Putative MRI markers of iron in deep gray matter have demonstrated age related changes during discrete periods of healthy childhood or adulthood, but few studies have included subjects across the lifespan. This study reports both transverse relaxation rate (R2*) and quantitative susceptibility mapping (QSM) of four primary deep gray matter regions (thalamus, putamen, caudate, and globus pallidus) in 498 healthy individuals aged 5–90 years. In the caudate, putamen, and globus pallidus, increases of QSM and R2* were steepest during childhood continuing gradually throughout adulthood, except caudate susceptibility which reached a plateau in the late 30s. The thalamus had a unique profile with steeper changes of R2* (reflecting additive effects of myelin and iron) than QSM during childhood, both reaching a plateau in the mid‐30s to early 40s and decreasing thereafter. There were no hemispheric or sex differences for any region. Notably, both R2* and QSM values showed more inter‐subject variability with increasing age from 5 to 90 years, potentially reflecting a common starting point in iron/myelination during childhood that diverges as a result of lifestyle and genetic factors that accumulate with age.

Purpose The transmit field B1+ at 3 T in brain affects the spatial uniformity and contrast of most image acquisitions. Here, B1+ spatial variation in brain at 3 T is characterized in a large healthy population. Methods Bloch‐Siegert B1+ maps were acquired at 3 T from 385 healthy subjects aged 5–90 years on a single MRI system. After transforming all B1+ maps to a standard brain atlas space, region‐of‐interest analysis was performed, and intersubject voxel‐wise coefficient of variation was calculated across the whole brain. The B1+ variability due to age and brain size was studied separately in males and females, along with B1+ variability due to nonideal transmit calibration. Results The voxel‐based mean coefficient of variation was 4.0% across all subjects, and the difference in B1+ between central (left thalamus) and outer regions (left frontal gray matter) was 24.2% ± 2.3%. The least intersubject variability occurred in central regions, whereas regions toward brain edges increased markedly in variation. The B1+ variability with age was mostly attributed to lifespan changes in CSF volume (which alters brain conductivity) and head orientation. Larger brain size correlated with more B1+ inhomogeneity (p < .001). Varying head position and anatomy resulted in an inaccurate transmit calibration. Conclusion In standard atlas space, intersubject B1+ variability at 3 T was relatively small in a large population aged 5–90 years. The B1+ varied with age‐related changes of CSF volume and head orientation, as well as differences in brain size and transmit calibration.

Parasites can evolve phenotypically plastic strategies for transmission such that a single genotype can give rise to a range of phenotypes depending on the environmental condition. State-dependent plasticity in particular can arise from individual differences in the parasite's internal state or the condition of the host. Facultative parasites serve as ideal model systems for investigating state-dependent plasticity because individuals can exhibit two life history strategies (free-living or parasitic) depending on the environment. Here, we experimentally show that the ectoparasitic mite Macrocheles subbadius is more likely to parasitize a fruit fly host if the female mite is mated; furthermore, the propensity to infect increased with the level of starvation experienced by the mite. Host condition also played an important role; hosts infected with moderate mite loads were more likely to gain additional infections in pairwise choice tests than uninfected flies. We also found that mites preferentially infected flies subjected to mechanical injury over uninjured flies. These results suggest that a facultative parasite's propensity to infect a host (i.e. switch from a free-living strategy) depends on both the parasite's internal state and host condition. Parasites often live in highly variable and changing environments, an infection strategy that is plastic is likely to be adaptive.

Purpose As home-based care continues to be a growing trend in health care, involvement of friend and family caregivers in the management of illness becomes essential. However, before nurses can prepare caregivers to engage in various types of care, an evidence base needs to be established via randomized controlled trials (RCTs). Research suggests that recruiting cancer patients and their friend or family caregivers into RCTs presents challenges. The purpose of this paper is to illustrate the barriers to recruitment of patient-caregiver dyads into a RCT of caregiver-delivered reflexology and to recommend strategies to address such barriers. Methods This paper reports on a nurse-directed RCT that involved recruitment efforts unique to a caregiver-delivered reflexology protocol for advanced-stage breast cancer patients. Ineligibility due to caregiver-related reasons, consent among eligible patients (out of 551 approached patients), and reasons for refusal were analyzed. Results Almost one-third of patients were found to be ineligible due to the lack of a caregiver to participate with them and provide this form of social support. Among eligible patients, the consent rate for this dyadic study is much lower than that of previous RCTs of reflexologist-delivered reflexology that enrolled just patients, not dyads. Conclusion Implications for nursing practice and research include addressing the need for greater social support for patients and strategies for problem-solving refusal reasons during study enrollment.

Brain magnetic resonance imaging (MRI) studies of clinical populations often require comparison to a normative ‘control’ cohort, usually of similar age/sex, scanned with the same protocol. The goal here was to create a normative brain MRI database of common quantitative methods to be used in comparisons with a variety of neurological disorders across the lifespan. 378 neurotypical controls (aged 5–90 years; median 31 years; 216 females, 162 males) completed brain MRI, cognitive testing, clinical assessment, and a demographics questionnaire. In addition, this large normative sample will yield novel insight into healthy brain development and aging.

Multi–site imaging consortiums strive to increase participant numbers by pooling data across sites, but scanner related differences can bias results. This study combines data from three research MRI centers, including three different scanner models from two vendors, to examine non–harmonized T1–weighted brain imaging protocols in two cohorts. First, 23 human traveling phantoms were scanned twice each at all three sites (six scans per person; 138 scans total) to quantify within–participant variability of brain volumes (total brain, white matter, gray matter, lateral ventricles, thalamus, caudate, putamen and globus pallidus), and to calculate site–specific correction factors for each structure. Sample size calculations were used to determine the number of traveling phantoms needed to achieve effect sizes for observed differences to help guide future studies. Next, cross–sectional lifespan volume trajectories were examined in 856 healthy participants (5—91 years of age) scanned at these sites. Cross–sectional trajectories of volume versus age for each structure were then compared before and after application of traveling phantom based site–specific correction factors, as well as correction using the open–source method ComBat. Although small systematic differences between sites were observed in the traveling phantom analysis, correction for site using either method had little impact on the lifespan trajectories. Only white matter had small but significant differences in the intercept parameter after ComBat correction (but not traveling phantom based correction), while no other fits differed. This suggests that age–related changes over the lifespan outweigh systematic differences between scanners for volumetric analysis. This work will help guide pooling of multisite datasets as well as meta–analyses of data from non–harmonized protocols.

Iron concentration in the human brain plays a crucial role in several neurodegenerative diseases and can be monitored noninvasively using quantitative susceptibility mapping (QSM) and effective transverse relaxation rate (R2*) mapping from multiecho T2*‐weighted images. Large population studies enable better understanding of pathologies and can benefit from pooling multisite data. However, reproducibility may be compromised between sites and studies using different hardware and sequence protocols. This work investigates QSM and R2* reproducibility at 3 T using locally optimized sequences from three centers and two vendors, and investigates possible reduction of cross‐site variability through postprocessing approaches. Twenty‐four healthy subjects traveled between three sites and were scanned twice at each site. Scan‐rescan measurements from seven deep gray matter regions were used for assessing within‐site and cross‐site reproducibility using intraclass correlation coefficient (ICC) and within‐subject standard deviation (SDw) measures. In addition, multiple QSM and R2* postprocessing options were investigated with the aim to minimize cross‐site sequence‐related variations, including: mask generation approach, echo‐timing selection, harmonizing spatial resolution, field map estimation, susceptibility inversion method, and linear field correction for magnitude images. The same‐subject cross‐site region of interest measurements for QSM and R2* were highly correlated (R2 ≥ 0.94) and reproducible (mean ICC of 0.89 and 0.82 for QSM and R2*, respectively). The mean cross‐site SDw was 4.16 parts per billion (ppb) for QSM and 1.27 s−1 for R2*. For within‐site measurements of QSM and R2*, the mean ICC was 0.97 and 0.87 and mean SDw was 2.36 ppb and 0.97 s−1, respectively. The precision level is regionally dependent and is reduced in the frontal lobe, near brain edges, and in white matter regions. Cross‐site QSM variability (mean SDw) was reduced up to 46% through postprocessing approaches, such as masking out less reliable regions, matching available echo timings and spatial resolution, avoiding the use of the nonconsistent magnitude contrast between scans in field estimation, and minimizing streaking artifacts.