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2,888 results for “Alzheimer's disease”
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 13.The results of the Corpus Calosum segmentation.
<p> The results of the Corpus Calosum segmentation. The six lines present in order: image zoom on the hippocampus area, manually segmented image, the result of the Caselle method , the result of the Chan&Vese method, the result of the Lankton method, the result of our method. Column 1 shows a healthy subject, column 2 MCI (primary stage), and the third column corresponds to an Alzheimer's subject (advanced stage). </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 12. Results of calculating the Hausdorff distances
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 11. Results of calculating the Hausdorff distance
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p> <p> Results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles Chan & Vese, Lanktom, our method) .and the ground truth about a MCI subject following segmentation of the hippocampus.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 10. The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p> <p>The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles, Chan & Vese, Lanktom, and our method) and the ground truth about a subject Normal following the segmentation of the hippocampus</p> <p> </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 9. A comparison between the results
<p>In figure 9 we present a comparison between the manual segmentation, Caselle, Chan&vese, Lankton and our method. </p> <p>A comparison between the results. Each column shows the superposition of the corresponding results: Caselle (yellow curve), Chan & Vese (curve blue), Lankton (red curve), our method (purple curve), and the ground truth (Green Curve) for a normal subject, MCI and AD.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 8. The results of the segmentation of the hippocampus.
<p>The results show the hippocampus segmentation using both Caselle, Chan&Vese, Lankton and our method. </p> <p>The results of the segmentation of the hippocampus. The six lines present: image zoom on the hippocampus area, manually segmented image, the result of the Caselle method, the result of the Chan&Vese method, the result of the Lankton method, the result of our method. Column 1 shows a healthy subject, column 2 shows a MCI (primary stage) and the third column corresponds to an Alzheimer's subject (advanced stage)</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 7. Training for classification
<p>AMED measures the average distance while HD measures the maximum distance between the two vectors. The aim of our method is to classify the test subject in three classes (N, MCI or AD), so for each vector element E we look for the four nearest neighbors. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 5. Improvement using variation
<p>In the figures below a constraint of the variation is used in order to show the limits. The contour may include more areas surrounding the hippocampus, which are not homogeneous with the desired area. Through the confidence interval of variation and priori knowledge can overcome these limitations. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 6. Improvement using Surface
<p>In the figures below, a constraint of the surface is used to show the limits. The contour may include the hippocampus and more areas surrounding it, which are homogeneous with the desired area. Through the surface of the confidence interval and priori knowledge can overcome these limitations. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 2. Three Corpus Calosum: Normal, MCI, AD
<p>The three figures above present the Corpus Callosum relating to three topics: Normal Topic by MCI (Mild Cognitive Impairment), Alzheimer’s topic. Secondly, we will present our clustering method to classify the test subject between 3 classes: N (Normal), MCI (Mild Cognitive Impairment), and AD (Alzheimer's disease). </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 1. Three hippocampus: Normal, MCI, AD
<p>In this context is our work: performing a diagnostic computer-aided system for detecting Alzheimer's disease. Like any diagnostic system, our system contains three parts: preprocessing, segmentation and classification. Initially, we will present a new segmentation method to segment the Hippocampus and Corpus Callosum regardless of the patient's condition. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 4. Training
<p> A. Segmentation It seeks to establish a model that describes the shape and typical fluctuations. This requires first the preparation of a learning base to reflect the possible variations in shape of the structure. The preparation of the training set Each shape will be modeled by a vector X, built by concatenating the coordinates of the characteristic points placed on its outline: X=(X1, X2,…...Xn) (1) The training set can be modeled by a set of vectors: {Xi} Where i = 1. . N {N number of sample images} and {Si} surface, {Vi} standard deviation of the Area. The principle of this step is be illustrated by the figure below. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 3.Proposed Computer Assisted Diagnosis
<p>The figure below presents our proposed Computer Assisted Diagnosis. Our CAD includes 3 steps: Preprocessing, Segmentation and Classification. For the step of preprocessing, we used the NLMS (Non Local Means) to improve the quality of image. For the step of segmentation: we have a learning phase to extract the different shapes and to determine the average shape. Our proposed automatic method is based on the deformable model. For the step of classification, we present a new supervised method to distinguish between Normal, MCI and AD. The figure below presents our proposed system.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 4. Training
<p>Each shape will be modeled by a vector X, built by concatenating the coordinates of the characteristic points placed on its outline: X=(X1, X2,…...Xn) (1) The training set can be modeled by a set of vectors: {Xi} Where i = 1. . N {N number of sample images} and {Si} surface, {Vi} standard deviation of the Area. The principle of this step is be illustrated by the figure below. </p>
Altered T-cell Reactivity to β-amyloid-related Antigens in Early Alzheimer's Disease
<p>The code generated during this study is available at this online repository with free access. Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Dr. Christoph Gericke (christoph.gericke@irem.uzh.ch).</p>
Spatiotemporal dysregulation of neuron-glia related genes and pro-/anti-inflammatory miRNAs in the 5xFAD mouse model of Alzheimer's disease - Supplementary data
<p><strong>Supplementary Table 1. </strong> Gene expression profile by RT-qPCR analysis revealed no significant differences when simultaneously considering the genotype (WT/5xFAD), age (6/9 months) and brain region (HPC, hippocampus/PFC, prefrontal cortex). </p> <p><strong>Supplementary Table 2.</strong> miRNA-target table for the Analyzed microRNAs and targets selected for this study. Obtained in the online platform https://www.mirnet.ca/</p> <p><strong>Supplementary Table 3. </strong> Node table for the analyzed microRNAs and targets selected for this study. We only considered miRNAs and/or targets with a node degree of at least 2. Obtained in the online platform https://www.mirnet.ca/</p> <p><strong>Supplementary Table 4.</strong> Bivariate Pearson’s correlation coefficients and respective p-values obtained between all miRNAs and genes.</p> <p><strong>Supplementary Table 5.</strong> List of microRNAs analyzed by RT-qPCR and their primer sequences.</p> <p><strong>Supplementary Table 6.</strong> List of genes and respective primer sequences used for mRNA analysis by RT-qPCR.</p> <p><strong>Supplementary Table 7.</strong> Raw data used for correlational analysis in hippocampus (HPC) and prefrontal cortex (PFC) using the cor function in RStudio software.</p>
Tractography Templates for White Matter Microstructure Analysis in Aging and Alzheimer's Disease
<p>This dataset contains tractography templates derived from several studies focused on white matter microstructure and its associations with neurodegenerative diseases, particularly Alzheimer's disease and Parkinsonism. These templates span key tracts relevant to both cognitive decline and motor function, including but not limited to the medial temporal lobe white matter, transcallosal fibers, sensorimotor tracts, and the fornix. The templates were developed and validated using advanced diffusion MRI techniques across various populations, including aging individuals, dementia patients, and those at risk of neurodegenerative conditions. This resource serves as a valuable tool for researchers investigating the structural integrity of white matter in both health and disease, allowing for cross-study comparability and enhancing the understanding of neurodegenerative processes.</p>
Data for "Deep learning-based model for diagnosing Alzheimer's disease and tauopathies"
<p>Image datasets and tuned models used in the paper (Koga et al., 2021). Data.zip contains image and text files for training models. Test.zip contains 12 images from 4 patients, which are a part of the hold-out dataset images used in the paper. There are 9 CSV files, which contain the results of tau burden quantification. Python code is available at GitHub (<a href="https://github.com/Koga-MD/DL-Tauopathies">https://github.com/Koga-MD/DL-Tauopathies</a>). </p>
Single-synapse analyses of Alzheimer's disease implicate pathologic tau, DJ1, CD47, and ApoE
<p>Synaptic molecular characterization is limited for Alzheimer's disease (AD). Our newly invented mass cytometry-based method, Synaptometry by Time of Flight (SynTOF), was used to measure 38 antibody probes in approximately 17 million single-synapse events from human brains without pathologic change or with pure AD or Lewy body disease (LBD), non-human primates (NHP), and PS/APP mice. Synaptic molecular integrity in humans and NHP was similar. Although not detected in human synapses, Aβ was in PS/APP mice single-synapse events. Clustering and pattern identification of human synapses showed expected disease-specific differences, like increased hippocampal pathologic tau in AD and reduced caudate dopamine transporter in LBD, and revealed novel findings including increased hippocampal CD47 and lowered DJ1 in AD and higher ApoE in AD with dementia. Our results were independently supported by multiplex ion beam imaging of intact tissue. This highlights the higher depth and breadth of insight on neurodegenerative diseases obtainable through SynTOF.</p>
Dataset for accumulation of pTau231 at the post synaptic density in Alzheimer's disease
<p>Dataset included from Journal of Alzheimer's disease publication "Accumulation of pTau231 at postsynaptic density in Alzheimer's Disease. This includes validation of synaptosomal fraction richness in Figure 5, tTau and pTau231 Simoa Quanterix synaptosomal fraction data for Supplementary figure 2, entorhinal cortex PSD95 P4 adjusted total tau and pTau231 values from Simoa Quanterix for generation of model shown in figure 4, quantitative AMPA immunohistochemical staining data used for generating figure 2, NanoString protein panel data used to generate heatmap in figure 7, quantitative immunofluorescence staining data for pTau214 and PSD95 colocalization studies in figure 3, Braak stage 2 case NanoString protein data for intra-case regional comparisons of disease progression, and synaptosome Simoa total tau and pTau231 data in addition to western blot P4 PSD95 data that was used for generation of figures 4 and 6.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.