GoKawiilMaterials
The individuals in the study were selected from five imaging datasets: the BCP49, the development, young-adult and ageing cohorts of the Human Connectome Project (HCP-D, HCP-YA and HCP-A, respectively)50 and the Healthy Brain Network (HBN) dataset51. Individuals in the BCP ranged from 16 days to 6 years old, with 557 individuals scanned at 1,095 unique time points before gradient-based quality control. We initially used 652 individuals (age 5.6–21.9 years) from HCP-D, 1,206 individuals (age 22–37 years) from HCP-YA, 725 individuals (age 36–100 years) from HCP-A and 772 individuals from the HBN (age 5.6–21.9 years). Across all five cohorts, our dataset included 3,912 individuals.
Ethics
All data were obtained from publicly available studies (BCP, HCP-D, HCP-YA, HCP-A and HBN). Each contributing study received ethics approval from its local institutional review board and obtained informed consent (and assent where appropriate). The present work analysed de-identified data and complied with all relevant data use agreements.
Quality control
We excluded infant data with excessive motion, cutting the BCP dataset from 557 individuals with 1,095 time points to 343 individuals with 760 time points. For the entire lifespan dataset, we used visual and clustering-based gradient quality control, excluding problematic gradients from the main analysis. Using this procedure, we excluded one additional data point from the BCP for a total of 759 time points and 343 individuals (158 male individuals, 176 female individuals and 9 unreported). We excluded 2 sets of gradients from the HCP-D for a total of 650 individuals (301 male individuals and 349 female individuals). We excluded 2 individuals from the HBN dataset for a total of 770 individuals (458 male individuals, 275 female individuals and 37 unreported). Preprocessing-related failures (for example, unsuccessful surface reconstruction or unusable resting-state fMRI data) led to the exclusion of 88 HCP-YA participants. Visual inspection and k-means clustering of Procrustes-aligned gradients identified an additional 50 corrupted gradient sets, yielding a final HCP-YA sample of 1,068 participants (482 male and 586 female). We excluded no data from the HCP-A (725 individuals; 319 male and 406 female). These decisions were justified by examining individual gradients as well as their associated metrics (dispersion, range and similarity to template) in combination with k-means clustering applied to the set of all individual gradients. In total, our final analysis included 3,972 unique gradient sets from 3,556 individuals. Supplementary Fig. 3 shows the distribution of participants’ ages by cohort after quality control. Cohort-wise median mean Power’s frame-wise displacement (FD) after quality control is shown in Supplementary Table 3.
fMRI preprocessing
We used an rs-fMRI preprocessing pipeline that is consistent with the HCP52. The preprocessing pipeline includes (1) head motion correction using FMRIB Software Library (FSL) mcflirt; (2) echo-planar imaging (EPI) distortion correction using FSL topup to generate distortion correction deformation fields using a pair of reverse phase-encoded field maps (that is, anterior–posterior and posterior–anterior or left–right and right–left); (3) rigid (six degrees of freedom) registration of a single-band reference (SBref) image to field maps; (4) rigid boundary-based registration (BBR) of distortion correcting the SBref image to the corresponding T1-weighted (T1w) images, with pre-alignment using a mutual information cost function; and (5) one-step sampling using combined deformation fields and translation matrices, producing motion- and distortion-corrected fMRI data in each individual’s native space (T1w space).
We denoised the fMRI data before further analysis. First, we detrended the data by high-pass filtering with a cut-off frequency of 0.001 Hz to remove slow signal drift. Next, we used independent component analysis based automatic removal of motion artefacts (ICA-AROMA) denoising53 to remove any residual motion artefacts. This involved performing a 150-component ICA on the fMRI data and classifying each component as either BOLD signal or artefact on the basis of high-frequency contents, correlation with realignment parameters from head motion correction, edge effects and cerebrospinal fluid fractions. Independent components classified as artefacts are (aggressively) removed by regression.
Structural and diffusion preprocessing
... continue reading