scholarly journals Gray matter network reorganization in multiple sclerosis from 7‐Tesla and 3‐Tesla MRI data

2020 ◽  
Vol 7 (4) ◽  
pp. 543-553
Author(s):  
Gabriel Gonzalez‐Escamilla ◽  
Dumitru Ciolac ◽  
Silvia De Santis ◽  
Angela Radetz ◽  
Vinzenz Fleischer ◽  
...  
2010 ◽  
Vol 32 (4) ◽  
pp. 971-977 ◽  
Author(s):  
Emma C. Tallantyre ◽  
Paul S. Morgan ◽  
Jennifer E. Dixon ◽  
Ali Al-Radaideh ◽  
Matthew J. Brookes ◽  
...  

2018 ◽  
Vol 25 (3) ◽  
pp. 382-391 ◽  
Author(s):  
Carolina M Rimkus ◽  
Menno M Schoonheim ◽  
Martijn D Steenwijk ◽  
Hugo Vrenken ◽  
Anand JC Eijlers ◽  
...  

Background: Coordinated patterns of gray matter morphology can be represented as networks, and network disruptions may explain cognitive dysfunction related to multiple sclerosis (MS). Objective: To investigate whether single-subject gray matter network properties are related to impaired cognition in MS. Methods: We studied 148 MS patients (99 female) and 33 healthy controls (HC, 21 female). Seven network parameters were computed and compared within MS between cognitively normal and impaired subjects, and associated with performance on neuropsychological tests in six cognitive domains with regression models. Analyses were controlled for age, gender, whole-brain gray matter volumes, and education level. Results: Compared to MS subjects with normal cognition, MS subjects with cognitive impairment showed a more random network organization as indicated by lower lambda values (all p < 0.05). Worse average cognition and executive function were associated with lower lambda values. Impaired information processing speed, working memory, and attention were associated with lower clustering values. Conclusion: Our findings indicate that MS subjects with a more randomly organized gray matter network show worse cognitive functioning, suggesting that single-subject gray matter graphs may capture neurological dysfunction due to MS.


2011 ◽  
Vol 35 (3) ◽  
pp. 537-542 ◽  
Author(s):  
A. Scott Nielsen ◽  
R. Philip Kinkel ◽  
Emanuele Tinelli ◽  
Thomas Benner ◽  
Julien Cohen-Adad ◽  
...  

2015 ◽  
Vol 21 (9) ◽  
pp. 1139-1150 ◽  
Author(s):  
Daniel M Harrison ◽  
Jiwon Oh ◽  
Snehashis Roy ◽  
Emily T Wood ◽  
Anna Whetstone ◽  
...  

Objective: Pathology in both cortex and deep gray matter contribute to disability in multiple sclerosis (MS). We used the increased signal-to-noise ratio of 7-tesla (7T) MRI to visualize small lesions within the thalamus and to relate this to clinical information and cortical lesions. Methods: We obtained 7T MRI scans on 34 MS cases and 15 healthy volunteers. Thalamic lesion number and volume were related to demographic data, clinical disability measures, and lesions in cortical gray matter. Results: Thalamic lesions were found in 24/34 of MS cases. Two lesion subtypes were noted: discrete, ovoid lesions, and more diffuse lesional areas lining the periventricular surface. The number of thalamic lesions was greater in progressive MS compared to relapsing–remitting (mean ±SD, 10.7 ±0.7 vs. 3.0 ±0.7, respectively, p < 0.001). Thalamic lesion burden (count and volume) correlated with EDSS score and measures of cortical lesion burden, but not with white matter lesion burden or white matter volume. Conclusions: Using 7T MRI allows identification of thalamic lesions in MS, which are associated with disability, progressive disease, and cortical lesions. Thalamic lesion analysis may be a simpler, more rapid estimate of overall gray matter lesion burden in MS.


Author(s):  
L Umutlu ◽  
S Maderwald ◽  
A Fischer ◽  
M Forsting ◽  
M Ladd ◽  
...  
Keyword(s):  
3 Tesla ◽  

Author(s):  
UKM Teichgräber ◽  
JG Pinkernelle ◽  
F Neumann ◽  
T Benter ◽  
H Bruhn ◽  
...  

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