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<p>Head motion during magnetic resonance imaging (MRI) examinations of patients with autism spectrum disorder (ASD) can influence the identification of brain differences as well as early diagnosis and precise MRI-based interventions for ASD. This study aims to address head motion issues in resting-state functional MRI (rs-fMRI) data by comparing various correction methods. Specifically, we evaluate the independent component analysis-based automatic removal of motion artifacts (ICA-AROMA) against traditional preprocessing pipelines, including head motion realignment parameters and global signal regression (GSR). Our dataset consisted of 306 participants, including 148 individuals with ASD and 158 participants with typical development (TD). We find that ICA-AROMA, particularly when combined with GSR and physiological noise correction, outperformed other strategies in differentiating ASD from TD participants based on functional connectivity (FC) analyses. The correlation of quality control with functional connectivity (QC-FC) is statistically significant in proportion and distance after applying each denoising pipeline. The mean FC between groups is significant for Yeo’s 17-Network in each denoising strategy. ICA-AROMA head motion correction outperformed other strategies, revealing more significant FC networks and distinct brain regions linked to the posterior cingulate cortex and postcentral gyrus. This suggests ICA-AROMA enhances fMRI preprocessing, aiding ASD diagnosis and biomarker development.</p>

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Improving brain difference identification in autism spectrum disorder through enhanced head motion correction in ICA-AROMA

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月21日

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  • Jianwu Guan,
  • Hai Li,
  • Qiansu Yang,
  • Yanwei Lv,
  • Lei Zhang,
  • Yi Wang,
  • Shijun Li

摘要

Head motion during magnetic resonance imaging (MRI) examinations of patients with autism spectrum disorder (ASD) can influence the identification of brain differences as well as early diagnosis and precise MRI-based interventions for ASD. This study aims to address head motion issues in resting-state functional MRI (rs-fMRI) data by comparing various correction methods. Specifically, we evaluate the independent component analysis-based automatic removal of motion artifacts (ICA-AROMA) against traditional preprocessing pipelines, including head motion realignment parameters and global signal regression (GSR). Our dataset consisted of 306 participants, including 148 individuals with ASD and 158 participants with typical development (TD). We find that ICA-AROMA, particularly when combined with GSR and physiological noise correction, outperformed other strategies in differentiating ASD from TD participants based on functional connectivity (FC) analyses. The correlation of quality control with functional connectivity (QC-FC) is statistically significant in proportion and distance after applying each denoising pipeline. The mean FC between groups is significant for Yeo’s 17-Network in each denoising strategy. ICA-AROMA head motion correction outperformed other strategies, revealing more significant FC networks and distinct brain regions linked to the posterior cingulate cortex and postcentral gyrus. This suggests ICA-AROMA enhances fMRI preprocessing, aiding ASD diagnosis and biomarker development.