The association between White matter microstructure alterations detected by Diffusional kurtosis imaging in Neural circuit and post-stroke depression

2021 ◽  
pp. 1-8
Author(s):  
Weijing Liang ◽  
Zexin Fan ◽  
Sha Cui ◽  
Xueyong Shen ◽  
Li Wang
2020 ◽  
Vol 42 (8) ◽  
pp. 640-648
Author(s):  
Jingfan Yao ◽  
Xinxin Liu ◽  
Xiao Lu ◽  
Cheng Xu ◽  
Hongyan Chen ◽  
...  

2017 ◽  
Vol 1665 ◽  
pp. 80-87 ◽  
Author(s):  
Hong Jiang ◽  
Na-Ying He ◽  
Yu-Hao Sun ◽  
Fang-Fang Jian ◽  
Liu-Guan Bian ◽  
...  

2021 ◽  
Vol Volume 17 ◽  
pp. 1839-1857
Author(s):  
Xuan-qiang Tu ◽  
Ze-hua Lai ◽  
Yu Zhang ◽  
Kai-qi Ding ◽  
Fei-yue Ma ◽  
...  

2018 ◽  
Author(s):  
Jeroen Mollink ◽  
Stephen M. Smith ◽  
Lloyd T. Elliott ◽  
Michiel Kleinnijenhuis ◽  
Marlies Hiemstra ◽  
...  

AbstractMicroscopic features (i.e., microstructure) of axons affect neural circuit activity through characteristics such as conduction speed. Deeper understanding of structure-function relationships and translating this into human neuroscience has been limited by the paucity of studies relating axonal microstructure in white matter pathways to functional connectivity (synchrony) between macroscopic brain regions. Using magnetic resonance imaging data in 11354 subjects, we constructed multi-variate models that predict the functional connectivity of pairs of brain regions from the microstructural signature of white matter pathways that connect them. Microstructure-derived models provide predictions of functional connectivity that were significant in up to 86% of the brain region pairs considered. These relationships are specific to the relevant white matter pathway and have high reproducibility. The microstructure-function relationships are associated to genetic variants (single-nucleotide polymorphisms), co-located with genes DAAM1 and LPAR1, that have previously been reported to play a role in neural development. Our results demonstrate that variation in white matter microstructure across individuals consistently and specifically predicts functional connectivity, and that this relationship is underpinned by genetic variability.


2021 ◽  
Author(s):  
Chaichana Jaroonpipatkul ◽  
Jaruwan Onwanna ◽  
Chavit Tunvirachaisakul ◽  
Nutchawan Jittapiromsak ◽  
Yothin Rakvongthai ◽  
...  

ABSTRACTObjectivePost-stroke depression (PSD) is one of the most frequent psychiatric symptoms after a stroke event. The role of white matter hyperintensities (WMHs) associated with PSD in older patients remains unclear. This study aimed to examine the volume and location of white matter microstructure abnormalities among older patients with early-onset PSD.MethodsOlder (≥55 years) patients with acute cerebral infarction and hospitalized in King Chulalongkorn Memorial Hospital’s stroke unit from October 2019 to September 2020 were recruited. Participants were assessed with the Montgomery-Åsberg Depression Rating Scale (MADRS) within three months after the onset of stroke. All patients had MRI scans. The brain images were segmented into four regions via left/right, frontal/dorsal plains. Two WMHs volume detections (visual rating vs. semi-automated WMHs volumetric detection) were employed on the fluid-attenuated inversion recovery images (FLAIR) for each segment. The study then investigated the association between WMHs volume and MADRS score with regression analysis.ResultsThe study included twenty-nine patients with acute stroke. Total WMHs volume and segmented regions were not statistically associated with the MADRS score. However, there was a trend in different WHMs volume of the left anterior segment between depressed and non-depressed groups (t-test 2.058, p = 0.055). Further, demographic and clinical data showed no association with depressive symptoms.ConclusionThe volume of WHMs might not contribute to the development of early-onset PSD in older patients. This study showed a potential of a quantitative MRI analysis in clinical practice. Further investigation with a larger group of patients is needed.


2010 ◽  
Vol 81 (12) ◽  
pp. 1312-1315 ◽  
Author(s):  
W. K. Tang ◽  
Y. K. Chen ◽  
J. Y. Lu ◽  
W. C. W. Chu ◽  
V. C. T. Mok ◽  
...  

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