Same brain, different look? The impact of scanner, sequence and preprocessing on diffusion imaging outcome parameters

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19–54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability

Interactive effect of age and APOE-ε4 allele load on white matter myelin content in cognitively normal middle-aged subjects

The apolipoprotein E gene (APOE) ε4 allele has a strong and manifold impact on cognition and neuroimaging phenotypes in cognitively normal subjects, including alterations in the white matter (WM) microstructure. Such alterations have often been regarded as a reflection of potential thinning of the myelin sheath along axons, rather than pure axonal degeneration. Considering the main role of APOE in brain lipid transport, characterizing the impact of APOE on the myelin coating is therefore of crucial interest, especially in healthy APOE-ε4 homozygous individuals, who are exposed to a twelve-fold higher risk of developing Alzheimer's disease (AD), compared to the rest of the population. We examined T1w/T2w ratio maps in 515 cognitively healthy middle-aged participants from the ALFA study (ALzheimer and FAmilies) cohort, a single-site population-based study enriched for AD risk (68 APOE-ε4 homozygotes, 197 heterozygotes, and 250 non-carriers). Using tract-based spatial statistics, we assessed the impact of age and APOE genotype on this ratio taken as an indirect descriptor of myelin content. Healthy APOE-ε4 carriers display decreased T1w/T2w ratios in extensive regions in a dose-dependent manner. These differences were found to interact with age, suggesting faster changes in individuals with more ε4 alleles. These results obtained with T1w/T2w ratios, confirm the increased vulnerability of WM tracts in APOE-ε4 healthy carriers. Early alterations of myelin content could be the result of the impaired function of the ε4 isoform of the APOE protein in cholesterol transport. These findings help to clarify the possible interactions between the APOE-dependent non-pathological burden and age-related changes potentially at the source of the AD pathological cascade

Structural reorganization of the early visual cortex following Braille training in sighted adults

Training can induce cross-modal plasticity in the human cortex. A well-known example of this phenomenon is the recruitment of visual areas for tactile and auditory processing. It remains unclear to what extent such plasticity is associated with changes in anatomy. Here we enrolled 29 sighted adults into a nine-month tactile Braille-reading training, and used voxel-based morphometry and diffusion tensor imaging to describe the resulting anatomical changes. In addition, we collected resting-state fMRI data to relate these changes to functional connectivity between visual and somatosensory-motor cortices. Following Braille-training, we observed substantial grey and white matter reorganization in the anterior part of early visual cortex (peripheral visual field). Moreover, relative to its posterior, foveal part, the peripheral representation of early visual cortex had stronger functional connections to somatosensory and motor cortices even before the onset of training. Previous studies show that the early visual cortex can be functionally recruited for tactile discrimination, including recognition of Braille characters. Our results demonstrate that reorganization in this region induced by tactile training can also be anatomical. This change most likely reflects a strengthening of existing connectivity between the peripheral visual cortex and somatosensory cortices, which suggests a putative mechanism for cross-modal recruitment of visual areas

The MNI data-sharing and processing ecosystem

AbstractNeuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of “Big Data”, there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing

Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines

© 2019 Haddad et al. The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data

Using diffusion imaging to explore the anatomical nature of early course schizophrenia

Schizophrenia (SCZ) is a serious brain disorder that affects around 1% of the world population. Despite a long history of research in diagnosis and treatment of SCZ, we are still far from being able to explain the origin of the disease and the interindividual differences in the trajectory of the disease. The neurodevelopmental hypothesis states that SCZ is caused by early maturational abnormalities, which interact with later brain development. Neuroimaging provides a noninvasive opportunity to study this theory in vivo. Traditionally, Magnetic resonance imaging (MRI) has been used to examine macrostructural gray matter features such as gray matter volume or cortical thickness and SCZ has been established as a brain disorder hereinafter. Diffusion tensor imaging (DTI) allows to investigate the microstructure of brain tissue. It measures the magnitude and direction of water molecule`s diffusion and is highly sensitive to alterations of gray and white matter organization. Gray matter contains the neurons and the white matter contains myelinated axons and provides long and middle range connectivity between cortical neurons. White matter alterations observed in SCZ, therefore, support the disconnection theory stating that SCZ is a brain disorder with disrupted integration of different brain systems. Finally, while early imaging research focused on chronic states of SCZ a shift of the field towards studying early stages can be observed in more recent years. Understanding early course SCZ raises the hope to improve diagnosis and subsequently prevention and intervention. In line with this research the aim of the presented studies is to characterize microstructural white and gray matter alterations in early course SCZ using diffusion MRI combined with advanced post-processing techniques, which are sufficiently sensitive to detect subtle brain conspicuities. Implications of and associations with neuropsychological and clinical symptoms and diagnosis of SCZ will be discussed subsequently. Paper 1 The purpose of the first project is to characterize white matter organization in patients with early course SCZ. To my knowledge this is the first study investigating five main intra-hemispheric corti-cocortical white matter tracts using manual guided tractography in early course SCZ. The tracts were selected based on previous findings: uncinate fasciculus (UF), cingulum bundle (CB), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF) and arcuate fasciculus (AF). Diffusion parameters (fractional anisotropy [FA], trace, axial diffusivity [AD] and radial diffusivity [RD]) were computed for each tract and compared between patients with early course SCZ (number [n]=30) and healthy controls (HC) (n=30). The association of the diffusion parameters of the tracts with clinical symptoms, memory performance, and processing speed was examined afterwards. A significant group effect, represented by reduced FA and increased RD and trace in the patients’ group compared to HC was observed for the right AF (FA [F=5.94, df=1, p=.016]; RD [F=5.60, df=1, p=.020]), CB (FA [F=9.35, df=1, p=.003]; RD [F=11.55, df=1, p=.0010] and ILF (FA [F=14.77, df=1, p=.004]; RD [F=13.25, df=1, p<.0001]). The pattern of lower FA and higher RD is indicative for myelin abnormalities. Structural alterations were correlated with positive symptoms (ILF, AF), and cognitive performance (CB), which points to the clinical relevance of the observed white matter conspicuities. Paper 2 In the past, DTI has mainly been used to study white mater, because technical challenges limited the use of DTI for the characterization of gray matter organization. However, as an extension of the classical disconnection theory one would not only expect dysconnectivity in white matter, but also a disruption of gray matter organization. The aim of this study therefore is to use novel DTI method- heterogeneity- to study the microstructural gray matter organization over the course of SCZ. In comparison to traditional diffusion indices, which focus on intra-voxel diffusion properties, heterogeneity captures the microstructural organization of a larger cortical area. After applying a free water correc-tion to control for partial volume effects, T1 and diffusion images were registered to each other and the variability (=heterogeneity) of diffusion parameters within the four brain lobes defined by auto-matic parcellation method was calculated. Patients with chronic SCZ (n=27) did not show differences of cortical organization when compared to HC (n=22). However, patients with early course SCZ (n=19) showed increased heterogeneity in the frontal lobe when compared to HC (n=15) (F=10.68, df=1, p<.0030). This indicates a lower grade of cortical organization in patients than in HC. It is suggested that this can be explained by neurodevelopmental abnormalities, plausibly caused by abnormal synaptic reorganization and pruning during adolescence and early adulthood in SCZ

Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network

Purpose: The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers. / Methods: Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A “traveling brains” dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability. / Results: The results show an inter-center and inter-subject variability of <10%, except for “clustering coefficient” (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners’ hardware. / Conclusions: The results show low variability of connectivity topological metrics across sites running a harmonised protocol

Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: A European multi-site 3T study

Brain vascular damage accumulate in aging and often manifest as white matter hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to segment WMHs, a gold standard has not been achieved and their longitudinal reproducibility has been poorly investigated. The aim of present work is to evaluate accuracy and reproducibility of two freely available segmentation algorithms. A harmonized MRI protocol was implemented in 3T-scanners across 13 European sites, each scanning five volunteers twice (test-retest) using 2D-FLAIR. Automated segmentation was performed using Lesion segmentation tool algorithms (LST): the Lesion growth algorithm (LGA) in SPM8 and 12 and the Lesion prediction algorithm (LPA). To assess reproducibility, we applied the LST longitudinal pipeline to the LGA and LPA outputs for both the test and retest scans. We evaluated volumetric and spatial accuracy comparing LGA and LPA with manual tracing, and for reproducibility the test versus retest. Median volume difference between automated WMH and manual segmentations (mL) was −0.22[IQR = 0.50] for LGA-SPM8, −0.12[0.57] for LGA-SPM12, −0.09[0.53] for LPA, while the spatial accuracy (Dice Coefficient) was 0.29[0.31], 0.33[0.26] and 0.41[0.23], respectively. The reproducibility analysis showed a median reproducibility error of 20%[IQR = 41] for LGA-SPM8, 14% [31] for LGA-SPM12 and 10% [27] with the LPA cross-sectional pipeline. Applying the LST longitudinal pipeline, the reproducibility errors were considerably reduced (LGA: 0%[IQR = 0], p < 0.001; LPA: 0% [3], p < 0.001) compared to those derived using the cross-sectional algorithms. The DC using the longitudinal pipeline was excellent (median = 1) for LGA [IQR = 0] and LPA [0.02]. LST algorithms showed moderate accuracy and good reproducibility. Therefore, it can be used as a reliable cross-sectional and longitudinal tool in multi-site studies

A test-retest reliability analysis of diffusion measures of white matter tracts relevant for cognitive control

Recent efforts to replicate structural brain-behavior correlations have called into question the replicability of structural brain measures used in cognitive neuroscience. Here, we report an evaluation of test-retest reliability of diffusion tensor imaging (DTI) measures, including fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, in several white matter tracts previously shown to be involved in cognitive control. In a data set consisting of 34 healthy participants scanned twice on a single day, we observe overall stability of DTI measures. This stability remained in a subset of participants who were also scanned a third time on the same day as well as in a 2-week follow-up session. We conclude that DTI measures in these tracts show relative stability, and that alternative explanations for the recent failures of replication must be considered