Edited by: Lynne E. Bernstein, George Washington University, USA
Reviewed by: Nienke Van Atteveldt, VU University Amsterdam, Netherlands; Laura Christine Anderson, University of Maryland, USA
*Correspondence: Paddy D. Ross, Institute of Neuroscience and Psychology, University of Glasgow, 58 Hillhead Street, G12 8QB Glasgow, UK e-mail:
This article was submitted to the journal Frontiers in Human Neuroscience.
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Our ability to read other people’s non-verbal signals gets refined throughout childhood and adolescence. How this is paralleled by brain development has been investigated mainly with regards to face perception, showing a protracted functional development of the face-selective visual cortical areas. In view of the importance of whole-body expressions in interpersonal communication it is important to understand the development of brain areas sensitive to these social signals. Here we used functional magnetic resonance imaging (fMRI) to compare brain activity in a group of 24 children (age 6–11) and 26 adults while they passively watched short videos of body or object movements. We observed activity in similar regions in both groups; namely the extra-striate body area (EBA), fusiform body area (FBA), posterior superior temporal sulcus (pSTS), amygdala and premotor regions. Adults showed additional activity in the inferior frontal gyrus (IFG). Within the main body-selective regions (EBA, FBA and pSTS), the strength and spatial extent of fMRI signal change was larger in adults than in children. Multivariate Bayesian (MVB) analysis showed that the spatial pattern of neural representation within those regions did not change over age. Our results indicate, for the first time, that body perception, like face perception, is still maturing through the second decade of life.
Perception of signals conveyed by other people’s faces or bodies is still improving in late childhood (Burgund et al.,
Brain areas identified as being specialized for the recognition and interpretation of human form and motion include the extra-striate body area (EBA) located bilaterally in the lateral occipitotemporal cortex, the fusiform body area (FBA), areas in the inferior parietal lobe (IPL) and posterior superior temporal sulcus (pSTS; Downing et al.,
Hence, our first goal was to further investigate the recruitment of EBA, pSTS and FBA during body movement perception in pre-pubertal primary-school children as compared to adults in terms of its amplitude and recruited spatial territory. We also wanted to replicate the decreasing right lateralisation effects observed by Pelphrey et al. (
Secondly, global level inferences obtained using current univariate approaches may not tell the full developmental story. They negate interactions between voxels, which can only be observed if one looks at patterns of neural representation. For example, (Morcom and Friston,
Here we applied a recently developed analytic method that allows multivariate Bayesian (MVB) model comparison across different patterns of activity both within, and across, regions (Friston et al.,
In summary we hypothesize, in line with face processing, that the body-selective areas will be right lateralised in children, but this effect will decrease over age. We further hypothesize, contrary to previous work in body recognition, that the neural representation pattern of body movements, along with the height and spatial extent of activation in the body-selective brain areas, will not be ‘adult-like’ in pre-pubescent children.
Twenty-Seven primary school children were recruited from schools and afterschool clubs in the West End of Glasgow (Scotland). Three children were excluded because of excessive head motion in the scanner. Therefore data from 24 children (aged 6–11 years:
We used 45 short video-clips from a set created and validated by (Kret et al.,
In addition, various clips of non-human moving objects (e.g., windscreen wipers, windmills, metronomes etc.) were taken from the internet. They were cropped to the same size (960 × 540 pixels, 50 frames, 25 fps) as the human videos using Adobe Premiere Pro and a green border was added to make these stimuli as similar as possible to the body stimuli.
Stimuli were organized into blocks of five clips (10 s). To assess the amount of low-level visual motion in each clip, we computed the average change in luminance between consecutive frames. To do so, for each clips we first estimated change in luminance in the background (corresponding to noise level) and for each pairs of frames extracted the number of pixels where the change in intensity was higher than noise. For each clip we computed the average number of pixels with change across the frames. Then we computed the cumulative motion for the five clips in each block. Overall the blocks of non-human clips had slightly more motion than the blocks of body movements clips, although this did not reach statistical significance (
We measured brain activity using a 3T fMRI scanner (Tim Trio, Siemens, Erlangen, Germany) equipped with a 32-channels head coil, using standard EPI sequence for functional scans (TR/TE: 2600 ms / 40 ms; slice thickness = 3 mm; in plane resolution = 3 × 3 mm). In addition, we acquired a high-resolution T1-weighted structural scan (1 mm3 3D MPRAGE sequence) for anatomical localization.
Parents/guardians were allowed to sit with their children in the scanning room if they or their child wished (This was the case for 3 subjects). Head motion was restricted thanks to appropriate cushioning. Children were familiarized with the environment and we acquired a 3 min-dummy scan while they watched a cartoon. This allowed us to give them feedback about their head motion and train them to stay still.
A MATLAB script using the Psychophysics Toolbox Extensions (Brainard,
Pre-processing and statistical analysis of MRI data was performed using SPM 8 (Welcome Department of Imaging Neuroscience).
By normalizing the data from our adults and children into the same stereotactic template, we were able to directly compare the strength and extent of activation across age groups. Several studies examining the feasibility of this approach have found no significant differences in brain foci locations when the brains of children as young as 6 were transformed to an adult template (Burgund et al.,
A general linear model was created with one predictor for each condition of interest (Body and Non-Body). Head motion parameters were also included as regressors of non-interest. The model was estimated for each participant and individual contrasts (Body vs. Non-Body) were taken to second-level random effect analyses to create group-averages separately for children and adults. For the main group analyses, group was added as a factor in the GLM and the resulting statistical maps are presented at a threshold of
We defined six regions of interest (ROIs): bilateral EBA, bilateral FBA and bilateral pSTS. These were derived by taking the set of contiguous voxels within a sphere of radius 8 mm surrounding the voxel in each anatomical region that showed the highest probability of activation in a meta-analysis of 20 studies examining contrasts between moving body and controls in adults (detailed in Grosbras et al.,
To test for differences in activity across ROIs and age group for each participant we extracted the individual peak
We investigated the coding activity patterns within the ROIs with an MVB decoding approach (see Friston et al.,
Then the set of voxel patterns chosen constitutes a hypothesis about the nature of the mapping between the brain activity (in this case voxel-wise activity in each of our ROIs) and the target variable (our Body > Non-Body contrast from the whole-brain analysis). MVB can therefore decode the neuronal activity pattern of the target variable according to the spatial priors afforded by each model. The evidence for each model can then be treated as a summary statistic and compared to other models using analysis of variance (ANOVA) (Friston et al.,
This allowed us to evaluate competing coding hypotheses (distributed vs. clustered as in Morcom and Friston,
Three subjects who showed head motion larger than 2 mm in any translation or 2 degrees in any rotation direction were excluded from the analysis. For the remaining participants, rigid body motion parameters were estimated and used to realign each volume to the averaged image. Those motion parameters were included in the general linear model as parameters of non-interest in order to exclude any potential effect on the activation of interest. In addition, independent
Further, variance in BOLD signal could also explain any potential difference we observe between children and adults. To account for this confound we compared the standard deviation of the BOLD signal during blank-screen blocks in our six ROIs across age-groups. We found no significant difference between adults and children in any ROI (rEBA:
In addition, we looked at the residual sum of squares of the full model fit in each of the six ROIs across age. This gave us an estimate of noise in our model for each participant. Again, we found no significant difference between adults and children (rEBA:
These controls give us confidence that any potential differences in fMRI signal change observed between adults and children are due to functional processing of the stimuli, and not simply due to differences in motion, variance in signal, or within subject error in model fit.
In adults, viewing dynamic bodies compared to viewing dynamic objects activated the bilateral fusiform gyri (including FBA), bilateral occipitotemporal cortices (including EBA), bilateral posterior superior temporal sulci (pSTS), right precentral, right inferior frontal gyrus (IFG), right superior parietal lobule, and bilateral amygdalae. In children, bilateral activity in the occipitotemporal regions and, only in the right hemisphere, fusiform gyrus, pSTS, amygdala and precentral gyrus reached significance level (see Figure
Region | Adults | Children | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
mm3 | mm3 | |||||||||
Right Fusiform Gyrus | 45 | −46 | −17 | 13.00 | 1890 | 42 | −49 | −17 | 5.51 | 918 |
Left Fusiform Gyrus | −42 | −40 | −20 | 5.54 | 567 | |||||
Right Occipitotemporal | 45 | −76 | −8 | 11.32 | 1566 | 48 | −73 | 4 | 10.16 | 1890 |
Left Occipitotemporal | −45 | −76 | 7 | 7.59 | 1728 | −54 | −67 | 16 | 7.00 | 1512 |
Right P Superior Temproal Sulcus | 57 | −43 | 10 | 10.10 | 2187 | 60 | −40 | 13 | 7.28 | 2187 |
Left P Superior Temporal Sulcus | −63 | −49 | 19 | 7.42 | 1458 | −51 | −55 | 13 | 4.00 | 837 |
Right Superior Parietal Lobe | 30 | −49 | 67 | 4.55 | 432 | |||||
Right Inferior Frontal Gyrus | 45 | 17 | 28 | 7.34 | 2025 | 36 | 17 | 25 | 4.33 | 621 |
Right Precentral Gyrus | 48 | 5 | 46 | 8.16 | 2025 | 51 | 2 | 49 | 6.18 | 1323 |
Right Amygdala | 18 | −7 | −14 | 7.14 | 1620 | 21 | −7 | −11 | 6.07 | 1377 |
Left Amygdala | −18 | −7 | −14 | 6.28 | 1215 | |||||
Right Temporal Pole | 36 | 17 | −32 | 4.43 | 648 | |||||
Right Precuneus | 3 | −58 | 31 | 5.08 | 2295 | |||||
Left Supramarginal Gyrus | −54 | −43 | 31 | 4.17 | 972 |
Adults showed significantly more activation than children in the bilateral occipitotemporal areas, right pSTS, right fusiform gyrus, bilateral amygdalae, right thalamus and the right IFG.
No region showed higher activity in children than in adults (see Figure
mm3 | |||||
---|---|---|---|---|---|
Adults > Children | |||||
Right Fusiform Gyrus | 45 | −52 | −17 | 4.33 | 1215 |
Right Occipitotemporal | 45 | −76 | −11 | 3.91 | 918 |
Right P Superior Temporal Sulcus | 54 | −46 | 7 | 3.79 | 351 |
Left P Superior Temporal Sulcus | −63 | −49 | 19 | 3.63 | 81 |
Right Anterior Inferior Frontal Gyrus | 51 | 32 | 10 | 3.76 | 81 |
Right Inferior Frontal Gyrus | 54 | 17 | 25 | 3.42 | 162 |
The average MNI coordinates of the highest positive
ROI | x(SD) | y(SD) | z(SD) | ||
---|---|---|---|---|---|
rEBA | Children | 49(3) | −72(3) | −1(3) | 24 |
Adults | 50(3) | −72(3) | −1(4) | 26 | |
lEBA | Children | −49(3) | −77(3) | −1(3) | 21 |
Adults | −48(3) | −75(3) | −1(1) | 26 | |
rFBA | Children | 42(2) | −42(4) | −19(4) | 24 |
Adults | 43(2) | −44(3) | −20(3) | 26 | |
lFBA | Children | −40(1) | −45(4) | −16(4) | 21 |
Adults | −41(2) | −44(3) | −19(3) | 26 | |
rpSTS | Children | 55(4) | −57(5) | 11(3) | 24 |
Adults | 56(4) | −56(5) | 11(3) | 26 | |
lpSTS | Children | −46(4) | −57(5) | 14(3) | 22 |
Adults | −46(4) | −56(5) | 14(3) | 26 |
For the three statistical thresholds that we considered (
rEBA | lEBA | rFBA | lFBA | rpSTS | lpSTS | |
---|---|---|---|---|---|---|
No. | 21 | 14 | 17 | 11 | 17 | 19 |
Mean (SD) | 1044.9 (712.8) | 494.1 (378) | 475.2 (540) | 291.6 (280.8) | 1439.1 (823.5) | 747.9 (726.3) |
No. | 26 | 22 | 25 | 18 | 25 | 25 |
Mean (SD) | 1539 (523.8) | 939.6 (569.7) | 912.6 (548) | 380.7 (353.7) | 1458 (634.5) | 688.5(586) |
No. | 18 | 12 | 12 | 6 | 17 | 13 |
Mean (SD) | 918 (672.3) | 342.9 (315.9) | 359.1 (502.2) | 248.4 (205.2) | 1139.4 (820.8) | 683 (650.7) |
No. | 26 | 21 | 24 | 12 | 24 | 23 |
Mean (SD) | 1304.1 (612.9) | 815.4 (548.1) | 696.6 (494.1) | 345.6 (251.1) | 1247.4 (666.9) | 459 (553.5) |
No. | 13 | 4 | 3 | 1 | 8 | 2 |
Mean (SD) | 432 (558.9) | 94.5 (135) | 270 (421.2) | 81 | 718.2 (577.8) | 54 (37.8) |
No. | 22 | 16 | 14 | 2 | 21 | 8 |
Mean (SD) | 899.1 (594) | 548.1 (442.8) | 286.2 (210.6) | 162 (37.8) | 731.7 (637.2) | 286.2 (281) |
In those participants, the average extent of activity was significantly higher in adults than in children at all three thresholds (Main effect of Age Group:
The peak
An Age Group × ROI ANOVA revealed a main effect of ROI (
Follow-up (
In addition we verified that this effect was not due to group differences in the processing of moving stimuli: none of the ROIs displayed an age-difference for the contrast Non-Bodies vs. Blank Screen (threshold of
Furthermore, we found no gender differences in any of our ROIs for either age group.
Using the peak-t data, a 3 × 2 × 2 mixed design ANOVA with within subject factors ROI (EBA/FBA/pSTS) and Hemisphere (Right/Left), and between subjects factor Age Group (Adults/Children) yielded a main effect of Hemisphere (
A further 3 × 2 × 2 mixed design ANOVA using a count of the contiguous voxels surrounding each peak yielded similar results. A main effect of Hemisphere (
As evidence is only relevant within groups (see above), the main effect of age is meaningless in the following analysis, while the interaction terms allow us to test for differences of models across age groups.
Figure
We investigated the development of the body-selective areas by comparing brain activity in primary-school children (age 6–11) and adults during passive viewing of body movements compared to object movements. In both groups we observed activity in similar regions to those reported in previous studies using static (Downing et al.,
A number of studies have confirmed that viewing static or dynamic bodies engages specific regions in the occipito-temporal cortex. Here we observe that these regions are also active in children. With regard to the EBA and the FBA, this confirms previous reports that had used static stimuli (Peelen et al.,
In addition, we observed activity in the pSTS in both adults and children. As previously stated, the pSTS is implicated in the processing of body related motion (Carter and Pelphrey,
We also observed activity in both groups in the IFG and inferior parietal lobule (IPL). These regions have been reported during both action observation and action execution (Grèzes and Decety,
Finally, both adults and children showed activity in the amygdala, bilaterally for the adults, while only the right hemisphere cluster reached significance in the children. Amygdala activity is commonly reported in fMRI studies of socially relevant facial expression perception (Kang et al.,
Our finding of a significant difference in both peak and extent activity in EBA, FBA and pSTS between children and adults contradicts previous work. Pelphrey et al. (
Using a more restricted ROI definition than Pelphrey et al. (
Another possibility to be considered is that top-down influences are responsible for the differences observed between children and adults. In other words, although the scans were kept deliberately short in an attempt to minimise a lack of attention in participants, attentional differences could still have arisen from the clips having different significance to adults and children. (Sinke et al.,
So, although we cannot rule out the possibility of some top-down influence, we can say with confidence that this is not the sole factor in our observed differences between adults and children.
Taken together with the developmental studies of face perception (Golarai et al.,
Previous studies showing increase in specificity of cortical activity over age (Carter and Pelphrey,
Previous studies had suggested that the body-selective regions in the visual cortex are “adult-like” by the age of 7 years old. Here, using a larger and more homogenous sample, we present evidence for the first time that 11 year-olds still exhibit reduced strength of activation in these areas compared to adults. We also find a significant increase with age in the extent of activation in the body-selective regions, but only in the right hemisphere. Furthermore, using MVB techniques we find evidence that patterns of neural representation do not differ between adults and children. Therefore we conclude that a significant quantitative, but not qualitative maturation occurs during adolescence for processing signals from the human body.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We would like to thank the ESRC for funding this research. Also Kate Sully, Eniko Zsoldos and Csenge Lantos for helping to recruit, scan and test the participants, and Mariske Kret for her contribution to creating the stimuli.
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