Quantitative Relaxometry Assessment of Brain Microstructural Abnormality of Preschool Children With Autism Spectrum Disorder With Synthetic Magnetic Resonance Imaging.

OBJECTIVE: This study aimed to perform an assessment of brain microstructure in children with autism aged 2 to 5 years using relaxation times acquired by synthetic magnetic resonance imaging. MATERIALS AND METHODS: Thirty-four children with autism spectrum disorder (ASD) (ASD group) and 17 children with global developmental delay (GDD) (GDD group) were enrolled, and synthetic magnetic resonance imaging was performed to obtain T1 and T2 relaxation times. The differences in brain relaxation times between the 2 groups of children were compared, and the correlation between significantly changed T1/T2 and clinical neuropsychological scores in the ASD group was analyzed. RESULTS: Compared with the GDD group, shortened T1 relaxation times in the ASD group were distributed in the genu of corpus callosum (GCC) ( P = 0.003), splenium of corpus callosum ( P = 0.002), and right thalamus (TH) ( P = 0.014), whereas shortened T2 relaxation times in the ASD group were distributed in GCC ( P = 0.011), left parietal white matter ( P = 0.035), and bilateral TH (right, P = 0.014; left, P = 0.016). In the ASD group, the T2 of the left parietal white matter is positively correlated with gross motor (developmental quotient [DQ] 2) and personal-social behavior (DQ5), respectively ( r = 0.377, P = 0.028; r = 0.392, P = 0.022); the T2 of the GCC was positively correlated with DQ5 ( r = 0.404, P = 0.018); and the T2 of the left TH is positively correlated with DQ2 and DQ5, respectively ( r = 0.433, P = 0.009; r = 0.377, P = 0.028). All significantly changed relaxation values were not significantly correlated with Childhood Autism Rating Scale scores. CONCLUSIONS: The shortened relaxometry times in the brain of children with ASD may be associated with the increased myelin content and decreased water content in the brain of children with ASD in comparison with GDD, contributing the understanding of the pathophysiology of ASD. Therefore, the T1 and T2 relaxometry may be used as promising imaging markers for ASD diagnosis.

Application of diffusion kurtosis imaging in neonatal brain development.

Background: Deviations from the regular pattern of growth and development could lead to early childhood diseases, suggesting the importance of evaluating early brain development. Through this study, we aimed to explore the changing patterns of white matter and gray matter during neonatal brain development using diffusion kurtosis imaging (DKI). Materials and methods: In total, 42 full-term neonates (within 28 days of birth) underwent conventional brain magnetic resonance imaging (MRI) and DKI. The DKI metrics (including kurtosis parameters and diffusion parameters) of white matter and deep gray matter were measured. DKI metrics from the different regions of interest (ROIs) were evaluated using the Kruskal-Wallis test and Bonferroni method. Spearman rank correlation analysis of the DKI metrics was conducted, and the age at the time of brain MRI acquisition was calculated. The subjects were divided into three groups according to their age at the time of brain MRI acquisition: the first group, neonates aged ≤7 days; the second group, neonates aged 8-14 days; and the third group, neonates aged 15-28 days. The rate of change in DKI metrics relative to the first group was computed. Results: The mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), and fractional anisotropy (FA) values showed positive correlations, whereas mean diffusion (MD), axial diffusion (Da), and radial diffusion (Dr) values showed negative correlations with the age at the time of brain MRI acquisition. The absolute correlation coefficients between MK values of almost all ROIs (except genu of the corpus callosum and frontal white matter) and the age at the time of brain MRI acquisition were greater than other metrics. The kurtosis parameters and FA values of central white matter were significantly higher than that of peripheral white matter, whereas the MD and Dr values were significantly lower than that of peripheral white matter. The MK value of the posterior limb of the internal capsule was the highest among the white matter areas. The FA value of the splenium of the corpus callosum was significantly higher than that of the other white matter areas. The kurtosis parameters and FA values of globus pallidus and thalamus were significantly higher than those of the caudate nucleus and putamen, whereas the Da and Dr values of globus pallidus and thalamus were significantly lower than those of the caudate nucleus and putamen. The relative change rates of kurtosis parameters and FA values of all ROIs were greater than those of MD, Da, and Dr values. The amplitude of MK values of almost all ROIs (except for the genu of the corpus callosum and central white matter of the centrum semiovale level) was greater than that of other metrics. The relative change rates of the Kr values of most ROIs were greater than those of the Ka value, and the relative change rates of the Dr values of most ROIs were greater than those of the Da value. Conclusion: DKI parameters showed potential advantages in detecting the changes in brain microstructure during neonatal brain development.

Classifying early stages of cervical cancer with MRI-based radiomics.

This study aims to establish a MRI-based classifier to distinguish early stages of cervical cancer with improved diagnostic performance to assist clinical diagnosis and treatment. 57 patients with pathological diagnosis of cervical cancer from January 2018 to May 2019 were enrolled in this study. MRI examinations, including T1-weighted image(T1WI), T2-weighted image(T2W), diffusion weighted imaging (DWI) and dynamic contrast enhanced (DCE), were performed before surgery. MR images from patients of stage Ib or IIa cervical cancer with tumor segmented were used as input. Feature extraction process extracted first-order statistics and texture and applied filters. The dimensionality of the radiomic features was reduced using the least absolute shrinkage and selection operator (LASSO). Models were trained by three machine-learning (k-nearest neighbor (KNN), support vector machine (SVM), and logistic regression (LR)) and diagnostic performance in differentiating stage Ib and stage IIa cases was evaluated. A total of 27 features were extracted to establish models, including 2 features from T1WI, 5 features from T2WI, 5 features from DWI (b = 50), 4 features from DWI (b = 800), 5 features from DCE, and 6 features from ADC. For each machine learning (ML) classifier, six sequences of training set and testing set are modeled and analyzed. Among all the models, the training set and testing set of T2WI model built by SVM classifier were the best (Area under the curve (AUC) 0.915) / (AUC 0.907). Radiomic analysis of ML-based texture features and first-order statistics features can be used to stage the early cervical cancer pre-operatively.

Diagnostic Value of Diffusion Tensor Imaging for Infants' Brain Development Retardation Caused by Pre-Eclampsia.

Objective: Pre-eclampsia (PE) can cause brain development delay in infants. This work aims to characterize the pattern differences of brain white matter development in premature infants under PE conditions and those without. Methods: Eighty preterm infants delivered by women with PE were selected as the PE group, and ninety-six preterm infants of the same period born to women without high-risk perinatal factors were used as control. All infants underwent diffusion tensor imaging (DTI) examination. The fractional anisotropy (FA) was measured in five regions of interests (ROIs), including posterior limbs of internal capsule (PLIC), splenium of the corpus callosum (SCC), superior frontal gyrus (SFG), superior parietal lobule (SPL), and superior occipital gyrus (SOG). The relationship between the FA values and postmenstrual age (PMA) was analyzed. Results: After adjusting for the birth weight and gestational ages, in the SCC and PLIC, the PMA and FA values showed a low-to-medium intensity positive correlation in the control group (r = 0.30, p=0.003; r = 0.53, p < 0.0001), while no positive relevance was detected in the PE group (r = 0.08, p=0.47; r = 0.19, p < 0.08). In the PE and control groups, in the SPL and SOG, the PMA and FA values showed a near-consistent positive correlation (r = 0.57, r = 0.55 vs. r = 0.31, r = 0.55; all p < 0.05). In the control group, in SFG, the PMA and FA values had a medium intensity positive correlation (r = 0.47, p < 0.0001), but there was no statistical difference in correlation in PE (r = 0.10, p=0.39). Conclusion: PE may cause lagging brain development in the SCC, PLIC, and SFG during infancy. DTI may be an effective and sensitive detection tool.

Brain Development in Infants of Mothers With Gestational Diabetes Mellitus: A Diffusion Tensor Imaging Study.

OBJECTIVE: The objective of this study was to investigate clinical neurocognitive performance and microstructural white matter (WM) alterations in infants of mothers with gestational diabetes mellitus (GDM) using diffusion tensor imaging. MATERIALS AND METHODS: Infants (corrected gestational age, 33.42-36.00 weeks) of mothers with GDM (n = 31) and gestational age- and sex-matched unexposed controls (n = 31) accomplished 3-T diffusion tensor imaging scans and neurocognitive tests. Diffusion tensor imaging measures, mainly referring to fractional anisotropy (FA) values, were compared between 2 groups, and within-group analysis of correlation between FA values and neurocognitive testing outcomes in GDM-exposed infants was conducted subsequently. RESULTS: Fractional anisotropy was significantly decreased in the splenium of corpus callosum, posterior limb of internal capsule, thalamus in infants of mothers with GDM when compared with controls (P < 0.05), reflecting microstructural WM abnormalities in the GDM group. Decreased FA was associated with worse neurocognitive performance in the exposed group (P < 0.05). CONCLUSIONS: Individuals of mothers with GDM showed microstructural WM abnormalities in different brain regions, which were significantly related to worse neurocognitive performance. This might reveal that GDM directly insults the brain development of the offspring.