Ethics statement

The experimental protocol and informed consent for this study were approved by the research ethics review board of Zhejiang University in China. All children and their parents were provided informed written consent and signed it before the experiment. We acquired signed written consent to publish their MRI images.

Participants

Twenty-nine healthy children (8.01±0.59 years, 15 males) participated in this study on the training effect of abacus-based mental calculation (AMC). These young volunteers from a single district of Qiqihar city in China constituted two groups: sixteen in a trained group (7.92±0.67 years, 9 males; 7.88±0.85 years, 7 females) with one-year of AMC training, and thirteen children in a control group (8.17±0.37 years, 6 males; 7.88±0.44 years, 7 females) without any abacus knowledge. The AMC group was able to perform calculations with no physical abacus but by mentally visualizing the abacus. It is believed this training system triggers a new strategy to process numbers [25]. The two groups were matched with respect to other educational background.

Experimental design

The volunteers were asked to perform a block-designed working memory task to compare two pictures (Fig 1). The paradigm consisted of nine blocks, and each block had a 24-s task condition alternating with a 16-s rest condition. Every task block included six trials of 4 s each. Each trial started with a ‘#’ for 1000 ms, followed by an abacus-bead picture for 500 ms, a fixation cross ‘+’ for the next 1000 ms, a second abacus-bead picture for 500 ms, and another fixation cross ‘+’ for the final 1000 ms. The subjects were asked to compare the two bead pictures in the scanner, decide whether they were identical, and respond by pressing a button as soon as possible after the second abacus-bead picture was presented. This task is based on the classic n-back task, which is a widely used paradigm in cognitive neuroscience to assess working memory [26]. Detailed information about this task was reported in our previous fMRI study [27]. The behavioral response time was recorded and used in the correlation analysis with the BOLD signal. The resting-state block had only a fixation cross ‘+’ on the screen for the entire 16 s period.

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Fig 1. Illustration of the bead-picture match task.

Schematic illustration of 1 trial of the experiment design, the bead-picture match task.

https://doi.org/10.1371/journal.pone.0134195.g001

MRI acquisition

MR data were acquired on a 1.5-T MRI clinical scanner (Achieva, Philips) with an 8-channel head coil in the first hospital of Qiqihar city. The functional images were collected using a single-shot gradient-echo echo-planar imaging (EPI) sequence with the following parameters: TR/TE = 2000/50 ms, flip angle = 90°, FOV = 230 mm × 230 mm, matrix = 64 × 64, slice thickness/gap = 5 mm/0.8 mm, and 22 slices in an interleaved ascending order to cover the whole brain. These images were set obliquely and parallel to the anterior and posterior commissure line. The entire functional scan included 180 measurements and lasted for 360 s. Another high-resolution anatomical scan was also acquired for each subject using a 3D fast field echo (FFE) sequence with the following parameters: TR/TE = 25/4.6 ms, flip angle = 15°, FOV = 256 mm × 256 mm, acquisition matrix = 256×256, reconstruction voxel size = 1 × 1× 1 mm³, 150 slices in the sagittal plane.

Data pre-processing

Imaging processing was performed using SPM8 (http://www.fil.ion.ucl.ac.uk/spm) on Matlab (version 2010b; The MathWorks, Natick, MA, USA). The first three scans were discarded to remove the impact of magnetization stabilization. Data were subsequently corrected for slice-timing differences. Functional image volumes were then registered and aligned to the first time point using a rigid-body transformation to correct head motion. Although it was difficult for our young volunteers to remain motionless during the task, none were found to have head motion greater than 2.0 mm in displacement or 2° in rotation in any direction. After motion-correction, it was necessary to perform spatial normalization and transform the native brain image into a standard template for subsequent voxel-wise group comparisons. During normalization, an individual’s native anatomical image was registered into a template space, and the transformation was applied to EPI images. This transformation involved a 12-parameter linear affine transformation followed by non-linear deformation [28, 29]. Three templates were adopted separately in this procedure, and it was our primary purpose to evaluate the performance of the template based on a small sample size. The templates included the standard MNI template (MNI) from the SPM package, a Chinese children template (CCT) proposed by Luo et al that claims to better represent the Chinese children population [6], and a study-specific template (SST) obtained from the average of all subjects in this study, as performed previously by Huang et al [5]. All three templates followed the MNI coordinate to enable a comparison of the activation results among templates. The normalized images were further smoothed with a Gaussian kernel of 6 mm full width at half maximum to enhance the signal-to-noise ratio.

SFN

The SFN value of the affine transformation matrix was used to measure the Euclidean difference between the template and a native brain. The initial transformation matrix obtained from normalization in SPM was a 4 × 4 square matrix. As mentioned above, the last column and row reflecting the translation part were discarded, and the SFN was derived from the remaining 3 × 3 matrix. By definition, the SFN was equal to the sum of the absolute squares of the matrix elements. We used the norm function with the type of ‘fro’ in Matlab, and took its square value as the SFN for implementation.

We analyzed the dependent measures of the SFN by employing repeated ANOVAs, including Templates (three templates: MNI, CCT, and SST) as a within-subject factor and Groups (trained and control) as a between-subject factor. Greenhouse-Geisser adjustments were used if sphericity could not be assumed. Follow-up post-hoc multiple comparisons were computed using Bonferroni correction.

Statistical process

Task activation was estimated using a two-level statistical model in SPM. In the first level, individual activation maps were computed with a general linear model [30]. A high-pass filter with a cut-off frequency of 1/128 Hz was applied to remove slow drift. Head-motion parameters were treated as confounds and included as covariates in the linear regression. For each subject, the effect of functional task was then estimated, and the contrast of the task condition was obtained. Group-level comparisons were then conducted with individual contrasts to determine within-group activation by one-sample t-test and between-group differences by two-sample t-test. Furthermore, these group comparisons were also re-analyzed with the SFN included as a covariate as a source of individual variability. The SFNs were separated into two groups and centered on the mean value of each group.

AlphaSim correction was applied in the final activation maps to control the false-positive rate with thresholds of adjusted p < 0.05 (corresponding to voxel-wise threshold of p < 0.01, FWHM = 6 mm) and a cluster size of 34 voxels. This correction was computed using the REST program (http://restfmri.net/forum/rest) [31].

Region of interest (ROI) analysis

The right OFC (rOFC) was of particular interest in this study. The ROIs of the OFC were defined in two ways. For the correlation analysis between the SFN and regional BOLD contrast size, both the left and right ROIs were defined from the AAL template. The averaged BOLD contrast size for each subject was calculated from the selected ROI. In a further investigation of the relationship between BOLD activity and behavioral response time, we selected the significant cluster by between-group differences inside of the rOFC.

The significant relationships reported in this correlation analysis remained if the outliers (Cook’s distance > 1) were excluded from the correlation analyses, suggesting that these correlations were not a statistical artifact.

Index of SFN

We determined that the SFN differed significantly depending on the template used during normalization. The SFN was generated from an affine transformation matrix and defined as the similarity between each individual image and the template. We further investigated whether the SFN affected the BOLD contrast size. Whole-brain analysis revealed significant correlations between the SFN and BOLD contrast size across all subjects for the three templates respectively (Fig 5), and the most correlated voxels were located near the tissue boundaries and in regions in the frontal lobe and cerebellum. The area of the OFC was also included (Fig 4a). The biological meaning of this correlation is unclear, but it is most like errors and bias introduced by image registration. Interestingly, in contrast to the MNI template, the correlations between the SFN and BOLD contrast size in the rOFC were weaker when CCT and SST were used in normalization (Fig 4). On the other hand, we also observed that the SFNs were significantly smaller in the CCT and SST than those in the MNI template. The degree of similarity between the individual and template is inversely proportional to the SFN values [32]. Our results show that employing CCT and SST in the normalization process could increase the similarity between individual images and the template and improve the characterization of children’s brain structure compared to the MNI template. Consequently, registration bias might increase when the MNI adult template is used for fMRI analysis in children, compared to CCT and SST, in line with a previous volumetric study [1]. Therefore, it might be feasible to use a study-specific template, even if the study population has limited sample, or to use a population-matched template to control and minimize the influence of registration errors associated with template usage.

Template choice and SFN correction

We hypothesized that a study-specific brain template of small sample size could increase the detection power of between-group activation differences. In our bead-picture match task, co-localizing activations were detected, including the bilateral inferior occipital gyrus, left fusiform, right inferior parietal lobule and left cingulate gyrus, in all three templates. However, the child-based templates, SST and CCT, detected between-group activation differences in regions of the rOFC; such differences were not observed when the adult-based MNI template was utilized. The observed activation differences in the rOFC are due solely to the use of a different template in the normalization procedure. The MNI template is an average brain template constructed from 305 young Western adult brains. However, our volunteers were all children from a single region of China, whose brains differ considerably in shape and size from Western adult brains. In line with previous studies [5, 33], our study demonstrated that a population-matched template (CCT) reduced registration errors and improved the results. Importantly, the CCT and SST produced very similar results. It is remarkable that the SST from a small sample was able to capture brain characteristics and exhibit superior performance in our children data analysis.

Due to registration errors and bias would be introduced by individual variation, it is necessary to characterize anatomic differences and minimize individual variation in study populations. The SFN was introduced and evaluated as an individual index in this study. Our study shows that using the SFN as a covariate is practical in group statistics. As an example, group activity was consistently in the rOFC regardless of the template used after SFN was introduced as a covariate in the design matrix. This result is consistent with previous morphometric analyses using specific statistical correction procedures that suggested that employing brain anatomic information as a covariate in group analyses may be valuable in specific populations [34, 35]. Another study also reported that gray-matter volume changes in aging could be detected after whole-brain anatomic difference correction [36], a finding replicated in our children functional data. Thus, our findings suggest that inclusion of the SFN as a covariate might be effective for reducing the effect of individual variation on fMRI results in group analyses and enhance the detection power in group statistics. Interestingly, significant between-group differences were identified in the left frontal inferior orbital region, only when the SFN was included as a covariate in the CCT template. This difference could only be detected with an uncorrected P-value of 0.05 when the two other templates were applied. This could probably be the particular merit provided by CCT, which is based on a larger cohort of subjects than SST.

Another major evidence of the current study is about the robustness of the correlation results between regional BOLD activity and behavioral response time across subjects. We observed an enhancement of the correlation strength in the trained group after adopting a proper template for normalization or after employing the SFN correction (Fig 7a and 7b). Note that this correlation was not sufficiently strong to reach a significance level of p < 0.05 when a traditional MNI template was applied. This suggests the importance of a proper brain template choice in children data analysis. Large differences between individual brains and the template increase the risks of misalignment and inaccuracy in the registration process. Our results also demonstrated that the SFN correction might sufficiently enrich the power of statistical analyses. The plausible result of an improved negative correlation in the rOFC under all three templates confirms usefulness of this simple correction. Taken together, these observations demonstrate the importance of template selection and post-hoc correction with the SFN.

Right OFC

The major findings in this study are associated with the rOFC. Optimizing the normalization process and group statistics increased our confidence in the group activation differences between the trained and control groups. Specifically, the one sample t-test in Fig 2 demonstrates that the rOFC was only activated in the trained group, and not the control. Further investigation revealed that individual BOLD amplitudes in the trained group were correlated with the average response times during the task. No significant correlation was observed in the control group, who had no experience with an abacus. The BOLD signal changes in the rOFC of control group were near zero, with a tendency to be negative. It indicates that this region was not involved when the controls were performing the task during functional scans. Hence, it is no surprise that the BOLD contrast size and behavior scores were not correlated in this region for the control group, even after our proposed correction (Fig 7c and 7d). By contrast, the rOFC was one of the activated nodes in the trained group, as shown in Fig 2, and was also a major point of difference between these two groups (Figs 3 and 6 and Table 1).

Our results confirm that the two groups, with or without abacus training, might use a different strategy when performing our bead-picture match task, and this conclusion can be emerged from the results of between-group activation differences in the rOFC. In the trained group, subjects have been trained to master the abacus for arithmetic when beads or digits are displayed. In the bead-picture match task, trained subjects might process bead image information by automatically converting it into digit information before completing the task. This automatic strategy has been demonstrated closely related to a visuospatial process [24]. Previous studies have observed functional differences between trained and control groups in the right hemisphere, and suggested the lateralization of the non-verbal AMC strategy [26]. These prior results are consistent with our finding of significant differences in the rOFC (Fig 2). In addition, there was a significant negative correlation between BOLD contrast size in the rOFC and behavior performance across subjects only in the trained group (Fig 7). The negative correlation indicates that subjects with better performance utilized more cognitive resources. Therefore, our findings of between-group activation differences and correlation results in the rOFC are not artifacts of this study. Our data emphasize the important role of the rOFC in our bead-picture match task in the trained AMC group.