Article Figures & Data
Figures
- Figure 1.
Growth curves for individual children's (n = 43) standardized score on the WIAT-II Numerical Operations subtest. Lines depict linear regression of Numerical Operations standardized score versus age across longitudinal visits for each child. Children who made gains relative to their peers over time are plotted in green. Those who declined or remained stable are plotted in red. The annualized change (slopes of individual regression lines) is used in subsequent regression and confirmatory cross-validation analyses.
- Figure 2.
a–d , Structural integrity in regions within ( a ) VTOC (FG), ( b ) PPC (IPS), ( c ) PFC [DLPFC, VLPFC, and pre/primary motor cortex (PMC)], and ( d ) visual cortex (cuneus) predicts longitudinal gains in numerical abilities. Children with higher volume in these regions at age 8 had steeper growth curves from childhood to adolescence. GMV, Gray matter volume.
- Figure 3.
Intrinsic functional connectivity of brain regions identified in the structural MRI analysis predicts gains in numerical abilities. a , b , The FG connectivity map had the most predictive voxels, displayed here in ( a ) an overlay of all functional connectivity maps and ( b ) quantified in a bar graph. c , Individual connectivity maps of regions within the VTOC, PPC, PFC, and visual cortex are displayed with the seed shown in pink. ROIs for functional connectivity analyses were generated by placing 6 mm spheres at the cluster peaks (most-significant voxels) from the VBM-based predictive analysis. PMC, Premotor cortex.
Tables
- Table 1.
Brain areas where structural integrity predicts longitudinal growth in numerical abilities a
Region Brodmann's area Number of voxels (mm3) Peak Z-score Peak MNI coordinates (mm) Math Reasoning Word Reading Reading Comprehension Working memory x y z VTOC Left FG 37 97 3.62 −36 −58 −12 p = 0.14, r = 0.13 p = 0.99, r = −0.34 p = 0.14, r = 0.12 p = 0.84, r = −0.2 PPC Left IPS 39 161 3.78 −42 −40 54 p = 0.78, r = −0.19 p = 0.93, r = −0.24 p = 0.67, r = −0.14 p = 0.98, r = −0.29 PFC Left DLPFC 46 215 3.97 −52 41 13 p = 0.54, r = −0.09 p = 0.60, r = −0.11 p = 0.84, r = −0.21 p = 0.44, r = −0.04 Left DLPFC 45 92 3.5 −40 27 6 p = 0.97, r = −0.27 p = 0.52, r = −0.07 p = 0.92, r = −0.24 p = 0.74, r = −0.18 Left VLPFC 10 516 4.10 −27 45 −8 p = 0.22, r = −0.07 p = 0.50, r = −0.06 p = 0.84, r = −0.21 p = 0.23, r = 0.07 Right pre/primary motor cortex 6 698 4.14 34 8 46 p = 0.007, r = 0.34 p = 0.32, r = 0.01 p = 0.05, r = 0.22 p = 0.67, r = −0.14 Visual cortex Right cuneus 17 551 4.27 18 −96 13 p = 0.25, r = −0.05 p = 0.45, r = −0.05 p = 0.11, r = 0.16 p = 0.95, r = −0.27 Right cuneus 18 116 3.79 22 −94 25 p = 0.08, r = −0.55 p = 0.85, r = −0.19 p = 0.93, r = −0.24 p = 0.93, r = −0.24 -
↵ a Clusters in which gray matter volume is predictive of numerical gains. Math and reading measures are subtests from the WIAT-II. The Working Memory Test Battery for Children was used to measure working memory.
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- Table 2.
Brain regions in which intrinsic functional connectivity with the FG is predictive of gains in numerical skills
Region Brodmann's area Number of voxels (mm3) Peak Z-score Peak MNI coordinates (mm) x y z VTOC Right FG 19 752 3.77 24 −72 −2 PPC Bilateral precuneus 7 141 3.62 4 −54 46 PFC Left DLPFC 46 106 3.39 −38 28 36 Right DLPFC 44 167 5.31 40 8 26 Visual cortex Right cuneus 18 363 4.08 16 −88 28 Temporal cortex Right anterior temporal cortex 21 404 3.64 48 6 −20 Left anterior temporal cortex 21 208 3.29 −50 2 −24 Subcortical Right basal ganglia (caudate) 116 3.58 10 8 6 Medial Mid-cingulate 23 271 5.25 2 −8 36
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