The Neuro/PsyGRID calibration experiment: identifying sources of variance and bias in multicenter MRI studies.

Calibration experiments precede multicenter trials to identify potential sources of variance and bias. In support of future imaging studies of mental health disorders and their treatment, the Neuro/PsyGRID consortium commissioned a calibration experiment to acquire functional and structural MRI from twelve healthy volunteers attending five centers on two occasions. Measures were derived of task activation from a working memory paradigm, fractal scaling (Hurst exponent) from resting fMRI, and grey matter distributions from T(1) -weighted sequences. At each intracerebral voxel a fixed-effects analysis of variance estimated components of variance corresponding to factors of center, subject, occasion, and within-occasion order, and interactions of center-by-occasion, subject-by-occasion, and center-by-subject, the latter (since there is no intervention) a surrogate of the expected variance of the treatment effect standard error across centers. A rank order test of between-center differences was indicative of crossover or noncrossover subject-by-center interactions. In general, factors of center, subject and error variance constituted >90% of the total variance, whereas occasion, order, and all interactions were generally <5%. Subject was the primary source of variance (70%-80%) for grey-matter, with error variance the dominant component for fMRI-derived measures. Spatially, variance was broadly homogenous with the exception of fractal scaling measures which delineated white matter, related to the flip angle of the EPI sequence. Maps of P values for the associated F-tests were also derived. Rank tests were highly significant indicating the order of measures across centers was preserved. In summary, center effects should be modeled at the voxel-level using existing and long-standing statistical recommendations.

A systematic review of the utility of 1.5 versus 3 Tesla magnetic resonance brain imaging in clinical practice and research.

OBJECTIVE: MRI at 3 T is said to be more accurate than 1.5 T MR, but costs and other practical differences mean that it is unclear which to use. METHODS: We systematically reviewed studies comparing diagnostic accuracy at 3 T with 1.5 T. We searched MEDLINE, EMBASE and other sources from 1 January 2000 to 22 October 2010 for studies comparing diagnostic accuracy at 1.5 and 3 T in human neuroimaging. We extracted data on methodology, quality criteria, technical factors, subjects, signal-to-noise, diagnostic accuracy and errors according to QUADAS and STARD criteria. RESULTS: Amongst 150 studies (4,500 subjects), most were tiny, compared old 1.5 T with new 3 T technology, and only 22 (15 %) described diagnostic accuracy. The 3 T images were often described as "crisper", but we found little evidence of improved diagnosis. Improvements were limited to research applications [functional MRI (fMRI), spectroscopy, automated lesion detection]. Theoretical doubling of the signal-to-noise ratio was not confirmed, mostly being 25 %. Artefacts were worse and acquisitions took slightly longer at 3 T. CONCLUSION: Objective evidence to guide MRI purchasing decisions and routine diagnostic use is lacking. Rigorous evaluation accuracy and practicalities of diagnostic imaging technologies should be the routine, as for pharmacological interventions, to improve effectiveness of healthcare. KEY POINTS : • Higher field strength MRI may improve image quality and diagnostic accuracy. • There are few direct comparisons of 1.5 and 3 T MRI. • Theoretical doubling of the signal-to-noise ratio in practice was only 25 %. • Objective evidence of improved routine clinical diagnosis is lacking. • Other aspects of technology improved images more than field strength.

Assessment of the impact of the scanner-related factors on brain morphometry analysis with Brainvisa.

BACKGROUND: Brain morphometry is extensively used in cross-sectional studies. However, the difference in the estimated values of the morphometric measures between patients and healthy subjects may be small and hence overshadowed by the scanner-related variability, especially with multicentre and longitudinal studies. It is important therefore to investigate the variability and reliability of morphometric measurements between different scanners and different sessions of the same scanner. METHODS: We assessed the variability and reliability for the grey matter, white matter, cerebrospinal fluid and cerebral hemisphere volumes as well as the global sulcal index, sulcal surface and mean geodesic depth using Brainvisa. We used datasets obtained across multiple MR scanners at 1.5 T and 3 T from the same groups of 13 and 11 healthy volunteers, respectively. For each morphometric measure, we conducted ANOVA analysis and verified whether the estimated values were significantly different across different scanners or different sessions of the same scanner. The between-centre and between-visit reliabilities were estimated from their contribution to the total variance, using a random-effects ANOVA model. To estimate the main processes responsible for low reliability, the results of brain segmentation were compared to those obtained using FAST within FSL. RESULTS: In a considerable number of cases, the main effects of both centre and visit factors were found to be significant. Moreover, both between-centre and between-visit reliabilities ranged from poor to excellent for most morphometric measures. A comparison between segmentation using Brainvisa and FAST revealed that FAST improved the reliabilities for most cases, suggesting that morphometry could benefit from improving the bias correction. However, the results were still significantly different across different scanners or different visits. CONCLUSIONS: Our results confirm that for morphometry analysis with the current version of Brainvisa using data from multicentre or longitudinal studies, the scanner-related variability must be taken into account and where possible should be corrected for. We also suggest providing some flexibility to Brainvisa for a step-by-step analysis of the robustness of this package in terms of reproducibility of the results by allowing the bias corrected images to be imported from other packages and bias correction step be skipped, for example.

Power calculations for multicenter imaging studies controlled by the false discovery rate.

Magnetic resonance imaging (MRI) is widely used in brain imaging research (neuroimaging) to explore structural and functional changes across dispersed neural networks visible only via multisubject experiments. Multicenter investigations are an effective way to increase recruitment rates. This article describes image-based power calculations for a two-group, cross-sectional design specified by the mean effect size and its standard error, sample size, false discovery rate (FDR), and size of the network (i.e., proportion of image locations) that truly demonstrates an effect. Minimum sample size (for fixed effect size) and the minimum effect size (for fixed sample size) are calculated by specifying the acceptable power threshold. Within-center variance was estimated in five participating centers by repeat MRI scanning of 12 healthy participants from whom distributions of gray matter were estimated. The effect on outcome measures when varying FDR and the proportion of true positives is presented. Their spatial patterns reflect within-center variance, which is consistent across centers. Sample sizes 3-6 times larger are needed when detecting effects in subcortical regions compared to the neocortex. Hypothesized multicenter studies of patients with first episode psychosis and control participants were simulated with varying proportions of the cohort recruited at each center. There is little penalty to sample size for recruitment at five centers compared to the center with the lowest variance alone. At 80% power 80 participants per group are required to observe differences in gray matter in high variance regions.

Between- and within-scanner variability in the CaliBrain study n-back cognitive task.

Psychiatric neuroimaging techniques are likely to improve understanding of the brain in health and disease, but studies tend to be small, based in one imaging centre and of unclear generalisability. Multicentre studies have great appeal but face problems if functional magnetic resonance imaging (fMRI) data from different centres are to be combined. Fourteen healthy volunteers had two brain scans on different days at three scanners. Considerable effort was first made to use similar scanning sequences and standardise task implementation across centres. The n-back cognitive task was used to investigate between- and within-scanner reproducibility and reliability. Both the functional imaging and behavioural results were in good accord with the existing literature. We found no significant differences in the activation/deactivation maps between scanners, or between repeat visits to the same scanners. Between- and within-scanner reproducibility and reliability was very similar. However, the smoothness of images from the scanners differed, suggesting that smoothness equalization might further reduce inter-scanner variability. Our results for the n-back task suggest it is possible to acquire fMRI data from different scanners which allows pooling across centres, when the same field strength scanners are used and scanning sequences and paradigm implementations are standardised.

Reproducibility of GABA measurements using 2D J-resolved magnetic resonance spectroscopy.

We determined the reproducibility of GABA (gamma-aminobutyric acid) measurements using 2D J-resolved magnetic resonance spectroscopy (MRS) on a clinical 1.5-T MR imaging scanner. Two-dimensional J-resolved spectra were acquired in vitro across five GABA concentrations using a volume head coil and a 5-in. surface coil. Additional spectra using a sixth GABA phantom with a very low concentration and from a healthy volunteer were recorded in the 5-in. surface coil only. In each case, the 3.01-ppm GABA resonance was quantified; for comparison, the peak integrals of choline (3.2 ppm) and creatine (3.03 ppm) were recorded. At a physiological concentration (1.2 mM), in vitro GABA measurement was significantly more reproducible in the surface coil than in the volume coil (P=.005), with coefficients of variation (CVs) being less than 16% with the surface coil and up to 68% with the volume head coil. At the smallest concentration of in vivo GABA reported using other spectroscopy techniques (0.8 mM) and detected only using the surface coil, the CV for GABA was 23% and was less than 10% for choline and creatine, which compare favorably with results from published studies. In vivo, the CV for GABA measurement was 26%, suggesting that 2D J-resolved MRS would be suitable for detecting physiological changes in GABA, similar to those reported using other methods.

Do acute phase markers explain body temperature and brain temperature after ischemic stroke?

OBJECTIVE: Both brain and body temperature rise after stroke but the cause of each is uncertain. We investigated the relationship between circulating markers of inflammation with brain and body temperature after stroke. METHODS: We recruited patients with acute ischemic stroke and measured brain temperature at hospital admission and 5 days after stroke with multivoxel magnetic resonance spectroscopic imaging in normal brain and the acute ischemic lesion (defined by diffusion-weighted imaging [DWI]). We measured body temperature with digital aural thermometers 4-hourly and drew blood daily to measure interleukin-6, C-reactive protein, and fibrinogen, for 5 days after stroke. RESULTS: In 44 stroke patients, the mean temperature in DWI-ischemic brain soon after admission was 38.4° C (95% confidence interval [CI] 38.2-38.6), in DWI-normal brain was 37.7° C (95% CI 37.6-37.7), and mean body temperature was 36.6° C (95% CI 36.3-37.0). Higher mean levels of interleukin-6, C-reactive protein, and fibrinogen were associated with higher temperature in DWI-normal brain at admission and 5 days, and higher overall mean body temperature, but only with higher temperature in DWI-ischemic brain on admission. CONCLUSIONS: Systemic inflammation after stroke is associated with elevated temperature in normal brain and the body but not with later ischemic brain temperature. Elevated brain temperature is a potential mechanism for the poorer outcome observed in stroke patients with higher levels of circulating inflammatory markers.