Quantitative susceptibility mapping shows lower brain iron content in children with autism

Tang, Shilong; Xu, Ye; Liu, Xianfan; Chen, Zhuo; Zhou, Yufeng; Nie, Lisha; He, Ling

doi:10.1007/s00330-020-07267-w

Cited by 29 publications

(13 citation statements)

References 35 publications

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“…In previous studies, only a few researchers explored whether the brain blood flow or gray matter of ASD children was abnormal by 3D-pseudo continuous Arterial Spin-Labeled (3D-pcASL), quantitative susceptibility mapping (QSM) and diffusion kurtosis imaging (DKI) sequences ( 7 , 12 – 15 ). For example, Tang et al ( 13 ) found a decrease in iron content in some brain regions of ASD children. Mori et al ( 14 ) confirmed that the cerebral blood flow in some brain areas of ASD children decreased.…”

Section: Introductionmentioning

confidence: 99%

Application of Quantitative Magnetic Resonance Imaging in the Diagnosis of Autism in Children

Tang

Nie²,

Liu

et al. 2022

Front. Med.

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ObjectiveTo explore the application of quantitative magnetic resonance imaging in the diagnosis of autism in children.MethodsSixty autistic children aged 2–3 years and 60 age- and sex-matched healthy children participated in the study. All the children were scanned using head MRI conventional sequences, 3D-T1, diffusion kurtosis imaging (DKI), enhanced T2*- weighted magnetic resonance angiography (ESWAN) and 3D-pseudo continuous Arterial Spin-Labeled (3D-pcASL) sequences. The quantitative susceptibility mapping (QSM), cerebral blood flow (CBF), and brain microstructure of each brain area were compared between the groups, and correlations were analyzed.ResultsThe iron content and cerebral blood flow in the frontal lobe, temporal lobe, hippocampus, caudate nucleus, substantia nigra, and red nucleus of the study group were lower than those in the corresponding brain areas of the control group (P < 0.05). The mean kurtosis (MK), radial kurtosis (RK), and axial kurtosis (AK) values of the frontal lobe, temporal lobe, putamen, hippocampus, caudate nucleus, substantia nigra, and red nucleus in the study group were lower than those of the corresponding brain areas in the control group (P < 0.05). The mean diffusivity (MD) and fractional anisotropy of kurtosis (FAK) values of the frontal lobe, temporal lobe and hippocampus in the control group were lower than those in the corresponding brain areas in the study group (P < 0.05). The values of CBF, QSM, and DKI in frontal lobe, temporal lobe and hippocampus could distinguish ASD children (AUC > 0.5, P < 0.05), among which multimodal technology (QSM, CBF, DKI) had the highest AUC (0.917) and DKI had the lowest AUC (0.642).ConclusionQuantitative magnetic resonance imaging (including QSM, 3D-pcASL, and DKI) can detect abnormalities in the iron content, cerebral blood flow and brain microstructure in young autistic children, multimodal technology (QSM, CBF, DKI) could be considered as the first choice of imaging diagnostic technology.Clinical Trial Registration[http://www.chictr.org.cn/searchprojen.aspx], identifier [ChiCTR2000029699].

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Section: Introductionmentioning

confidence: 99%

Application of Quantitative Magnetic Resonance Imaging in the Diagnosis of Autism in Children

Tang

Nie²,

Liu

et al. 2022

Front. Med.

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“…Both R2* and QSM have demonstrated good reproducibility (Feng, Deistung, & Reichenbach, 2018; Spincemaille et al, 2019) and strong correlations with postmortem iron measures (Langkammer et al, 2010; Langkammer et al, 2012; Sun et al, 2015). These techniques suggest that excess iron accumulation or loss is associated with various neurodevelopmental (e.g., ADHD—Cortese et al, 2012; autism—Tang et al, 2020) and neurodegenerative disorders (e.g., Parkinson's disease, Huntington's disease, Multiple Sclerosis, and dementia; for review see Wang et al, 2017). In healthy subjects, studies have demonstrated correlations between R2* or QSM of basal ganglia structures and various aspects of brain function including working memory in children and adults (Darki, Nemmi, Möller, Sitnikov, & Klingberg, 2016) processing speed (Hect, Daugherty, Hermez, & Thomason, 2018) and IQ in children (Carpenter et al, 2016; Hect et al, 2018), cognitive switching (Daugherty, Hoagey, Kennedy, & Rodrigue, 2019), executive function, and psychomotor speed in adults (Ghadery et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

R2* and quantitative susceptibility mapping in deep gray matter of 498 healthy controls from 5 to 90 years

Treit

Naji

Seres

et al. 2021

Human Brain Mapping

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Putative MRI markers of iron in deep gray matter have demonstrated age related changes during discrete periods of healthy childhood or adulthood, but few studies have included subjects across the lifespan. This study reports both transverse relaxation rate (R2*) and quantitative susceptibility mapping (QSM) of four primary deep gray matter regions (thalamus, putamen, caudate, and globus pallidus) in 498 healthy individuals aged 5–90 years. In the caudate, putamen, and globus pallidus, increases of QSM and R2* were steepest during childhood continuing gradually throughout adulthood, except caudate susceptibility which reached a plateau in the late 30s. The thalamus had a unique profile with steeper changes of R2* (reflecting additive effects of myelin and iron) than QSM during childhood, both reaching a plateau in the mid‐30s to early 40s and decreasing thereafter. There were no hemispheric or sex differences for any region. Notably, both R2* and QSM values showed more inter‐subject variability with increasing age from 5 to 90 years, potentially reflecting a common starting point in iron/myelination during childhood that diverges as a result of lifestyle and genetic factors that accumulate with age.

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“…The highest iron concentrations in the adult brain can be found within the deep grey nuclei [8] and the iron content changes during normal aging [9][10][11]. Iron deficiencies are the most frequent cause of deficiency-related diseases, leading possibly to hypomyelination, delayed cognitive or motor development in children [12][13][14]. While iron deposition does have a paramagnetic effect on tissue susceptibility, the effect of myelination is diamagnetic.…”

Section: Introductionmentioning

confidence: 99%

Analysis of deep grey nuclei susceptibility in early childhood: a quantitative susceptibility mapping and R2* study at 3 Tesla

et al. 2021

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Purpose Aging is the most significant determinant for brain iron accumulation in the deep grey matter. Data on brain iron evolution during brain maturation in early childhood are limited. The purpose of this study was to investigate age-related iron deposition in the deep grey matter in children using quantitative susceptibility (QSM) and R2* mapping. Methods We evaluated brain MRI scans of 74 children (age 6–154 months, mean 40 months). A multi-echo gradient-echo sequence obtained at 3 Tesla was used for the QSM and R2* calculation. Susceptibility of the pallidum, head of caudate nucleus, and putamen was correlated with age and compared between sexes. Results Susceptibility changes in all three nuclei correlated with age (correlation coefficients for QSM/R2*: globus pallidus 0.955/0.882, caudate nucleus 0.76/0.65, and putamen 0.643/0.611). During the first 2 years, the R2* values increased more rapidly than the QSM values, indicating a combined effect of iron deposition and myelination, followed by a likely dominating effect of iron deposition. There was no significant gender difference. Conclusion QSM and R2* can monitor myelin maturation processes and iron accumulation in the deep grey nuclei of the brain in early life and may be a promising tool for the detection of deviations of this normal process. Susceptibility in the deep nuclei is almost similar early after birth and increases more quickly in the pallidum. The combined use of QSM and R2* analysis is beneficial.

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Quantitative susceptibility mapping shows lower brain iron content in children with autism

Cited by 29 publications

References 35 publications

Application of Quantitative Magnetic Resonance Imaging in the Diagnosis of Autism in Children

Application of Quantitative Magnetic Resonance Imaging in the Diagnosis of Autism in Children

R2* and quantitative susceptibility mapping in deep gray matter of 498 healthy controls from 5 to 90 years

Analysis of deep grey nuclei susceptibility in early childhood: a quantitative susceptibility mapping and R2* study at 3 Tesla

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