Quantitative synthetic MRI reveals grey matter abnormalities in children with drug-naïve attention-deficit/hyperactivity disorder

To investigate the quantitative profiles of brain grey matter (GM) in pediatric drug-naïve ADHD patients using synthetic magnetic resonance imaging (SyMRI). A total of 37 drug-naïve pediatric ADHD and 27 age- and gender-matched healthy controls (HC) were enrolled in this study. Each subject underwen...

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Published inBrain imaging and behavior Vol. 16; no. 1; pp. 406 - 414
Main Authors Su, Shu, Chen, Yingqian, Dai, Yan, Lin, Liping, Qian, Long, Zhou, Qin, Zou, Mengsha, Zhang, Hongyu, Liu, Meina, Xiang, Xianhong, Yang, Zhiyun
Format Journal Article
LanguageEnglish
Published New York Springer US 01.02.2022
Springer Nature B.V
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ISSN1931-7557
1931-7565
1931-7565
DOI10.1007/s11682-021-00514-8

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Abstract To investigate the quantitative profiles of brain grey matter (GM) in pediatric drug-naïve ADHD patients using synthetic magnetic resonance imaging (SyMRI). A total of 37 drug-naïve pediatric ADHD and 27 age- and gender-matched healthy controls (HC) were enrolled in this study. Each subject underwent both SyMRI and conventional 3D T1-FSPGR scans. Quantitative parameters, T1 and T2 maps, were extracted from the SyMRI data. Between-group quantitative maps were compared using a general linear model analysis. Pearson correlation analysis was conducted to assess the association between significantly altered MR indices and clinical measurements in ADHD. Compared with the HC group, altered T1 and T2 relaxometry times in the ADHD group were mainly distributed in GM regions of the cerebellum, attention and execution control network, default mode network, and limbic areas. Moreover, the T1 value of the right cerebellum 8 was negatively correlated with the attention concentration level in ADHD (R = 0.140, P  = 0.0225). With regards to T2 map, the associations were observed between the attention level of ADHD patients and left fusiform gyrus (R = 0.251, P  = 0.0016), and right cerebellum crus2 (R = 0.142, P  = 0.0214). Altered T1, T2 values found in specific regions of GM, including cerebellum, attention and execution control network, default mode network, and limbic areas, may reveal widespread micromorphology changes, i.e., brain iron deficiency, low myelin content, and enlarged vascular interstitial space in ADHD patients. Thus, T1, T2 values might be promising imaging markers for future ADHD studies.
AbstractList To investigate the quantitative profiles of brain grey matter (GM) in pediatric drug-naïve ADHD patients using synthetic magnetic resonance imaging (SyMRI). A total of 37 drug-naïve pediatric ADHD and 27 age- and gender-matched healthy controls (HC) were enrolled in this study. Each subject underwent both SyMRI and conventional 3D T1-FSPGR scans. Quantitative parameters, T1 and T2 maps, were extracted from the SyMRI data. Between-group quantitative maps were compared using a general linear model analysis. Pearson correlation analysis was conducted to assess the association between significantly altered MR indices and clinical measurements in ADHD. Compared with the HC group, altered T1 and T2 relaxometry times in the ADHD group were mainly distributed in GM regions of the cerebellum, attention and execution control network, default mode network, and limbic areas. Moreover, the T1 value of the right cerebellum 8 was negatively correlated with the attention concentration level in ADHD (R = 0.140, P = 0.0225). With regards to T2 map, the associations were observed between the attention level of ADHD patients and left fusiform gyrus (R = 0.251, P = 0.0016), and right cerebellum crus2 (R = 0.142, P = 0.0214). Altered T1, T2 values found in specific regions of GM, including cerebellum, attention and execution control network, default mode network, and limbic areas, may reveal widespread micromorphology changes, i.e., brain iron deficiency, low myelin content, and enlarged vascular interstitial space in ADHD patients. Thus, T1, T2 values might be promising imaging markers for future ADHD studies.
To investigate the quantitative profiles of brain grey matter (GM) in pediatric drug-naïve ADHD patients using synthetic magnetic resonance imaging (SyMRI). A total of 37 drug-naïve pediatric ADHD and 27 age- and gender-matched healthy controls (HC) were enrolled in this study. Each subject underwent both SyMRI and conventional 3D T1-FSPGR scans. Quantitative parameters, T1 and T2 maps, were extracted from the SyMRI data. Between-group quantitative maps were compared using a general linear model analysis. Pearson correlation analysis was conducted to assess the association between significantly altered MR indices and clinical measurements in ADHD. Compared with the HC group, altered T1 and T2 relaxometry times in the ADHD group were mainly distributed in GM regions of the cerebellum, attention and execution control network, default mode network, and limbic areas. Moreover, the T1 value of the right cerebellum 8 was negatively correlated with the attention concentration level in ADHD (R = 0.140, P = 0.0225). With regards to T2 map, the associations were observed between the attention level of ADHD patients and left fusiform gyrus (R = 0.251, P = 0.0016), and right cerebellum crus2 (R = 0.142, P = 0.0214). Altered T1, T2 values found in specific regions of GM, including cerebellum, attention and execution control network, default mode network, and limbic areas, may reveal widespread micromorphology changes, i.e., brain iron deficiency, low myelin content, and enlarged vascular interstitial space in ADHD patients. Thus, T1, T2 values might be promising imaging markers for future ADHD studies.To investigate the quantitative profiles of brain grey matter (GM) in pediatric drug-naïve ADHD patients using synthetic magnetic resonance imaging (SyMRI). A total of 37 drug-naïve pediatric ADHD and 27 age- and gender-matched healthy controls (HC) were enrolled in this study. Each subject underwent both SyMRI and conventional 3D T1-FSPGR scans. Quantitative parameters, T1 and T2 maps, were extracted from the SyMRI data. Between-group quantitative maps were compared using a general linear model analysis. Pearson correlation analysis was conducted to assess the association between significantly altered MR indices and clinical measurements in ADHD. Compared with the HC group, altered T1 and T2 relaxometry times in the ADHD group were mainly distributed in GM regions of the cerebellum, attention and execution control network, default mode network, and limbic areas. Moreover, the T1 value of the right cerebellum 8 was negatively correlated with the attention concentration level in ADHD (R = 0.140, P = 0.0225). With regards to T2 map, the associations were observed between the attention level of ADHD patients and left fusiform gyrus (R = 0.251, P = 0.0016), and right cerebellum crus2 (R = 0.142, P = 0.0214). Altered T1, T2 values found in specific regions of GM, including cerebellum, attention and execution control network, default mode network, and limbic areas, may reveal widespread micromorphology changes, i.e., brain iron deficiency, low myelin content, and enlarged vascular interstitial space in ADHD patients. Thus, T1, T2 values might be promising imaging markers for future ADHD studies.
To investigate the quantitative profiles of brain grey matter (GM) in pediatric drug-naïve ADHD patients using synthetic magnetic resonance imaging (SyMRI). A total of 37 drug-naïve pediatric ADHD and 27 age- and gender-matched healthy controls (HC) were enrolled in this study. Each subject underwent both SyMRI and conventional 3D T1-FSPGR scans. Quantitative parameters, T1 and T2 maps, were extracted from the SyMRI data. Between-group quantitative maps were compared using a general linear model analysis. Pearson correlation analysis was conducted to assess the association between significantly altered MR indices and clinical measurements in ADHD. Compared with the HC group, altered T1 and T2 relaxometry times in the ADHD group were mainly distributed in GM regions of the cerebellum, attention and execution control network, default mode network, and limbic areas. Moreover, the T1 value of the right cerebellum 8 was negatively correlated with the attention concentration level in ADHD (R = 0.140, P  = 0.0225). With regards to T2 map, the associations were observed between the attention level of ADHD patients and left fusiform gyrus (R = 0.251, P  = 0.0016), and right cerebellum crus2 (R = 0.142, P  = 0.0214). Altered T1, T2 values found in specific regions of GM, including cerebellum, attention and execution control network, default mode network, and limbic areas, may reveal widespread micromorphology changes, i.e., brain iron deficiency, low myelin content, and enlarged vascular interstitial space in ADHD patients. Thus, T1, T2 values might be promising imaging markers for future ADHD studies.
Author Zhou, Qin
Xiang, Xianhong
Su, Shu
Dai, Yan
Lin, Liping
Yang, Zhiyun
Zou, Mengsha
Liu, Meina
Chen, Yingqian
Zhang, Hongyu
Qian, Long
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Keywords Attention-deficit/hyperactivity disorder
Quantitative MRI
Grey matter
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References Norman, L. J., Carlisi, C., Lukito, S., et al. (2016). Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: A comparative meta-analysis. JAMA Psychiatry, 73(8), 815–825.
Does, M. D. (2018). Inferring brain tissue composition and microstructure via MR relaxometry. NeuroImage, 182, 136–148.
Kupeli, A., Kocak, M., Goktepeli, M., et al. (2020). Role of T1 mapping to evaluate brain aging in a healthy population. Clinical Imaging, 59(1), 56–60.
Edwards, L. J., Kirilina, E., Mohammadi, S., et al. (2018). Microstructural imaging of human neocortex in vivo. NeuroImage, 182, 184–206.
Megna, R., Alfano, B., Lanzillo, R., et al. (2019). Brain tissue volumes and relaxation rates in multiple sclerosis: Implications for cognitive impairment. Journal of Neurology, 266(2), 361–368.
Deoni, S. C., Zinkstok, J. R., Daly, E., et al. (2015). White-matter relaxation time and myelin water fraction differences in young adults with autism. Psychological Medicine, 45(4), 795–805.
Caye, A., Rocha, T. B., Anselmi, L., et al. (2016). Attention-deficit/hyperactivity disorder trajectories from childhood to young adulthood: Evidence from a birth cohort supporting a late-onset syndrome. JAMA Psychiatry, 73(7), 705–712.
Cheng, Q. Q., Huang, J. X., Liang, J. Y., et al. (2020). Evaluation of abnormal iron distribution in specific regions in the brains of patients with Parkinson’s disease using quantitative susceptibility mapping and R2*mapping. Experimental and Therapeutic Medicine, 19(6), 3778–3786.
Wang, Y., Sun, K., Liu, Z., et al. (2020a). Classification of unmedicated bipolar disorder using whole-brain functional activity and connectivity: A radiomics analysis. Cerebral Cortex, 30(3), 1117–1128.
Albajara Saenz, A., Villemonteix, T., & Massat, I. (2019). Structural and functional neuroimaging in attention-deficit/hyperactivity disorder. Development Medicine and Child Neurology, 61(4), 399–405.
Wu, Z. M., Llera, A., Hoogman, M., et al. (2019). Linked anatomical and functional brain alterations in children with attention-deficit/hyperactivity disorder. Neuroimage Clinical, 23, 101851.
Hoogman, M., Bralten, J., Hibar, D. P., et al. (2017). Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: A cross-sectional mega-analysis. Lancet Psychiatry, 4(4), 310–319.
Samea, F., Soluki, S., Nejati, V., et al. (2019). Brain alterations in children/adolescents with ADHD revisited: A neuroimaging meta-analysis of 96 structural and functional studies. Neuroscience and Biobehavioral Reviews, 100, 1–8.
Bralten, J., Greven, C. U., Franke, B., et al. (2016). Voxel-based morphometry analysis reveals frontal brain differences in participants with ADHD and their unaffected siblings. Journal of Psychiatry & Neuroscience, 41(4), 272–279.
Sun, L., Cao, Q., Long, X., et al. (2012). Abnormal functional connectivity between the anterior cingulate and the default mode network in drug-naïve boys with attention deficit hyperactivity disorder. Psychiatry Research, 201(2), 120–127.
Wardlaw, J. M., Benveniste, H., Nedergaard, M., et al. (2020). Perivascular spaces in the brain: Anatomy, physiology and pathology. Nature Reviews Neurology, 16(3), 137–153.
Stuber, C., Morawski, M., Schafer, A., et al. (2014). Myelin and iron concentration in the human brain: A quantitative study of MRI contrast. NeuroImage, 93(Pt 1), 95–106.
Hagiwara, A., Hori, M., Yokoyama, K., et al. (2017). Utility of a multiparametric quantitative MRI Model that assesses myelin and edema for evaluating plaques, periplaque white matter, and normal-appearing white matter in patients with multiple sclerosis: A feasibility study. American Journal of Neuroradiology, 38(2), 237–242.
Jiang, W. H., Duan, K. K., Chen, J. Y., et al. (2019). Structural brain alterations and their association with cognitive function and symptoms in attention-deficit/hyperactivity disorder families. European Neuropsychopharmacology, 29, 1189–1190.
Ambrosino, S., de Zeeuw, P., Wierenga, L. M., et al. (2017). What can cortical development in attention-deficit/hyperactivity disorder teach us about the early developmental mechanisms involved? Cerebral Cortex, 27(9), 4624–4634.
Frodl, T., & Skokauskas, N. (2012). Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivity disorder indicates treatment effects. Acta Psychiatrica Scandinavica, 125(2), 114–126.
Cao, M., Shu, N., Cao, Q., et al. (2014). Imaging functional and structural brain connectomics in attention-deficit/hyperactivity disorder. Molecular Neurobiology, 50(3), 1111–1123.
Andica, C., Hagiwara, A., Hori, M., et al. (2019). Review of synthetic MRI in pediatric brains: Basic principle of MR quantification, its features, clinical applications, and limitations. Journal of Neuroradiology 46(4), 268–275.
Thapar, A., & Cooper, M. (2016). Attention deficit hyperactivity disorder. The Lancet, 387(10024), 1240–1250.
Lorio, S., Kherif, F., Ruef, A., et al. (2016). Neurobiological origin of spurious brain morphological changes: A quantitative MRI study. Human Brain Mapping, 37(5), 1801–1815.
Posner, J., Park, C., & Wang, Z. (2014). Connecting the dots: A review of resting connectivity MRI studies in attention-deficit/hyperactivity disorder. Neuropsychology Review, 24(1), 3–15.
Vogt, B. A. (2019). Cingulate impairments in ADHD: Comorbidities, connections, and treatment. Handbook of Clinical Neurology, 166, 297–314.
Warntjes, J. B., Leinhard, O. D., West, J., et al. (2008). Rapid magnetic resonance quantification on the brain: Optimization for clinical usage. Magnetic Resonance in Medicine, 60(2), 320–329.
Thomas, R., Sanders, S., Doust, J., et al. (2015). Prevalence of attention-deficit/hyperactivity disorder: A systematic review and meta-analysis. Pediatrics, 135(4), e994–1001.
Lei, X., Wang, Y., Yuan, H., et al. (2014). Neuronal oscillations and functional interactions between resting state networks. Human Brain Mapping, 35(7), 3517–3528.
Qian, X., Castellanos, F. X., Uddin, L. Q., et al. (2019). Large-scale brain functional network topology disruptions underlie symptom heterogeneity in children with attention-deficit/hyperactivity disorder. Neuroimage Clinical, 21, 101600.
Wang, L. J., Li, S. C., Kuo, H. C., et al. (2020b). Gray matter volume and microRNA levels in patients with attention-deficit/hyperactivity disorder. European Archives of Psychiatry and Clinical Neuroscience, 270(8), 1037–1045.
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References_xml – reference: Kupeli, A., Kocak, M., Goktepeli, M., et al. (2020). Role of T1 mapping to evaluate brain aging in a healthy population. Clinical Imaging, 59(1), 56–60.
– reference: Thapar, A., & Cooper, M. (2016). Attention deficit hyperactivity disorder. The Lancet, 387(10024), 1240–1250.
– reference: Norman, L. J., Carlisi, C., Lukito, S., et al. (2016). Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: A comparative meta-analysis. JAMA Psychiatry, 73(8), 815–825.
– reference: Thomas, R., Sanders, S., Doust, J., et al. (2015). Prevalence of attention-deficit/hyperactivity disorder: A systematic review and meta-analysis. Pediatrics, 135(4), e994–1001.
– reference: Wang, Y., Sun, K., Liu, Z., et al. (2020a). Classification of unmedicated bipolar disorder using whole-brain functional activity and connectivity: A radiomics analysis. Cerebral Cortex, 30(3), 1117–1128.
– reference: Wu, Z. M., Llera, A., Hoogman, M., et al. (2019). Linked anatomical and functional brain alterations in children with attention-deficit/hyperactivity disorder. Neuroimage Clinical, 23, 101851.
– reference: Posner, J., Park, C., & Wang, Z. (2014). Connecting the dots: A review of resting connectivity MRI studies in attention-deficit/hyperactivity disorder. Neuropsychology Review, 24(1), 3–15.
– reference: Wang, L. J., Li, S. C., Kuo, H. C., et al. (2020b). Gray matter volume and microRNA levels in patients with attention-deficit/hyperactivity disorder. European Archives of Psychiatry and Clinical Neuroscience, 270(8), 1037–1045.
– reference: Cao, M., Shu, N., Cao, Q., et al. (2014). Imaging functional and structural brain connectomics in attention-deficit/hyperactivity disorder. Molecular Neurobiology, 50(3), 1111–1123.
– reference: Edwards, L. J., Kirilina, E., Mohammadi, S., et al. (2018). Microstructural imaging of human neocortex in vivo. NeuroImage, 182, 184–206.
– reference: Megna, R., Alfano, B., Lanzillo, R., et al. (2019). Brain tissue volumes and relaxation rates in multiple sclerosis: Implications for cognitive impairment. Journal of Neurology, 266(2), 361–368.
– reference: Wardlaw, J. M., Benveniste, H., Nedergaard, M., et al. (2020). Perivascular spaces in the brain: Anatomy, physiology and pathology. Nature Reviews Neurology, 16(3), 137–153.
– reference: Lei, X., Wang, Y., Yuan, H., et al. (2014). Neuronal oscillations and functional interactions between resting state networks. Human Brain Mapping, 35(7), 3517–3528.
– reference: Samea, F., Soluki, S., Nejati, V., et al. (2019). Brain alterations in children/adolescents with ADHD revisited: A neuroimaging meta-analysis of 96 structural and functional studies. Neuroscience and Biobehavioral Reviews, 100, 1–8.
– reference: Bralten, J., Greven, C. U., Franke, B., et al. (2016). Voxel-based morphometry analysis reveals frontal brain differences in participants with ADHD and their unaffected siblings. Journal of Psychiatry & Neuroscience, 41(4), 272–279.
– reference: Hoogman, M., Bralten, J., Hibar, D. P., et al. (2017). Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: A cross-sectional mega-analysis. Lancet Psychiatry, 4(4), 310–319.
– reference: Vogt, B. A. (2019). Cingulate impairments in ADHD: Comorbidities, connections, and treatment. Handbook of Clinical Neurology, 166, 297–314.
– reference: Qian, X., Castellanos, F. X., Uddin, L. Q., et al. (2019). Large-scale brain functional network topology disruptions underlie symptom heterogeneity in children with attention-deficit/hyperactivity disorder. Neuroimage Clinical, 21, 101600.
– reference: Ambrosino, S., de Zeeuw, P., Wierenga, L. M., et al. (2017). What can cortical development in attention-deficit/hyperactivity disorder teach us about the early developmental mechanisms involved? Cerebral Cortex, 27(9), 4624–4634.
– reference: Hagiwara, A., Hori, M., Yokoyama, K., et al. (2017). Utility of a multiparametric quantitative MRI Model that assesses myelin and edema for evaluating plaques, periplaque white matter, and normal-appearing white matter in patients with multiple sclerosis: A feasibility study. American Journal of Neuroradiology, 38(2), 237–242.
– reference: Warntjes, J. B., Leinhard, O. D., West, J., et al. (2008). Rapid magnetic resonance quantification on the brain: Optimization for clinical usage. Magnetic Resonance in Medicine, 60(2), 320–329.
– reference: Does, M. D. (2018). Inferring brain tissue composition and microstructure via MR relaxometry. NeuroImage, 182, 136–148.
– reference: Frodl, T., & Skokauskas, N. (2012). Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivity disorder indicates treatment effects. Acta Psychiatrica Scandinavica, 125(2), 114–126.
– reference: Sun, L., Cao, Q., Long, X., et al. (2012). Abnormal functional connectivity between the anterior cingulate and the default mode network in drug-naïve boys with attention deficit hyperactivity disorder. Psychiatry Research, 201(2), 120–127.
– reference: Cheng, Q. Q., Huang, J. X., Liang, J. Y., et al. (2020). Evaluation of abnormal iron distribution in specific regions in the brains of patients with Parkinson’s disease using quantitative susceptibility mapping and R2*mapping. Experimental and Therapeutic Medicine, 19(6), 3778–3786.
– reference: Stuber, C., Morawski, M., Schafer, A., et al. (2014). Myelin and iron concentration in the human brain: A quantitative study of MRI contrast. NeuroImage, 93(Pt 1), 95–106.
– reference: Andica, C., Hagiwara, A., Hori, M., et al. (2019). Review of synthetic MRI in pediatric brains: Basic principle of MR quantification, its features, clinical applications, and limitations. Journal of Neuroradiology 46(4), 268–275.
– reference: Caye, A., Rocha, T. B., Anselmi, L., et al. (2016). Attention-deficit/hyperactivity disorder trajectories from childhood to young adulthood: Evidence from a birth cohort supporting a late-onset syndrome. JAMA Psychiatry, 73(7), 705–712.
– reference: Deoni, S. C., Zinkstok, J. R., Daly, E., et al. (2015). White-matter relaxation time and myelin water fraction differences in young adults with autism. Psychological Medicine, 45(4), 795–805.
– reference: Jiang, W. H., Duan, K. K., Chen, J. Y., et al. (2019). Structural brain alterations and their association with cognitive function and symptoms in attention-deficit/hyperactivity disorder families. European Neuropsychopharmacology, 29, 1189–1190.
– reference: Albajara Saenz, A., Villemonteix, T., & Massat, I. (2019). Structural and functional neuroimaging in attention-deficit/hyperactivity disorder. Development Medicine and Child Neurology, 61(4), 399–405.
– reference: Lorio, S., Kherif, F., Ruef, A., et al. (2016). Neurobiological origin of spurious brain morphological changes: A quantitative MRI study. Human Brain Mapping, 37(5), 1801–1815.
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Snippet To investigate the quantitative profiles of brain grey matter (GM) in pediatric drug-naïve ADHD patients using synthetic magnetic resonance imaging (SyMRI). A...
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SubjectTerms Abnormalities
Attention Deficit Disorder with Hyperactivity - diagnostic imaging
Attention deficit hyperactivity disorder
Biomedical and Life Sciences
Biomedicine
Brain
Brain - diagnostic imaging
Brain Mapping
Cerebellum
Cerebral Cortex
Child
Correlation analysis
Gray Matter - diagnostic imaging
Humans
Hyperactivity
Iron deficiency
Magnetic Resonance Imaging
Medical imaging
Myelin
Neuroimaging
Neuropsychology
Neuroradiology
Neurosciences
Nutrient deficiency
Original Research
Patients
Pediatrics
Pharmaceutical Preparations
Psychiatry
Substantia grisea
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Title Quantitative synthetic MRI reveals grey matter abnormalities in children with drug-naïve attention-deficit/hyperactivity disorder
URI https://link.springer.com/article/10.1007/s11682-021-00514-8
https://www.ncbi.nlm.nih.gov/pubmed/34491528
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