Archives

  • 2018-07
  • 2018-10
  • 2018-11
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • 2020-05
  • 2020-06
  • 2020-07
  • 2020-08
  • 2020-09
  • 2020-10
  • 2020-11
  • 2020-12
  • 2021-01
  • 2021-02
  • 2021-03
  • 2021-04
  • 2021-05
  • 2021-06
  • 2021-07
  • 2021-08
  • 2021-09
  • 2021-10
  • 2021-11
  • 2021-12
  • 2022-01
  • 2022-02
  • 2022-03
  • 2022-04
  • 2022-05
  • 2022-06
  • 2022-07
  • 2022-08
  • 2022-09
  • 2022-10
  • 2022-11
  • 2022-12
  • 2023-01
  • 2023-02
  • 2023-03
  • 2023-04
  • 2023-05
  • 2023-06
  • 2023-08
  • 2023-09
  • 2023-10
  • 2023-11
  • 2023-12
  • 2024-01
  • 2024-02
  • 2024-03
  • 2024-04

    • br The Pediatric Imaging Neurocognition and Genetics PING

    2018-10-25


    The Pediatric Imaging, Neurocognition, and Genetics (PING) Project Since these early observations, much elegant imaging work has been done revealing robust indices of ongoing biological development of the JSH-23 that can be monitored noninvasively in children. Many of these neurodevelopmental biomarkers and functional imaging phenotypes show very protracted trajectories of change with age and exhibit regional variation. Though a number of studies have now examined age-effects on measures of cortical architecture during the postnatal years, and a few have included longitudinal data, details about the pattern of change have been inconsistent, probably in part because of modest sample sizes, different age-ranges examined, and variable imaging protocols and analysis methods. Recently, investigators throughout the country collaborated on the large, multisite Pediatric Imaging, Neurocognition, and Genetics (PING) project in which well over 1000 children were studied. This imaging genetics study of children between the ages of 3 and 20 enrolled participants at 10 sites throughout the US. The design was cross-sectional and involved only a limited number of developmental and cognitive phenotypes, but the dataset is now shared freely with the research community and has been accessed by people all around the world, through a web-based tool called the PING Portal (pingstudy.ucsd.edu). Users can apply for access by filing a data use request and a data use agreement on the Portal. Approved users can download the dataset for offline analysis and/or explore the data using advanced interactive statistical and visualization utilities in the Data Exploration Module (Brown et al., 2012; Bartsch et al., 2014; Jernigan et al., 2015). This dataset provides several advantages for defining postnatal changes on imaging phenotypes, including: the large number of participants studied with harmonized and standardized methods; the wide age range of the participants (and therefore the long developmental trajectories that can be estimated); and the availability of genome-wide genotyping, which among other things made it possible to compute sensitive measures of genetic ancestry, in the form of 6 “genetic ancestry factors” (Alexander et al., 2009; Jernigan et al., 2015). Thus in this dataset it has been possible to estimate age-differences and extrapolated trajectories while holding constant the scanner used, socioeconomic status of the family, and genetic ancestry, variables that could otherwise introduce cohort effects in a cross-sectional study. Application of extended FreeSurfer methods for computational morphometry produced a set of cortical biomarkers that, for example, isolated variability in surface area from variability in apparent cortical thickness. Fig. 4 shows plots produced with the Data Exploration Module of the PING Portal of age-differences (and smooth functions of age) for two global cortical phenotypes, total surface area and mean thickness (across the entire cortical surface). The effect of age on surface area is nonmonotonic; surface area expands during early childhood years, and expansion decelerates during middle childhood giving way to gradual contraction during adolescence and thereafter. In contrast, the apparent thickness of the cortex exhibits (mostly linear) monotonic decrease across the entire range, from 3 to 21 years. Rate of change maps for surface area are shown in Fig. 5 and confirm that the global pattern is observed across the entire cortical surface, i.e., early expansion followed by contraction during adolescence. However, there is some evidence that different regions may exhibit different trajectories. Note that the maps of change at ages 4 and 6 are coded differently than those at ages 8 and above to better visualize regional variability in the generally higher rates of expansion at these earlier ages. To further highlight regional differences we computed the smooth age functions from the GAM models for 3 larger ROIs generated by the 12-cluster genetic parcellation of surface area (shown in Fig. 1 above adapted from Chen et al., 2012). Shown in Fig. 6 are the trajectories for (covariate-adjusted) mean expansion coefficients for parcels in the dorsolateral prefrontal cortex (blue), dorsomedial frontal cortex (red), and occipital cortex (green); labeled as parcels 2, 3, and 12, respectively. Comparing the models visually suggests that early to middle childhood expansion is more rapid in the dorsolateral prefrontal than in the occipital parcel.