Dataset Information

Experimental radar data for monitoring brain atrophy progression.

ABSTRACT: This data set contains complex frequency domain signals obtained from unidirectional antennas mainly fabricated for radar-based head imaging. Data were obtained as part of a project investigating radar-based microwave imaging for monitoring neurodegenerative diseases, especially Alzheimer's disease. The wearable device, measurement setup, and phantoms used are described. Multiple experiments were performed to get the data from three lamb brain phantoms that realistically mimic the whole-brain atrophy due to Alzheimer's disease. Microwave imaging has shown great potential for breast and brain screening due to its low cost, non-ionizing, portable, and wearable nature. Most of the studies are based on simulations with good results, but further evaluation on experimental data is required before its clinical viability. This work provides an open-source experimental dataset that can be used to evaluate novel signal processing and imaging techniques and validate simulation results. The data provide both the magnitude and phase value at each discrete frequency, making this data set useful for both time-delay and phase-shift based imaging algorithms.

SUBMITTER: Ullah R

PROVIDER: S-EPMC9234078 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Experimental radar data for monitoring brain atrophy progression.

Ullah Rahmat R Saied Imran I Arslan Tughrul T

Data in brief 20220610

This data set contains complex frequency domain signals obtained from unidirectional antennas mainly fabricated for radar-based head imaging. Data were obtained as part of a project investigating radar-based microwave imaging for monitoring neurodegenerative diseases, especially Alzheimer's disease. The wearable device, measurement setup, and phantoms used are described. Multiple experiments were performed to get the data from three lamb brain phantoms that realistically mimic the whole-brain at ...[more]

PMID: 35770026

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Project description:BackgroundWith a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field.ObjectiveWide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy.MethodsRADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided.ResultsGeneral principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts.ConclusionsRADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

Project description:Brain atrophy has been reported in the early stages of Parkinson's disease, but there have been few longitudinal studies. How intrinsic properties of the brain, such as anatomical connectivity, local cell-type distribution and gene expression combine to determine the pattern of disease progression also remains unknown. One hypothesis proposes that the disease stems from prion-like propagation of misfolded alpha-synuclein via the connectome that might cause varying degrees of tissue damage based on local properties. Here, we used MRI data from the Parkinson Progression Markers Initiative to map the progression of brain atrophy over 1, 2 and 4 years compared with baseline. We derived atrophy maps for four time points using deformation-based morphometry applied to T1-weighted MRI from 120 de novo Parkinson's disease patients, 74 of whom had imaging at all four time points (50 Men: 24 Women) and 157 healthy control participants (115 Men: 42 Women). In order to determine factors that may influence neurodegeneration, we related atrophy progression to brain structural and functional connectivity, cell-type expression and gene ontology enrichment analyses. After regressing out the expected age and sex effects associated with normal ageing, we found that atrophy significantly progressed over 2 and 4 years in the caudate, nucleus accumbens, hippocampus and posterior cortical regions. This progression was shaped by both structural and functional brain connectivity. Also, the progression of atrophy was more pronounced in regions with a higher expression of genes related to synapses and was inversely related to the prevalence of oligodendrocytes and endothelial cells. In sum, we demonstrate that the progression of atrophy in Parkinson's disease is in line with the prion-like propagation hypothesis of alpha-synuclein and provide evidence that synapses may be especially vulnerable to synucleinopathy. In addition to identifying vulnerable brain regions, this study reveals different factors that may be implicated in the neurotoxic mechanisms leading to progression in Parkinson's disease. All brain maps generated here are available on request.

Project description:ImportanceIt remains unclear which risk factors accelerate brain atrophy along with a progression from normal cognition to mild cognitive impairment (MCI).ObjectiveTo examine risk factors associated with the acceleration of brain atrophy and progression from normal cognition to MCI based on long-term longitudinal data for middle-aged and older adults.Design, setting, and participantsData for this cohort study were extracted from the Biomarkers for Older Controls at Risk for Dementia (BIOCARD) cohort, initiated at the National Institutes of Health from January 1, 1995, to December 31, 2005, and continued at Johns Hopkins University from January 1, 2015, to October 31, 2023. All participants were cognitively normal at baseline. The participants whose structural magnetic brain imaging (MRI) of the brain and cerebrospinal fluid (CSF) measures were available for over 10 years were included.ExposuresLongitudinal structural MRI of the brain and measurement of CSF biomarkers for Alzheimer disease pathology (ratio of amyloid β peptide 42 [Aβ42] to Aβ40, tau phosphorylated at threonine 181, and total tau).Main outcomes and measuresAnnual change rates of segmental brain volumes, Kaplan-Meier survival curves plotting time to event for progression to MCI symptom onset, and hazard ratios (HRs) determined by Cox proportional hazards regression models.ResultsA total of 185 participants (mean [SD] age, 55.4 [8.4] years; 116 women [63%]) were included and followed up for a maximum of 27 years (median, 20 [IQR, 18-22] years). The groups with high levels of atrophy in the white matter and enlargement in the ventricles had an earlier progression from normal cognition to MCI symptom onset (HR for white matter, 1.86 [95% CI, 1.24-2.49]; P = .001; HR for ventricles, 1.71 [95% CI, 1.19-2.24]; P = .009). Diabetes was associated with progression to MCI (HR, 1.41 [95% CI, 1.06-1.76]; P = .04), as was a low CSF Aβ42:Aβ40 ratio (HR, 1.48 [95% CI, 1.09-1.88]; P = .04), and their combination had a higher HR of 1.55 (95% CI, 1.13-1.98]; P = .03), indicating a synergic association of diabetes and amyloid pathology with MCI progression.Conclusions and relevanceIn this cohort study of middle-aged and older adults, higher rates of volume change in the white matter and ventricles, along with the presence of diabetes and a low CSF Aβ42:Aβ40 ratio, were identified as important risk factors for the progression to MCI. These results support the importance of identifying individuals who have accelerated brain atrophy to optimize preventive strategies for progression to MCI.

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Experimental radar data for monitoring brain atrophy progression.

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Experimental radar data for monitoring brain atrophy progression.

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