Purpose: To investigate white matter (WM) structural alterations using diffusion tensor imaging (DTI) in obstructive sleep apnea (OSA) patients, with or without residual sleepiness, following adherent continuous positive airway pressure (CPAP) treatment. Possible quantitative relationships were explored between the DTI metrics and two clinical assessments of somnolence.

Materials And Methods: Twenty-nine male patients (30-55 years old) with a confirmed diagnosis of OSA were recruited. The patients were treated with CPAP therapy only. The Psychomotor Vigilance Task (PVT) and Epworth Sleepiness Scale (ESS) were performed after CPAP treatment and additionally administered at the time of the magnetic resonance imaging (MRI) scan. Based on the PVT results, the patients were divided into a nonsleepy group (lapses ≤5) and a sleepy group (lapses >5). DTI was performed at 3T, followed by an analysis using tract-based spatial statistics (TBSS) to investigate the differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λ ), and radial diffusivity (λ ) between the two groups.

Results: A higher MD (P < 0.05) was observed in the sleepy group than the nonsleepy group in the whole-brain TBSS analysis in the WM. The increased MD (17.8% of the fiber tracts; P < 0.05) was caused primarily by an elevated λ . Axial diffusivity (λ ) exhibited no significant difference (P > 0.17). The alterations in FA or MD of individual fiber tracts occurred mainly in the internal/external capsule, corona radiata, corpus callosum, and sagittal stratum regions. The FA and MD values correlated with the PVT and ESS assessments from all patients (R ≥ 0.517, P < 0.05).

Conclusion: Global and regional WM alterations, as revealed by DTI, can be a possible mechanism to explain why OSA patients with high levels of CPAP use can have differing responses to treatment. Compromised myelin sheath, indicated by increased radial diffusivity, can be involved in the underlying WM changes. Evidence level: 1 J. MAGN. RESON. IMAGING 2017;45:1371-1378.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5350066PMC
http://dx.doi.org/10.1002/jmri.25463DOI Listing

Publication Analysis

Top Keywords

white matter
8
obstructive sleep
8
sleep apnea
8
patients residual
8
residual sleepiness
8
osa patients
8
cpap treatment
8
nonsleepy group
8
group lapses
8
sleepy group
8

Similar Publications

Background: Vanishing white matter disease (VWMD) is a rare autosomal recessive leukoencephalopathy. It is typified by a gradual loss of white matter in the brain and spinal cord, which results in impairments in vision and hearing, cerebellar ataxia, muscular weakness, stiffness, seizures, and dysarthria cogitative decline. Many reports involve minors.

View Article and Find Full Text PDF

Background: Thalamocortical functional and structural connectivity alterations may contribute to clinical phenotype of Autism Spectrum Disorder. As previous studies focused mainly on thalamofrontal connections, we comprehensively investigated between-group differences of thalamic functional networks and white matter pathways projecting also to temporal, parietal, occipital lobes and their associations with core and co-occurring conditions of this population.

Methods: A total of 38 children (19 with Autism Spectrum Disorder) underwent magnetic resonance imaging and behavioral assessment.

View Article and Find Full Text PDF

Local structural-functional coupling with counterfactual explanations for epilepsy prediction.

Neuroimage

January 2025

College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics, Shenzhen, 518038, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, 210016, China. Electronic address:

The structural-functional brain connections coupling (SC-FC coupling) describes the relationship between white matter structural connections and the corresponding functional activation or functional connections. It has been widely used to identify brain disorders. However, the existing research on SC-FC coupling focuses on global and regional scales, and few studies have investigated the impact of brain disorders on this relationship from the perspective of multi-brain region cooperation (i.

View Article and Find Full Text PDF

Understanding structural-functional connectivity coupling in patients with major depressive disorder: A white matter perspective.

J Affect Disord

January 2025

Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital), Yantai, Shandong 264000, PR China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China. Electronic address:

Purpose: To elucidate the structural-functional connectivity (SC-FC) coupling in white matter (WM) tracts in patients with major depressive disorder (MDD).

Methods: A total of 178 individuals diagnosed with MDD and 173 healthy controls (HCs) were recruited for this study. The Euclidean distance was calculated to assess SC-FC coupling.

View Article and Find Full Text PDF

Automatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer.

BMC Neurol

January 2025

Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, School of Medicine, College of Medicine, National Sun Yat-Sen University, No. 123 Ta-Pei Road, Niao-Sung Dist, Kaohsiung, 83305, Taiwan.

Background And Purpose: White matter hyperintensities in brain MRI are key indicators of various neurological conditions, and their accurate segmentation is essential for assessing disease progression. This study aims to evaluate the performance of a 3D convolutional neural network and a 3D Transformer-based model for white matter hyperintensities segmentation, focusing on their efficacy with limited datasets and similar computational resources.

Materials And Methods: We implemented a convolution-based model (3D ResNet-50 U-Net with spatial and channel squeeze & excitation) and a Transformer-based model (3D Swin Transformer with a convolutional stem).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!