions between DTI markers of WM microstructure and ASD trait severity. Cognitive abilities, measured by intelligence quotient, were well-matched between the groups and were controlled in all analyses. There were no significant group differences in whole brain volume. TBSS showed widespread regions of significantly reduced fractional anisotropy (FA) and increased mean diffusivity (MD) and radial diffusivity (RD) in ASD compared with controls. Linear regression analyses in the combined sample showed that average whole WM skeleton FA was negatively influenced by AQ (p=0.004), whilst MD and RD were positively related to AQ (p=0.002; p=0.001). Regression slopes were similar within both groups and strongest for AQ social, communication and attention switching scores. In conclusion, similar regression characteristics were found between WM microstructure and ASD trait severity in a combined sample of ASD and neurotypical adults. WM anomalies were relatively more severe in the clinically diagnosed sample. Both findings suggest that there is a dimensional relationship between WM microstructure and severity of ASD traits from neurotypical subjects through to clinical ASD, with reduced coherence of WM associated with greater ASD symptoms. General cognitive abilities were independent of the relationship between WM indices and ASD traits

Group-ICA estimated RSNs based on 27-component analysis (A) and 70-component analysis (B).

<p>All sixteen non-artefactual components from the 27-component analysis (A) and 12 non-artefactual components exhibiting significant or marginal significant amplitude-vs-SSRT correlations from the 70-component analysis (B) were shown. The number of each component was based on the ranking of variance explained by the component. A summary of the functions of the components shown in subfigure (A) can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066572#pone.0066572.s013" target="_blank">Table S1</a>.</p

Significant negative correlation of RSN amplitude (timeseries standard deviation) vs. SSRT.

<p>The results obtained at two decomposing dimensions, arranged according to their correspondence in the function of components. The threshold (determining inclusion in this table) was <i>p</i><0.05 (FDR corrected), which corresponds to uncorrected for the 27-component analysis and uncorrected for the 70-component analysis. For components exhibiting significant SSRT correlations, their counterparts obtained at the other decomposing dimension were also listed here even if they did not survive the threshold, and are indicated by ^a.</p

The effects of spatial smoothing and z-transformation upon spatial-map-vs-SSRT correlations.

<p>The <i>negative</i> spatial-map-vs-SSRT correlation maps were thresholded at <i>p</i><0.05 (TFCE corrected for multiple comparisons across space, and for two-sided tests, but not corrected across multiple RSNs). <i>Non-Z-Map</i> indicates SSRT correlations on the spatial maps produced directly by dual-regression, and <i>Z-Map</i> indicates the correlations produced by the z-transformed version of these spatial maps. <i>No Smooth</i> indicates SSRT correlations based on the unsmoothed (i.e., only the 5-mm FWHM smoothing at the preprocessing stage) spatial maps, and <i>10-mm smooth</i> indicates the SSRT correlations based on the spatial maps additionally smoothed with a Gaussian kernel of FWHM 10 mm. The correlation maps were superimposed on their respective <i>group-mean</i> spatial maps obtained by Group-ICA and then on the MNI152 template. The <i>group-mean</i> spatial maps were provided here to show whether the significant regions lie within or outside the group-level RSNs. Red-yellow indicates significant regions in the <i>group-mean</i> spatial map, blue indicates significant spatial-map-vs-SSRT correlations that do not overlap with the group-mean map, and green indicates regions of overlap. The results were based on components corresponding to primary-medial (high eccentricity) visual networks, namely, component No. 14 from the 27-component analysis and component No. 54 from the 70-component analysis. The spatial-map-vs-SSRT correlations based on the z-transformed and 10-mm smoothed components were also shown though no significant voxel was observed. It can be seen that a greater number of significant voxels, if any, could be detected based on non-z-transformed and 10-mm smoothed spatial maps.</p

Network matrix vs. SSRT correlations from the 27-component analysis that survived a threshold of uncorrected <i>p</i><0.01.

*<p>indicated that the SSRT correlation survived a threshold of <i>p</i><0.05 (FDR corrected). R-values in bold indicate that the SSRT correlation survived the threshold of uncorrected <i>p</i><0.01.</p

Correlation of SSRT vs. the full/partial (within-subject) RSN timeseries correlation matrices across subjects.

<p>Correlation of SSRT vs. the full/partial (within-subject) RSN timeseries correlation matrices across subjects.</p