scholarly journals The association between biomarkers in cerebrospinal fluid and structural changes in the brain in patients with Alzheimer's disease

2013 ◽  
Vol 275 (4) ◽  
pp. 418-427 ◽  
Author(s):  
X. Li ◽  
T.-Q. Li ◽  
N. Andreasen ◽  
M. K. Wiberg ◽  
E. Westman ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Structural Changes ◽  
The Brain

scholarly journals Cerebrospinal fluid p-tau217 performs better than p-tau181 as a biomarker of Alzheimer’s disease

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Shorena Janelidze ◽  
Erik Stomrud ◽  
Ruben Smith ◽  
Sebastian Palmqvist ◽  
Niklas Mattsson ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Amyloid Β ◽  
Pet Tracer ◽  
Positron Emission ◽  
Diagnostic Work ◽  
Work Up ◽  
The Brain ◽  
Better Than

AbstractCerebrospinal fluid (CSF) p-tau181 (tau phosphorylated at threonine 181) is an established biomarker of Alzheimer’s disease (AD), reflecting abnormal tau metabolism in the brain. Here we investigate the performance of CSF p-tau217 as a biomarker of AD in comparison to p-tau181. In the Swedish BioFINDER cohort (n = 194), p-tau217 shows stronger correlations with the tau positron emission tomography (PET) tracer [18F]flortaucipir, and more accurately identifies individuals with abnormally increased [18F]flortaucipir retention. Furthermore, longitudinal increases in p-tau217 are higher compared to p-tau181 and better correlate with [18F]flortaucipir uptake. P-tau217 correlates better than p-tau181 with CSF and PET measures of neocortical amyloid-β burden and more accurately distinguishes AD dementia from non-AD neurodegenerative disorders. Higher correlations between p-tau217 and [18F]flortaucipir are corroborated in an independent EXPEDITION3 trial cohort (n = 32). The main results are validated using a different p-tau217 immunoassay. These findings suggest that p-tau217 might be more useful than p-tau181 in the diagnostic work up of AD.

scholarly journals Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer’s disease

2020 ◽  
Vol 6 (43) ◽  
pp. eaaz9360 ◽  
Author(s):  
Lenora Higginbotham ◽  
Lingyan Ping ◽  
Eric B. Dammer ◽  
Duc M. Duong ◽  
Maotian Zhou ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Wide Spectrum ◽  
Csf Biomarkers ◽  
Protein Biomarkers ◽  
Cerebrospinal Fluid Biomarkers ◽  
Brain Proteome ◽  
The Brain ◽  
Underlying Pathophysiology

Alzheimer’s disease (AD) lacks protein biomarkers reflective of its diverse underlying pathophysiology, hindering diagnostic and therapeutic advancements. Here, we used integrative proteomics to identify cerebrospinal fluid (CSF) biomarkers representing a wide spectrum of AD pathophysiology. Multiplex mass spectrometry identified ~3500 and ~12,000 proteins in AD CSF and brain, respectively. Network analysis of the brain proteome resolved 44 biologically diverse modules, 15 of which overlapped with the CSF proteome. CSF AD markers in these overlapping modules were collapsed into five protein panels representing distinct pathophysiological processes. Synaptic and metabolic panels were decreased in AD brain but increased in CSF, while glial-enriched myelination and immunity panels were increased in brain and CSF. The consistency and disease specificity of panel changes were confirmed in >500 additional CSF samples. These panels also identified biological subpopulations within asymptomatic AD. Overall, these results are a promising step toward a network-based biomarker tool for AD clinical applications.

scholarly journals Comparison of machine learning approaches for enhancing Alzheimer’s disease classification

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10549
Author(s):  
Qi Li ◽  
Mary Qu Yang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Spatial Information ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Disease Classification ◽  
Support Vector ◽  
The Brain

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.

Cerebrospinal Fluid pH and Monoamine and Glucolytic Metabolites in Alzheimer's Disease

1974 ◽  
Vol 124 (580) ◽  
pp. 280-287 ◽  
Author(s):  
C. G. Gottfries ◽  
Åke Kjällquist ◽  
Urban Pontén ◽  
B. E. Roos ◽  
G. Sundbärg
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Brain Tissue ◽  
Homovanillic Acid ◽  
Monoamine Metabolites ◽  
Hydroxyindoleacetic Acid ◽  
The Brain ◽  
Valid Information

Determinations of acid monoamine metabolites, such as homovanillic acid (HVA) and 5-hydroxyindoleacetic acid (5-HIAA), in cerebrospinal fluid (CSF) give valid information on the metabolism of the corresponding amines in the brain tissue (Moir et al., 1970; Roos, 1970). The monoamine metabolites in the CSF are related to age. The concentrations of HVA and 5-HIAA increase with age (Gottfries et al., 1971). Probenecid blocks the elimination of HVA and 5-HIAA from brain tissue to blood (Neff et al., 1964, 1967; Werdinius, 1966) and from CSF to blood (Guldberg et al., 1966; Olsson and Roos, 1968). Probenecid thus normally induces an increase in the concentrations of the acid monoamine metabolites in the CSF, which is related to the turnover of monoamines in the brain tissue.

In Vivo Microdialysis in Mice Captures Changes in Alzheimer’s Disease Cerebrospinal Fluid Biomarkers Consistent with Developing Pathology

2021 ◽  
pp. 1-14
Author(s):  
Christiana Bjorkli ◽  
Claire Louet ◽  
Trude Helen Flo ◽  
Mary Hemler ◽  
Axel Sandvig ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Mouse Model ◽  
Amyloid Β ◽  
Csf Biomarkers ◽  
Intracerebral Microdialysis ◽  
Preclinical Models ◽  
The Brain

Background: Preclinical models of Alzheimer’s disease (AD) can provide valuable insights into the onset and progression of the disease, such as changes in concentrations of amyloid-β (Aβ) and tau in cerebrospinal fluid (CSF). However, such models are currently underutilized due to limited advancement in techniques that allow for longitudinal CSF monitoring. Objective: An elegant way to understand the biochemical environment in the diseased brain is intracerebral microdialysis, a method that has until now been limited to short-term observations, or snapshots, of the brain microenvironment. Here we draw upon patient-based findings to characterize CSF biomarkers in a commonly used preclinical mouse model for AD. Methods: Our modified push-pull microdialysis method was first validated ex vivo with human CSF samples, and then in vivo in an AD mouse model, permitting assessment of dynamic changes of CSF Aβ and tau and allowing for better translational understanding of CSF biomarkers. Results: We demonstrate that CSF biomarker changes in preclinical models capture what is observed in the brain; with a decrease in CSF Aβ observed when plaques are deposited, and an increase in CSF tau once tau pathology is present in the brain parenchyma. We found that a high molecular weight cut-off membrane allowed for simultaneous sampling of Aβ and tau, comparable to CSF collection by lumbar puncture in patients. Conclusion: Our approach can further advance AD and other neurodegenerative research by following evolving neuropathology along the disease cascade via consecutive sampling from the same animal and can additionally be used to administer pharmaceutical compounds and assess their efficacy (Bjorkli, unpublished data).

scholarly journals Prediction of Alzheimer’s Disease Based on Coordinate-Dense Attention Network

2021 ◽  
Author(s):  
Yongmei Tang ◽  
Xiangyun Liao ◽  
Weixin Si ◽  
Zhigang Ning
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Brain Aging ◽  
Significant Feature ◽  
Convolutional Network ◽  
Basic Model ◽  
Prediction Scheme ◽  
Feature Information ◽  
The Brain

Alzheimer’s disease (AD) is a degenerative disease of the nervous system. Mild cognitive impairment (MCI) is a condition between brain aging and dementia. The prediction will be divided into stable sMCI and progressive pMCI as a binary task. Structural magnetic resonance imaging (sMRI) can describe structural changes in the brain and provide a diagnostic method for the detection and early prevention of Alzheimer’s disease. In this paper, an automatic disease prediction scheme based on MRI was designed. A dense convolutional network was used as the basic model. By adding a channel attention mechanism to the model, significant feature information in MRI images was extracted, and the unimportant features were ignored or suppressed. The proposed framework is compared with the most advanced methods, and better results are obtained.

scholarly journals Integrated Proteomics Reveals Brain-Based Cerebrospinal Fluid Biomarkers in Asymptomatic and Symptomatic Alzheimer’s Disease

2019 ◽  
Author(s):  
Lenora Higginbotham ◽  
Lingyan Ping ◽  
Eric B. Dammer ◽  
Duc M. Duong ◽  
Maotian Zhou ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Postmortem Brain ◽  
Csf Biomarkers ◽  
Amyloid Accumulation ◽  
Cerebrospinal Fluid Biomarkers ◽  
Novel Biomarkers ◽  
The Brain ◽  
Brain Tissues

AbstractAlzheimer’s disease (AD) features a complex web of pathological processes beyond amyloid accumulation and tau-mediated neuronal death. To meaningfully advance AD therapeutics, there is an urgent need for novel biomarkers that comprehensively reflect these disease mechanisms. Here we applied an integrative proteomics approach to identify cerebrospinal fluid (CSF) biomarkers linked to a diverse set of pathophysiological processes in the diseased brain. Using multiplex proteomics, we identified >3,500 proteins across 40 CSF samples from control and AD patients and >12,000 proteins across 48 postmortem brain tissues from control, asymptomatic AD (AsymAD), AD, and other neurodegenerative cases. Co-expression network analysis of the brain tissues resolved 44 protein modules, nearly half of which significantly correlated with AD neuropathology. Fifteen modules robustly overlapped with proteins quantified in the CSF, including 271 CSF markers highly altered in AD. These 15 overlapping modules were collapsed into five panels of brain-linked fluid markers representing a variety of cortical functions. Neuron-enriched synaptic and metabolic panels demonstrated decreased levels in the AD brain but increased levels in diseased CSF. Conversely, glial-enriched myelination and immunity panels were highly increased in both the brain and CSF. Using high-throughput proteomic analysis, proteins from these panels were validated in an independent CSF cohort of control, AsymAD, and AD samples. Remarkably, several validated markers were significantly altered in AsymAD CSF and appeared to stratify subpopulations within this cohort. Overall, these brain-linked CSF biomarker panels represent a promising step toward a physiologically comprehensive tool that could meaningfully enhance the prognostic and therapeutic management of AD.

scholarly journals Altered d-glucose in brain parenchyma and cerebrospinal fluid of early Alzheimer’s disease detected by dynamic glucose-enhanced MRI

2020 ◽  
Vol 6 (20) ◽  
pp. eaba3884 ◽  
Author(s):  
Jianpan Huang ◽  
Peter C. M. van Zijl ◽  
Xiongqi Han ◽  
Celia M. Dong ◽  
Gerald W. Y. Cheng ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Glucose Uptake ◽  
Brain Parenchyma ◽  
Protein Accumulation ◽  
Altered Glucose ◽  
Early Alzheimer’S Disease ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Altered cerebral glucose uptake is one of the hallmarks of Alzheimer’s disease (AD). A dynamic glucose-enhanced (DGE) magnetic resonance imaging (MRI) approach was developed to simultaneously monitor d-glucose uptake and clearance in both brain parenchyma and cerebrospinal fluid (CSF). We observed substantially higher uptake in parenchyma of young (6 months) transgenic AD mice compared to age-matched wild-type (WT) mice. Notably lower uptakes were observed in parenchyma and CSF of old (16 months) AD mice. Both young and old AD mice had an obviously slower CSF clearance than age-matched WT mice. This resembles recent reports of the hampered CSF clearance that leads to protein accumulation in the brain. These findings suggest that DGE MRI can identify altered glucose uptake and clearance in young AD mice upon the emergence of amyloid plaques. DGE MRI of brain parenchyma and CSF has potential for early AD stratification, especially at 3T clinical field strength MRI.

scholarly journals Plasma p-Tau181 Outperforms Cerebrospinal Fluid Total Tau in Terms of Alzheimer’s Disease Diagnosis

2021 ◽  
Author(s):  
Fardin Nabizadeh ◽  
Mohammad Balabandian ◽  
Mohammad Reza Rostami ◽  
Richard T. Ward ◽  
Niloufar Ahmadi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Structural Changes ◽  
Clinical Symptoms ◽  
Disease Diagnosis ◽  
Cross Sectional ◽  
Total Tau ◽  
Preclinical Ad ◽  
Brain Changes

Abstract Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with dementia, and is a serious concern for the health of individuals and government health care systems worldwide. Gray matter atrophy and white matter damage are major contributors to cognitive deficits experienced by patients with AD, as seen through magnetic resonance imaging (MRI). Many of these brain changes associated with AD begin to occur about 15 years before the onset of initial clinical symptoms. Therefore, it is critical to find biomarkers reflective of these brain changes associated with AD to identify this disease and monitor its prognosis and development. The level of hyperphosphorylated tau 181 (p-Tau181) in the plasma has been recently considered as a novel biomarker for the presence of AD, with increased levels in patients with AD, preclinical AD, and those experiencing mild cognitive impairment (MCI). In the current study, we examined the association of cerebrospinal fluid (CSF) and plasma levels of p-Tau181 with structural brain changes pertaining to cortical thickness, cortical volume, surface area, and subcortical volume in MCI patients. In this cross-sectional study we included the information of 461 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The results of voxel-wise partial correlation analyses showed a significant negative correlation between the increased levels of plasma p-Tau181, CSF total tau, and CSF p-Tau181 and structural changes in widespread brain regions. These results provide evidence for the use of plasma p-Tau181 as a diagnostic marker for structural changes in the brain associated with the early stages of AD and neurodegeneration.

scholarly journals Cerebrospinal fluid p-tau217 performs better than p-tau181 as a biomarker of Alzheimer’s disease

2020 ◽  
Author(s):  
Shorena Janelidze ◽  
Erik Stomrud ◽  
Ruben Smith ◽  
Sebastian Palmqvist ◽  
Niklas Mattsson ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Amyloid Β ◽  
Pet Tracer ◽  
Diagnostic Work ◽  
Work Up ◽  
The Brain ◽  
Diagnostic Work Up ◽  
Better Than

ABSTRACTCerebrospinal fluid (CSF) p-tau181 (tau phosphorylated at threonine 181) is an established biomarker of Alzheimer’s disease (AD) reflecting abnormal tau metabolism in the brain. Tau can be phosphorylated at multiple other sites including threonine 217, and here we investigated the performance of CSF p-tau217 as a biomarker of AD in comparison to p-tau181. In the Swedish BioFINDER cohort (n=194), p-tau217 had stronger correlations with the tau PET tracer [18F]flortaucipir, and more accurately identified individuals with abnormally increased [18F]flortaucipir retention. Furthermore, longitudinal increases in p-tau217 were higher compared to p-tau181 and better correlated with [18F]flortaucipir retention. P-tau217 correlated better than p-tau181 with PET measures of neocortical amyloid-β burden and more accurately distinguished AD dementia from non-AD neurodegenerative disorders. Higher correlations between p-tau217 and [18F]flortaucipir were corroborated in an independent EXPEDITION3 trial cohort (n=32). These findings suggest that p-tau217 might be more useful than p-tau181 in the diagnostic work up of AD.

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