Brain structure characteristics in children with attention-deficit/hyperactivity disorder elucidated using traveling-subject harmonization

doi:10.21203/rs.3.rs-4804399/v1

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Brain structure characteristics in children with attention-deficit/hyperactivity disorder elucidated using traveling-subject harmonization

https://doi.org/10.21203/rs.3.rs-4804399/v1

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Background

Brain imaging studies for attention-deficit/hyperactivity disorder (ADHD) have not always yielded consistent findings, potentially owing to measurement bias in magnetic resonance imaging (MRI) scanners. This study aimed to elucidate the structural brain characteristics in children with ADHD by addressing measurement bias in multi-site MRI data using the traveling-subject (TS) method.

Methods

The MRI data of 14 traveling subjects, 181 typical development (TD), and 117 ADHD children were collected from multiple sites. The TS method and ComBat were used to correct for measurement bias. Gray matter volumes were estimated using FreeSurfer, and the ADHD and TD groups were compared using mixed-effect models.

Results

Compared to raw data and ComBat-corrected data, the TS method significantly reduced measurement bias while maintaining sampling bias. The results from the TS-corrected data showed that the brain volume of the ADHD group was significantly smaller than that of the TD group in the bilateral middle temporal cortex, bilateral orbitofrontal cortex, right inferior frontal gyrus, right middle frontal gyrus, left inferior temporal gyrus, left precuneus cortex, and bilateral insular cortex. Brain regions that showed significant differences differed across the TS method, ComBat, and raw data. In contrast, notably significant volumetric reductions in the right middle temporal gyrus of ADHD patients were consistently observed across all methods.

Conclusions

The TS method effectively reduces measurement bias across MRI scanners, which may affect the findings of a multi-site study. The right middle temporal gyrus showed significant differences in all methods, suggesting it is a crucial region for ADHD.

Biological sciences/Neuroscience

Health sciences/Diseases/Psychiatric disorders/ADHD

ADHD

MRI

traveling-subject method

ComBat harmonization

gray matter volume

multi-site neuroimaging

Attention deficit hyperactivity disorder (ADHD) is a common disorder most frequently diagnosed in children and adolescents, with a prevalence of over 5% [1, 2]. It is characterized by inattention, overactivity, or impulsiveness symptoms that are age-inappropriate, persistent, and pervasive [1]. In the long term, ADHD is associated with a significant risk of educational failure, interpersonal problems, mental illness, and delinquency [3], placing a substantial burden on families as well as on health, social care, and criminal justice systems [4]. Therefore, it is important to clarify the neural basis of ADHD and establish effective early assessments and interventions.

Although many brain imaging studies have been conducted to elucidate the pathology of ADHD and abnormalities in brain structure in children with ADHD have been reported, these studies have not always yielded consistent findings [5]. Some studies have found that patients with ADHD showed decreased grey matter volume in the extensive parts of the cortex, including the dorsolateral prefrontal cortex, anterior cingulate cortex, inferior parietal lobule [6–12], and subcortical areas, including the amygdala, caudate nucleus, hippocampus, and putamen [7, 9], compared with control subjects. By contrast, another study found no significant reduction in gray matter volume after multiple comparison corrections [13]. Other studies on patients with ADHD have found larger gray matter volumes in the dorsolateral prefrontal, temporal, and intra-calcarine cortices, parietal lobule [13–15], left caudate nucleus, and putamen [10].

As described above, ADHD-related neuroimaging studies have provided conflicting evidence, implying that the neurobiological mechanisms underlying ADHD lack clarity, possibly because of small sample sizes, differences in magnetic resonance imaging (MRI) scanners, selection of participants, and so on[5]. To control for the MRI machines, previous studies have used the mixed-effect model to control for MRI machines as a random effect or used ComBat harmonization [16] to control site and MRI differences in large samples through meta-analyses and multi-institutional studies [17]. However, there are two problems with such methods: 1) it is difficult to extract all MRI differences and correct for measurement bias, and 2) it is possible to reduce sampling bias incorrectly [17]. To solve these problems, a new correction approach, the traveling-subject (TS) method, has been developed[17, 18]. The TS method is an innovative approach for correcting differences between MRI machines. Using the TS method, sampling bias and individual differences can be controlled for the same participant using MRI scans from multiple institutions, and the regression model only extracts the measurement bias between models, enabling more accurate data correction[17]. A previous study provided evidence for the applicability of the TS method to the structural brain[18].

Therefore, in this study, we first examine the validity of the TS method by computing the reliability of the harmonized brain structure in the TS dataset. Next, we apply the TS method to an independent dataset, including 181 children with typical development (TD) and 117 children with ADHD from hospitals of the University of Fukui, Osaka University, and Chiba University, and compare the measurement and sampling biases among the TS-corrected data, ComBat-corrected data, and raw data to assess the applicability of the TS method to the independent structural brain dataset. Though a previous study provided evidence of the applicability of the TS method in structural brain data[18], few studies applied the TS method to an independent dataset of brain structures. Moreover, this study will analyze the differences in brain structural characteristics between children with ADHD and children with TD and compare the results of TS-corrected, ComBat-corrected, and raw data. This is the first study to apply the TS method to correct for measurement bias from different MRI scanners in multi-site brain structure data, and the methods of accurate removal of measurement bias discovered in this study may help ensure the reproducibility of results from ADHD brain structure studies.

Participants

We used data collected from the Child Developmental MRI (CDM) project (https://bmjopen.bmj.com/content/13/6/e070157.long)[5]. Fourteen healthy TS participants (female = 7, age = 31.71 ± 8.20 years, right-handedness = 13) were scanned by four MRI machines at three sites within three months. This study used this TS dataset to correct measurement bias in each MRI machine. Participants with ADHD were recruited from hospitals of the University of Fukui, Osaka University, and Chiba University in Japan. Children with TD were recruited from the local community and assessed to ensure that none of them had developmental delays, received any special support education, or had a history of epilepsy or other psychiatric disorders. Participants with ADHD fulfilled the diagnostic criteria for ADHD according to the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5). Participants in the current study participated in the experiments from 2014 to 2022. None of the participants had a history of severe head trauma, neurological illness, or potential for hazards associated with MRI examinations (such as the presence of metal on the body surface or internal structures, pregnancy, claustrophobia, or fear of the dark). The demographic data of the participants with ADHD and TD are summarized in Table 1. All participants provided written informed consent prior to the experiment. This study was approved by the Research Ethics Committee of the University of Fukui Hospital (approval no. 20220601).

Table 1

Demographic data of the participants
	TD(n = 181)	ADHD(n = 117)	Difference between TD and ADHD (p-value)
Age (years)	12.65 ± 2.96	10.34 ± 2.30	p < 0.001
Sex
Male/Female(n)	122/59	109/8	p < 0.001
Handedness(n)
Right/Left/Ambidextrous (n)	165/12/3	103/13/1	p < 0.001
IQ	105.10 ± 11.60	95.17 ± 13.16	p < 0.001
MRI Machine(n)
Uni. of Fukui Ⅰ/Uni. of Fukui Ⅱ/Osaka Uni./Chiba Uni.(n)	37/118/12/45	19/98/19/1
ICV (cm³)	1386.29 ± 168.06	1389.53 ± 172.53	p > 0.05
TD = typical development
ADHD = attention-deficit/hyperactivity disorder
n = number
IQ = intelligence quotient
ICV = intracranial volume

MRI data acquisition

Participants were scanned with T1-weighted imaging at the University of Fukui, Osaka University, or Chiba University using a 3T GE Signa PET/MR scanner (General Electric HealthCare, Chicago, Illinois, USA; University of Fukui), 3 T GE Discovery MR750 scanner (General Electric HealthCare; University of Fukui or Chiba University), or 3 T GE Signa Architect scanner (General Electric HealthCare; Osaka University). The scanning parameters are provided in Table S1.

MRI analysis

The fully automated segmentation procedure implemented in FreeSurfer version 7.3.8 was used to estimate the gray matter volumes of the cortical and subcortical regions (http://surfer.nmr.mgh.harvard.edu/). The structural data were obtained using a standardized processing pipeline. Structural data using the Desikan-Killiany atlas-based classification of cortical regions and the atlas-based segmentation of subcortical regions was used in this study. Details of the segmentation method are provided by Fischl et al. (2012) [19].

Harmonization methods

We followed the TS harmonization method reported by Yamashita et al. [5], which extends a general linear model harmonization using the TS dataset. Python was used to estimate the measurement bias of each MRI machine using the TS dataset and reduce measurement bias from the ADHD dataset.

ComBat harmonization was also used to control measurement bias for comparison. ComBat was initially developed to correct the batch effect in genomics (20)[20] and has recently been applied to MRI datasets [18]. ComBat corrects a type of multivariate dataset using an empirical Bayesian estimation approach and can be used to analyze datasets obtained using different scanning machines. In the current study, we used the module “neuroCombat” to correct structural brain data using Python [21]

Measurement and sampling biases of different harmonization methods

To quantitatively investigate the validity of different harmonization methods in structural brain data, we calculated measurement biases, sampling biases, and disorder factors, following recommendations from Yamashita et al. [17]. We estimate the measurement and sampling biases using the following model:

Brain structures = x_m^Tm + x_s^Ts + x_d^Td + x_p^Tp + e

where m represents the measurement bias (4 machines × 1), s represents the sampling bias of TD (3 sites × 1) and ADHD (3 sites × 1), d represents the disorder factor (2 × 1), and p represents the participant factor (43 participants with repeated measures × 1). The brain structures are normalized before adding to the model. Measurement bias was calculated as the average of the effect sizes of the brain structures across different MRI scanners. The sampling biases in participants with TD and patients with ADHD were defined separately as the average effect sizes of the brain structures across different sites.

Statistical analyses

We used R (version 4.3.1; The R Foundation for Statistical Computing, Vienna, Austria) and Python (version 3.11.6; Python Software Foundation, Wilmington, DE, USA) for statistical analyses. First, we examined the necessity and validity of harmonization. We used a repeated-measures analysis of variance (ANOVA) on the TS dataset to examine the necessity of harmonization. Additionally, we computed the intraclass correlation coefficient (ICC) of the harmonized structural brain of the TS dataset, a descriptive statistic that can be used when quantitative measurements are made on units organized into groups (the individuals in this study) to examine validity. We compared the ICC among the raw, TS-corrected, and ComBat-corrected data of traveling subjects using ANOVA and a post-hoc test using Tukey’s Honest Significant Difference (HSD) method. We subsequently adapted TS and ComBat to correct the brain structure data in 181 children with TD and 117 children with ADHD from the CDM project. We calculated the measurement and sampling biases for TD and ADHD and compared these biases among TS-corrected, ComBat-corrected, and raw data using ANOVA and a post-hoc test using Tukey’s HSD method.

Additionally, we examined the association between brain structures and ADHD. First, we adapted a linear mixed-effects model to examine the relationship between brain structures harmonized by TS and ADHD. We analyzed this model using the R-package “lmertest.” For the mixed-effects model with a group (ADHD or TD) as the independent variable and brain structures as the dependent variable, we considered participants’ age, sex, handedness, intelligence quotient (IQ) measured using the Wechsler Intelligence Scale for Children (WISC), and intracranial volume of the brain as covariates. As some participants participated in the experiment multiple times, the subject ID (used to distinguish whether it was the same person) was modeled as a random effect. Raw brain structural data and brain structures harmonized using ComBat were also used in the mixed-effects model to compare children with ADHD and TD. Additionally, considering the differences in age, sex, and handedness between the ADHD and TD groups, we adapted the propensity score matching method to match the age, sex, and handedness of the TD group with the ADHD group (N = 94), and analyzed them similarly.

Necessity of harmonization

Table S1 summarizes the results of the repeated-measures ANOVA of the raw data from the TS dataset. The results showed that 40/82 brain regions showed significant differences among the same participants after false discovery rate (FDR) correction (Table S2 and Figure S1).

Validity of traveling-subject harmonization

First, we compared the ICC among TS-corrected, ComBat-corrected, and raw data from 14 traveling subjects. ANOVA showed a significant difference among the data (F(2,243) = 7.16, p < 0.01). The post-hoc test showed that the ICC of the TS-corrected data (M = 0.681, SD = 0.155) was larger than that of the raw data (M = 0.597, SD = 0.189) (Family-Wise Error [FWE] p < 0.05). There was no significant difference between TS-corrected and ComBat-corrected data (M = 683, SD = 0.151) of the traveling subjects (FWE p = 0.996). Additionally, the ICC of the ComBat-corrected data was larger than that of the raw data (FWE, p < 0.05).

Subsequently, we applied TS and ComBat to children from an independent dataset, the CDM project, including children with ADHD and TD, to correct measurement bias due to different MRI machines. We calculated and compared the measurement and sampling biases among TS-corrected, ComBat-corrected, and raw data. ANOVA showed that there were significant differences among the data in terms of measurement bias (F(2,243) = 21.898, p < 0.001) and sampling bias in the ADHD (F(2,243) = 9.073, p < 0.001) and TD (F(2,243) = 13.661, p < 0.001) groups. Subsequently, we conducted a post hoc test to compare the measurement and sampling biases among the different harmonization methods and raw data. The results showed that the measurement bias of TS-corrected data (M = 0.048, SD = 0.022) was smaller than that of the raw data (M = 0.056, SD = 0.024) (FWE p = 0.034), while it was larger than that of the ComBat-corrected data (M = 0.035, SD = 0.012) (FWE p < 0.001). The measurement bias of the ComBat-corrected data was significantly smaller than that of the raw data (FWE, p < 0.001) (Fig. 2A). Further, the TS-corrected data (M = 0.035, SD = 0.015) and raw data (M = 0.038, SD = 0.019) had a larger sampling bias than the ComBat-corrected data for the TD group (M = 0.028, SD = 0.012) (TS vs. ComBat: FWE p < 0.001; raw vs. ComBat: FWE p < 0.001), while no significant difference between TS-corrected data and raw data was observed (FWE p = 0.488) (Fig. 2B). The result of sampling bias in the ADHD group was similar to that in the TD group, with the TS-corrected data (M = 0.036, SD = 0.016) and raw data (M = 0.040, SD = 0.018) displaying a larger sampling bias than ComBat-corrected data (M = 0.028, SD = 0.011) (TS vs. ComBat: FWE p < 0.001; raw vs. ComBat: FWE p < 0.001), while no significant difference between TS-corrected data and raw data was observed (FWE p = 0.213) (Fig. 2C).

Comparison between ADHD and TD

We compared brain structures between the ADHD and TD groups. The results of the raw, TS-corrected, and ComBat-corrected data are summarized in Fig. 3 and Table S3. In the raw data, the ADHD group had significantly smaller brain volumes than the TD group in the right middle temporal gyrus (β = -0.255, FDR p = 0.001) and bilateral orbitofrontal cortex (lateral region) (right: β = -0.193, FDR p = 0.008; left: β = -0.226, FDR p = 0.001) (Fig. 3A), whereas in the TS-corrected data, the brain volume in the bilateral middle temporal gyrus (left: β = -0.179, FDR p = 0.031; right: β = -0.277, FDR p < 0.001), bilateral orbitofrontal cortex (lateral region) (right: β = -0.219, FDR p = 0.002; left: β = -0.246, FDR p < 0.001), right inferior frontal gyrus (orbital part) (β = -0.209, FDR p = 0.027), right middle frontal gyrus (rostral part) (β = -0.195, FDR p = 0.031), left inferior temporal gyrus (β = -0.169, FDR p = 0.037), left precuneus cortex (β = -0.155, FDR p = 0.037), and bilateral insular cortex (left: β = -0.188, FDR p = 0.037; right: β = -0.180, FDR p = 0.037) were significantly smaller than that in the TD group (Fig. 3B). Regarding ComBat-corrected data, the right middle temporal gyrus (β = -0.255, FDR p = 0.001) and bilateral orbitofrontal cortex (lateral region) (right: β = -0.193, FDR p = 0.008; left: β = -0.226, FDR p = 0.001) showed significant differences between the ADHD and TD groups (Fig. 3C).

To obtain more robust results, we matched the participants by propensity score, including age, sex, and handedness, and the data of 188 participants (94 ADHD and 94 TD) were included in the analysis. The results are summarized in the Supplementary Material. The result showed that only the right middle temporal gyrus (TS: β = -0.270, FDR p = 0.025; ComBat: β = -0.274, FDR p = 0.014) was smaller in children with ADHD compared with the children with TD in TS-corrected and ComBat-corrected data. For raw data, the right middle temporal gyrus (β = -0.277, FDR p = 0.013) and left orbitofrontal cortex (β = -0.255, FDR p = 0.043) showed significant differences between the ADHD and TD groups. The results are summarized in Table S4.

This study aimed to examine the structural brain differences between children with ADHD and children with TD by addressing the measurement bias in MRI data from different scanners using the TS method. This study demonstrates that the TS harmonization method can improve the reliability of structural brain data across multiple MRI scanners by applying the TS method to an independent dataset. This study also examined the differences between ADHD and TD to elucidate the brain structural characteristics of ADHD by comparing different harmonization methods and raw data. The results of TS-corrected data showed volumetric differences between the ADHD and TD groups in the cortical regions, including the bilateral middle temporal and bilateral orbitofrontal cortex, right inferior frontal gyrus, right middle frontal gyrus, left inferior temporal gyrus, left precuneus cortex, and bilateral insular cortex. In contrast, comparisons between ADHD and TD using ComBat-corrected and raw data showed different findings than when using TS-corrected data.

Before harmonization, the current study provided evidence that half of the brain regions showed significant differences among the same participants, demonstrating the necessity of harmonization for measurement bias in MRI scanners. The TS method, which estimates measurement bias more accurately [17], significantly reduces measurement bias compared with the raw data. This method involves considerable logistical effort, including TS recruitment and scan scheduling within a short duration; however, it corrects for measurement bias. The TS method addresses the variations introduced by different scanners and scanning protocols [17]. This is crucial for multi-site studies, wherein such variations can compromise the credibility of research findings. Although a previous study proved the validity of TS in structural brain data [18], few studies have applied TS to correct for measurement bias in an independent dataset. One of the contributions of this study is the validation of the TS method in structural brain data from an independent dataset, providing evidence that this method can be effectively used to harmonize multi-site MRI bias in structural brain data.

This study underscores the importance of harmonization methods in multi-site neuroimaging studies. Site differences consist of two types of bias: measurement and sampling. Measurement bias includes differences in the properties of MRI scanners, such as imaging variables, field strength, MRI manufacturers, and scanner models, whereas sampling bias refers to differences in participant groups between sites. The measurement bias can influence the accuracy of results in multi-site studies. Sampling bias can be considered as the biological part of site difference because of sampling from different subpopulations [17], which makes it possible to include the biological part of disorders. therefore it is inevitable that the data will contain sampling bias. Since the sampling bias included the biological part of the site difference, the reduction of sampling bias may influence the analysis of the difference in brain structures between ADHD and TD groups. The current study calculated the sampling bias is consistent to the previous study, which proved the existence of sampling bias in our dataset, and ComBat method could reduce it as measurement bias. Although ComBat can provide more power to reduce the measurement bias, the overcorrection of sampling bias may influence the accuracy of findings.

The results indicated that TS-corrected data exhibited a smaller measurement bias compared with the raw data, but a larger measurement bias than ComBat-corrected data. This suggests that while the TS method is effective in reducing some measurement biases inherent in multi-site data collection, the ComBat method achieves a greater reduction in measurement bias. This result is consistent with that of a previous study that proved the effectiveness of robust normalization methods for MRI data [16]. Our analysis also showed that ComBat-corrected data had a smaller sampling bias than the raw and TS-corrected data.

These results emphasize the importance of selecting appropriate correction methods for multi-site studies. While TS methods offer improvements over raw data and do not influence the sampling bias of different sites, the ComBat method’s rigorous statistical framework provides superior performance in reducing measurement bias while also reducing sampling biases such as measurement bias. Considering that reducing the sampling bias in patients influences the findings of the neural basis of a disorder [17] and that sampling bias does exist in raw data, harmonization using the TS method may be more appropriate for the current analysis.

Analysis using TS-corrected data revealed significant volumetric differences between the ADHD and TD groups in several brain regions. Notably, patients with ADHD exhibited smaller volumes in the bilateral middle temporal gyrus, bilateral orbitofrontal cortex, right inferior frontal gyrus, right middle frontal gyrus, left inferior temporal gyrus, left precuneus, and bilateral insular cortex. Reductions in the bilateral middle temporal gyrus and orbitofrontal cortex have been consistently reported. These regions are crucial for cognitive functions such as information processing, attention regulation, and emotional control, which are often impaired in ADHD[22, 23]. Even when comparing the ADHD group to a matched TD group, differences in the middle temporal gyrus remained significant. Moreover, the middle temporal gyrus was smaller in children with ADHD, even when using ComBat-corrected and raw data. This highlights the robustness of this finding across different correction methods. Castellanos and Aoki’s [24] suggestion that ADHD is a disorder of the default mode network (DMN) and Cubillo et al.’s [25] findings of frontostriatal dysfunction align with the current study’s results, particularly the consistent finding of abnormalities in the middle temporal gyrus [12]. The middle temporal gyrus is a critical node within the DMN, a network of brain regions that is typically more active at rest and less active during goal-directed tasks [26]. The middle temporal gyrus’s connectivity with these regions supports various DMN functions. The right middle temporal gyrus showed significant differences in all methods, emphasizing its potential role as a biomarker for ADHD. However, significant differences in brain volume were found in several brain regions among the TS-corrected, ComBat-corrected, and raw data. The TS method can reduce the measurement bias derived from multiple MRI scanners, and the results of the raw data may be influenced by this bias. Additionally, as the comparison of TS-corrected data with ComBat-corrected data revealed some differences in results, we considered that the excessive reduction of sampling bias when using ComBat may influence the accuracy of findings. Previous studies that did not use the TS method could not accurately correct these biases, which may have led to the discrepancies in the results.

In addition to the brain regions mentioned above, the right inferior frontal gyrus and the right middle frontal gyrus also show reduced volumes in patients with ADHD. These frontal regions are associated with executive functions, including inhibitory control and working memory, and their reduced volume may contribute to the characteristic impulsivity and inattention observed in ADHD [27]. Furthermore, studies have reported volume reductions in the left inferior temporal gyrus and left precuneus cortex in individuals with ADHD. The inferior temporal gyrus is involved in visual processing and object recognition, whereas the precuneus is involved in self-referential thinking and visuospatial processing. These structural differences may underlie some cognitive and perceptual challenges faced by patients with ADHD [28]. Finally, the bilateral insular cortex, which is involved in interoceptive awareness and emotional regulation, has also been shown to have a reduced volume in individuals with ADHD. The role of the insula in integrating emotional and cognitive processes suggests that its structural abnormalities could be linked to the emotional dysregulation frequently observed in ADHD[10]. Overall, these findings highlight a widespread pattern of cortical volume reduction in ADHD, which encompasses the regions involved in attention, executive function, emotional regulation, and cognitive processing. This broad network of structural abnormalities underscores the complexity of ADHD and the need for a comprehensive approach to its study and treatment. The observed structural differences in these brain regions align with previous neuroimaging studies, implicating alterations in the neural circuitry associated with attention, executive function, emotional regulation, and cognitive control in individuals with ADHD. However, these regions showed no significant differences between the ADHD and TD groups after comparing the ADHD group with a matched TD group, implying that these regions may be influenced by age, sex, and handedness.

Despite these promising results, this study had some limitations. The study sample may not fully represent the broader population of children with ADHD. The participants were drawn from specific geographical regions and clinical settings, which could limit the generalizability of the findings to other populations. Additionally, this study only examined the brain structure characteristics in children with ADHD elucidated using harmonization. For functional brain, Castellanos et al. (2016) [24]reported that children with ADHD have abnormalities in the default mode network and network of brain regions involved in the reward system, while a recent meta-analysis study of resting-state functional MRI reported that no findings specific to ADHD could be obtained [29]. Future studies should consider examining the functional brain characteristics in children with ADHD by using the TS method.

In conclusion, this study demonstrated the effectiveness of the TS method in correcting measurement bias in multi-site MRI studies involving children with ADHD. These findings highlight significant structural differences in the brains of patients with ADHD, particularly in the middle temporal gyrus, and underscore the importance of using robust harmonization techniques to improve the reproducibility and accuracy of neuroimaging research.

Acknowledgments

This work was supported by the Japan Society for the Promotion of Science through Grants-in-Aid for Scientific Research (KAKENHI; grant numbers : 24K16647 to Qiulu Shou, 21K02380 to Yoshifumi Mizuno, 23K12814 to Masatoshi Yamashita, 23H00949 to Kuriko Kagitani-Shimono, 22H01090, 23K02956, 23K07004, 24K21493 to Yoshiyuki Hirano), Kawano Masanori Memorial Public Interest Incorporated Foundation for Promotion of Pediatrics (AY 2023 to Yoshifumi Mizuno), research grants from the University of Fukui (AY 2023 to Qiulu Shou and Yoshifumi Mizuno), the Life Science Innovation Center, University of Fukui (AY 2023 to Qiulu Shou), Taiju Life Social Welfare Foundation (AY 2023 to Masatoshi Yamashita), and the Collaborative Research Program of the Collaborative Research Network for Asian Children with Developmental Disorders: MEXT Policy Initiative FY2022, under joint research conducted through the initiative.

We are grateful to Kenji Tsuchiya MD, PhD from the Research Centre for Child Mental Development, Hamamatsu University School of Medicine, Eiji Shimizu MD, PhD from the Research Centre for Child Mental Development, Chiba University, and Masako Taniike MD, PhD from United Graduate School of Child Development, Osaka University for their help for this research.

Disclosures

The authors declare no biomedical financial interests or potential conflicts of interest.

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The authors have declared there is NO conflict of interest to disclose

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Brain structure characteristics in children with attention-deficit/hyperactivity disorder elucidated using traveling-subject harmonization

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Version 1

Abstract

Figures

Introduction

Subjects and Methods

Participants

MRI data acquisition

MRI analysis

Harmonization methods

Measurement and sampling biases of different harmonization methods

Statistical analyses

Results

Necessity of harmonization

Validity of traveling-subject harmonization

Comparison between ADHD and TD

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1