Regional analysis

Processed task data were mapped to n = 33 regions per hemisphere based on the Deskian-Killany atlas\cite{Desikan_2006} and for subcortical structures for each hemisphere: namely thalamus, caudate, putamen, pallidum, hippocampus, amygdala, accumbens area and ventral diencephalon. Segmentaions of these structures were based on FreeSurfer (aseg) sub-cortical parcellations. These "imaging derived phenotypes" are provided by the ABCD study team in the data-release. Subjects with activation values above or below 3 standard deviations were excluded. The mean activation per region was subsequently calculated across all subjects. A one-tailed t-test was used to assess difference from 0 and these results were used to create a map of regions of significant activation (at \(\alpha\) < 0.05). 
Statistical Analysis
Multivariate linear regression was used to assess the relationship between PPS, depressive symptoms and fMRI activity in the MID task. The following additional covariates were included in all analysis; age, sex, race, BMI, household income , parental education, mean frame-wise motion during task. In order to control for variance due to site, we included the variable "device serial number" which indicates the individual MR scanners used to acquire data (n = 25). The number of subjects acquired per scanner varied from a minimum of 9 to a maximum of 283. Scanner device explained a significant (\(\alpha\) at 0.05) amount of variance of PPS (F = 7.7, p < 0.00001), but not number of depressive symptoms. 
False discovery rate (FDR) methods  were used to correct results for multiple comparisons across the brain regions.

Results

Prodromal psychosis scores

PPS were significantly predicted by age (\(\beta\) = -0.05, t = -2.3, p = 0.02), but were not predicted by sex, parental education, in-scanner motion or BMI levels. A significant amount of variance in PPS were further predicted by device (F24, 1887 = 7.4, p < 0.001), household income (F2, 1887 = 7.9, p < 0.001) and race (F3, 1887 = 5.4, p = 0.001).

Number of depressive symptoms

Number of depressive symptoms was significantly predicted by household income (lower vs. middle \(\beta\) = -0.18, t = -2.4, p = 0.02; lower vs. higher \(\beta\) = -0.3, t = -3.7, p =0.0003) and BMI levels (lean vs. overweight \(\beta\) = -0.01, t = -0.17, p = 0.87; lean vs. obese \(\beta\) = 0.19, t = 2.9, p = 0.004). 

Mean reward anticipation activation

Results of mean activation for top 10  brain regions are reported in Table \ref{517637} (full list available in Supplementary Material).