Reducing Individual Variation for fMRI Studies in Children by Minimizing Template Related Errors.

Spatial normalization is an essential process for group comparisons in functional MRI studies. In practice, there is a risk of normalization errors particularly in studies involving children, seniors or diseased populations and in regions with high individual variation. One way to minimize normalization errors is to create a study-specific template based on a large sample size. However, studies with a large sample size are not always feasible, particularly for children studies. The performance of templates with a small sample size has not been evaluated in fMRI studies in children. In the current study, this issue was encountered in a working memory task with 29 children in two groups. We compared the performance of different templates: a study-specific template created by the experimental population, a Chinese children template and the widely used adult MNI template. We observed distinct differences in the right orbitofrontal region among the three templates in between-group comparisons. The study-specific template and the Chinese children template were more sensitive for the detection of between-group differences in the orbitofrontal cortex than the MNI template. Proper templates could effectively reduce individual variation. Further analysis revealed a correlation between the BOLD contrast size and the norm index of the affine transformation matrix, i.e., the SFN, which characterizes the difference between a template and a native image and differs significantly across subjects. Thereby, we proposed and tested another method to reduce individual variation that included the SFN as a covariate in group-wise statistics. This correction exhibits outstanding performance in enhancing detection power in group-level tests. A training effect of abacus-based mental calculation was also demonstrated, with significantly elevated activation in the right orbitofrontal region that correlated with behavioral response time across subjects in the trained group

Retrieval of surface PM2.5 mass concentrations over North China using visibility measurements and GEOS-Chem simulations

Despite much effort made in studying human health associated with fine particulate matter (PM2.5), our knowledge about PM2.5 and human health from a long-term perspective is still limited by inadequately long data. Here, we presented a novel method to retrieve surface PM2.5 mass concentrations using surface visibility measurements and GEOS-Chem model simulations. First, we used visibility measurements and the ratio of PM2.5 and aerosol extinction coefficient (AEC) in GEOS-Chem to calculate visibility-inferred PM2.5 at individual stations (SC-PM2.5). Then we merged SC-PM2.5 with the spatial pattern of GEOS-Chem modeled PM2.5 to obtain a gridded PM2.5 dataset (GC-PM2.5). We validated the GC-PM2.5 data over the North China Plain on a 0.3125° longitude x 0.25° latitude grid in January, April, July and October 2014, using ground-based PM2.5 measurements. The spatial patterns of temporally averaged PM2.5 mass concentrations are consistent between GC-PM2.5 and measured data with a correlation coefficient of 0.79 and a linear regression slope of 0.8. The spatial average GC-PM2.5 data reproduce the day-to-day variation of observed PM2.5 concentrations with a correlation coefficient of 0.96 and a slope of 1.0. The mean bias is less than 12 μg/m3 (<14%). Future research will validate the proposed method using multi-year data, for purpose of studying long-term PM2.5 variations and their health impacts since 1980.Peer reviewe

Regions of significant between-group differences, coordinates (x, y, z) and T-values of local maxima detected across three templates for the bead-picture match task without and with SFN correction (<i>p</i> < 0.05, AlphaSim corrected).

<p>Regions of significant between-group differences, coordinates (x, y, z) and T-values of local maxima detected across three templates for the bead-picture match task without and with SFN correction (<i>p</i> < 0.05, AlphaSim corrected).</p

Between-group differences after SFN correction.

<p>Group differences in the rOFC region (marked) were consistently detected for all three templates when the SFN was included as a covariate (<i>p</i> < 0.05, AlphaSim corrected). Red indicates trained group > control group; blue indicates trained group < control group.</p

Whole-brain correlation maps of the SFN and BOLD contrast size.

<p>Voxel-based whole-brain correlation maps of the SFN and BOLD contrast size (<i>p</i> < 0.05, AlphaSim corrected) among the three templates.</p

Illustration of the bead-picture match task.

<p>Schematic illustration of 1 trial of the experiment design, the bead-picture match task.</p

Relationship between response time and BOLD contrast size in the rOFC.

<p>The relationship between the subject’s response time in the trained group and BOLD contrast size in the rOFC were correlated (<i>p</i> < 0.05) for the CCT (<i>r</i> = -0.54, <i>p</i> = 0.019) and SST (<i>r</i> = -0.51, <i>p</i> = 0.023) templates, but not for the MNI template (<i>r</i> = -0.39, <i>p</i> = 0.192) <b>(a)</b>. After SFN correction, significant correlations were observed across all three templates in the trained group (MNI: <i>r</i> = -0.59, <i>p</i> = 0.017; CCT: <i>r</i> = -0.59, <i>p</i> = 0.016; SST: <i>r</i> = -0.57, <i>p</i> = 0.02) <b>(b)</b>. However, no negative correlation was observed in the control group without (MNI: <i>r</i> = -0.48, <i>p</i> = 0.119; CCT: <i>r</i> = -0.52, <i>p</i> = 0.087; SST: <i>r</i> = -0.47, <i>p</i> = 0.151) or with (MNI: <i>r</i> = -0.46, <i>p</i> = 0.115; CCT: <i>r</i> = -0.49, <i>p</i> = 0.091; SST: <i>r</i> = -0.51, <i>p</i> = 0.077) SFN correction, regardless of the brain template used <b>(c, d)</b>.</p

Between-group differences in the rOFC region across three templates.

<p>Significant between-group differences in the rOFC region (marked by red circle) were observed using both the CCT and SST templates, but not the MNI template (<i>p</i> < 0.05, AlphaSim corrected). Red indicates trained group > control group; blue indicates trained group < control group.</p