Diffusion kurtosis imaging tractography reveals disrupted white matter structural networks in children with obstructive sleep apnea syndrome.

To assess the disruptions of brain white matter (WM) structural network in children with obstructive sleep apnea (OSA) using diffusion kurtosis imaging (DKI). We use DKI tractography to construct individual whole-brain, region-level WM networks in 40 OSA and 28 healthy children. Then, we apply graph theory approaches to analyze whether OSA children would show altered global and regional network topological properties and whether these alterations would significantly correlate with the clinical characteristics of OSA. We found that both OSA and healthy children showed an efficient small-world organization and highly similar hub distributions in WM networks. However, characterized by kurtosis fractional anisotropy (KFA) weighted networks, OSA children exhibited decreased global and local efficiency, increased shortest path length compared with healthy children. For regional topology, OSA children exhibited significant decreased nodal betweenness centrality (BC) in the bilateral medial orbital superior frontal gyrus (ORBsupmed), right orbital part superior frontal gyrus (ORBsup), insula, postcentral gyrus, left middle temporal gyrus (MTG), and increased nodal BC in the superior parietal gyrus, pallidum. Intriguingly, the altered nodal BC of multiple regions (right ORBsupmed, ORBsup and left MTG) within default mode network showed significant correlations with sleep parameters for OSA patients. Our results suggest that children with OSA showed decreased global integration and local specialization in WM networks, typically characterized by DKI tractography and KFA metric. This study may advance our current understanding of the pathophysiological mechanisms of impaired cognition underlying OSA.

Brain white matter variation in patients with obstructive sleep apnea-hypopnea syndrome with diffusional kurtosis imaging / 中国耳鼻咽喉头颈外科

OBJECTIVE To explore the changes of brain white matter of the patients with obstructive sleep apnea-hypopnea syndrome(OSAHS)with magnetic resonance diffusional kurtosis imaing(DKI).METHODS There were 38 patients with OSAHS examined by polysomnogram(PSG)from November 2017 to June 2021,and 32 non-OSAHS controls matched in gender and age who were included in this study.All the subjects were scanned by DKI.The different encephalic regions through comparing kurtosis fractional anisotropy(KFA)values of all encephalic regions between the two groups were found,and the relationship between the KFA values of the different encephalic regions in OSAHS patients and the apnea-hypopnea index(AHI),lowest saturation oxygen(LSaO2)and the percent of the total record time spent below 90%oxygen saturation(TS90%)in PSG were analyzed.RESULTS The KFA values of white matter fiber tracts in corpus callosum,bilateral corona radiata,cingulate gyrus,right superior cerebellar peduncle and inferior cerebellar peduncle of OSAHS group were obviously lower than control group(P<0.05).After controlling for age and body mass index(BMI),the partial correlation between AHI and the KFA values of corpus callosum,left posterior corona radiata,right anterior corona radiata,cingulate gyrus in OSAHS group were also negative(r=-0.344,-0.380,-0.406,-0.53,P<0.05).The partial correlation between LSaO2 and the KFA values of corpus callosum,left posterior corona radiata,right anterior corona radiata,cingulate gyrus,right superior cerebellar peduncle in OSAHS group were also positive(r=0.366,0.406,0.446,0.404,0.342,P<0.05).The partial correlation between TS90%and the KFA values of corpus callosum,left posterior corona radiata,right anterior corona radiata,cingulate gyrus,right superior cerebellar peduncle and inferior cerebellar peduncle in OSAHS group were also negative(r=-0.414,-0.352,-0.355,-0.336,-0.456,-0.360,P<0.05).CONCLUSION Recurrent apnea and hypoxemia at night of OSAHS patients can cause damage to white matter fibers in parts of encephalic regions.

Characterisation of brain microstructural alterations in children with obstructive sleep apnea syndrome using diffusion kurtosis imaging.

Obstructive sleep apnea (OSA) is a common chronic sleep-related breathing disorder in children. Previous studies showed widespread alterations in white matter (WM) in children with OSA mainly by using diffusion tensor imaging (DTI), while diffusional kurtosis imaging (DKI) extended DTI and exhibited improved sensitivity in detecting developmental and pathological changes in neural tissues. Therefore, we conducted whole-brain DTI and DKI analyses and compared the differences in kurtosis and diffusion parameters within the skeleton between 41 children with OSA and 32 healthy children. Between-group differences were evaluated by tract-based spatial statistics (TBSS) analysis (p < 0.05, TFCE corrected), and partial correlations between DKI metrics and sleep parameters were assessed considering age and gender as covariates. Compared with the controls, children with OSA showed significantly decreased kurtosis fractional anisotropy (KFA) mainly in white matter regions with a complex fibre arrangement including the posterior corona radiate (PCR), superior longitudinal fasciculus (SLF), and inferior fronto-occipital fasciculus (IFOF), while decreased FA in white matter regions with a coherent fibre arrangement including the posterior limb of internal capsule (PLIC), anterior thalamic radiation (ATR), and corpus callosum (CC). Notably, the receiver operating characteristic (ROC) curve analysis demonstrated the KFA value in complex tissue regions significantly (p < 0.001) differentiated children with OSA from the controls. In addition, the KFA value in the left PCR, SLF, and IFOF showed significant partial correlations to the sleep parameters for children with OSA. Combining DKI derived kurtosis and diffusion parameters can provide complementary neuroimaging biomarkers for assessing white matter alterations, and reveal pathological changes and monitor disease progression in paediatric OSA.

Convolutional neural network optimizes the application of diffusion kurtosis imaging in Parkinson's disease.

OBJECTIVES: The literature regarding the use of diffusion-tensor imaging-derived metrics in the evaluation of Parkinson's disease (PD) is controversial. This study attempted to assess the feasibility of a deep-learning-based method for detecting alterations in diffusion kurtosis measurements associated with PD. METHODS: A total of 68 patients with PD and 77 healthy controls were scanned using scanner-A (3 T Skyra) (DATASET-1). Meanwhile, an additional five healthy volunteers were scanned with both scanner-A and an additional scanner-B (3 T Prisma) (DATASET-2). Diffusion kurtosis imaging (DKI) of DATASET-2 had an extra b shell compared to DATASET-1. In addition, a 3D-convolutional neural network (CNN) was trained from DATASET-2 to harmonize the quality of scalar measures of scanner-A to a similar level as scanner-B. Whole-brain unpaired t test and Tract-Based Spatial Statistics (TBSS) were performed to validate the differences between the PD and control groups using the model-fitting method and CNN-based method, respectively. We further clarified the correlation between clinical assessments and DKI results. RESULTS: An increase in mean diffusivity (MD) was found in the left substantia nigra (SN) in the PD group. In the right SN, fractional anisotropy (FA) and mean kurtosis (MK) values were negatively correlated with Hoehn and Yahr (H&Y) scales. In the putamen (Put), FA values were positively correlated with the H&Y scales. It is worth noting that these findings were only observed with the deep learning method. There was neither a group difference nor a correlation with clinical assessments in the SN or striatum exceeding the significance level using the conventional model-fitting method. CONCLUSIONS: The CNN-based method improves the robustness of DKI and can help to explore PD-associated imaging features.

Diffusion kurtosis imaging as a neuroimaging biomarker in patients with carbon monoxide intoxication.

Attempting suicide by burning charcoal can lead to carbon monoxide (CO) intoxication and cognitive deficits. Changes in white matter (WM) quantified by diffusion tensor imaging (DTI)-derived parameters have been validated to reflect cognitive test scores. As diffusion kurtosis imaging (DKI) measures biological microstructures using non-Gaussian diffusivity, we assessed the added-information of DKI with neuropsychological test scores as the major outcome measure. A total of 45 patients were enrolled and compared with 30 age-matched controls. The patients were stratified into acute or chronic phase according to the intervals of intoxication and assessments. WM status was assessed using tract-based spatial statistics for DKI and DTI topographies, and the sensitivity/specificity of either model was tested using area under the curve (AUC) analysis. To evaluate their clinical significance, values of DKI- and DTI-derived parameters were extracted from seven regions of interest (ROI) and correlated with neuropsychiatric scores. The kurtosis parameters were lower in the patients than in the controls but none of the parameters provided differentiations between the acute or chronic phase. Kurtosis fractional anisotropy (KFA) had a higher AUC than fractional anisotropy while the other 3 DTI parameters had higher AUC than the corresponding DKI ones. In clinical correlations, KFA value of right posterior WM correlated with visual memory (r = 0.326, p = 0.029), and KFA values of bilateral posterior WM correlated with the digit forward score (right: r = 0.302, p = 0.043; left: r = 0.314, p = 0.036). Although DTI was more sensitive in reflecting disease status, KFA may be more sensitive and specific than fractional anisotropy in cognitive test score predictions.