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195 results for “Brain health”
The Brain Health Study: A Pragmatic, Patient-Centered Trial
ClinicalTrials.gov study NCT05356702. IPD Sharing: YES. Countries: 1. Publications: 3.
Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health
<p>This repository houses fully-adjusted methylome-wide association study (MWAS) summary statistics for 4,231 SomaScan protein measurements. These were generated as part of the study titled ‘Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health’ by Gadd <em>et al</em>. The Stratifying Resilience and Depression Longitudinally (STRADL) cohort used in this study is a subset of individuals from Generation Scotland: The Scottish Family Health Study. There were 744 individuals with complete protein and DNA methylation measurements available at 772,619 CpG probes. MWAS were performed with protein residuals as the outcome and DNA methylation as the exposure, using the Omics-data-based complex trait analysis (OSCA) software.</p> <p>Fully-adjusted models were run using M-values that were adjusted for age, sex, DNA methylation-derived immune cell estimates, depression status, DNA methylation batch and set, body mass index and a DNA methylation-derived smoking score. Protein levels were rank-based inverse normalised and scaled to have a mean of 0 and standard deviation of 1. Protein levels were residualised by age, sex, available pQTLs, technical covariates and 20 genetic principal components.</p> <p>Four of the 4,235 protein MWAS models did not converge (15509-2 - NAGLU, 15584-9 - CFHR2, 4407-10 - MST1 and 6402-8 - PILRA). Therefore, summary statistics are provided for 4,231 protein levels.</p> <p>Each protein MWAS summary statistics file has been saved with the following naming system: "MWAS_SeqId_Protein_gene.csv". For example, the protein with gene name CRYBB2 and SeqId 10000-28 has the following file name: "MWAS_10000-28_CRYBB2.csv".</p> <p>The SeqIds, UniProt codes, gene names and full UniProt names can be found in "annotation_formatted_for_paper.csv" and the full summary statistics are found within "compressed-protein-ewas.tar.gz".</p> <p>Please contact either <a href="mailto:riccardo.marioni@ed.ac.uk">riccardo.marioni@ed.ac.uk</a> or <a href="mailto:danni.gadd@ed.ac.uk">danni.gadd@ed.ac.uk</a> for any queries. All code is available at the following Github repository: <a href="https://github.com/DanniGadd/Epigenome-and-phenome-wide-study-of-brain-health-outcomes">https://github.com/DanniGadd/Epigenome-and-phenome-wide-study-of-brain-health-outcomes</a>.</p>
Investigating the Effects of Beef Consumption on Cognitive and Brain Health
ClinicalTrials.gov study NCT06690892. IPD Sharing: YES. Countries: 1. Publications: 2.
Independent Walking for Brain Health
ClinicalTrials.gov study NCT03058146. IPD Sharing: NO. Countries: 1. Publications: 2.
The Effects of Fish Oil Supplementation on the Brain Health of Collegiate Football Athletes
ClinicalTrials.gov study NCT04796207. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Efficacy of a Multimodal Brain Health Intervention for Older African Americans
ClinicalTrials.gov study NCT04863378. IPD Sharing: NO. Countries: 1. Publications: 1.
Exercise Training to Improve Brain Health in Older HIV+ Individuals
ClinicalTrials.gov study NCT02663934. IPD Sharing: UNDECIDED. Countries: 1. Publications: 32.
Kimel Family Centre for Brain Health and Wellness
ClinicalTrials.gov study NCT06933667. IPD Sharing: YES. Countries: 1. Publications: 1.
Investigating the Cognitive and Brain Health Benefits of Lean Pork Consumption
ClinicalTrials.gov study NCT07031076. IPD Sharing: YES. Countries: 1. Publications: 3.
Brain Health Support Program
ClinicalTrials.gov study NCT05347966. IPD Sharing: Not stated. Countries: 1. Publications: 2.
The Brain Health Study: A Pragmatic, Patient-Centered Trial
ClinicalTrials.gov study NCT05905796. IPD Sharing: YES. Countries: 1. Publications: 4.
Community-Based Social Connection Intervention Program to Improve Cardiovascular and Brain Health
ClinicalTrials.gov study NCT07319663. IPD Sharing: NO. Countries: 1. Publications: 1.
Efficacy of Xeomin for Migraines in Patients With Traumatic Brain Injuries vs. Anomalous Health Incidents
ClinicalTrials.gov study NCT07267819. IPD Sharing: NO. Countries: 1. Publications: 16.
Exercise, Brain, and Cardiovascular Health
ClinicalTrials.gov study NCT03841669. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Relation between 20-year income volatility and brain health in midlife: the CARDIA study
Objective: Income volatility presents a growing public health threat. To our knowledge, no previous study examined the relationship between income volatility, cognitive function and brain integrity. Methods: We studied 3,287 participants aged 23 to 35 years in 1990 from the Coronary Artery Risk Development in Young Adults prospective cohort study. Income volatility data were created using income data collected from 1990 to 2010 and defined as standard deviation of percent change in income and number of income drops >=25% (categorized as 0, 1, or 2+). In 2010, cognitive tests (n=3,287) and brain scans (n=716) were obtained. Results: After covariate adjustment, higher income volatility was associated with worse performances on processing speed (β=-1.09, 95%CI=-1.73, -0.44) and executive functioning (β=2.53, 95%CI:0.60, 4.50) but not on verbal memory (β=-0.02, 95%CI:-0.16, 0.11). Similarly, additional income drops were associated with worse performances on processing speed and executive functioning. Higher income volatility and more income drops were also associated with worse microstructural integrity of total brain and total white matter. All findings were similar when restricted to those with high education, suggesting reverse causation may not explain these findings. Conclusion: Income volatility over a 20-year period of formative earning years was associated with worse cognitive function and brain integrity in midlife.
Dynamic Visualization of ResNet Layer Activations for Brain Health Classification
<p>This GIF file provides a dynamic visualization of the internal representations (activations) from the ResNet 18 model layers (2 to 69) during a brain health classification task. The sequence begins by showing the original input image, followed by successive activation maps visualized using the "jet" colormap. Each frame corresponds to the activations extracted from a specific layer in the ResNet, resized to match the input image dimensions for better interpretability. </p> <p>The dataset used for this visualization is from S. Bhuvaji, "Brain Tumor Classification MRI," published on Kaggle in 2023. This dataset contains MRI images of brain tumors and has been utilized to train and evaluate the ResNet model for the classification of brain health states. The input sample displayed in the GIF is one such MRI image from the dataset, highlighting the model's ability to extract and analyze features relevant to brain tumor diagnosis.</p> <p>The activations reveal how the ResNet processes the input image hierarchically. In the <strong>early layers (e.g., Layers 2-10)</strong>, the network preserves much of the spatial structure of the original image, focusing on edges and low-level features. Moving to the <strong>intermediate layers (e.g., Layers 11-40)</strong>, the network begins to emphasize localized patterns while filtering out irrelevant structures such as the skull, concentrating instead on regions associated with tumors or health-related features. Finally, the <strong>deep layers (e.g., Layers 41-69)</strong> extract highly abstract and classification-relevant patterns, concentrating on tumor-related features while discarding most of the background.</p> <p>The progression of the activations in the GIF demonstrates how the network transitions from general image features to highly specialized, diagnostic features that are critical for the classification task. This visualization helps provide an intuitive understanding of the hierarchical processing capabilities of convolutional neural networks (CNNs) in medical image analysis.</p> <h3>Key Features:</h3> <p>The visualization includes an input brain image and its corresponding activation maps, extracted from each ResNet layer. The activation maps are resized to match the original image for consistency, and the "jet" colormap is applied to enhance visual interpretation of activation intensities. Each frame in the GIF dynamically updates to show the activations of the next layer, offering an engaging representation of the network's internal behavior.</p> <h3>Use Cases:</h3> <p>This GIF is a valuable resource for education, research, and presentations. It can be used to illustrate how deep learning models process medical images, providing insights into the hierarchical feature extraction process. Researchers and educators can leverage this visualization to explain the concept of feature abstraction in CNNs. It is also ideal for inclusion in talks, posters, and papers to showcase the dynamic analysis of neural network activations.</p>
Brain Health and Exercise in Schizophrenia
ClinicalTrials.gov study NCT01392885. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Digital Solution for Salutogenic Brain Health (DiSaB): A Pilot Protocol for Clinical Implementation
ClinicalTrials.gov study NCT06582316. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Digitally Supported Lifestyle Programme to Promote Brain Health Among Older Adults
ClinicalTrials.gov study NCT05565170. IPD Sharing: YES. Countries: 4. Publications: 1.
Brain Health Program for Older Adults With Subjective Cognitive Decline
ClinicalTrials.gov study NCT05934136. IPD Sharing: NO. Countries: 1. Publications: 4.
ScienceDex guides
Understand access before you commit
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.