scholarly journals Compensatory reconfiguration of functional networks between white matter and grey matter in Alzheimer’s disease

2021 ◽  
Author(s):  
Xingxing Zhang ◽  
Qing Guan ◽  
Debo Dong ◽  
Fuyong Chen ◽  
Jing Yi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Euclidean Distance ◽  
Grey Matter ◽  
Temporal Cortex ◽  
Resting State Fmri ◽  
Functional Networks ◽  
Compensatory Mechanisms ◽  
The Brain

AbstractThe temporal synchronization of BOLD signals within white matter (WM) and between WM and grey matter (GM) exhibited intrinsic architecture and cognitive relevance. However, few studies examined the network property within- and between-tissue in Alzheimer’s disease (AD). The hub regions with high weighted degree (WD) were prone to the neuropathological damage of AD. To systematically investigate the changes of hubs within- and between-tissue functional networks in AD patients, we used the resting-state fMRI data of 30 AD patients and 37 normal older adults (NC) from the ADNI open database, and obtained four types of voxel-based WD metrics and four types of distant-dependent WD metrics (ddWD) based on a series of Euclidean distance ranges with a 20mm increment. We found that AD patients showed decreased within-tissue ddWD in the thalamic nucleus and increased between-tissue ddWD in the occipito-temporal cortex, posterior thalamic radiation, and sagittal stratum, compared to NC. We also found that AD patients showed the increased between-tissue FCs between the posterior thalamic radiation and occipito-temporal cortex, and between the sagittal stratum and the salience and executive networks. The dichotomy of decreased and increased ddWD metrics and their locations were consistent with previous studies on the neurodegnerative and compensatory mechanisms of AD, indicating that despite the disruptions, the brain still strived to compensate for the neural inefficiency by reorganizing functional circuits. Our findings also suggested the short-to-medium ranged ddWD metrics between WM and GM as useful biomarker to detect the compensatory changes of functional networks in AD.

scholarly journals White Matter Network Alterations in Alzheimer’s Disease Patients

2020 ◽  
Vol 10 (3) ◽  
pp. 919
Author(s):  
Ramesh Kumar Lama ◽  
Sang-Woong Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Grey Matter ◽  
Small World ◽  
Brain Network ◽  
Topological Properties ◽  
Graph Theoretical Analysis ◽  
Small World Property ◽  
The Brain

Previous studies have revealed the occurrence of alterations of white matter (WM) and grey matter (GM) microstructures in Alzheimer’s disease (AD) and their prodromal state amnestic mild cognitive impairment (MCI). In general, these alterations can be studied comprehensively by modeling the brain as a complex network, which describes many important topological properties, such as the small-world property, modularity, and efficiency. In this study, we systematically investigated white matter abnormalities using unbiased whole brain network analysis. We compared regional and network related WM features between groups of 19 AD and 25 MCI patients and 22 healthy controls (HC) using tract-based spatial statistics (TBSS), network based statistics (NBS) and graph theoretical analysis. We did not find significant differences in fractional anisotropy (FA) between two groups on TBSS analysis. However, observable alterations were noticed at a network level. Brain network measures such as global efficiency and small world properties were low in AD patients compared to HCs.

Comparison of the Unified Segmentation Method and the New Segmentation Method on Detection of Grey Matter and White Matter Changes in Alzheimer’s Disease Based on Voxel-Based Morphometry

2011 ◽  
Vol 301-303 ◽  
pp. 1189-1195
Author(s):  
Ling Jing Hu ◽  
Long Zheng Tong ◽  
Yun Yun Duan ◽  
Bo Wu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Brain Atrophy ◽  
Grey Matter ◽  
Segmentation Method ◽  
Voxel Based Morphometry ◽  
Segmentation Methods ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Voxel-based morphometry method (VBM) has been widely applied to detect the brain atrophy and achieved promising results; however, the effect of the segmentation step in VBM is not clear and the new segmentation method in SPM8 hasn’t been used in Alzheimer’s disease (AD) studies. The aim of this study is to investigate the locations and degrees of grey matter (GM), white matter (WM) atrophy and evaluate the results derived from two segmentation methods. Magnetic resonance imaging (MRI) was collected in 16 AD patients and 16 healthy controls (HC). Using two segmentation methods respectively, several reduction clusters of GM and WM were detected but the locations and degrees of reduction volumes were discrepant resulted from different segmentation methods. Our results suggest that VBM is an effective tool to analyze AD brain atrophy and based on VBM, the comparison of the locations and degrees of volume reduction among AD researches through different segmentation methods should be cautious.

Abstract 44: Associations of Atrial Fibrillation, Neuroimaging Measures of Cerebrovascular Disease and Alzheimer’S Disease-related Pathology, and Cognitive Impairment

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Jonathan Graff-Radford ◽  
Rosebud Roberts ◽  
Malini Madhavan ◽  
Alejandro Rabinstein ◽  
Ruth Cha ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
White Matter ◽  
Cerebrovascular Disease ◽  
Regression Models ◽  
Grey Matter ◽  
White Matter Hyperintensity ◽  
Carrier Status

The objective of this study was to investigate the cross-sectional associations of atrial fibrillation with neuroimaging measures of cerebrovascular disease and Alzheimer’s disease-related pathology, and their interaction with cognitive impairment. MRI scans of non-demented individuals (n=1044) from the population-based Mayo Clinic Study of Aging were analyzed for infarctions, total grey matter, hippocampal and white matter hyperintensity volumes. A subset of 496 individuals underwent FDG and C-11 Pittsburgh compound B (PiB) PET scans. We assessed the associations of atrial fibrillation with i) categorical MRI measures (cortical and subcortical infarctions) using multivariable logistic regression models, and with ii) continuous MRI measures ( hippocampal, total grey matter, and white matter hyperintensity volumes) and FDG-PET and PiB-PET measures using multivariable linear regression models, and adjusting for confounders. Among participants who underwent MRI (median age, 77.8, 51.6% male), 13.5% had atrial fibrillation. Presence of atrial fibrillation was associated with subcortical infarctions (odds ratio [OR], 1.83; p=0.002), cortical infarctions (OR, 1.91; p=0.03), total grey matter volume (Beta [β], -.025, p<.0001) after controlling for age, education, gender, APOE e4 carrier status, coronary artery disease, diabetes, history of clinical stroke, and hypertension. However, atrial fibrillation was not associated with white matter hyperintensity volume, hippocampal volume, Alzheimer’s pattern of FDG hypometabolism or PiB uptake. There was a significant interaction of cortical infarction (p for interaction=0.004) and subcortical infarction (p for interaction =0.015) with atrial fibrillation with regards to odds of mild cognitive impairment (MCI). Using subjects with no atrial fibrillation and no infarction as the reference, the OR (95% confidence intervals [CI]) for MCI was 2.98 (1.66, 5.35;p = 0.0002) among participants with atrial fibrillation and any infarction, 0.69 (0.36, 1.33;p= 0.27) for atrial fibrillation and no infarction, and 1.50 (0.96, 2.32;p = 0.07) for no atrial fibrillation and any infarction. These data highlight that atrial fibrillation is associated with MCI in the presence of infarctions.

scholarly journals Sequential relationships between grey matter and white matter atrophy and brain metabolic abnormalities in early Alzheimer's disease

Brain ◽  
2010 ◽  
Vol 133 (11) ◽  
pp. 3301-3314 ◽  
Author(s):  
N. Villain ◽  
M. Fouquet ◽  
J.-C. Baron ◽  
F. Mezenge ◽  
B. Landeau ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Grey Matter ◽  
Metabolic Abnormalities ◽  
Early Alzheimer’S Disease

scholarly journals Early Recognition of Alzheimer’s Disease using Brain MRI

2017 ◽  
Vol 2 (1) ◽  
pp. 6
Author(s):  
Rania Ahmed Kadry Abdel Gawad Birry
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Euclidean Distance ◽  
Early Recognition ◽  
Brain Mri ◽  
Extraction Techniques ◽  
City Block ◽  
Cosine Distance ◽  
City Block Distance ◽  
The Brain

Abstract—Alzheimer’s disease (AD) is a brain disease that causes a slow decline in memory, thinking and reasoning skills. It represents a major public health problem.  Magnetic Resonance Imaging (MRI) have shown that the brains of people with (AD) shrink significantly as the disease progresses. This shrinkage appears in specific brain regions such as the hippocampus which is a small, curved formation in the brain that plays an important role in the limbic system also involved in the formation of new memories and is also associated with learning and emotions.  Medical information on brain MRI is used in detecting the abnormalities in physiological structures. Structural MRI measurements can detect and follow the evolution of brain atrophy which is a marker of the disease progression; therefore, it allows diagnosis and prediction of AD.  The research’s main target is the early recognition of Alzheimer’s disease automatically, which will thereby avoid deterioration of the case resulting in complete brain damage stage.  Alzheimer’s disease yields visible changes in the brain structures. The aim is to recognize if the patient belongs to Alzheimer’s disease category or a normal healthy person at an early stage. Initially, image pre-processing and features extraction techniques are applied including data reduction using Discrete Cosine Transform (DCT) and Cropping, then traditional classification techniques like Euclidean Distance, Chebyshev Distance, Cosine Distance, City Block Distance, and Black pixel counter, were applied on the resulting vectors for classification. Image pre-processing includes noise reduction, Gray-scale conversion and binary scale conversion were applied for the MRI images. Feature extraction techniques follow including cropping and low spatial frequency components (DCT). This paper aims to automatically recognize and detect Alzheimer’s infected brain using MRI, without the need of clinical expert. This early recognition would be helpful to postpone the disease progression and maintain it at an almost steady stage. It was concluded after collecting a dataset of 50 MRI , 25 for normal MRI and  25 for AD MRI that Chebyshev Distance classifier yielded the highest success rate in the recognition of Alzheimer’s disease with accuracy 94% compared to other classification techniques used where, Euclidean Distance is 91.6%,  Cosine Distance is 86.8%, City block Distance is 89.6%, Correlation Distance is 86.4% and Black pixels counter is 90%.

scholarly journals Large-Scale Functional Networks, Cognition and Brain Structures Supporting Social Cognition and Theory of Mind Performance in Prodromal to Mild Alzheimer’s Disease

2021 ◽  
Vol 13 ◽  
Author(s):  
Jose Manuel Valera-Bermejo ◽  
Matteo De Marco ◽  
Micaela Mitolo ◽  
Chiara Cerami ◽  
Alessandra Dodich ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Social Cognition ◽  
Theory Of Mind ◽  
Large Scale ◽  
Grey Matter ◽  
Network Connectivity ◽  
Functional Networks ◽  
Default Mode ◽  
The Right

Impairment of social cognition (SC) skills such as recognition and attribution of intentions and affective states of others (Theory of Mind, ToM) has been evidenced in Alzheimer’s Disease (AD). This study investigated the neuropsychological, neuroanatomical and brain-functional underpinnings of SC processing to obtain an understanding of the social neurophenotype in early probable AD. Forty-six patients with mild cognitive impairment and mild probable AD underwent SC assessment including emotion recognition (Ekman-60-faces task) and cognitive and affective ToM (Reading-the-Mind-in-the-Eyes test and Story-based Empathy task). Linear models tested the association between SC scores and neuropsychological measures, grey matter maps and large-scale functional networks activity. The executive domain had the most predominant association with SC scores in the cognitive profile. Grey matter volume of the anterior cingulate, orbitofrontal, temporoparietal junction (TPJ), superior temporal, and cerebellar cortices were associated with ToM. Social cognition scores were associated with lower connectivity of the default-mode network with the prefrontal cortex. The right fronto-parietal network displayed higher inter-network connectivity in the right TPJ and insula while the salience network showed lower inter-network connectivity with the left TPJ and insula. Connectivity coupling alterations of executive-attentional networks may support default mode social-cognitive-associated decline through the recruitment of frontal executive mechanisms.

scholarly journals Identifying the Main Functional Pathways Associated with Cognitive Resilience to Alzheimer’s Disease

2021 ◽  
Vol 22 (17) ◽  
pp. 9120
Author(s):  
Marta Pérez-González ◽  
Sara Badesso ◽  
Elena Lorenzo ◽  
Elizabeth Guruceaga ◽  
Alberto Pérez-Mediavilla ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mononuclear Cells ◽  
Memory Function ◽  
Temporal Cortex ◽  
Mrna Levels ◽  
Peripheral Blood Mononuclear ◽  
Novel Treatments ◽  
Peripheral Immune Cells ◽  
The Brain

Understanding the mechanisms involved in cognitive resilience in Alzheimer’s disease (AD) represents a promising strategy to identify novel treatments for dementia in AD. Previous findings from our group revealed that the study of aged-Tg2576 cognitive resilient individuals is a suitable tool for this purpose. In the present study, we performed a transcriptomic analysis using the prefrontal cortex of demented and resilient Tg2576 transgenic AD mice. We have been able to hypothesize that pathways involved in inflammation, amyloid degradation, memory function, and neurotransmission may be playing a role on cognitive resilience in AD. Intriguingly, the results obtained in this study are suggestive of a reduction of the influx of peripheral immune cells into the brain on cognitive resilient subjects. Indeed, Cd4 mRNA expression is significantly reduced on Tg2576 mice with cognitive resilience. For further validation of this result, we analyzed CD4 expression in human AD samples, including temporal cortex and peripheral blood mononuclear cells (PBMC). Interestingly, we have found a negative correlation between CD4 mRNA levels in the periphery and the score in the Mini-Mental State Exam of AD patients. These findings highlight the importance of understanding the role of the immune system on the development of neurodegenerative diseases and points out to the infiltration of CD4+ cells in the brain as a key player of cognitive dysfunction in AD.

scholarly journals Loss and Reorganisation of Superficial White Matter in Alzheimer's disease: A Diffusion MRI study

2021 ◽  
Author(s):  
Thomas Veale ◽  
Ian B Malone ◽  
Teresa Poole ◽  
Thomas D Parker ◽  
Catherine F Slattery ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Fractional Anisotropy ◽  
Diffusion Mri ◽  
Grey Matter ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Young Onset ◽  
Neurite Density

Pathological involvement of cerebral white matter in Alzheimer's disease has been shown using diffusion tensor imaging. Superficial white matter (SWM) changes have been relatively understudied despite their importance in cortico-cortical connections. Measuring SWM degeneration using diffusion tensor imaging is challenging due to its complex structure and proximity to the cortex. To overcome this we investigated diffusion MRI changes in young-onset Alzheimer's disease using standard diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging to distinguish between disease-related changes that are due to degeneration (e.g. loss of myelinated fibres) and those due to reorganisation (e.g. increased fibre dispersion). Twenty-nine young-onset Alzheimer's disease patients and 22 healthy controls had both single-shell and multi-shell diffusion MRI. We calculated fractional anisotropy, mean diffusivity, neurite density index, orientation dispersion index and tissue fraction (1-free water fraction). Diffusion metrics were sampled in 15 a priori regions of interest at four points along the cortical profile: cortical grey matter, the grey/white boundary, SWM (1mm below grey/white boundary) and SWM/deeper white matter (2mm below grey/white boundary). To estimate cross-sectional group differences, we used average marginal effects from linear mixed effect models of participants' diffusion metrics along the cortical profile. The SWM of young-onset Alzheimer's disease individuals had lower neurite density index compared to controls in five regions (superior and inferior parietal, precuneus, entorhinal and parahippocampus) (all P<0.05), and higher orientation dispersion index in three regions (fusiform, entorhinal and parahippocampus) (all P<0.05). Young-onset Alzheimer's disease individuals had lower fractional anisotropy in the SWM of two regions (entorhinal and parahippocampus) (both P<0.05) and higher fractional anisotropy within the postcentral region (P<0.05). Mean diffusivity in SWM was higher in the young-onset Alzheimer's disease group in the parahippocampal region (P<0.05) and lower in three regions (postcentral, precentral and superior temporal) (all P<0.05). In the overlying grey matter, disease-related changes were largely consistent with SWM findings when using neurite density index and fractional anisotropy, but appeared at odds with orientation dispersion and mean diffusivity SWM changes. Tissue fraction was significantly lower across all grey matter regions in young-onset Alzheimer's disease individuals (all P<0.001) but group differences reduced in magnitude and coverage when moving towards the SWM. These results show that microstructural changes occur within SWM and along the cortical profile in individuals with young-onset Alzheimer's disease. Lower neurite density and higher orientation dispersion suggests underlying SWM fibres undergo neurodegeneration and reorganisation, two effects previously indiscernible using standard diffusion tensor metrics in SWM.

Centrum semiovale white matter CT changes associated with normal ageing, Alzheimer's disease and late life depression with and without reversible dementia

1991 ◽  
Vol 21 (2) ◽  
pp. 321-328 ◽  
Author(s):  
Godfrey D. Pearlson ◽  
Peter V. Rabins ◽  
Alistair Burns
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Grey Matter ◽  
Late Life Depression ◽  
Centrum Semiovale ◽  
Dementia Syndrome ◽  
Ct Attenuation ◽  
Patient Groups ◽  
Attenuation Values

SYNOPSISA standardized, reliable means of assessing CT attenuation numbers in the centrum semiovale and surrounding grey matter was developed. This was applied to cranial CT scans of 60 normal controls (36 aged > 60 years), 25 elderly patients with major depression (14 of whom had the dementia syndrome of depression), and 10 patients with Alzheimer's disease (AD). Subjects received neuropsychological evaluation.Centrum semiovale (CSO) CT attenuation numbers decreased with increasing age for both white and grey matter. White matter attenuation values best discriminated elderly controls from the three patient groups. Both white and grey matter CSO attenuation values correlated with performance on a number of cognitive tasks.

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