scholarly journals P2-157: COGNITIVE TESTS AND GRAY MATTER VOLUME IN LATE LIFE DEPRESSION: A VOXEL-BASED MORPHOMETRY STUDY

2014 ◽  
Vol 10 ◽  
pp. P528-P529
Author(s):  
Salma Rose Imanari Ribeiz ◽  
Fabio Duran ◽  
Claudio Campi Castro ◽  
David Steffens ◽  
Geraldo Busatto ◽  
...  
2013 ◽  
Vol 25 (12) ◽  
pp. 1929-1940 ◽  
Author(s):  
Hyun Kook Lim ◽  
Won Sang Jung ◽  
Howard J Aizenstein

ABSTRACTBackground:Although previous studies on late life depression (LLD) have shown morphological abnormalities in frontal–striatal–temporal areas, alterations in coordinated patterns of structural brain networks in LLD are still poorly understood. The aim of this study was to investigate differences in gray matter structural brain network between LLD and healthy controls.Methods:We used gray matter volume measurement from magnetic resonance imaging to investigate large-scale structural brain networks in 37 LLD patients and 40 normal controls. Brain networks were constructed by thresholding gray matter volume correlation matrices of 90 regions and analyzed using graph theoretical approaches.Results:Although both LLD and control groups showed a small-world organization of group networks, there were no differences in the clustering coefficient, the path length, and the small-world index across a wide range of network density. Compared with controls, LLD patients showed decreased nodal betweenness in the medial orbitofrontal and angular gyrus regions. In addition, LLD patients showed hub regions in superior temporal gyrus and middle cingulate gyrus, and putamen. On the other hand, the control group showed hub regions in the medial orbitofrontal gyrus, middle cingulate gyrus, and cuneus.Conclusion:Our findings suggest that the gray matter structural networks are not globally but regionally altered in LLD patients. This multivariate structural analysis using graph theory might provide a more appropriate paradigm for understanding complicated neurobiological mechanism of LLD.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Akihiro Takamiya ◽  
Thomas Vande Casteele ◽  
Michel Koole ◽  
François-Laurent De Winter ◽  
Filip Bouckaert ◽  
...  

AbstractLate-life depression (LLD) is associated with a risk of developing Alzheimer’s disease (AD). However, the role of AD-pathophysiology in LLD, and its association with clinical symptoms and cognitive function are elusive. In this study, one hundred subjects underwent amyloid positron emission tomography (PET) imaging with [18F]-flutemetamol and structural MRI: 48 severely depressed elderly subjects (age 74.1 ± 7.5 years, 33 female) and 52 age-/gender-matched healthy controls (72.4 ± 6.4 years, 37 female). The Geriatric Depression Scale (GDS) and Rey Auditory Verbal Learning Test (RAVLT) were used to assess the severity of depressive symptoms and episodic memory function respectively. Amyloid deposition was quantified using the standardized uptake value ratio. Whole-brain voxel-wise comparisons of amyloid deposition and gray matter volume (GMV) between LLD and controls were performed. Multivariate analysis of covariance was conducted to investigate the association of regional differences in amyloid deposition and GMV with clinical factors, including GDS and RAVLT. As a result, there were no significant group differences in amyloid deposition. In contrast, LLD showed significant lower GMV in the left temporal and parietal region. GMV reduction in the left temporal region was associated with episodic memory dysfunction, but not with depression severity. Regional GMV reduction was not associated with amyloid deposition. LLD is associated with lower GMV in regions that overlap with AD-pathophysiology, and which are associated with episodic memory function. The lack of corresponding associations with amyloid suggests that lower GMV driven by non-amyloid pathology may play a central role in the neurobiology of LLD presenting as a psychiatric disorder.Trial registration: European Union Drug Regulating Authorities Clinical Trials identifier: EudraCT 2009-018064-95.


2021 ◽  
Author(s):  
Akihiro Takamiya ◽  
Thomas Vande Casteele ◽  
Michel Koole ◽  
François-Laurent De Winter ◽  
Filip Bouckaert ◽  
...  

AbstractLate-life depression (LLD) is associated with a risk of developing Alzheimer’s disease (AD). However, the role of AD-pathophysiology in LLD, and its association with clinical symptoms and cognitive function are elusive. In this study, one hundred subjects underwent amyloid positron emission tomography (PET) imaging with [18F]-flutemetamol and structural MRI: 48 severely depressed elderly subjects (age 74.1±7.5 years, 33 female) and 52 age-/gender-matched healthy controls (72.4±6.4 years, 37 female). The Geriatric Depression Scale (GDS) and Rey Auditory Verbal Learning Test (RAVLT) were used to assess the severity of depressive symptoms and episodic memory function respectively. Amyloid deposition was quantified using the standardized uptake value ratio. Whole-brain voxel-wise comparisons of amyloid deposition and gray matter volume (GMV) between LLD and controls were performed. Multivariate analysis of covariance was conducted to investigate the association of regional differences in amyloid deposition and GMV with clinical factors, including GDS and RAVLT. As a result, there were no significant group differences in amyloid deposition. In contrast, LLD showed significant lower GMV in the left temporal and parietal region. GMV reduction in the left temporal region was associated with episodic memory dysfunction, but not with depression severity. Regional GMV reduction was not associated with amyloid deposition. LLD is associated with lower GMV in regions that overlap with AD-pathophysiology, and which are associated with episodic memory function. The lack of corresponding associations with amyloid suggests that lower GM driven by non-amyloid pathology may play a central role in the neurobiology of LLD presenting as a psychiatric disorder.


2021 ◽  
pp. 1-10
Author(s):  
Hidemasa Takao ◽  
Shiori Amemiya ◽  
Osamu Abe ◽  

Background: Scan acceleration techniques, such as parallel imaging, can reduce scan times, but reliability is essential to implement these techniques in neuroimaging. Objective: To evaluate the reproducibility of the longitudinal changes in brain morphology determined by longitudinal voxel-based morphometry (VBM) between non-accelerated and accelerated magnetic resonance images (MRI) in normal aging, mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Methods: Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 2 database, comprising subjects who underwent non-accelerated and accelerated structural T1-weighted MRI at screening and at a 2-year follow-up on 3.0 T Philips scanners, we examined the reproducibility of longitudinal gray matter volume changes determined by longitudinal VBM processing between non-accelerated and accelerated imaging in 50 healthy elderly subjects, 54 MCI patients, and eight AD patients. Results: The intraclass correlation coefficient (ICC) maps differed among the three groups. The mean ICC was 0.72 overall (healthy elderly, 0.63; MCI, 0.75; AD, 0.63), and the ICC was good to excellent (0.6–1.0) for 81.4%of voxels (healthy elderly, 64.8%; MCI, 85.0%; AD, 65.0%). The differences in image quality (head motion) were not significant (Kruskal–Wallis test, p = 0.18) and the within-subject standard deviations of longitudinal gray matter volume changes were similar among the groups. Conclusion: The results indicate that the reproducibility of longitudinal gray matter volume changes determined by VBM between non-accelerated and accelerated MRI is good to excellent for many regions but may vary between diseases and regions.


2008 ◽  
Vol 63 (5) ◽  
pp. 465-474 ◽  
Author(s):  
Robyn A. Honea ◽  
Andreas Meyer-Lindenberg ◽  
Katherine B. Hobbs ◽  
Lukas Pezawas ◽  
Venkata S. Mattay ◽  
...  

2005 ◽  
Vol 22 (8) ◽  
pp. 2089-2094 ◽  
Author(s):  
Hanik K. Yoo ◽  
Minue J. Kim ◽  
Seog Ju Kim ◽  
Young Hoon Sung ◽  
Minyoung E. Sim ◽  
...  

PLoS ONE ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. e99889 ◽  
Author(s):  
Xueting Li ◽  
Alain De Beuckelaer ◽  
Jiahui Guo ◽  
Feilong Ma ◽  
Miao Xu ◽  
...  

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