Diffusion kurtosis imaging of gray matter in young adults with autism spectrum disorder

McKenna, Faye; Miles, Laura; Donaldson, Jeffrey; Lazar, Mariana

doi:10.1038/s41598-020-78486-w

Cited by 8 publications

(13 citation statements)

References 80 publications

(143 reference statements)

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“…Also, observe that evaporation has a correlation of 0.7 to daily maximum temperature. Further, we can also plot the response of RainTomorrow along with temperature, evaporation, humidity, and pressure 20 . The scatter plots display how the response is classified to the predictors, and boxplots displays the statistical values of the feature, at which the response is Yes or No.…”

Section: Data Sourcementioning

confidence: 99%

Automated predictive analytics tool for rainfall forecasting

Raval

Sivashanmugam

Pham

et al. 2021

Sci Rep

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Australia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision.

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Section: Data Sourcementioning

confidence: 99%

Automated predictive analytics tool for rainfall forecasting

Raval

Sivashanmugam

Pham

et al. 2021

Sci Rep

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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.…”

Section: Introductionmentioning

confidence: 99%

“…Mori et al ( 14 ) confirmed that the cerebral blood flow in some brain areas of ASD children decreased. McKenna et al ( 15 ) demonstrated that gray matter microstructure in some brain regions of autistic children was abnormal. However, they did not prove whether there is spatial overlap in brain regions with reduced iron content, cerebral blood flow perfusion, abnormal gray matter microstructure, and whether there is a correlation between them.…”

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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“…For homogeneous structures, such as gray matter, MK and RK are more sensitive as compared with FA and MD. MK and RK played an important role in detecting the development of isotropic tissues (such as gray matter) ( 25 ). In the gray matter region, the changes of MK and RK parameters may be related to the increased concentration of mature neuronal macromolecules and the decrease of tissue water content ( 26 , 27 ) or to other special structures occurring in the development of gray matter.…”

Section: Discussionmentioning

confidence: 99%

The Value of Diffusion Kurtosis Imaging in Detecting Delayed Brain Development of Premature Infants

et al. 2021

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Objective: Preterm infants are at high risk of the adverse neurodevelopmental outcomes. Our aim is to explore the value of diffusion kurtosis imaging (DKI) in diagnosing brain developmental disorders in premature infants.Materials and Methods: A total of 52 subjects were included in this study, including 26 premature infants as the preterm group, and 26 full-term infants as the control group. Routine MRI and DKI examinations were performed. Mean kurtosis (MK), radial kurtosis (RK), fractional anisotropy (FA), and mean diffusivity (MD) values were measured in the brain regions including posterior limbs of the internal capsule (PLIC), anterior limb of internal capsule (ALIC), parietal white matter (PWM), frontal white matter (FWM), thalamus (TH), caudate nucleus (CN), and genu of the corpus callosum (GCC). The chi-squared test, t-test, Spearman's correlation analysis, and receiver operating characteristic curve were used for data analyses.Results: In the premature infant group, the MK and RK values of PLIA, ALIC, and PWM were lower than those in the control group (p < 0.05). The FA values of PWM, FWM, and TH were also lower than those of the control group (p < 0.05). The area under curves of MK in PLIC and ALIC, MD in PWM, and FA in FWM were 0.813, 0.802, 0.842, and 0.867 (p < 0.05). In the thalamus and CN, the correlations between MK, RK values, and postmenstrual age (PMA) were higher than those between FA, MD values, and PMA.Conclusion: Diffusion kurtosis imaging can be used as an effective tool in detecting brain developmental disorders in premature infants.

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Diffusion kurtosis imaging of gray matter in young adults with autism spectrum disorder

Cited by 8 publications

References 80 publications

Automated predictive analytics tool for rainfall forecasting

Automated predictive analytics tool for rainfall forecasting

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

The Value of Diffusion Kurtosis Imaging in Detecting Delayed Brain Development of Premature Infants

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