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2,888 results for “Alzheimer's disease”
Extraction of clinical phenotypes for Alzheimer disease dementia from clinical notes using natural language processing
<p><strong>Objectives</strong></p> <p>There is much interest in utilizing clinical data for developing prediction models for Alzheimer disease (AD) risk, progression, and outcomes. Existing studies have mostly utilized curated research registries, image analysis, and structured Electronic Health Record (EHR) data. However, much critical information resides in relatively inaccessible unstructured clinical notes within the EHR.</p> <p><strong>Materials and Methods</strong></p> <p>We developed a natural language processing (NLP)-based pipeline to extract AD-related clinical phenotypes, documenting strategies for success and assessing the utility of mining unstructured clinical notes. We evaluated the pipeline against gold-standard manual annotations performed by two clinical dementia experts for AD-related clinical phenotypes including medical comorbidities, biomarkers, neurobehavioral test scores, behavioral indicators of cognitive decline, family history, and neuroimaging findings.</p> <p><strong>Results</strong></p> <p>Documentation rates for each phenotype varied in the structured versus unstructured EHR. Inter-annotator agreement was high (Cohen's kappa = 0.72–1) and positively correlated with the NLP-based phenotype extraction pipeline's performance (average F1-score = 0.65-0.99) for each phenotype.</p> <p><strong>Discussion</strong></p> <p>We developed an automated NLP-based pipeline to extract informative phenotypes that may improve the performance of eventual machine-learning predictive models for AD. In the process, we examined documentation practices for each phenotype relevant to the care of AD patients and identified factors for success.</p> <p><strong>Conclusion</strong></p> <p>Success of our NLP-based phenotype extraction pipeline depended on domain-specific knowledge and focus on a specific clinical domain instead of maximizing generalizability. </p>
dataset related to article "A NOVEL BIO-INSPIRED STRATEGY TO PREVENT AMYLOIDOGENESIS AND SYNAPTIC DAMAGE IN ALZHEIMER'S DISEASE"
<p><strong>Levels of A</strong><strong>beta40, Abeta42 and aggregated Abeta</strong></p> <p><strong>results obtained from plaque count</strong></p> <p><strong>densitometric analysis of ctf and synaptic proteins</strong></p> <p><strong>levels of antibodies against Abeta42 and Abeta1-6</strong></p>
datset related to article "THE NOVEL I213S MUTATION IN PSEN1 GENE IS LOCATED IN A HOTSPOT CODON ASSOCIATED WITH FAMILIAL EARLY-ONSET ALZHEIMER'S DISEASE"
<p><strong>Electropherogram of the proband psen1 exon 7</strong></p> <p><strong>ngs analysis of causal and risk genes associated to dementia</strong></p>
Synaptic oligomeric tau in Alzheimer's disease – a potential culprit in the spread of tau pathology through the brain
<p>In Alzheimer’s disease (AD), fibrillar tau pathology accumulates and spreads through the brain and synapses are lost. Evidence from mouse models indicates that tau spreads trans-synaptically from pre- to postsynapses and that oligomeric tau is synaptotoxic, but data on synaptic tau in human brain is scarce. Here we used sub-diffraction-limit microscopy to study synaptic tau accumulation in post-mortem temporal and occipital cortices of human AD and control donors. Oligomeric tau is present in both pre- and postsynaptic terminals even in areas without abundant fibrillar tau deposition. Further, there is a higher proportion of oligomeric tau compared to phosphorylated or misfolded tau found at synaptic terminals. These data suggest that accumulation of oligomeric tau in synapses is an early event in disease pathogenesis, and that tau pathology may progress through the brain via trans-synaptic spread in human disease. Thus, specifically reducing oligomeric tau at synapses may be a promising therapeutic strategy for AD.</p>
Prazosin for Agitation in Alzheimer's Disease
ClinicalTrials.gov study NCT03710642. IPD Sharing: YES. Countries: 1. Publications: 6.
Safety, Tolerability, PK and PD of Posiphen® in Subjects With Early Alzheimer's Disease
ClinicalTrials.gov study NCT02925650. IPD Sharing: YES. Countries: 1. Publications: 2.
A Study to Evaluate the Efficacy and Safety of ABBV-8E12 in Participants With Early Alzheimer's Disease
ClinicalTrials.gov study NCT02880956. IPD Sharing: YES. Countries: 11. Publications: 1.
Nicotinamide as an Early Alzheimer's Disease Treatment
ClinicalTrials.gov study NCT03061474. IPD Sharing: YES. Countries: 1. Publications: 4.
Extraction of clinical phenotypes for Alzheimer disease dementia from clinical notes using natural language processing
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Searching for the cellular underpinnings of the selective vulnerability to tauopathic insults in Alzheimer's disease
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Data from: Pathways underlying selective neuronal vulnerability in Alzheimer's disease: Contrasting the vulnerable locus coeruleus to the resilient substantia nigra
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Data from: Longitudinal three-photon imaging for tracking amyloid plaques and vascular degeneration in a mouse model of Alzheimer’s disease
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Single-synapse analyses of Alzheimer’s disease implicate pathologic tau, DJ1, CD47, and ApoE
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Longitudinal analysis of the microbiome and metabolome in the 5xfAD mouse model of Alzheimer's disease
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Alzheimer's Disease versus Bipolar Disorder versus Health Control MRI data and processed results
<p><strong>README</strong></p> <p>The data is structured as follows:</p> <p>Clinical_data folder contains the .csv that can be read by spreadsheet software, as well as from Python, Matlab or R. There are separate files for each biomarker. The file "clinical_data_id_age_gender.csv" contains the numerical random key of the patient for anonymity, diagnostic key, age and gender for each entry in the other files. The file "clinical_data_corrected.csv" can be ignored.</p> <p>Diagnostic keywords: "crl" == healthy control, "tb" == bipolar disorder, "ea" == Alheimer's disease</p> <p>Imaging data is nifti encoded. The name of the file starts with the diagnostic key followed by the numerical random key and some nemotechnic for the contents. For instance: "crl_132_diff_dti_FA_FA_to_target.nii.gz" is the spatially normalized FA data of healthy control 132. Imaging data can be read with FSL, SPM, and any other nifti reading soft.</p> <p>Imaging folders contain the following data<br> DWI_origin - > the original diffusion weighted MRI data and their corresponding b-vector values</p> <p>FA - > the FA coefficients computed using FSL</p> <p>FA_to_target - > the FA volumes registered to MNI template using FSL tools</p> <p>T1_preprocessed - > the T1-weighted volumes at 1mm resolution registered to the MNI template using FSL no-linear registration tools</p> <p>T1_VBM_SPM_1mm - > the results of applying SPM implementation of voxel based morphometry (VBM) on the T1-weighted data, including results of the correlation between biomarkers and the detected clusters . Results can be checked using SPM (https://www.fil.ion.ucl.ac.uk/spm/)</p> <p>TBSS_results -> contains track based spatial statistics (TBSS) results obtained with FSL software (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TBSS)</p> <p> </p> <p><strong>Publications using this dataset</strong></p> <p>M. Graña, M. Termenon, A. Savio, A. Gonzalez-Pinto, J. Echeveste, J. M. Pérez, A. Besga, Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson’s correlation, Neuroscience letters,Volume 502, Issue 3, 20 September 2011, Pages 225-229</p> <p>A. Besga, M. Termenon, M. Graña, J. Echeveste, J. M. Perez, A. Gonzalez-Pinto "Discovering Alzheimer's disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features, <strong>Neuroscience Letters</strong>, Volume 520, Issue 1, 27 June 2012, Pages 71–76.</p> <p>M. Termenon, M. Graña, A. Besga, J. Echeveste, A. Gonzalez-Pinto, Lattice Independent Component Analysis feature selection on Diffusion Weighted Imaging for Alzheimer’s Disease Classification, Neurocomputing (2013) Volume 114, 19 August 2013, Pages 132–141</p> <p>Ariadna Besga, Itxaso González-Ortega, Enrique Echeburúa, Alexandre Savio, Borja Ayerdi, Darya Chyzhyk, Jose LM Madrigal, Juan C. Leza, Manuel Graña, Ana González-Pinto, "Discrimination between Alzheimer’s Disease and Late Onset Bipolar Disorder using multivariate analysis" Frontiers in Aging Neuroscience, 7:231</p> <p>Ariadna Besga-Basterra, Darya Chyzhyk, Itxaso González-Ortega, Alexandre Savio, Borja Ayerdi, Jon Echeveste, Manuel Graña, Ana González-Pinto, Eigenanatomy on fractional anisotropy imaging provides white matter anatomical features discriminating between Alzheimer’s Disease and Late Onset Bipolar Disorder, Current Alzheimer Research, 13(5): 557 - 565 (2016)</p> <p>Ariadna Besga, Darya Chyzhyk, Itxaso Gonzalez Ortega, Jon Echeveste, Marina Grana-Lecuona, Manuel Grana, Ana González-Pinto, White Matter Tract Integrity in Alzheimer’s Disease versus Late Onset Bipolar Disorder and its Correlation with Systemic Inflammation and Oxidative Stress Biomarkers, Frontiers in Aging Neuroscience, 9:179 (2017)</p>
Genome-wide association results from: Transcriptomic stratification of late-onset Alzheimer's cases reveals novel genetic modifiers of disease pathology
<p>Late-Onset Alzheimer's disease (LOAD) is a common, complex genetic disorder well-known for its heterogeneous pathology. The genetic heterogeneity underlying common, complex diseases poses a major challenge for targeted therapies and the identification of novel disease-associated variants. Case-control approaches are often limited to examining a specific outcome in a group of heterogenous patients with different clinical characteristics. Here, we developed a novel approach to define relevant transcriptomic endophenotypes and stratify decedents based on molecular profiles in three independent human LOAD cohorts. By integrating post-mortem brain gene co-expression data from 2114 human samples with LOAD, we developed a novel quantitative, composite phenotype that can better account for the heterogeneity in genetic architecture underlying the disease. We used iterative weighted gene co-expression network analysis (WGCNA) to reduce data dimensionality and to isolate gene sets that are highly co-expressed within disease subtypes and represent specific molecular pathways. We then performed single variant association testing using whole genome-sequencing data for the novel composite phenotype in order to identify genetic loci that contribute to disease heterogeneity. Distinct LOAD subtypes were identified for all three study cohorts (two in ROSMAP, three in Mayo Clinic, and two in Mount Sinai Brain Bank). Single variant association analysis identified a genome-wide significant variant in <i>TMEM106B</i> (p-value < 5´10<sup>-8</sup>, rs1990620<sup><span><span>G</span></span></sup>) in the ROSMAP cohort that confers protection from the inflammatory LOAD subtype. Taken together,<b> </b>our novel approach can be used to stratify LOAD into distinct molecular subtypes based on affected disease pathways.</p>
Data from: Sleep, inflammation, and cognitive behavior of aged wild-type mice subjected to diffuse brain injury and aged 3xTg-AD mice as a model of Alzheimer's disease
<p>Identifying differential responses between sexes following traumatic brain injury (TBI) can elucidate the mechanisms behind disease pathology. Peripheral and central inflammation in the pathophysiology of TBI can increase sleep in male rodents, but this remains untested in females. We hypothesized that diffuse TBI would increase inflammation and sleep in males more so than in females. Diffuse TBI was induced in C57BL/6J mice and serial blood samples were collected (baseline, 1, 5, 7 days post‐injury [DPI]) to quantify peripheral immune cell populations and sleep regulatory cytokines. Brains and spleens were harvested at 7DPI to quantify central and peripheral immune cells, respectively. Mixed‐effects regression models were used for data analysis. Female TBI mice had 77%–124% higher IL‐6 levels than male TBI mice at 1 and 5DPI, whereas IL‐1β and TNF‐α levels were similar between sexes at all timepoints. Despite baseline sex differences in blood‐measured Ly6Chigh monocytes (females had 40% more than males), TBI reduced monocytes by 67% in TBI mice at 1DPI. Male TBI mice had 31%–33% more blood‐measured and 31% more spleen‐measured Ly6G+ neutrophils than female TBI mice at 1 and 5DPI, and 7DPI, respectively. Compared with sham, TBI increased sleep in both sexes during the first light and dark cycles. Male TBI mice slept 11%–17% more than female TBI mice, depending on the cycle. Thus, sex and TBI interactions may alter the peripheral inflammation profile and sleep patterns, which might explain discrepancies in disease progression based on sex.</p>
miRNA counts identified by RNA seq in the caudate nucleus of patients neuropathologically diagnosed with late-onset Alzheimer's disease (non-carriers and carriers of intermediate expansions in the HTT gene) and healthy subjects.
<p>These data include the raw miRNA counts identified by RNA-seq in <em>post mortem</em> caudate nucleus samples. Samples with identifier <strong>A</strong> belong to patients with a neuropathological diagnosis of late-onset Alzheimer's disease. Group <strong>B</strong> samples belong to patients with the same neuropathological diagnosis, but carrying CAG expansions in the intermediate range (27-35 CAG) in the <em>HTT</em> gene. Finally, group <strong>C </strong>samples belong to healthy subjects without neuropathological diagnosis. </p> <p> </p>
Mechanical stimulation prevents impairment of axon growth and overcompensates microtubules destabilization in cellular models of Alzheimer's disease related Tau pathology
<p>Data and metadata associated to a publication 10.3389/fmed.2025.1519628</p>
Impact of Image Processing Settings for Radiomic Features in Alzheimer's Disease Using 18F-FDG and 11C-PIB PET Scans
<p>Radiomics is an established method for calculating features for computer-aided diagnosis and has been vastly applied to oncological studies. This study aimed to assess the impact of image processing in radiomic features in neuroimaging. Fifteen Alzheimer's disease subjects and 18 healthy individuals underwent [18F]-2-fluoro-2-deoxy-D-glucose (FDG) and 11C-labelled Pittsburgh Compound B (PIB) PET scans. T1-MRI scans were used for cerebellar and grey matter (GM), and white matter (WM) tissue delineation. PET images were registered to MRI (MR space) and transformed to MNI space. All images were normalized to cerebellar uptake (SUVR). All possible combinations of the following settings were considered to extract feature values: (1)tracer: FDG or PIB; (2)space: MR or MNI space; (3)discretization: fixed bin number (BN) of 64, fixed bin sizes (BS) of 0.05 or 0.25; and (4)volume of interest (VOI): GM, WM, or BRAIN (GM+WM). Features that correlated (>0.9) to traditional metrics (average VOI SUVR and volume) in any configuration were removed. Correlation of feature values between configurations, redundancy, and harmonization of feature values were tested. Image processing settings highly affect radiomic feature values and should be carefully taken into consideration during study design and should be properly reported.</p><p> </p><p>The enclosed datasets refer to the work developed at the University Medical Center Groningen and consists of extracted feature values used in the publication.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.