Cortical thickness in clinical moyamoya disease: A magnetic resonance imaging study

Atrophy in subcortical gray matter in adult patients with moyamoya disease

BACKGROUND

Acute cerebrovascular accidents, long-term hypoperfusion, and/or remote neuronal degeneration may lead to structural alterations in patients with moyamoya disease (MMD). This study sought to comprehensively investigate the distribution characteristics of subcortical gray matter volume and their correlations with angiographic changes in the intracranial artery in patients with MMD.

METHOD

One hundred forty-two patients with MMD and 142 age- and sex-matched healthy controls underwent 3-dimensional high-resolution structural magnetic resonance imaging. Volumes of subcortical gray matter and subregions of the hippocampus and amygdala were calculated, and the degree of stenosis/occlusion of intracranial arteries in patients with MMD was evaluated on MR angiography.

RESULTS

Volume reductions in the thalamus, caudate, putamen, hippocampus, amygdala, pallidum, and nucleus accumbens were found in patients with MMD. Hippocampal subfields and amygdala subnuclei in patients with MMD showed distinct vulnerability, and morphological alterations in specific subregions were more obvious than in the whole hippocampus/amygdala. Volume loss in several subcortical areas was related to disease duration and intracranial arterial changes.

CONCLUSIONS

Our findings revealed structural alteration patterns of subcortical gray matter in MMD. The specific atrophy in subregions of the hippocampus and the amygdala suggested potential cognitive and affective impairments in MMD, which warrants further investigation. Chronic cerebral hemodynamic alterations in MMD may play a pivotal role in morphological changes in subcortical areas.

Identification of moyamoya disease based on cerebral oxygen saturation signals using machine learning methods

Moyamoya is a cerebrovascular disease with a high mortality rate. Early detection and mechanistic studies are necessary. Near-infrared spectroscopy (NIRS) was used to study the signals of the cerebral tissue oxygen saturation index (TOI) and the changes in oxygenated and deoxygenated hemoglobin concentrations (HbO and Hb) in 64 patients with moyamoya disease and 64 healthy volunteers. The wavelet transforms (WT) of TOI, HbO and Hb signals, as well as the wavelet phase coherence (WPCO) of these signals from the left and right frontal lobes of the same subject, were calculated. Features were extracted from the spontaneous oscillations of TOI, HbO and Hb in five physiological activity-related frequency segments. Machine learning models based on support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost) have been built to classify the two groups. For 20-min signals, the 10-fold cross-validation accuracies of SVM, RF and XGBoost were 87%, 85% and 85%, respectively. For 5-min signals, the accuracies of the three methods were 88%, 88% and 84%, respectively. The method proposed in this article has potential for detecting and screening moyamoya with high proficiency. Evaluating the cerebral oxygenation with NIRS shows great potential in screening moyamoya diseases.

Structural Abnormalities in Pediatric Moyamoya Disease Revealed by Clinical Magnetic Resonance Imaging, Regionally Distributed Relative Signal Intensities and Volumes

Moyamoya disease (MMD) is a rare, progressive cerebrovascular disorder, with an unknown etiology and pathogenesis. It is characterized by steno-occlusive changes at the terminal portion of the internal carotid artery (ICA), which is accompanied by variable development of the basal collaterals called moyamoya vessels. In this study, we investigate the potential for structural T1 magnetic resonance imaging (MRI) to help characterize MMD clinically, with the help of regionally distributed relative signal intensities (RRSIs) and volumes (RRVs). These RRSIs and RRVs provide the ability to characterize aspects of regional brain development and represent an extension to existing automated biomarker extraction technologies. This study included 269 MRI examinations from MMD patients and 993 MRI examinations from neurotypical controls, with regional biomarkers compared between groups with the area under the receiver operating characteristic curve (AUC). Results demonstrate abnormal presentation of RRSIs and RRVs in the insula (15-20 year old cohort, left AUC: 0.74, right AUC: 0.71), and the lateral orbitofrontal region (5-10 year old cohort, left AUC: 0.67; 15-20 year cohort, left AUC: 0.62, right AUC: 0.65). Results indicate that RRSIs and RRVs may help in characterizing brain development, assist in the assessment of the presentation of the brains of children with MMD, and may help overcome standardization challenges in multi-protocol clinical MRI. Further investigation of the potential for RRSIs and RRVs in clinical imaging is warranted and supported through the release of open source software.