fix figure legends

Author: rciric

fmriprep/viz/config.json

@@ -96,7 +96,7 @@
                 "name": "epi/rois",
                 "file_pattern": "func/.*_rois",
                 "title": "ROIs in BOLD space",
-                "description": "Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask."
+                "description": "Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The tCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask."
             },
             {
                 "name": "epi_mean_t1_registration/flirt",
@@ -138,13 +138,13 @@
                 "name": "compcor",
                 "file_pattern": "func/.*compcor",
                 "title": "CompCor",
-                "description": "The cumulative variance explained by the first k components of the t/aCompCor decomposition, for all values of k. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression."
+                "description": "The cumulative variance explained by the first k components of the t/aCompCor decomposition, plotted for all values of k. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression."
             },
             {
                 "name": "confounds_correlation",
                 "file_pattern": "func/.*confounds_correlation",
                 "title": "Correlations among nuisance regressors",
-                "description": "Left: Heatmap summarising the correlation structure among confound variables. (Cosine bases and PCA-derived CompCor components are orthogonal.) Right: magnitude of the correlation between each confound time series and the mean global signal. Strong correlations might be indicative of partial volume effects and can inform decisions about feature orthogonalisation prior to confound regression."
+                "description": "Left: Heatmap summarising the correlation structure among confound variables. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.) Right: magnitude of the correlation between each confound time series and the mean global signal. Strong correlations might be indicative of partial volume effects and can inform decisions about feature orthogonalisation prior to confound regression."
             }
         ]
     },