AI Article Synopsis
- Depression is a common nonmotor symptom in Parkinson's disease (PD), and its connection to white matter abnormalities requires further investigation.
- The study compared whole brain white matter integrity between 31 depressed PD patients and 37 nondepressed PD patients using diffusion tensor imaging, revealing significant differences in certain brain regions.
- Findings indicate that depression in PD is associated with reduced white matter integrity, particularly in the left hemisphere, which may help clarify the mechanisms behind depression in this population.
Article Abstract
Aims: Depression is one of the most common nonmotor symptoms in Parkinson's disease (PD). But the pathogenesis is still unclear. Studies have shown that depression in PD is closely related to the white matter abnormalities, but the number of studies is still very small and lack of whole brain white matter lesions study.
Methods: In this study, we investigated whole brain white matter integrity in 31 depressed PD patients and 37 nondepressed PD patients by diffusion tensor imaging.
Results: There was no difference in age, gender, age of onset, disease duration, Hoehn-Yahr scale, Unified Parkinson's Disease Rating Scale scores-III, and Mini-Mental State Examination scores between the two groups. The only difference was the Hamilton Depression Rating Scale. Depressed PD patients showed reduced fractional anisotropy values in the left anterior corona radiata, left posterior thalamic radiation, left cingulum, left superior longitudinal fasciculus, left sagittal stratum (including inferior longitudinal fasciculus and inferior fronto-occipital fasciculus), and left uncinate fasciculus. In patients with depression, the Hamilton Depression Rating Scale (HDRS) was negatively correlated with the FA value in the left cingulum (r = -0.712, P = .032) and left superior longitudinal fasciculus (r = -0.699, P = .025).
Conclusions: This study suggested depression in PD was related to impaired white matter integrity especially the long contact fibers in the left hemisphere. These findings may be helpful for further understanding the potential mechanisms underlying depression in PD.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6489851 | PMC |
| http://dx.doi.org/10.1111/cns.12778 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A rare case of adult-onset vanishing white matter leukoencephalopathy with movement disorder, expressing homozygous EIF2B3 and PRKN pathogenic variants.
BMC Neurol
January 2025
Faculty of Medicine, Department of Neurology, Al-Quds University, Jerusalem, Palestine.
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 PDFAltered thalamotemporal structural connectivity is associated with autistic traits in children with ASD.
Behav Brain Res
January 2025
Center for Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle, WA, United States of America.
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.
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 PDFUnderstanding 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.
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).
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!