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Genetic and environmental influences on MRI scan quantity and quality.

ABSTRACT: The current study provides an overview of quantity and quality of MRI data in a large developmental twin sample (N?=?512, aged 7-9), and investigated to what extent scan quantity and quality were influenced by genetic and environmental factors. This was examined in a fixed scan protocol consisting of two functional MRI tasks, high resolution structural anatomy (3DT1) and connectivity (DTI) scans, and a resting state scan. Overall, scan quantity was high (88% of participants completed all runs), while scan quality decreased with increasing session length. Scanner related distress was negatively associated with scan quantity (i.e., completed runs), but not with scan quality (i.e., included runs). In line with previous studies, behavioral genetic analyses showed that genetics explained part of the variation in head motion, with heritability estimates of 29% for framewise displacement and 65% for absolute displacement. Additionally, our results revealed that subtle head motion (after exclusion of excessive head motion) showed lower heritability estimates (0-14%), indicating that findings of motion-corrected and quality-controlled MRI data may be less confounded by genetic factors. These findings provide insights in factors contributing to scan quality in children, an issue that is highly relevant for the field of developmental neuroscience.

SUBMITTER: Achterberg M

PROVIDER: S-EPMC6969338 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Genetic and environmental influences on MRI scan quantity and quality.

Achterberg Michelle M van der Meulen Mara M

Developmental cognitive neuroscience 20190527

The current study provides an overview of quantity and quality of MRI data in a large developmental twin sample (N = 512, aged 7-9), and investigated to what extent scan quantity and quality were influenced by genetic and environmental factors. This was examined in a fixed scan protocol consisting of two functional MRI tasks, high resolution structural anatomy (3DT1) and connectivity (DTI) scans, and a resting state scan. Overall, scan quantity was high (88% of participants completed all runs), ...[more]

PMID: 31170550

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Project description:BackgroundQuality improvement is attracting the attention of the primary health care system as a means by which to achieve higher quality patient care. Ontario, Canada has demonstrated leadership in terms of its improvement in healthcare, but the province lacks a structured framework by which it can consistently evaluate its quality improvement initiatives specific to the primary healthcare system. The intent of this research was to complete an environmental scan and capacity map of quality improvement activities being built in and by the primary healthcare sector (QI-PHC) in Ontario as a first step to developing a coordinated and sustainable framework of primary healthcare for the province.MethodsData were collected between January and July 2011 in collaboration with an advisory group of stakeholder representatives and quality improvement leaders in primary health care. Twenty participants were interviewed by telephone, followed by review of relevant websites and documents identified in the interviews. Data were systematically examined using Framework Analysis augmented by Prior's approach to document analysis in an iterative process.ResultsThe environmental scan identified many activities (n=43) designed to strategically build QI-PHC capacity, identify promising QI-PHC practices and outcomes, scale up quality improvement-informed primary healthcare practice changes, and make quality improvement a core organizational strategy in health care delivery, which were grouped into clusters. Cluster 1 was composed of initiatives in the form of on-going programs that deliberately incorporated long-term quality improvement capacity building through province-wide reach. Cluster 2 represented activities that were time-limited (research, pilot, or demonstration projects) with the primary aim of research production. The activities of most primary health care practitioners, managers, stakeholder organizations and researchers involved in this scan demonstrated a shared vision of QI-PHC in Ontario. However, this vision was not necessarily collaboratively developed nor were activities necessarily strategically linked.ConclusionsWithin the scope of this research, the scan affirmed that there is currently no province-wide, integrated, and measured quality improvement program for the primary healthcare sector in Ontario. This could be improved by the development of a coordinated plan, an accompanying accountability framework, and an appropriate sustainable funding envelope for QI-PHC at the provincial level.

Project description:Estimating and projecting population trends using population viability analysis (PVA) are central to identifying species at risk of extinction and for informing conservation management strategies. Models for PVA generally fall within two categories, scalar (count-based) or matrix (demographic). Model structure, process error, measurement error, and time series length all have known impacts in population risk assessments, but their combined impact has not been thoroughly investigated. We tested the ability of scalar and matrix PVA models to predict percent decline over a ten-year interval, selected to coincide with the IUCN Red List criterion A.3, using data simulated for a hypothetical, short-lived organism with a simple life-history and for a threatened snail, Tasmaphena lamproides. PVA performance was assessed across different time series lengths, population growth rates, and levels of process and measurement error. We found that the magnitude of effects of measurement error, process error, and time series length, and interactions between these, depended on context. We found that high process and measurement error reduced the reliability of both models in predicted percent decline. Both sources of error contributed strongly to biased predictions, with process error tending to contribute to the spread of predictions more than measurement error. Increasing time series length improved precision and reduced bias of predicted population trends, but gains substantially diminished for time series lengths greater than 10-15 years. The simple parameterization scheme we employed contributed strongly to bias in matrix model predictions when both process and measurement error were high, causing scalar models to exhibit similar or greater precision and lower bias than matrix models. Our study provides evidence that, for short-lived species with structured but simple life histories, short time series and simple models can be sufficient for reasonably reliable conservation decision-making, and may be preferable for population projections when unbiased estimates of vital rates cannot be obtained.

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Genetic and environmental influences on MRI scan quantity and quality.

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Genetic and environmental influences on MRI scan quantity and quality.

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