Whole brain atlas-based diffusion kurtosis imaging parameters for evaluation of minimal hepatic encephalopathy

Gupta, Prateek K.; Vyas, Sameer; Salan, Teddy; Jain, Chirag; Taneja, Sunil; Dhiman, Radha K.; Singh, Paramjeet; Ahuja, Chirag; Ray, Nirmalya; Govind, Varan

doi:10.1177/19714009211026924

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…FAK is like FA to some extent measuring anisotropy but adopts the kurtosis tensor for calculation, which leads to less errors from complex white matter fiber arrangements ( 29 ). When the bulk density of fiber bundles and axons increases, it can also provide the necessary supplement for poor FA performance ( 30 ).…”

Section: Discussionmentioning

confidence: 99%

Application of TBSS-based machine learning models in the diagnosis of pediatric autism

Zhao²,

Sun³

et al. 2023

Front. Neurol.

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ObjectiveTo explore the microstructural changes of white matter in children with pediatric autism by using diffusion kurtosis imaging (DKI), and evaluate whether the combination of tract-based spatial statistics (TBSS) and back-propagation neural network (BPNN)/support vector machine (SVM)/logistic regression (LR) was feasible for the classification of pediatric autism.MethodsDKI data were retrospectively collected from 32 children with autism and 27 healthy controls (HCs). Kurtosis fractional anisotropy (FAK), mean kurtosis (MK), axial kurtosis (KA), radial kurtosis (RK), fractional anisotropy (FA), axial diffusivity (DA), mean diffusivity (MD) and Radial diffusivity (DR) were generated by iQuant workstation. TBSS was used to detect the regions of parameters values abnormalities and for the comparison between these two groups. In addition, we also introduced the lateralization indices (LI) to study brain lateralization in children with pediatric autism, using TBSS for additional analysis. The parameters values of the differentiated regions from TBSS were then calculated for each participant and used as the features in SVM/BPNN/LR. All models were trained and tested with leave-one-out cross validation (LOOCV).ResultsCompared to the HCs group, the FAK, DA, and KA values of multi-fibers [such as the bilateral superior longitudinal fasciculus (SLF), corticospinal tract (CST) and anterior thalamic radiation (ATR)] were lower in pediatric autism group (p < 0.05, TFCE corrected). And we also found DA lateralization abnormality in Superior longitudinal fasciculus (SLF) (the LI in HCs group was higher than that in pediatric autism group). However, there were no significant differences in FA, MD, MK, DR, and KR values between HCs and pediatric autism group (P > 0.05, TFCE corrected). After performing LOOCV to train and test three model (SVM/BPNN/LR), we found the accuracy of BPNN (accuracy = 86.44%) was higher than that of LR (accuracy = 76.27%), but no different from SVM (RBF, accuracy = 81.36%; linear, accuracy = 84.75%).ConclusionOur proposed method combining TBSS findings with machine learning (LR/SVM/BPNN), was applicable in the classification of pediatric autism with high accuracy. Furthermore, the FAK, DA, and KA values and Lateralization index (LI) value could be used as neuroimaging biomarkers to discriminate the children with pediatric autism or not.

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Section: Discussionmentioning

confidence: 99%

Application of TBSS-based machine learning models in the diagnosis of pediatric autism

Zhao²,

Sun³

et al. 2023

Front. Neurol.

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“…Schindler et al showed that older rats with cognitive dysfunction after WBRT fail to show a radiation-induced decrease in neurogenesis in the hippocampus ( 38 ). DKI metrics of the clinical psychometric hepatic encephalopathy score (PHES) are correlated with the average AK and MK values in the lateral frontoorbital gyrus, caudate and thalamus rather than individual brain microstructures ( 39 ). Twelve non hippocampal regions discovered in our study, such as the left IFGoperc, right SFGmedial, right PHG, right FFG and left MTG, that are associated with cognitive changes after radiotherapy should be delineated as dose-avoidance ROIs for cognitive function protection.…”

Section: Discussionmentioning

confidence: 99%

Whole brain atlas-based diffusion kurtosis imaging parameters for the evaluation of multiple cognitive-related brain microstructure injuries after radiotherapy in lung cancer patients with brain metastasis

Xie¹,

Feng²,

Rong³

et al. 2023

Quant Imaging Med Surg

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Background Whole brain radiation therapy (WBRT) can cause cognitive dysfunctions in lung cancer patients with brain metastasis (BM). Diffusion kurtosis imaging (DKI) can detect brain microstructural alterations sensitivly. We aimed to identify the potential of DKI parameters for early radiation-induced brain injury and investigate the association between microstructure changes and neurocognitive function (NCF) decline. Methods Lung cancer patients with BM (n=35) who underwent WBRT in a single center in Zhejiang, China, were consecutively and prospectively enrolled between June 24th, 2020 and December 22nd, 2021, and the median follow-up time was 6.0 months (3.6–6.6 months). DKI and T1-weighted (T1W) MRI scans were acquired prior to and following WBRT. Diffusivity-based (mean diffusivity, MD; fractional anisotropy, FA) and kurtosis-based (mean kurtosis, MK; axial kurtosis, AK) parameters were calculated within the automated anatomical labeling (AAL) atlas-based regions. Reliable change indices practice effects (RCI-PE) scores of the Mini-Mental State Examination (MMSE) were calculated to determine significant neurocognitive decline by a one-sample t -test from baseline to 2–6 months post-WBRT. To assess the subacute induced effects within the whole brain, percentage changes of DKI parameters were evaluated at 170 atlas-based regions by a one-sample t -test. Linear regression analyses were used to evaluate the association between DKI parameter changes and RCI-PE scores. Results Finally, the study included 19 patients in the longitudinal follow-up. RCI-PE scores declined at 2–6 months post-WBRT (mean RCI-PE =−0.842, 95% CI, −0.376 to −1.310; P=0.002). With the atlas-based analysis of subacute effects after post-WBRT, a total of 28 regions changed in at least one diffusion parameter, revealing region-wise microstructural alterations in the brain. Significant correlations of at least one diffusion parameters with RCI-PEs were observed in 9 regions, such as the right orbital part of the inferior frontal gyrus [right IFGorb, r(AK) =0.47, P=0.03] and left middle temporal gyrus [left MTG, r(MK) =−0.49, P=0.03]. Conclusions DKI parameters can be used to detect early microstructure changes and represent important imaging predictors for cognitive decline. The reported 9 regions are more particularly vulnerable to neurocognitive radiation-induced impairment for lung cancer patients with BM, representing potential dose-avoidance targets for cognitive function preservation.

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“…The correlation between DKI and neurocognitive abnormalities may provide early and more accurate localization of brain involvement and may help in predicting prognosis and neuropsychological sequelae (6).…”

Section: Introductionmentioning

confidence: 99%

Diffusion-Weighted Imaging in Minimal Hepatic Encephalopathy Using Mono-, Bi-, and Stretched Exponential Models

Zhang,

Wang

2024

I J Radiol

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Background: Currently, there is no gold standard for diagnosing minimal hepatic encephalopathy (MHE). Diffusion-weighted imaging (DWI) can non-invasively evaluate changes in brain volume and damage to brain function. Objectives: The current study aims to evaluate changes in cerebral blood flow and brain function as predictors of MHE using DWI. Patients and Methods: Participants from October 2016 to October 2021 were scanned using a 3.0T superconductive MR machine (Discovery MR750, GE Medical Systems). Intravoxel incoherent motion DWI (IVIM-DWI) images of 30 patients with MHE and 30 controls were analyzed in a retrospective case-control study. The parameters of mono-, bi-, and stretched exponential models of the regions of interest, delineated from cerebral perfusion pseudo-color maps, were measured. The significance of the differences in parameter values between the groups was assessed using an independent t-test. Furthermore, the receiver operating characteristic curve was used to analyze the predictive efficiency of each parameter for MHE. Results: Compared to the control group, the distributed diffusion coefficient (DDC) of the bilateral frontal lobe, temporal lobe, occipital lobe, parietal lobe, cingulate gyrus, and thalamus in the MHE group was statistically different (P < 0.05). The pseudo-diffusion coefficient (D*) and DDC demonstrated good diagnostic efficacy for MHE. D* had the highest area under the curve (AUC) in the bilateral parietal lobe, bilateral cingulate gyrus, and left occipital lobe. In contrast, DDC had the highest AUC in the right occipital and bilateral temporal lobes. Conclusion: Intravoxel incoherent motion DWI is an imaging method that can detect MHE. In addition, D* and DDC are better diagnostic parameters for screening MHE. Intravoxel incoherent motion DWI will be helpful in uncovering deeper intrinsic pathophysiological mechanisms and improving predictive efficiency.

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Whole brain atlas-based diffusion kurtosis imaging parameters for evaluation of minimal hepatic encephalopathy

Cited by 6 publications

References 30 publications

Application of TBSS-based machine learning models in the diagnosis of pediatric autism

Application of TBSS-based machine learning models in the diagnosis of pediatric autism

Whole brain atlas-based diffusion kurtosis imaging parameters for the evaluation of multiple cognitive-related brain microstructure injuries after radiotherapy in lung cancer patients with brain metastasis

Diffusion-Weighted Imaging in Minimal Hepatic Encephalopathy Using Mono-, Bi-, and Stretched Exponential Models

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