Emergence and organization of adult brain function throughout child development

Abstract

Adult cognitive neuroscience has guided the study of human brain development by identifying regions associated with cognitive functions at maturity. The activity, connectivity, and structure of a region can be compared across ages to characterize the developmental trajectory of the corresponding function. However, developmental differences may reflect both the maturation of the function and also its organization across the brain. That is, a function may be present in children but supported by different brain regions, leading its maturity to be underestimated. Here we test the presence, maturity, and localization of adult functions in children using shared response modeling, a machine learning approach for functional alignment. After learning a lower-dimensional feature space from fMRI activity as adults watched a movie, we translated these shared features into the anatomical brain space of children 3-12 years old. To evaluate functional maturity, we correlated this reconstructed activity with children's actual fMRI activity as they watched the same movie. We found reliable correlations throughout cortex, even in the youngest children. The strength of the correlation in the precuneus, inferior frontal gyrus, and lateral occipital cortex predicted chronological age. These age-related changes were driven by three types of developmental trajectories: emergence from absence to presence, consistency in anatomical expression, and reorganization from one anatomical region to another. We also found evidence that the processing of pain-related events in the movie underwent reorganization across childhood. This data-driven, naturalistic approach provides a new perspective on the development of functional neuroanatomy throughout childhood.

Keywords: Brain maturation; Child development; Developmental neuroscience; Machine learning; Naturalistic perception; Shared response modeling; fMRI.

Fig. 1.

Schematic of the signal reconstruction pipeline. An SRM was trained on the first half of the fMRI data from a group of adults (N = 33; 2 example matrices shown) and then each of the children (N = 122) was fit into this space. Adult fMRI data from the second half of the movie (i.e., not used to train the model) were transformed into the shared space and averaged. This shared adult activity was then projected into each child’s brain and correlated with their actual activity. This procedure was then repeated for training on the second half and testing on the first half.

Fig. 2.

Reconstruction of adult function in children. In all brain plots, the strength of signal reconstruction is denoted by color and only regions that survived statistical thresholding through cluster correction are plotted. (A) Signal reconstruction for a group of adults predicting an independent adult’s functional activity is reliable throughout much of the brain. (B) Signal reconstruction remains statistically reliable (though numerically weaker) for adults predicting functional activity in children ranging from 3–12 years old.

Fig. 3.

Relationship between signal reconstruction and age. (A) Brain regions with a reliable correlation between signal reconstruction of adult function in a child’s brain activity and the child’s chronological age across all 122 children, colored by the strength of the relationship. (B) Similar regions are found in leave-one-child-out iterations of the age prediction analysis. Yellow–red colors signify regions that were significant in a majority of iterations. Using signal reconstruction scores from these regions, we could accurately predict the held-out child’s chronological age.

Fig. 4.

Signal reconstruction of adult features was statistically reliable even in the youngest children, but spread anatomically and grew in strength throughout childhood. To quantify this developmental change, we correlated the unthresholded voxelwise signal reconstruction in each age group with that of adults, revealing increasing maturity: 3.5–4.5 years, r = 0.507; 4.5–5.5 years, r = 0.587; 5.5–7.5 years, r = 0.601; 7.5–9.5 years, r = 0.646; 9.5–12.3, r = 0.797.

Fig. 5.

Trajectories of functional development within and across brain regions. (A–C) To understand the nature of developmental changes in signal reconstruction, we predicted activity from one adult feature at a time rather than all features. We used a functional parcellation to identify which regions expressed a given feature most strongly in each age group. Parcels with significant signal reconstruction of adult features within each age group (p<0.05, corrected) were ranked by the strength of the reconstruction. For ease of visualization, here we color up to the top five parcels for each feature and age group. The anatomical labels for these parcels were obtained from the Talairach atlas. Three example adult features are depicted across ages, illustrating developmental trajectories we refer to as emergence (Feature 4), consistency (Feature 6), and reorganization (Feature 7). The top five parcels for the remaining features are depicted in Fig. S5 and all parcels that are significant for each group are displayed in Fig. S6. Asterisks indicate which parcels differed significantly in signal reconstruction of each feature across age groups (p <0.05, corrected; Fig. S7).

Fig. 6.

Distribution of resampled correlation values between the average representation of shared features in a group of adults and two of the three cognitive constructs defined in Jacoby et al. (2016). (A) For mental events, we did not find a significant relationship with any of the shared features of adults. However, we did find a significant positive correlation between Feature 9 and pain events (p <0.05, corrected). This feature was strongly expressed in the cuneus of adults and older children, the postcentral gyrus in the middle child age group, and the posterior cingulate of younger children. (B) Visualization of the z-scored time courses of individual features during the second half of the movie and the two cognitive constructs. Orange lines represent the HRF-convolved mentalizing events, and purple lines represent the HRF-convolved pain events. The feature time course lines are colored similarly to (A).