Figure 1.
Continuous face morphing, optical flow and associated functional activations.
(A) Exemplary keyframes of a video sequence (see Movie S1) morphing a 20 year-old female into a 60 year-old male. Both gradual age and gender changes are illustrated at intervals of 1 second. (B) Line magnitude images of optical flow velocities computed by the Horn-Schunck algorithm. Differential motion/optical flow was quantified as an overall parameter by the sum of flow magnitudes between successive keyframes. (C) Motion-/flow-related activations of hMT+ derived from the group-level analyses (n = 24 subjects, FWER-corrected p<0.05, [−log10 (p)] colorbar) on posterior cortical flat maps of both hemispheres. Additionally, ventral (v) and dorsal (d) visuotopic labels (V1–8, Vp, LO, hMT+) of the SuMS database, transformed from Caret's PALS atlas into FreeSurfer's average surface space, are displayed. Note that according to recent data [112], V4v and V8 are labeled together as hV4 while VP has been labeled V3v.
Figure 2.
Psychometrics of facial age and gender changes.
(A) Facial age difference ratings (magnitude of age-gradients spanned by morphing rated on a 6-point visual analogue scale, maximum scaled to 3.0 arbitrary units [a.u.]) followed Stevens' (∧0.3) better than Weber-Fechner's law (log10) or a natural logarithmic transformation (ln) of start and target age. All face stimuli (n = 121) were morphed to an average-aged male face of 33 years, the morphing sequence was randomly played forwards or backwards for the rating (circles with error bars; n = 24 subjects). (B) Facial aging (x-axis; objective age in [years]) increased the variability of subjective age ratings (y-axis; SD, standard deviation of estimated age in [years] across n = 24 subjects). Rating accuracy of factual (n = 121 stimuli of real faces) and interpolated age (n = 80 intermediate face stimuli from the morphing algorithm; one randomly selected for each annual increment between 2 and 81 years of age) did not differ significantly (p = 0.97). (C) Face gender ratings (on a 6-point visual analogue scale, maximum scaled to 1.0 arbitrary units [a.u.]) along temporal morph continua (n = 60) across faces of clearly different sex. Subjective ratings by (n = 24) subjects (boxes with error bars, blue line) were augmented above linear transition values (dashed line with black dots), reflecting the tendency to apperceptive gender categorization.
Figure 3.
Modeling changes of age and gender during face morphing.
Both were time-binned at the video frame rate (24 fps) and scaled to maxima of 1.0 arbitrary units [a.u.]. (A) Differential age change encoded according to Stevens' law of psychophysics (using a power exponent of 0.3; Figure 2A). Note that relative facial aging was up-weighted to initial periods of the example morph (also see Figure 1A; here: solid red line) and, for identical age differences, to younger absolute ages, i.e. aging from 10 to 26 was assumed to provide a stronger stimulus with more visual cues than aging from 64 to 80 years (dashed vs. double-dotted/dashed line). (B) Differential gender change expressed by the first derivative of the function plotted in Figure 2C. Note that peak androgyny was defined as the effective stimulus-of-interest, i.e. the transition of facial gender was emphasized at the center of the morph (see also Figure 1A and Movie S1). Half of the morphs contained no gender transitions, retaining a flat line at zero level to indicate the lack of gender change (dashed line).
Figure 4.
Functional activations associated with changes of facial age and gender.
(A) Group-level (n = 24) functional activations1 related to age and gender change, respectively. (B) Quantification and between-cluster/-hemisphere comparisons of observed effect sizes evoked by facial age and gender changes across (n = 24) subjects. Individual values of each cluster's mean activation (± error bars across subjects) were normalized to the lowest average of corresponding response magnitudes (as extracted from the first-level analyses). (C) Increased age-related activations1 of the most accurate (n = 5) above average age-raters (n = 14). The corresponding cortical flat map is outlined by the borders of the left age-responsive pANG cluster. (D) Relative to average post-hoc raters (avg, n = 14), high explicit age-rating accuracy (upper quintile P80, n = 5) was accompanied by almost five times the response magnitude during implicit age-change processing within left pANG (p<0.001, based on mean individual activation levels of the sub-cluster shown in Figure 4C, as back-projected to native subject space). Activations of lower quintile raters (P20, n = 5) were more variable but not statistically different from the average (P20–80). 1Significant activations (FWER-corrected p<0.05) displayed on FreeSurfer's average inflated surface (color bars depict uncorrected activation probabilities [−log10 (p)]). pANG, posterior angular gyrus area; pITS, posterior inferior temporal sulcus; DLPFC, dorsolateral prefrontal cortex; LOT, lateral occipito-temporal area; FFG, fusiform gyrus; orientation labels: L, left; R, right.
Figure 5.
Association pathways subserving facial age processing.
Ventral portion of Wernicke's perpendicular fasciculus (WpF) connecting pANG and pITS (average probabilistic path distributions connecting the functional clusters; n = 24, 3D-tract volume rendering thresholded at ≥100 connecting samples passing through each voxel, displayed on sagittal [x = −36 mm] and coronal [y = −54 mm] projection view planes in MNI standard space). pANG, posterior angular gyrus area; pITS, posterior inferior temporal sulcus; orientation labels: L, left; R, right.
Figure 6.
Surface-based cross-correlation of fMRI activation probabilities and structural connectivities.
Spatial cross-correlation plots (± SEM)1 between activation probabilities ([−log10(p)]) and structural connectivity scores ([log10(cs/ns)], with [(cs/ns)] reflecting ratios of connecting samples to the number of samples sent out from each vertex) for pANG, pITS and FFG (cf. Figure 4A), based on two samples: (n = 24) paired with functional data [red] and (n = 46) independent subjects [blue]. Connectivity ratios tend to get bound earlier at maximum values than corresponding activation probabilities. Positive correlations were slightly stronger for the sample with paired fMRI and DWI data (n = 24) compared to the independent sample from the FMRIB DWI database (n = 46; with Spearman's ρ displayed for the paired/independent sample when significant). However, the latter largely replicate and confirm generalizability of the results. 1SEM, standard error of the mean; */**/***: FWER-corrected p<0.05/0.01/0.001. pANG, posterior angular gyrus area; pITS, posterior inferior temporal sulcus; FFG, fusiform gyrus.
Table 1.
Synopsis of functional activations related to age, gender and motion/optical flow.
Figure 7.
Left-hemispheric nodes of the presumed brain network processing the age of faces.
3D model illustrating how the ventral stream, pITS in particular, may interact via Wernicke's perpendicular fasciculus (WpF) with the posterior magnitude-encoding and approximate number system [98], [99], pANG in particular, to quantify the varying age of faces. FFG exhibits some connectivity to pANG (cf. Figure 6) but is primarily engaged in processing fixed face attributes such as categorical gender (even if continuously changed over variable androgyny levels like in Figure 1A; see also Figures 2C, 3B and 4A). pANG, posterior angular gyrus area; pITS, posterior inferior temporal sulcus; DLPFC, dorsolateral prefrontal cortex; LOT, lateral occipitotemporal area; FFG, fusiform gyrus; 17–19, Brodmann's areas forming three visual tiers; hMT+, human motion-sensitive temporal cortex; ITG/MTG/STG, inferior/middle/superior temporal gyrus; ITS/STS, inferior/superior temporal sulcus.