Dirk Smeets

The effect of morphological and microstructural integrity of the corpus callosum on cognition, fatigue and depression in mildly disabled MS patients

AIMnTo assess the value of callosal morphological and microstructural integrity in assessing different cognitive domains, fatigue and depression in mildly disabled multiple sclerosis (MS) patients.nnnMATERIALS AND METHODSnWe assessed 29 mildly disabled MS patients and 15 healthy controls using 3T magnetic resonance images (T1-weighted, FLAIR and DTI) and neuropsychological tests assessing different cognitive functions, depression and fatigue. We compared the added value of morphological measures (corpus callosum area corrected for total intracranial volume, index, circularity and the more detailed thickness profile) and diffusion features (fractional anisotropy and mean diffusivity) in multilinear models including standard clinical and whole-brain parameters in assessing neuropsychological scores.nnnRESULTSnEven in mildly disabled MS patients, a significant reduction of the corpus callosum (p<0.001) was observed in comparison to healthy controls. Callosal area, index and circularity were significantly (p<0.002) related to whole-brain white matter volume, T2 lesion load and deep grey matter volume, but not with cortical grey matter. The combination of commonly used imaging and clinical parameters explained between 7% (Fatigue) and 50% (processing speed, verbal memory) of the adjusted variance. Inclusion of the mean diffusivity increased the adjusted R2 significantly to 69% (p=0.004) and 71% (p=0.002) for visuospatial and verbal memory respectively.nnnCONCLUSIONnOur results show that callosal features may be used as an alternative to measuring whole-brain volumes. Furthermore, the microstructural integrity of the corpus callosum can help to predict an MS patients memory performance.

Artificial neural networks applied to the analysis of synchrotron nuclear resonant scattering data

The capabilities of artificial neural networks (ANNs) have been investigated for the analysis of nuclear resonant scattering (NRS) data obtained at a synchrotron source. The major advantage of ANNs over conventional analysis methods is that, after an initial training phase, the analysis is fully automatic and practically instantaneous, which allows for a direct intervention of the experimentalist on-site. This is particularly interesting for NRS experiments, where large amounts of data are obtained in very short time intervals and where the conventional analysis method may become quite time-consuming and complicated. To test the capability of ANNs for the automation of the NRS data analysis, a neural network was trained and applied to the specific case of an Fe/Cr multilayer. It was shown how the hyperfine field parameters of the system could be extracted from the experimental NRS spectra. The reliability and accuracy of the ANN was verified by comparing the output of the network with the results obtained by conventional data analysis.

Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid-β-induced pathology

BackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the elderly population. In this study, we used the APP/PS1 transgenic mouse model to explore the feasibility of using diffusion kurtosis imaging (DKI) as a tool for the early detection of microstructural changes in the brain due to amyloid-β (Aβ) plaque deposition.MethodsWe longitudinally acquired DKI data of wild-type (WT) and APP/PS1 mice at 2, 4, 6 and 8xa0months of age, after which these mice were sacrificed for histological examination. Three additional cohorts of mice were also included at 2, 4 and 6xa0months of age to allow voxel-based co-registration between diffusion tensor and diffusion kurtosis xa0metrics and immunohistochemistry.ResultsChanges were observed in diffusion tensorxa0(DT) and diffusion kurtosisxa0(DK) metrics in many of the 23 regions of interest that were analysed. Mean and axialxa0kurtosis were greatly increased owing to Aβ-induced pathological changes in the motor cortex of APP/PS1 mice at 4, 6 and 8xa0months of age. Additionally, fractional anisotropy (FA) was decreased in APP/PS1 mice at these respective ages. Linear discriminant analysis of the motor cortex data indicated that combining diffusion tensorxa0and diffusion kurtosis metrics permits improved separation of WT from APP/PS1 mice compared with either diffusion tensor or diffusion kurtosis metrics alone. We observed that mean kurtosis and FA are the critical metrics for a correct genotype classification. Furthermore, using a newly developed platform to co-register the in vivo diffusion-weighted magnetic resonance imaging with multiple 3D histological stacks, we found high correlations between DK metrics and anti-Aβ (clone 4G8) antibody, glial fibrillary acidic protein, ionised calcium-binding adapter molecule 1 and myelin basic protein immunohistochemistry. Finally, we observed reduced FA in the septal nuclei of APP/PS1 mice at all ages investigated. The latter was at least partially also observed by voxel-based statistical parametric mapping, which showed significantly reduced FA in the septal nuclei, as well as in the corpus callosum, of 8-month-old APP/PS1 mice compared with WT mice.ConclusionsOur results indicate that DKI metrics hold tremendous potential for the early detection and longitudinal follow-up of Aβ-induced pathology.

Processing of structural neuroimaging data in young children: Bridging the gap between current practice and state-of-the-art methods

Highlights • The structure of a child brain is significantly different from an adult brain.• Standard software tools for processing brain MRI data might not be appropriate for analyzing pediatric neuroimaging data.• Age-specific and 4D brain MRI atlases have shown to improve the results for brain extraction, normalization, and segmentation.• Image quality enhancement and longitudinal registration are important processing steps for analysis of pediatric samples.

Measurement of Whole-Brain and Gray Matter Atrophy in Multiple Sclerosis: Assessment with MR Imaging

Purpose To compare available methods for whole-brain and gray matter (GM) atrophy estimation in multiple sclerosis (MS) in terms of repeatability (same magnetic resonance [MR] imaging unit) and reproducibility (different system/field strength) for their potential clinical applications. Materials and Methods The softwares ANTs-v1.9, CIVET-v2.1, FSL-SIENAX/SIENA-5.0.1, Icometrix-MSmetrix-1.7, and SPM-v12 were compared. This retrospective study, performed between March 2015 and March 2017, collected data from (a) eight simulated MR images and longitudinal data (2 weeks) from 10 healthy control subjects to assess the cross-sectional and longitudinal accuracy of atrophy measures, (b) test-retest MR images in 29 patients with MS acquired within the same day at different imaging unit field strengths/manufacturers to evaluate precision, and (c) longitudinal data (1 year) in 24 patients with MS for the agreement between methods. Tissue segmentation, image registration, and white matter (WM) lesion filling were also evaluated. Multiple paired t tests were used for comparisons. Results High values of accuracy (0.87-0.97) for whole-brain and GM volumes were found, with the lowest values for MSmetrix. ANTs showed the lowest mean error (0.02%) for whole-brain atrophy in healthy control subjects, with a coefficient of variation of 0.5%. SPM showed the smallest mean error (0.07%) and coefficient of variation (0.08%) for GM atrophy. Globally, good repeatability (P > .05) but poor reproducibility (P < .05) were found for all methods. WM lesion filling technique mainly affected ANTs, MSmetrix, and SPM results (P < .05). Conclusion From this comparison, it would be possible to select a software for atrophy measurement, depending on the requirements of the application (research center, clinical trial) and its goal (accuracy and repeatability or reproducibility). An improved reproducibility is required for clinical application.

Evaluation of methods for volumetric analysis of pediatric brain data: The childmetrix pipeline versus adult-based approaches

Pediatric brain volumetric analysis based on Magnetic Resonance Imaging (MRI) is of particular interest in order to understand the typical brain development and to characterize neurodevelopmental disorders at an early age. However, it has been shown that the results can be biased due to head motion, inherent to pediatric data, and due to the use of methods based on adult brain data that are not able to accurately model the anatomical disparity of pediatric brains. To overcome these issues, we proposed childmetrix, a tool developed for the analysis of pediatric neuroimaging data that uses an age-specific atlas and a probabilistic model-based approach in order to segment the gray matter (GM) and white matter (WM). The tool was extensively validated on 55 scans of children between 5 and 6u202fyears old (including 13 children with developmental dyslexia) and 10 pairs of test-retest scans of children between 6 and 8u202fyears old and compared with two state-of-the-art methods using an adult atlas, namely icobrain (applying a probabilistic model-based segmentation) and Freesurfer (applying a surface model-based segmentation). The results obtained with childmetrix showed a better reproducibility of GM and WM segmentations and a better robustness to head motion in the estimation of GM volume compared to Freesurfer. Evaluated on two subjects, childmetrix showed good accuracy with 82–84% overlap with manual segmentation for both GM and WM, thereby outperforming the adult-based methods (icobrain and Freesurfer), especially for the subject with poor quality data. We also demonstrated that the adult-based methods needed double the number of subjects to detect significant morphological differences between dyslexics and typical readers. Once further developed and validated, we believe that childmetrix would provide appropriate and reliable measures for the examination of childrens brain.

A Retrospective Belgian Multi-Center MRI Biomarker Study in Alzheimer's Disease (REMEMBER).

Background: Magnetic resonance imaging (MRI) acquisition/processing techniques assess brain volumes to explore neurodegeneration in Alzheimer’s disease (AD). Objective: We examined the clinical utility of MSmetrix and investigated if automated MRI volumes could discriminate between groups covering the AD continuum and could be used as a predictor for clinical progression. Methods: The Belgian Dementia Council initiated a retrospective, multi-center study and analyzed whole brain (WB), grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), cortical GM (CGM) volumes, and WM hyperintensities (WMH) using MSmetrix in the AD continuum. Baseline (nu200a=u200a887) and follow-up (FU, nu200a=u200a95) T1-weighted brain MRIs and time-linked neuropsychological data were available. Results: The cohort consisted of cognitively healthy controls (HC, nu200a=u200a93), subjective cognitive decline (nu200a=u200a102), mild cognitive impairment (MCI, nu200a=u200a379), and AD dementia (nu200a=u200a313). Baseline WB and GM volumes could accurately discriminate between clinical diagnostic groups and were significantly decreased with increasing cognitive impairment. MCI patients had a significantly larger change in WB, GM, and CGM volumes based on two MRIs (nu200a=u200a95) compared to HC (FU>24months, pu200a=u200a0.020). Linear regression models showed that baseline atrophy of WB, GM, CGM, and increased CSF volumes predicted cognitive impairment. Conclusion: WB and GM volumes extracted by MSmetrix could be used to define the clinical spectrum of AD accurately and along with CGM, they are able to predict cognitive impairment based on (decline in) MMSE scores. Therefore, MSmetrix can support clinicians in their diagnostic decisions, is able to detect clinical disease progression, and is of help to stratify populations for clinical trials.

PERFORMANCE EVALUATION OF AUTOMATIC BRAIN MRI SUBSTRUCTURE SEGMENTATION WITH ICOBRAIN

Figure 3. The Effect of AD onmedial temporal lobe cortex shape of the two anatomical variants. A support vector machine (SVM) is trained to discriminate amyloid-negative cognitively normal adults and amyloid-positive AD cases (cases from the other groups were excluded). The vector orthogonal to the SVM hyperplane, which is assumed to be the direction that best discriminate the two groups, is visualized.

FDG-PET POWER TO PREDICT MEMORY DECLINE IN ALZHEIMER’S DISEASE DEPENDS ON DISEASE PHASE AND AMYLOID AND TAU STATUS

temporal progression of disease is unclear. We used a novel MRI phasing algorithm, based on pathological staging studies, to test the hypothesis that non-amnestic AD has disease originating and spreading in neocortex.Methods:We inferred the anatomical origin and progression of disease in each AD variant based on the frequency of regional atrophy patterns in cross-sectional MRI from 131 patients with AD, confirmed through autopsy or CSF results. Disease progression models were computed separately for typical amnestic AD (aAD, 38 scans), logopenic variant primary progressive aphasia (lvPPA, 97 scans), posterior cortical atrophy (PCA, 54 scans), corticobasal syndrome (CBS, 31 scans), and behavioural/ dysexecutive-variant AD (bvAD, 39 scans). For each AD variant, 4 phases of atrophy were defined in 120 anatomical regions-of-interest (ROIs) using a grey matter volume threshold of Z< -1.0 relative to elderly controls. The origin of disease (Phase 1) was inferred from the most frequently atrophied 10% of ROIs; similarly, Phases 2, 3, and 4 comprised ROIs in the 2nd, 3rd, and 4th deciles of atrophy frequency. Results:We observed a unique distribution of atrophy for each phenotype. Phase 1 ROIs in our model represent the anatomical origin of disease, including: MTL for the aAD group (relatively spared in other phenotypes), left lateral temporal lobe for lvPPA, occipito-parietal cortex for PCA, temporo-parietal cortex for CBS, and fronto-temporal cortex for bvAD. Disease phase was significantly correlated with MMSE score and disease duration, independently of age. Conclusions: Our neuroimaging data showed unique maps of progressive atrophy for each AD variant. The classification of patients into phases was validated by correlations with disease duration and neuropsychological performance. We propose these results represent maps of distinct anatomic progression, with non-amnestic phenotypes showing neocortical origin and spread of disease.

THE “A/T/N” SYSTEM: ADDED PREDICTIVE VALUE OF N BIOMARKERS OF PROGRESSION FROM MCI TO DEMENTIA OVER 2 AND 4 YEARS

FTD Fusiform gyms (L and R) 3.65 and 2.10 Middle frontal gyrus (L and R) 3.25 and 2.74 Inferior frontal gyrus (L and R) 2.52 and 2.49 Lateral orbital gyrus (R and L) 2.18 and 2.00 Mix Middle and inferior temporal gyrus (R) 1.37 Posterior temporal lobe (L) 1.06 Cuneus (R) 1.04 Lateral remainder occipital lobe (R) 1.02 VaD Posterior temporal lobe (R) 1.64 Superior temporal gyrus middle part (R) 1.20 Middle and inferior temporal gyrus (R) 1.11 Fusiform gyrus (L) 1.11 NUD Inferior frontal gyrus (L and R) 0.98 and 0.68 Superior temporal gyrus middle part (L) 0.95 Superior temporal gyrus anterior part (L) 0.94 Middle and inferior temporal gyrus (R) 0.89 Middle frontal gyrus (L) 0.86 AD Superior temporal gyrus middle part (L) 0.96 Middle and inferior temporal gyrus (R and L) 0.92 and 0.78 Posterior temporal lobe (L) 0.86 Angular gyrus (L) 0.86 Precentral gyrus (L) 0.84 MCI Middle and inferior temporal gyrus (L and R) 0.78 and 0.55 Cuneus(R) 0.60 Precentral gyrus (L) 0.60 Posterior temporal lobe (L) 0.53 Superior temporal gyrus middle part (L) 0.53 SCI Middle and inferior temporal gyrus (L and R) 0.52 and 0.41 Precentral gyrus (L) 0.48 Postcentral gyrus (L) 0.45 ND Amygdala (R) 0.21 Postcentral gyrus (L and R) 0.12 and 0.11 Poster Presentations: Wednesday, July 19, 2017 P1546