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674 results for “brain disease”
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>
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>
Follow Up Study for Treatment of Parkinson's Disease With Deep Brain Stimulation
ClinicalTrials.gov study NCT01022073. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
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>
Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease
<p>Connectome, traits and behavior data for APOE234 mice.</p> <ul> <li>1. connectome.zip: mouse brain structural connectivity matrices from diffusion MRI.</li> <li>2. FAGNN_Phenotype.csv: a sheet of trait information of mice used in the study.</li> </ul> <p>columns: winding numbers, total distance, normalized NE time, normalized NE distance, normalized NW time, normalized NW distance, normalized SE time, normalized SE distance, normlaized SW time, normalized SW distance, island latency to first entry, island entries, normalized thigmataxis time, and normalized thigmotaxis distance</p> <div>rows: 4 trials for each day from day 1 to day 5 with 1 probing test each at day 5 and day 8</div> <ul> <li>3. mouse_anatomy.csv: brain region information regarding the connectivity matrix.</li> <li>4. behavior.zip: behavioral data for each mouse from Morris Water Maze experiments.</li> </ul>
ALAMEDA Data: Bridging the Early Diagnosis and Treatment Gaps of Brain Diseases (Parkinson's Disease, Multiple Slerosis and Stroke)
<p><strong>ALAMEDA</strong> is an Horizon 2020 Research and Innovation project that aims to bridge the early diagnosis and treatment gap of brain diseases via smart, connected, proactive and evidence-based technological interventions. Its vision is to research and prototype new generation Artificial Intelligence (AI) systems to support brain disorders patients' healthcare, focusing on Parkinson's Disease (PD), Multiple Sclerosis (MS) and Stroke.</p> <p>To this end, three (one for each disease) small scale validation pilots were performed in real world settings. Throughout these pilots, various types of data, such as accelerometer, gyroscopic, heart rate, etc., were collected via smart wearable sensors from the patients enrolled. The smart devices that were employed include: a Fitbit smartwatch, a GENEActiv smart bracelet, Novel Loadsol insole sensors and a prototype smart belt with triaxial accelerometers and gyroscopes embedded. Moreover, the patients underwent several clinical assessments and filled in numerous both disease-specific and non-disease-specific questionnaires.</p> <p>In this record, both raw and processed sensory data are combined with both clinical and patient reported outcomes (PROs) to form different disease-specific datasets. More specifically:</p> <ul> <li>For <strong>Parkinson's disease</strong>: Three datasets are provided (one for tremor detection, one for dyskinesia detection, and one for Hoehn & Yahr score estimation) alongside the vertical ground reaction force recordings.</li> <li>For <strong>Multiple Sclerosis</strong>: Two datasets are provided (one for Expanded Disability Status Scale (EDSS) scores classification and one that accumulates clinical data and individual scores from various MS-related questionnaires) alongside the vertical ground reaction force and the smart belt recordings.</li> <li>For <strong>Stroke</strong>: Two datasets are provided (one for rehabilitation exercises' recognition and one for walking classification, both with and without manual annotations) alongside the smart belt recordings.</li> </ul> <p>More information about the datasets provided can be found in the respective READ ME files that are included in the current record.</p>
Data from: Structural and functional brain connectome in motor neuron diseases: a multicenter MRI study
Objective. To investigate structural and functional neural organization in amyotrophic lateral sclerosis (ALS), primary lateral sclerosis (PLS) and progressive muscular atrophy (PMA) patients. Methods. 173 ALS, 38 PLS, 28 PMA sporadic patients and 79 healthy controls were recruited from three Italian centers. Subjects underwent clinical, neuropsychological and brain MRI evaluations. Using graph analysis and connectomics, global and lobar topological network properties and regional structural and functional brain connectivity were assessed. The association between structural and functional network organization and clinical/cognitive data was investigated. Results. Compared to healthy controls, ALS and PLS patients showed altered structural global network properties, as well as local topological alterations and decreased structural connectivity in sensorimotor, basal ganglia, frontal and parietal areas. PMA patients showed preserved global structure. Patient groups did not show significant alterations of functional network topological properties relative to controls. Increased local functional connectivity was observed in ALS patients in the precentral, middle and superior frontal areas, and in PLS patients in the sensorimotor, basal ganglia and temporal networks. In both ALS and PLS patients, structural connectivity alterations correlated with motor impairment, while functional connectivity disruption was closely related to executive dysfunctions and behavioral disturbances. Conclusions. This multicenter study showed widespread motor/extra-motor network degeneration in ALS and PLS, suggesting that graph analysis and connectomics might represent a powerful approach to detect upper motor neuron degeneration, extra-motor brain changes and network reorganization associated with the disease. Network-based advanced MRI provides an objective in vivo assessment of motor neuron diseases, delivering potential prognostic markers.
Microelectrode register (MER) data from Deep Brain Stimulation (DBS) surgery in Parkinson's disease patients
<p>MER data consist of brain signal in different depths when DBS surgery is being done. In each depth, a data file is created, with different duration depending on the depth, and up to three channels.</p> <p>Data come from 14 patients (9 males and 5 females), they are anonymized and labelled from P1 to P14. They correspond to patients in age 65.1 +- 5.6 years.</p> <p>Data are organized in STN-IN and STN-OUT (different depths in each folder), subthalamus-in, and subthalamus-out since the STN area is the target area when implanting a DBS. Classification in STN-IN and STN-OUT was made by the neurophysiologists and surgeons.</p> <p>Data were recorded for left and right lobes, 8 patients in left and right lobe, 1 patient in right lobe, and 5 patients in left lobe.</p> <p>The format is mat file (MATLAB file)</p> <p>Data sampling frequency is 12kHz.</p> <p>No filtering or data processing was made, they are directly obtained from the MER acquisition system.</p> <p> </p>
Data for "Brain cell-type shifts in Alzheimer's disease, autism and schizophrenia interrogated using methylomics and genetics"
<p>Data for "Genetic and methylomic interrogation of brain cell-type shifts in autism, schizophrenia, and Alzheimer’s disease" (Yap et al. 2023).</p> <p>Source data from ROSMAP, LIBD and UCLA_ASD post-mortem brain datasets.</p> <p>This data repository contains 3 files:</p> <p><strong>220819_Supplementary_Tables.xlsx</strong></p> <p>Supplementary Tables for the manuscript:</p> <ol> <li>Supplementary Table 1: Comparison of CTP deconvolution methods in the ROSMAP dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 2: Comparison of CTP deconvolution methods in the LIBD dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 3: Comparison of CTP deconvolution methods in the UCLA_ASD dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 4: Deconvolved brain CTPs (raw), with covariates.</li> <li>Supplementary Table 5: Deconvolved brain CTPs (clr-transform, offset 1e-3), with covariates and raw PGS for all ancestries. Comp* indicates compositionally-aware principal components of the CTP data; *pgs_raw indicates PGS calculated for using genotyping across all ancestries. This table has a total of n=1,098 across all ancestries, including n=885 EUR. After applying a rel<0.05 threshold on the n=885 EUR, there were n=878 EUR which were used in the PGS analysis so that population stratification PCs did not simply capture family structure.</li> <li>Supplementary Table 6: Deconvolved brain CTPs (clr-transform, offset 1e-3), with covariates and standardised PGS for n=878 Europeans. *pgs_raw indicates unstandardised PGS, PC* indicates genotyping PCs within the European dataset, *_PGS indicates standardised PGS within the European subset.</li> <li>Supplementary Table 7: Deconvolved brain CTPs (clr-transform, offset 1e-3), adjusted for oligodendrocyte proportions, with covariates.</li> <li>Supplementary Table 8: Deconvolved brain CTPs (raw), adjusted for oligodendrocyte proportions, with covariates.</li> </ol> <p><strong>20220108_maf05_gwas_ctp.tar.gz</strong></p> <p>GWAS summary statistics for the 7 brainCTPs (clr-transformed): Exc, Inh, Astro, Endo, Micro, Oligo, OPC</p> <p>METAL output format:<br> MarkerName: SNP<br> Allele1<br> Allele2<br> Freq1: Allele1 frequency<br> FreqSE: frequency standard error<br> MinFreq: minimum Allele1 frequency in meta-analysis<br> MaxFreq: maximum Allele1 frequency in meta-analysis<br> Effect: effect size<br> StdErr: standard error of effect size<br> P-value: calculated in inverse variance weighted meta-analysis<br> Direction: directions of effects across the 3 datasets<br> HetISq: heterozygosity I-squared<br> HetChiSq: heterozygosity chi-squared<br> HetDf: heterozygosity degress of freedom<br> HetPVal: heterozygosity test p-value</p> <p><strong>20220108_maf05_gwas_ctp_pc.tar.gz</strong></p> <p>GWAS summary statistics for the 5 CTP_PCs</p> <p>METAL output format (see above)</p> <p> </p> <p><strong>Source data</strong>:</p> <p>ROSMAP: Raw methylation .idat files were obtained from Synapse accession syn7357283. Whole genome sequencing .vcf files (variants jointly called with MSBB and Mayo studies) were obtained from Synapse accession syn11707420.</p> <p>LIBD: Raw methylation .idat files were obtained from GEO accession GSE74193. SNP genotypes were downloaded from dbGaP accession phs000417.v2.p1.</p> <p>UCLA-ASD: The processed methylation beta matrix was downloaded from Synapse accession syn8263588. SNP genotypes were downloaded from Synapse accession syn10537134.</p> <p>GWAS summary statistics are available at: 10.5281/zenodo.7604231</p> <p>Code is available on GitHub: gandallab/brain_CTP_deconv</p>
Data for: Deep brain stimulation in early-stage Parkinson disease
<p><strong>Objective</strong>: To report 5-year outcomes from the subthalamic nucleus (STN) deep brain stimulation (DBS) in early-stage Parkinson disease (PD) pilot clinical trial.</p> <p><strong>Methods</strong>: The pilot was a prospective, single-blind clinical trial that randomized patients with early-stage PD (Hoehn & Yahr II off medications) to receive bilateral STN DBS plus optimal drug therapy (ODT) vs ODT alone (IDEG050016, NCT0282152, IRB040797). Participants who completed the 2-year trial participated in this observational follow-up study, which included annual outpatient visits through 5 years. This analysis includes 28 patients who were taking PD medications for 6 months to 4 years at enrollment. Outcomes were analyzed using both proportional odds logistic regression and linear mixed effects models.ResultsEarly STN DBS + ODT participants required lower levodopa equivalent daily doses (p = 0.04, β = −240 mg, 95% confidence interval [CI] −471 to −8) and had 0.06 times the odds of requiring polypharmacy at 5 years compared to early ODT participants (p = 0.01, odds ratio [OR] 0.06, 95% CI 0.00 to 0.65). The odds of having worse rest tremor for early STN DBS + ODT participants were 0.21 times those of early ODT participants (p < 0.001, OR 0.21, 95% CI 0.09 to 0.45). The safety profile was similar between groups.</p> <p><strong>Conclusions</strong>: These results suggest that early DBS reduces the need for and complexity of PD medications while providing long-term motor benefit over standard medical therapy. Further investigation is warranted, and the Food and Drug Administration has approved the conduct of a prospective, multicenter, pivotal clinical trial of DBS in early-stage PD (IDEG050016).</p> <p><strong>Classification of evidence</strong>: This study provides Class II evidence that DBS implanted in early-stage PD decreases the risk of disease progression and polypharmacy compared to optimal medical therapy alone.</p>
Physical Therapy and Deep Brain Stimulation in Parkinson Disease
ClinicalTrials.gov study NCT03181282. IPD Sharing: NO. Countries: 1. Publications: 1.
Cognitive Decline and Alzheimer's Disease in the Dallas Lifespan Brain Study
ClinicalTrials.gov study NCT04080544. IPD Sharing: NO. Countries: 1. Publications: 15.
Deep Brain Stimulation (DBS) for the Treatment of Parkinson's Disease
ClinicalTrials.gov study NCT01839396. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Non-invasive Blood-brain Barrier Opening in Alzheimer's Disease Patients Using Focused Ultrasound
ClinicalTrials.gov study NCT04118764. IPD Sharing: NO. Countries: 1. Publications: 4.
Brain Imaging Study Of Rosiglitazone Efficacy And Safety In Alzheimer's Disease
ClinicalTrials.gov study NCT00265148. IPD Sharing: YES. Countries: 3. Publications: 1.
3-month Study of MSDC-0160 Effects on Brain Glucose Utilization, Cognition & Safety in Subjects With Alzheimer's Disease
ClinicalTrials.gov study NCT01374438. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Asymmetric Subthalamic Deep Brain Stimulation for Axial Motor Dysfunction in Parkinson's Disease
ClinicalTrials.gov study NCT03462082. IPD Sharing: YES. Countries: 1. Publications: 4.
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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.