Development of quality standards for multi-center, longitudinal magnetic resonance imaging studies in clinical neuroscience

Magnetic resonance imaging (MRI) data is generated by a complex procedure. Many possible sources of error exist which can lead to a worse signal. For example, hidden defective components of a MRI-scanner, changes in the static magnetic field caused by a person simply moving in the MRI scanner room as well as changes in the measurement sequences can negatively affect the signal-to-noise ratio (SNR). A comprehensive, reproducible, quality assurance (QA) procedure is necessary, to ensure reproducible results both from the MRI equipment and the human operator of the equipment. To examine the quality of the MRI data, there are two possibilities. On the one hand, water or gel-filled objects, so-called "phantoms", are regularly measured. Based on this signal, which in the best case should always be stable, the general performance of the MRI scanner can be tested. On the other hand, the actually interesting data, mostly human data, are checked directly for certain signal parameters (e.g., SNR, motion parameters). This thesis consists of two parts. In the first part a study-specific QA-protocol was developed for a large multicenter MRI-study, FOR2107. The aim of FOR2107 is to investigate the causes and course of affective disorders, unipolar depression and bipolar disorders, taking clinical and neurobiological effects into account. The main aspect of FOR2107 is the MRI-measurement of more than 2000 subjects in a longitudinal design (currently repeated measurements after 2 years, further measurements planned after 5 years). To bring MRI-data and disease history together, MRI-data must provide stable results over the course of the study. Ensuring this stability is dealt with in this part of the work. An extensive QA, based on phantom measurements, human data analysis, protocol compliance testing, etc., was set up. In addition to the development of parameters for the characterization of MRI-data, the used QA-protocols were improved during the study. The differences between sites and the impact of these differences on human data analysis were analyzed. The comprehensive quality assurance for the FOR2107 study showed significant differences in MRI-signal (for human and phantom data) between the centers. Occurring problems could easily be recognized in time and be corrected, and must be included for current and future analyses of human data. For the second part of this thesis, a QA-protocol (and the freely available associated software "LAB-QA2GO") has been developed and tested, and can be used for individual studies or to control the quality of an MRI-scanner. This routine was developed because at many sites and in many studies, no explicit QA is performed nevertheless suitable, freely available QA-software for MRI-measurements is available. With LAB-QA2GO, it is possible to set up a QA-protocol for an MRI-scanner or a study without much effort and IT knowledge. Both parts of the thesis deal with the implementation of QA-procedures. High quality data and study results can be achieved only by the usage of appropriate QA-procedures, as presented in this work. Therefore, QA-measures should be implemented at all levels of a project and should be implemented permanently in project and evaluation routines

Measurement variability following MRI system upgrade

Major hardware/software changes to MRI platforms, either planned or unplanned, will almost invariably occur in longitudinal studies. Our objective was to assess the resulting variability on relevant imaging measurements in such context, specifically for three Siemens Healthcare Magnetom Trio upgrades to the Prismafit platform. We report data acquired on three healthy volunteers scanned before and after three different platform upgrades. We assessed differences in image signal (contrast-to-noise ratio (CNR)) on T1-weighted images (T1w) and fluid-attenuated inversion recovery images (FLAIR); brain morphometry on T1w image; and small vessel disease (white matter hyperintensities; WMH) on FLAIR image. Prismafit upgrade resulted in higher (30%) and more variable neocortical CNR and higher brain volume and thickness mainly in frontal areas. A significant relationship was observed between neocortical CNR and cortical volume. For FLAIR images, no significant CNR difference was observed, but WMH volumes were significantly smaller (-68%) after Prismafit upgrade, when compared to results on the Magnetom Trio. Together, these results indicate that Prismafit upgrade significantly influenced image signal, brain morphometry measures and small vessel diseases measures and that these effects need to be taken into account when analyzing results from any longitudinal study undergoing similar changes

Positron emission tomography and magnetic resonance imaging methods and datasets within the Dominantly Inherited Alzheimer Network (DIAN)

The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case-control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual\u27s point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of \u27sporadic\u27 AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers

Positron emission tomography and magnetic resonance imaging methods and datasets within the Dominantly Inherited Alzheimer Network (DIAN)

The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case-control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual's point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of 'sporadic' AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers

Normative data for subcortical regional volumes over the lifetime of the adult human brain

Normative data for volumetric estimates of brain structures are necessary to adequately assess brain volume alterations in individuals with suspected neurological or psychiatric conditions. Although many studies have described age and sex effects in healthy individuals for brain morphometry assessed via magnetic resonance imaging, proper normative values allowing to quantify potential brain abnormalities are needed. We developed norms for volumetric estimates of subcortical brain regions based on cross-sectional magnetic resonance scans from 2790 healthy individuals aged 18 to 94 years using 23 samples provided by 21 independent research groups. The segmentation was conducted using FreeSurfer, a widely used and freely available automated segmentation software. Models predicting subcortical regional volumes of each hemisphere were produced including age, sex, estimated total intracranial volume (eTIV), scanner manufacturer, magnetic field strength, and interactions as predictors. The mean explained variance by the models was 48%. For most regions, age, sex and eTIV predicted most of the explained variance while manufacturer, magnetic field strength and interactions predicted a limited amount. Estimates of the expected volumes of an individual based on its characteristics and the scanner characteristics can be obtained using derived formulas. For a new individual, significance test for volume abnormality, effect size and estimated percentage of the normative population with a smaller volume can be obtained. Normative values were validated in independent samples of healthy adults and in adults with Alzheimer's disease and schizophrenia

Positron emission tomography and magnetic resonance imaging methods and datasets within the dominantly inherited Alzheimer network (DIAN)

The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case–control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual’s point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of ‘sporadic’ AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers

Impact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) was established in 2004 to facilitate the development of effective treatments for Alzheimer's disease (AD) by validating biomarkers for AD clinical trials. METHODS: We searched for ADNI publications using established methods. RESULTS: ADNI has (1) developed standardized biomarkers for use in clinical trial subject selection and as surrogate outcome measures; (2) standardized protocols for use across multiple centers; (3) initiated worldwide ADNI; (4) inspired initiatives investigating traumatic brain injury and post-traumatic stress disorder in military populations, and depression, respectively, as an AD risk factor; (5) acted as a data-sharing model; (6) generated data used in over 600 publications, leading to the identification of novel AD risk alleles, and an understanding of the relationship between biomarkers and AD progression; and (7) inspired other public-private partnerships developing biomarkers for Parkinson's disease and multiple sclerosis. DISCUSSION: ADNI has made myriad impacts in its first decade. A competitive renewal of the project in 2015 would see the use of newly developed tau imaging ligands, and the continued development of recruitment strategies and outcome measures for clinical trials

Basic MR sequence parameters systematically bias automated brain volume estimation

Automated brain MRI morphometry, including hippocampal volumetry for Alzheimer disease, is increasingly recognized as a biomarker. Consequently, a rapidly increasing number of software tools have become available. We tested whether modifications of simple MR protocol parameters typically used in clinical routine systematically bias automated brain MRI segmentation results. The study was approved by the local ethical committee and included 20 consecutive patients (13 females, mean age 75.8 ± 13.8 years) undergoing clinical brain MRI at 1.5 T for workup of cognitive decline. We compared three 3D T1 magnetization prepared rapid gradient echo (MPRAGE) sequences with the following parameter settings: ADNI-2 1.2 mm iso-voxel, no image filtering, LOCAL- 1.0 mm iso-voxel no image filtering, LOCAL+ 1.0 mm iso-voxel with image edge enhancement. Brain segmentation was performed by two different and established analysis tools, FreeSurfer and MorphoBox, using standard parameters. Spatial resolution (1.0 versus 1.2 mm iso-voxel) and modification in contrast resulted in relative estimated volume difference of up to 4.28 % (p < 0.001) in cortical gray matter and 4.16 % (p < 0.01) in hippocampus. Image data filtering resulted in estimated volume difference of up to 5.48 % (p < 0.05) in cortical gray matter. A simple change of MR parameters, notably spatial resolution, contrast, and filtering, may systematically bias results of automated brain MRI morphometry of up to 4-5 %. This is in the same range as early disease-related brain volume alterations, for example, in Alzheimer disease. Automated brain segmentation software packages should therefore require strict MR parameter selection or include compensatory algorithms to avoid MR parameter-related bias of brain morphometry results