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The present study aimed to determine whether a recently proposed cerebral small vessel disease (CSVD) classification scheme could differentiate the 5-year all-cause mortality in middle-to-old aged asymptomatic CSVD. Stroke-free and non-demented participants recruited from the community-based I-Lan Longitudinal Aging Study underwent baseline brain magnetic resonance imaging (MRI) between 2011 and 2014 and were followed-up between 2018 and 2019. The study population was classified into control (non-CSVD) and CSVD type 1–4 groups based on MRI markers. We determined the association with mortality using Cox regression models, adjusting for the age, sex, and vascular risk factors. A total of 735 participants were included. During a mean follow-up of 5.7 years, 62 (8.4%) died. There were 335 CSVD type 1 (57.9 ± 5.9 years), 249 type 2 (65.6 ± 8.1 years), 52 type 3 (67.8 ± 9.2 years), and 38 type 4 (64.3 ± 9.0 years). Among the four CSVD types, CSVD type 4 individuals had significantly higher all-cause mortality (adjusted hazard ratio = 5.0, 95% confidence interval 1.6–15.3) compared to controls. This novel MRI-based CSVD classification scheme was able to identify individuals at risk of mortality at an asymptomatic, early stage of disease and might be applied for future community-based health research and policy. [ABSTRACT FROM AUTHOR]

Background: The increase of headache frequency is associated with higher headache related disability and lower quality of life in patients with migraine. However, the pathophysiology of migraine progression, persistence, or remission is elusive. The purpose of this study is to identify the brain signatures that are predictive of the long-term outcomes among patients with high-frequency migraine (HFM: 10–30 headache days/month). Methods: We prospectively enrolled patients with HFM and healthy controls and collected their baseline clinical profiles and brain-MRI data at first visit. We longitudinally followed the patients and determined their outcomes at 2-year follow-up. Good outcome was defined as ≥50% reduction of baseline headache days and poor outcome was defined as reduction < 50% or frequency increase. Voxel-based morphometry was used to study gray matter volume (GMV), and structural covariance was used to investigate structural connectivity. Results: Among 56 patients with HFM, 37 had good outcome and 19 poor outcome. Compared to the healthy controls (n = 37), patients with poor outcome had decreased GMV over the left posterior cingulate gyrus, and increased GMV over the bilateral cerebellum and the right precentral gyrus. Further, patients with poor outcome had greater GMV over the right and the left cerebella compared to patients with good outcome, and the GMVs of the cerebella were correlated to 2-year headache frequencies (right: r = 0.38, P = 0.005; left: r = 0.35, P = 0.009). Structural connectivity were increased between the cerebellum and the cuneus, the calcarine cortex, and the temporal lobe, respectively, in patients with poor outcome, and was decreased between the cerebellum and the prefrontal cortex in patients with poor outcome. The structural covariance integrities between the right cerebellum and the right cuneus were correlated to 2-year headache frequencies (r = 0.36, P = 0.008). Conclusions: Structural volume and connectivity changes of the cerebellum may underlie headache persistence in patients with HFM. [ABSTRACT FROM AUTHOR]

Objectives: To identify disease-related spatial covariance patterns of grey matter volume as an aid in the classification of Parkinson's disease (PD).Methods: Seventy structural covariance networks (SCNs) based on grey matter volume covariance patterns were defined using independent component analysis with T1-weighted structural MRI scans (discovery sample, 70 PD patients and 70 healthy controls). An image-based classifier was constructed from SCNs using a multiple logistic regression analysis with a leave-one-out cross-validation-based feature selection scheme. A validation sample (26 PD patients and 26 healthy controls) was further collected to evaluate the generalization ability of the constructed classifier.Results: In the discovery sample, 13 SCNs, including the cerebellum, anterior temporal poles, parahippocampal gyrus, parietal operculum, occipital lobes, supramarginal gyri, superior parietal lobes, paracingulate gyri and precentral gyri, had higher classification performance for PD. In the validation sample, the classifier had moderate generalization ability, with a mean sensitivity of 81%, specificity of 69% and overall accuracy of 75%. Furthermore, certain individual SCNs were also associated with disease severity.Conclusions: Although not applicable for routine care at present, our results provide empirical evidence that disease-specific, large-scale structural networks can provide a foundation for the further improvement of diagnostic MRI in movement disorders.Key Points: • Disease-specific, large-scale SCNs can be identified from structural MRI. • A new network-based framework for PD classification is proposed. • An SCN-based classifier had moderate generalization ability in PD classification. • The selected SCNs provide valuable functional information regarding PD patients. [ABSTRACT FROM AUTHOR]

Background: Despite evidently distinct symptoms, tension-type headache (TTH) and migraine are highly comorbid and exhibit many similarities in clinical practice. The purpose of this study was to investigate whether both types of headaches are similar in brain morphology. Methods: Consecutive patients with TTH and age- and sex-matched patients with migraine and healthy controls were enrolled for brain magnetic resonance imaging examination. Patients with TTH were excluded if they reported any headache features or associated symptoms of migraine. Changes in gray matter (GM) volume associated with headache diagnosis (TTH vs. migraine) and frequency (episodic vs. chronic) were examined using voxel-based morphometry. The correlation with headache profile and the discriminative ability between TTH and migraine were also investigated for these GM changes. Results: In comparison with controls ( n = 43), the patients with TTH (25 episodic and 24 chronic) exhibited a GM volume increase in the anterior cingulate cortex, supramarginal gyrus, temporal pole, lateral occipital cortex, and caudate. The patients with migraine (31 episodic and 25 chronic) conversely exhibited a GM volume decrease in the orbitofrontal cortex. These GM changes did not correlate with any headache profile. A voxel-wise 2 × 2 factorial analysis further revealed the substantial effects of headache types and frequency in the comparison of GM volume between TTH and migraine. Specifically, the migraine group (vs. TTH) had a GM decrease in the superior and middle frontal gyri, cerebellum, dorsal striatum, and precuneus. The chronic group (vs. episodic group) otherwise demonstrated a GM decrease in the bilateral insula and anterior cingulate cortex. In receiver operating characteristic analysis, the GM volumes of the left superior frontal gyrus and right cerebellum V combined had good discriminative ability for distinguishing TTH and migraine (area under the curve = 0.806). Conclusions: TTH and migraine are separate headache disorders with different characteristics in relation to GM changes. The major morphological difference between the two types of headaches is the relative GM decrease of the prefrontal and cerebellar regions in migraine, which may reflect a higher allostatic load associated with this disabling headache. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the effectiveness of structural imaging as an aid in the diagnosis of Parkinson's disease (PD). Methods: High-resolution T -weighted magnetic resonance imaging was performed in 72 patients with idiopathic PD (mean age, 61.08 years) and 73 healthy subjects (mean age, 58.96 years). The whole brain was parcellated into 95 regions of interest using composite anatomical atlases, and region volumes were calculated. Three diagnostic classifiers were constructed using binary multiple logistic regression modeling: the (i) basal ganglion prior classifier, (ii) data-driven classifier, and (iii) basal ganglion prior/data-driven hybrid classifier. Leave-one-out cross validation was used to unbiasedly evaluate the predictive accuracy of imaging features. Pearson's correlation analysis was further performed to correlate outcome measurement using the best PD classifier with disease severity. Results: Smaller volume in susceptible regions is diagnostic for Parkinson's disease. Compared with the other two classifiers, the basal ganglion prior/data-driven hybrid classifier had the highest diagnostic reliability with a sensitivity of 74%, specificity of 75%, and accuracy of 74%. Furthermore, outcome measurement using this classifier was associated with disease severity. Conclusions: Brain structural volumetric analysis with multiple logistic regression modeling can be a complementary tool for diagnosing PD. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to investigate whether physical evaluations could be used for predicting the presence and severity of obstructive sleep apnea (OSA) in non-obese snoring patients. Methods: This is a retrospective study, and a total of 244 non-obese (body mass index, BMI, <27 kg/m) snoring patients (178 men and 66 women; mean age = 43.1 ± 12.1 years) were included. Each patient underwent polysomnography and a thorough physical examination, including flexible nasopharyngoscopy and Müller maneuver. Patients were divided into four groups based on apnea-hypopnea index (AHI) scores: normal (simple snoring), AHI < 5; mild OSA, 5 ≦ AHI < 15; moderate OSA, 15 ≦ AHI < 30; severe OSA, AHI ≧ 30. Logistic regression was used to identify risk factors for OSA severity. Results: Fifty-nine patients (24%) were simple snorers. The prevalence of sleep apnea (mild, moderate, or severe OSA) for our non-obese snoring patients was 76%. Univariate logistic analyses showed that higher BMI, male gender and retropalatal Müller grades were significantly associated with OSA severity. Multivariate logistic regression analysis identified male gender and retropalatal Müller grade as risk factors for OSA in non-obese snoring patients. Conclusions: Physical examination may be useful for studying the upper airway in non-obese snoring patients. Flexible nasopharyngoscopy with Müller maneuver appears to be useful for evaluating the severity of OSA in non-obese patients. Retropalatal Müller grade is highly related to both the presence and severity of OSA, particularly in males. [ABSTRACT FROM AUTHOR]

The article discusses a study which assessed mortality, disability, and intensive care in patients with mitochondrial 3243A>G mutation. Topics discussed include complications that may develop in patients harboring m.3243A>G and requiring intensive care unit (ICU) admission, and the importance of early diagnosis to avoid the administration of mitochondriontoxic drugs in the ICU.