Population-based blood screening for preclinical Alzheimer's disease in a British birth cohort at age 70.

Alzheimer's disease has a preclinical stage when cerebral amyloid-ß deposition occurs before symptoms emerge, and when amyloid-ß-targeted therapies may have maximum benefits. Existing amyloid-ß status measurement techniques, including amyloid PET and CSF testing, are difficult to deploy at scale, so blood biomarkers are increasingly considered for screening. We compared three different blood-based techniques-liquid chromatography-mass spectrometry measures of plasma amyloid-ß, and single molecule array (Simoa) measures of plasma amyloid-ß and phospho-tau181-to detect cortical 18F-florbetapir amyloid PET positivity (defined as a standardized uptake value ratio of >0.61 between a predefined cortical region of interest and eroded subcortical white matter) in dementia-free members of Insight 46, a substudy of the population-based British 1946 birth cohort. We used logistic regression models with blood biomarkers as predictors of amyloid PET status, with or without age, sex and APOE ε4 carrier status as covariates. We generated receiver operating characteristics curves and quantified areas under the curves to compare the concordance of the different blood tests with amyloid PET. We determined blood test cut-off points using Youden's index, then estimated numbers needed to screen to obtain 100 amyloid PET-positive individuals. Of the 502 individuals assessed, 441 dementia-free individuals with complete data were included; 82 (18.6%) were amyloid PET-positive. The area under the curve for amyloid PET status using a base model comprising age, sex and APOE ε4 carrier status was 0.695 (95% confidence interval: 0.628-0.762). The two best-performing Simoa plasma biomarkers were amyloid-ß42/40 (0.620; 0.548-0.691) and phospho-tau181 (0.707; 0.646-0.768), but neither outperformed the base model. Mass spectrometry plasma measures performed significantly better than any other measure (amyloid-ß1-42/1-40: 0.817; 0.770-0.864 and amyloid-ß composite: 0.820; 0.775-0.866). At a cut-off point of 0.095, mass spectrometry measures of amyloid-ß1-42/1-40 detected amyloid PET positivity with 86.6% sensitivity and 71.9% specificity. Without screening, to obtain 100 PET-positive individuals from a population with similar amyloid PET positivity prevalence to Insight 46, 543 PET scans would need to be performed. Screening using age, sex and APOE ε4 status would require 940 individuals, of whom 266 would proceed to scan. Using mass spectrometry amyloid-ß1-42/1-40 alone would reduce these numbers to 623 individuals and 243 individuals, respectively. Across a theoretical range of amyloid PET positivity prevalence of 10-50%, mass spectrometry measures of amyloid-ß1-42/1-40 would consistently reduce the numbers proceeding to scans, with greater cost savings demonstrated at lower prevalence.

Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging.

Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated. For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners.

Correlated metals as transparent conductors.

The fundamental challenge for designing transparent conductors used in photovoltaics, displays and solid-state lighting is the ideal combination of high optical transparency and high electrical conductivity. Satisfying these competing demands is commonly achieved by increasing carrier concentration in a wide-bandgap semiconductor with low effective carrier mass through heavy doping, as in the case of tin-doped indium oxide (ITO). Here, an alternative design strategy for identifying high-conductivity, high-transparency metals is proposed, which relies on strong electron-electron interactions resulting in an enhancement in the carrier effective mass. This approach is experimentally verified using the correlated metals SrVO3 and CaVO3, which, despite their high carrier concentration (>2.2 × 10(22) cm(-3)), have low screened plasma energies (<1.33 eV), and demonstrate excellent performance when benchmarked against ITO. A method is outlined to rapidly identify other candidates among correlated metals, and strategies are proposed to further enhance their performance, thereby opening up new avenues to develop transparent conductors.

Study protocol: Insight 46 - a neuroscience sub-study of the MRC National Survey of Health and Development.

BACKGROUND: Increasing age is the biggest risk factor for dementia, of which Alzheimer's disease is the commonest cause. The pathological changes underpinning Alzheimer's disease are thought to develop at least a decade prior to the onset of symptoms. Molecular positron emission tomography and multi-modal magnetic resonance imaging allow key pathological processes underpinning cognitive impairment - including ß-amyloid depostion, vascular disease, network breakdown and atrophy - to be assessed repeatedly and non-invasively. This enables potential determinants of dementia to be delineated earlier, and therefore opens a pre-symptomatic window where intervention may prevent the onset of cognitive symptoms. METHODS/DESIGN: This paper outlines the clinical, cognitive and imaging protocol of "Insight 46", a neuroscience sub-study of the MRC National Survey of Health and Development. This is one of the oldest British birth cohort studies and has followed 5362 individuals since their birth in England, Scotland and Wales during one week in March 1946. These individuals have been tracked in 24 waves of data collection incorporating a wide range of health and functional measures, including repeat measures of cognitive function. Now aged 71 years, a small fraction have overt dementia, but estimates suggest that ~1/3 of individuals in this age group may be in the preclinical stages of Alzheimer's disease. Insight 46 is recruiting 500 study members selected at random from those who attended a clinical visit at 60-64 years and on whom relevant lifecourse data are available. We describe the sub-study design and protocol which involves a prospective two time-point (0, 24 month) data collection covering clinical, neuropsychological, ß-amyloid positron emission tomography and magnetic resonance imaging, biomarker and genetic information. Data collection started in 2015 (age 69) and aims to be completed in 2019 (age 73). DISCUSSION: Through the integration of data on the socioeconomic environment and on physical, psychological and cognitive function from 0 to 69 years, coupled with genetics, structural and molecular imaging, and intensive cognitive and neurological phenotyping, Insight 46 aims to identify lifetime factors which influence brain health and cognitive ageing, with particular focus on Alzheimer's disease and cerebrovascular disease. This will provide an evidence base for the rational design of disease-modifying trials.

Addressing asthma and obesity in children with community health workers: proof-of-concept intervention development.

BACKGROUND: The objective of this study was to design and test the feasibility and impact of a community health worker (CHW) intervention for comorbid asthma and obesity. METHODS: Using a proof of concept study design, we collected pre/post outcomes from a single intervention cohort of urban low-income in a single community area. A community-based participatory research approach was employed. Forty-six children and their caregivers were recruited. Children were 5-12 years old with physician-diagnosed asthma and body mass index (BMI) > 85%. Families were offered 12 home visits from CHWs that integrated asthma and obesity core curriculums. The primary asthma outcome was asthma control, measured via the Childhood Asthma Control Test (cACT). The primary obesity outcome was child body mass index (BMI). RESULTS: Families received a median of 10 out of the 12 home visits over 1 year. At 1 year, there was a significant improvement in the number of children with controlled asthma as measured via cACT (85.7% at 1 year compared to 61.9% at baseline, p = 0.01). Activity limitations and emergency utilization were reduced while inhaler technique improved (p < 0.01 for all). Child BMI z-score was reduced: mean = 1.97 (SD 0.79) at 1 year compared to mean = 2.13 (SD 0.40) at baseline, p < 0.01. No association was seen between change in child BMI and change in asthma control. Worse baseline child depression scores were associated with less improvement in asthma control (p = 0.003) and higher baseline caregiver post-traumatic stress disorder scores were associated with increased child BMI (p = 0.012). CONCLUSIONS: The CHW intervention has promise for improving asthma and weight outcomes in high-risk children with comorbid asthma and obesity; this model warrants further development and investigation.

Non-small-cell lung cancer resectability: diagnostic value of PET/MR.

PURPOSE: To assess the diagnostic performance of PET/MR in patients with non-small-cell lung cancer. METHODS: Fifty consecutive consenting patients who underwent routine (18)F-FDG PET/CT for potentially radically treatable lung cancer following a staging CT scan were recruited for PET/MR imaging on the same day. Two experienced readers, unaware of the results with the other modalities, interpreted the PET/MR images independently. Discordances were resolved in consensus. PET/MR TNM staging was compared to surgical staging from thoracotomy as the reference standard in 33 patients. In the remaining 17 nonsurgical patients, TNM was determined based on histology from biopsy, imaging results (CT and PET/CT) and follow-up. ROC curve analysis was used to assess accuracy, sensitivity and specificity of the PET/MR in assessing the surgical resectability of primary tumour. The kappa statistic was used to assess interobserver agreement in the PET/MR TNM staging. Two different readers, without knowledge of the PET/MR findings, subsequently separately reviewed the PET/CT images for TNM staging. The generalized kappa statistic was used to determine intermodality agreement between PET/CT and PET/MR for TNM staging. RESULTS: ROC curve analysis showed that PET/MR had a specificity of 92.3 % and a sensitivity of 97.3 % in the determination of resectability with an AUC of 0.95. Interobserver agreement in PET/MR reading ranged from substantial to perfect between the two readers (Cohen's kappa 0.646 - 1) for T stage, N stage and M stage. Intermodality agreement between PET/CT and PET/MR ranged from substantial to almost perfect for T stage, N stage and M stage (Cohen's kappa 0.627 - 0.823). CONCLUSION: In lung cancer patients PET/MR appears to be a robust technique for preoperative staging.

How we developed a trainee-led book group as a supplementary education tool for psychiatric training in the 21st century.

BACKGROUND: Postgraduate medical education has, in recent years, become a dynamic field with the increasing availability of innovative and interactive teaching techniques. Anecdotal evidence suggests that the current focus of psychiatric training on the acquisition of scientific and clinical knowledge is inadequate to address the multidimensional nature of psychiatry. Supplementary teaching tools may be usefully applied to address this need. METHODS: A group of trainees at the Maudsley Hospital and Institute of Psychiatry (UK) pioneered the use of a book group as an innovative teaching tool to enhance the psychiatric training experience by, amongst other aspects, facilitating dialogue between peers on fundamental epistemological issues raised by critical engagement with seminal psychiatric texts. RESULTS: Feedback from members suggested that participation in the book group broadened the overall learning potential and experience of psychiatry. The key ingredients were identified as: (i) collaborative peer-to-peer learning; (ii) the use of 'flipped classroom' model; and (iii) joint ownership. CONCLUSION: The book group has demonstrated real potential to facilitate direct trainee engagement with the multi-faceted nature of psychiatry as a complex humanistic discipline within an informal learning space.

Are power calculations useful? A multicentre neuroimaging study.

There are now many reports of imaging experiments with small cohorts of typical participants that precede large-scale, often multicentre studies of psychiatric and neurological disorders. Data from these calibration experiments are sufficient to make estimates of statistical power and predictions of sample size and minimum observable effect sizes. In this technical note, we suggest how previously reported voxel-based power calculations can support decision making in the design, execution and analysis of cross-sectional multicentre imaging studies. The choice of MRI acquisition sequence, distribution of recruitment across acquisition centres, and changes to the registration method applied during data analysis are considered as examples. The consequences of modification are explored in quantitative terms by assessing the impact on sample size for a fixed effect size and detectable effect size for a fixed sample size. The calibration experiment dataset used for illustration was a precursor to the now complete Medical Research Council Autism Imaging Multicentre Study (MRC-AIMS). Validation of the voxel-based power calculations is made by comparing the predicted values from the calibration experiment with those observed in MRC-AIMS. The effect of non-linear mappings during image registration to a standard stereotactic space on the prediction is explored with reference to the amount of local deformation. In summary, power calculations offer a validated, quantitative means of making informed choices on important factors that influence the outcome of studies that consume significant resources.

A comparison of CT- and MR-based attenuation correction in neurological PET.

PURPOSE: To assess the quantitative accuracy of current MR attenuation correction (AC) methods in neurological PET, in comparison to data derived using CT AC. METHODS: This retrospective study included 25 patients who were referred for a neurological FDG PET examination and were imaged sequentially by PET/CT and simultaneous PET/MR. Differences between activity concentrations derived using Dixon and ultrashort echo time (UTE) MR-based AC and those derived from CT AC were compared using volume of interest and voxel-based approaches. The same comparisons were also made using PET data represented as SUV ratios (SUVr) using grey matter cerebellum as the reference region. RESULTS: Extensive and statistically significant regional underestimations of activity concentrations were found with both Dixon AC (P < 0.001) and UTE AC (P < 0.001) in all brain regions when compared to CT AC. The greatest differences were found in the cortical grey matter (Dixon AC 21.3%, UTE AC 15.7%) and cerebellum (Dixon AC 19.8%, UTE AC 17.3%). The underestimation using UTE AC was significantly less than with Dixon AC (P < 0.001) in most regions. Voxel-based comparisons showed that all cortical grey matter and cerebellum uptake was underestimated with Dixon AC compared to CT AC. Using UTE AC the extent and significance of these differences were reduced. Inaccuracies in cerebellar activity concentrations led to a mixture of predominantly cortical underestimation and subcortical overestimation in SUVr PET data for both MR AC methodologies. CONCLUSION: MR-based AC results in significant underestimation of activity concentrations throughout the brain, which makes the use of SUVr data difficult. These effects limit the quantitative accuracy of neurological PET/MR.

Black box no more: A cross-sectional multi-disciplinary survey for exploring governance and guiding adoption of AI in medical imaging and radiotherapy in the UK.

BACKGROUND: Medical Imaging and radiotherapy (MIRT) are at the forefront of artificial intelligence applications. The exponential increase of these applications has made governance frameworks necessary to uphold safe and effective clinical adoption. There is little information about how healthcare practitioners in MIRT in the UK use AI tools, their governance and associated challenges, opportunities and priorities for the future. METHODS: This cross-sectional survey was open from November to December 2022 to MIRT professionals who had knowledge or made use of AI tools, as an attempt to map out current policy and practice and to identify future needs. The survey was electronically distributed to the participants. Statistical analysis included descriptive statistics and inferential statistics on the SPSS statistical software. Content analysis was employed for the open-ended questions. RESULTS: Among the 245 responses, the following were emphasised as central to AI adoption: governance frameworks, practitioner training, leadership, and teamwork within the AI ecosystem. Prior training was strongly correlated with increased knowledge about AI tools and frameworks. However, knowledge of related frameworks remained low, with different professionals showing different affinity to certain frameworks related to their respective roles. Common challenges and opportunities of AI adoption were also highlighted, with recommendations for future practice.

Black box no more: a scoping review of AI governance frameworks to guide procurement and adoption of AI in medical imaging and radiotherapy in the UK.

Technological advancements in computer science have started to bring artificial intelligence (AI) from the bench closer to the bedside. While there is still lots to do and improve, AI models in medical imaging and radiotherapy are rapidly being developed and increasingly deployed in clinical practice. At the same time, AI governance frameworks are still under development. Clinical practitioners involved with procuring, deploying, and adopting AI tools in the UK should be well-informed about these AI governance frameworks. This scoping review aimed to map out available literature on AI governance in the UK, focusing on medical imaging and radiotherapy. Searches were performed on Google Scholar, Pubmed, and the Cochrane Library, between June and July 2022. Of 4225 initially identified sources, 35 were finally included in this review. A comprehensive conceptual AI governance framework was proposed, guided by the need for rigorous AI validation and evaluation procedures, the accreditation rules and standards, and the fundamental ethical principles of AI. Fairness, transparency, trustworthiness, and explainability should be drivers of all AI models deployed in clinical practice. Appropriate staff education is also mandatory to ensure AI's safe and responsible use. Multidisciplinary teams under robust leadership will facilitate AI adoption, and it is crucial to involve patients, the public, and practitioners in decision-making. Collaborative research should be encouraged to enhance and promote innovation, while caution should be paid to the ongoing auditing of AI tools to ensure safety and clinical effectiveness.

Test-retest repeatability of ADC in prostate using the multi b-Value VERDICT acquisition.

PURPOSE: VERDICT (Vascular, Extracellular, Restricted Diffusion for Cytometry in Tumours) MRI is a multi b-value, variable diffusion time DWI sequence that allows generation of ADC maps from different b-value and diffusion time combinations. The aim was to assess precision of prostate ADC measurements from varying b-value combinations using VERDICT and determine which protocol provides the most repeatable ADC. MATERIALS AND METHODS: Forty-one men (median age: 67.7 years) from a prior prospective VERDICT study (April 2016-October 2017) were analysed retrospectively. Men who were suspected of prostate cancer and scanned twice using VERDICT were included. ADC maps were formed using 5b-value combinations and the within-subject standard deviations (wSD) were calculated per ADC map. Three anatomical locations were analysed per subject: normal TZ (transition zone), normal PZ (peripheral zone), and index lesions. Repeated measures ANOVAs showed which b-value range had the lowest wSD, Spearman correlation and generalized linear model regression analysis determined whether wSD was related to ADC magnitude and ROI size. RESULTS: The mean lesion ADC for b0b1500 had the lowest wSD in most zones (0.18-0.58x10-4 mm2/s). The wSD was unaffected by ADC magnitude (Lesion: p = 0.064, TZ: p = 0.368, PZ: p = 0.072) and lesion Likert score (p = 0.95). wSD showed a decrease with ROI size pooled over zones (p = 0.019, adjusted regression coefficient = -1.6x10-3, larger ROIs for TZ versus PZ versus lesions). ADC maps formed with a maximum b-value of 500 s/mm2 had the largest wSDs (1.90-10.24x10-4 mm2/s). CONCLUSION: ADC maps generated from b0b1500 have better repeatability in normal TZ, normal PZ, and index lesions.

Operationalizing the centiloid scale for [18F]florbetapir PET studies on PET/MRI.

INTRODUCTION: The Centiloid scale aims to harmonize amyloid beta (Aß) positron emission tomography (PET) measures across different analysis methods. As Centiloids were created using PET/computerized tomography (CT) data and are influenced by scanner differences, we investigated the Centiloid transformation with data from Insight 46 acquired with PET/magnetic resonanceimaging (MRI). METHODS: We transformed standardized uptake value ratios (SUVRs) from 432 florbetapir PET/MRI scans processed using whole cerebellum (WC) and white matter (WM) references, with and without partial volume correction. Gaussian-mixture-modelling-derived cutpoints for Aß PET positivity were converted. RESULTS: The Centiloid cutpoint was 14.2 for WC SUVRs. The relationship between WM and WC uptake differed between the calibration and testing datasets, producing implausibly low WM-based Centiloids. Linear adjustment produced a WM-based cutpoint of 18.1. DISCUSSION: Transformation of PET/MRI florbetapir data to Centiloids is valid. However, further understanding of the effects of acquisition or biological factors on the transformation using a WM reference is needed. HIGHLIGHTS: Centiloid conversion of amyloid beta positron emission tomography (PET) data aims to standardize results.Centiloid values can be influenced by differences in acquisition.We converted florbetapir PET/magnetic resonance imaging data from a large birth cohort.Whole cerebellum referenced values could be reliably transformed to Centiloids.White matter referenced values may be less generalizable between datasets.

Neuregulin-1 genotype is associated with structural differences in the normal human brain.

The human neuregulin-1 (NRG-1) gene is highly expressed in the brain, is implicated in numerous functions associated with neuronal development, and is a leading candidate gene for schizophrenia. The T allele of SNP8NRG243177, part of a risk haplotype for schizophrenia, has been previously associated with decreases in white matter in the right anterior internal capsule and the left anterior thalamic radiation. To our knowledge no studies have described the effects of SNP8NRG243177 on grey matter volume at a voxelwise level. We assessed associations between this SNP and brain structure in 79 general population volunteers from the Northern Finland 1966 Birth Cohort (NFBC 1966). We show, for the first time, that genetic variation in SNP8NRG243177 is associated with variation in frontal brain structure in both grey and white matter. T allele carriers showed decreased grey matter volume in several frontal gyri, including inferior, middle and superior frontal gyri and the anterior cingulate gyrus, as well as decreased white matter volume in the regions of the genu and body of the corpus callosum, anterior and superior corona radiata, anterior limb of the internal capsule and external capsule regions traversed by major white matter tracts of the anterior thalamic radiation, and the inferior fronto-occipital fasciculus. These results suggest that this genetic variant may mediate risk for schizophrenia, in part, through its effect on brain structure in these regions.

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.

Abnormal structure of frontostriatal brain systems is associated with aspects of impulsivity and compulsivity in cocaine dependence.

A growing body of preclinical evidence indicates that addiction to cocaine is associated with neuroadaptive changes in frontostriatal brain systems. Human studies in cocaine-dependent individuals have shown alterations in brain structure, but it is less clear how these changes may be related to the clinical phenotype of cocaine dependence characterized by impulsive behaviours and compulsive drug-taking. Here we compared self-report, behavioural and structural magnetic resonance imaging data on a relatively large sample of cocaine-dependent individuals (n = 60) with data on healthy volunteers (n = 60); and we investigated the relationships between grey matter volume variation, duration of cocaine use, and measures of impulsivity and compulsivity in the cocaine-dependent group. Cocaine dependence was associated with an extensive system of abnormally decreased grey matter volume in orbitofrontal, cingulate, insular, temporoparietal and cerebellar cortex, and with a more localized increase in grey matter volume in the basal ganglia. Greater duration of cocaine dependence was correlated with greater grey matter volume reduction in orbitofrontal, cingulate and insular cortex. Greater impairment of attentional control was associated with reduced volume in insular cortex and increased volume of caudate nucleus. Greater compulsivity of drug use was associated with reduced volume in orbitofrontal cortex. Cocaine-dependent individuals had abnormal structure of corticostriatal systems, and variability in the extent of anatomical changes in orbitofrontal, insular and striatal structures was related to individual differences in duration of dependence, inattention and compulsivity of cocaine consumption.

Brain tissue volume changes following weight gain in adults with anorexia nervosa.

OBJECTIVE: To measure brain volume deficits among underweight patients with anorexia nervosa (AN) compared to control participants and evaluate the reversibility of these deficits with short-term weight restoration. METHOD: Brain volume changes in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) were examined in 32 adult women with AN and compared to 21, age and body mass index-range matched control women. RESULTS: Patients with AN had a significant increase in GM (p = .006, η(2) = 0.14) and WM volume (p = .001, η(2) = 0.19) following weight restoration. Patients on average had lower levels of GM at low weight (647.63 ± 62.07 ml) compared to controls (679.93 ± 53.31 ml), which increased with weight restoration (662.64 ± 69.71 ml), but did not fully normalize. DISCUSSION: This study suggests that underweight adult patients with AN have reduced GM and WM volumes that increase with short-term weight restoration.

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.

Noise removal in resting-state and task fMRI: functional connectivity and activation maps.

OBJECTIVE: Blood-oxygenated-level dependent (BOLD)-based functional magnetic resonance imaging (fMRI) is a widely used non-invasive tool for mapping brain function and connectivity. However, the BOLD signal is highly affected by non-neuronal contributions arising from head motion, physiological noise and scanner artefacts. Therefore, it is necessary to recover the signal of interest from the other noise-related fluctuations to obtain reliable functional connectivity (FC) results. Several pre-processing pipelines have been developed, mainly based on nuisance regression and independent component analysis (ICA). The aim of this work was to investigate the impact of seven widely used denoising methods on both resting-state and task fMRI. APPROACH: Task fMRI can provide some ground truth given that the task administered has well established brain activations. The resulting cleaned data were compared using a wide range of measures: motion evaluation and data quality, resting-state networks and task activations, FC. MAIN RESULTS: Improved signal quality and reduced motion artefacts were obtained with all advanced pipelines, compared to the minimally pre-processed data. Larger variability was observed in the case of brain activation and FC estimates, with ICA-based pipelines generally achieving more reliable and accurate results. SIGNIFICANCE: This work provides an evidence-based reference for investigators to choose the most appropriate method for their study and data.