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Christian Enzinger

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14 papers

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YNIMG Journal 2024 Journal Article

Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks

Maximilian Sackl
Christian Tinauer
Martin Urschler
Christian Enzinger
Rudolf Stollberger
Stefan Ropele

Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippocampus volume and track its volume loss, a robust and reliable segmentation is essential. Manual hippocampus segmentation is considered the gold standard but is extensive, time-consuming, and prone to rater bias. Therefore, it is often replaced by automated programs like FreeSurfer, one of the most commonly used tools in clinical research. Recently, deep learning-based methods have also been successfully applied to hippocampus segmentation. The basis of all approaches are clinically used T1-weighted whole-brain MR images with approximately 1 mm isotropic resolution. However, such T1 images show low contrast-to-noise ratios (CNRs), particularly for many hippocampal substructures, limiting delineation reliability. To overcome these limitations, high-resolution T2-weighted scans are suggested for better visualization and delineation, as they show higher CNRs and usually allow for higher resolutions. Unfortunately, such time-consuming T2-weighted sequences are not feasible in a clinical routine. We propose an automated hippocampus segmentation pipeline leveraging deep learning with T2-weighted MR images for enhanced hippocampus segmentation of clinical T1-weighted images based on a series of 3D convolutional neural networks and a specifically acquired multi-contrast dataset. This dataset consists of corresponding pairs of T1- and high-resolution T2-weighted images, with the T2 images only used to create more accurate manual ground truth annotations and to train the segmentation network. The T2-based ground truth labels were also used to evaluate all experiments by comparing the masks visually and by various quantitative measures. We compared our approach with four established state-of-the-art hippocampus segmentation algorithms (FreeSurfer, ASHS, HippoDeep, HippMapp3r) and demonstrated a superior segmentation performance. Moreover, we found that the automated segmentation of T1-weighted images benefits from the T2-based ground truth data. In conclusion, this work showed the beneficial use of high-resolution, T2-based ground truth data for training an automated, deep learning-based hippocampus segmentation and provides the basis for a reliable estimation of hippocampal atrophy in clinical studies.

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YNICL Journal 2024 Journal Article

Low-frequency MR elastography reveals altered deep gray matter viscoelasticity in multiple sclerosis

Christian Kiss
Sebastian Wurth
Bettina Heschl
Michael Khalil
Thomas Gattringer
Christian Enzinger
Stefan Ropele

INTRODUCTION: Brain viscoelasticity as assessed by magnetic resonance elastography (MRE) has been discussed as a promising surrogate of microstructural alterations due to neurodegenerative processes. Existing studies indicate that multiple sclerosis (MS) is associated with a global reduction in brain stiffness. However, no study to date systematically investigated the MS-related characteristics of brain viscoelasticity separately in normal-appearing white matter (NAWM), deep gray matter (DGM) and T2-hyperintense white matter (WM) lesions. METHODS: were reconstructed by multifrequency dual elasto-visco (MDEV) inversion and related to structural imaging and clinical parameters. RESULTS: | (p = 0.04) and φ (p = 0.004) in the thalamus of patients. No alteration in NAWM stiffness relative to WM in healthy controls was observed. CONCLUSION: Low-frequency elastography in MS patients reveals age-independent alterations in the viscoelasticity of deep gray matter at early stages of disease.

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YNICL Journal 2023 Journal Article

Spatial distributions of white matter hyperintensities on brain MRI: A pooled analysis of individual participant data from 11 memory clinic cohorts

Mirthe Coenen
Geert Jan Biessels
Charles DeCarli
Evan F. Fletcher
Pauline M. Maillard
Frederik Barkhof
Josephine Barnes
Thomas Benke

INTRODUCTION: The spatial distribution of white matter hyperintensities (WMH) on MRI is often considered in the diagnostic evaluation of patients with cognitive problems. In some patients, clinicians may classify WMH patterns as "unusual", but this is largely based on expert opinion, because detailed quantitative information about WMH distribution frequencies in a memory clinic setting is lacking. Here we report voxel wise 3D WMH distribution frequencies in a large multicenter dataset and also aimed to identify individuals with unusual WMH patterns. METHODS: Individual participant data (N = 3525, including 777 participants with subjective cognitive decline, 1389 participants with mild cognitive impairment and 1359 patients with dementia) from eleven memory clinic cohorts, recruited through the Meta VCI Map Consortium, were used. WMH segmentations were provided by participating centers or performed in Utrecht and registered to the Montreal Neurological Institute (MNI)-152 brain template for spatial normalization. To determine WMH distribution frequencies, we calculated WMH probability maps at voxel level. To identify individuals with unusual WMH patterns, region-of-interest (ROI) based WMH probability maps, rule-based scores, and a machine learning method (Local Outlier Factor (LOF)), were implemented. RESULTS: WMH occurred in 82% of voxels from the white matter template with large variation between subjects. Only a small proportion of the white matter (1.7%), mainly in the periventricular areas, was affected by WMH in at least 20% of participants. A large portion of the total white matter was affected infrequently. Nevertheless, 93.8% of individual participants had lesions in voxels that were affected in less than 2% of the population, mainly located in subcortical areas. Only the machine learning method effectively identified individuals with unusual patterns, in particular subjects with asymmetric WMH distribution or with WMH at relatively rarely affected locations despite common locations not being affected. DISCUSSION: Aggregating data from several memory clinic cohorts, we provide a detailed 3D map of WMH lesion distribution frequencies, that informs on common as well as rare localizations. The use of data-driven analysis with LOF can be used to identify unusual patterns, which might serve as an alert that rare causes of WMH should be considered.

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YNIMG Journal 2023 Journal Article

Super-resolution QSM in little or no additional time for imaging (NATIve) using 2D EPI imaging in 3 orthogonal planes

Beata Bachrata
Steffen Bollmann
Jin Jin
Monique Tourell
Assunta Dal-Bianco
Siegfried Trattnig
Markus Barth
Stefan Ropele

Quantitative Susceptibility Mapping has the potential to provide additional insights into neurological diseases but is typically based on a quite long (5-10 min) 3D gradient-echo scan which is highly sensitive to motion. We propose an ultra-fast acquisition based on three orthogonal (sagittal, coronal and axial) 2D simultaneous multi-slice EPI scans with 1 mm in-plane resolution and 3 mm thick slices. Images in each orientation are corrected for susceptibility-related distortions and co-registered with an iterative non-linear Minimum Deformation Averaging (Volgenmodel) approach to generate a high SNR, super-resolution data set with an isotropic resolution of close to 1 mm. The net acquisition time is 3 times the volume acquisition time of EPI or about 12 s, but the three volumes could also replace "dummy scans" in fMRI, making it feasible to acquire QSM in little or No Additional Time for Imaging (NATIve). NATIve QSM values agreed well with reference 3D GRE QSM in the basal ganglia in healthy subjects. In patients with multiple sclerosis, there was also a good agreement between the susceptibility values within lesions and control ROIs and all lesions which could be seen on 3D GRE QSMs could also be visualized on NATIve QSMs. The approach is faster than conventional 3D GRE by a factor of 25-50 and faster than 3D EPI by a factor of 3-5. As a 2D technique, NATIve QSM was shown to be much more robust to motion than the 3D GRE and 3D EPI, opening up the possibility of studying neurological diseases involving iron accumulation and demyelination in patients who find it difficult to lie still for long enough to acquire QSM data with conventional methods.

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YNIMG Journal 2022 Journal Article

Free water diffusion MRI and executive function with a speed component in healthy aging

Martin Berger
Lukas Pirpamer
Edith Hofer
Stefan Ropele
Marco Duering
Benno Gesierich
Ofer Pasternak
Christian Enzinger

Extracellular free water (FW) increases are suggested to better provide pathophysiological information in brain aging than conventional biomarkers such as fractional anisotropy. The aim of the present study was to determine the relationship between conventional biomarkers, FW in white matter hyperintensities (WMH), FW in normal appearing white matter (NAWM) and in white matter tracts and executive functions (EF) with a speed component in elderly persons. We examined 226 healthy elderly participants (median age 69. 83 years, IQR: 56. 99–74. 42) who underwent brain MRI and neuropsychological examination. FW in WMH and in NAWM as well as FW corrected diffusion metrics and measures derived from conventional MRI (white matter hyperintensities, brain volume, lacunes) were used in partial correlation (adjusted for age) to assess their correlation with EF with a speed component. Random forest analysis was used to assess the relative importance of these variables as determinants. Lastly, linear regression analyses of FW in white matter tracts corrected for risk factors of cognitive and white matter deterioration, were used to examine the role of specific tracts on EF with a speed component, which were then ranked with random forest regression. Partial correlation analyses revealed that almost all imaging metrics showed a significant association with EF with a speed component (r = -0. 213 – 0. 266). Random forest regression highlighted FW in WMH and in NAWM as most important among all diffusion and structural MRI metrics. The fornix (R2=0. 421, p = 0. 018) and the corpus callosum (genu (R 2 = 0. 418, p = 0. 021), prefrontal (R 2 = 0. 416, p = 0. 026), premotor (R 2 = 0. 418, p = 0. 021)) were associated with EF with a speed component in tract based regression analyses and had highest variables importance. In a normal aging population FW in WMH and NAWM is more closely related to EF with a speed component than standard DTI and brain structural measures. Higher amounts of FW in the fornix and the frontal part of the corpus callosum leads to deteriorating EF with a speed component.

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YNICL Journal 2022 Journal Article

Magnetic susceptibility anisotropy in normal appearing white matter in multiple sclerosis from single-orientation acquisition

Renat Sibgatulin
Daniel Güllmar
Andreas Deistung
Christian Enzinger
Stefan Ropele
Jürgen R. Reichenbach

Quantitative susceptibility mapping (QSM) has been successfully applied to study changes in deep grey matter nuclei as well as in lesional tissue, but its application to white matter has been complicated by the observed orientation dependence of gradient echo signal. The anisotropic susceptibility tensor is thought to be at the origin of this orientation dependence, and magnetic susceptibility anisotropy (MSA) derived from this tensor has been proposed as a marker of the state and integrity of the myelin sheath and may therefore be of particular interest for the study of demyelinating pathologies such as multiple sclerosis (MS). Reconstruction of the susceptibility tensor, however, requires repeated measurements with multiple head orientations, rendering the approach impractical for clinical applications. In this study, we combined single-orientation QSM with fibre orientation information to assess apparent MSA in three white matter tracts, i.e., optic radiation (OR), splenium of the corpus callosum (SCC), and superior longitudinal fascicle (SLF), in two cohorts of 64 healthy controls and 89 MS patients. The apparent MSA showed a significant decrease in optic radiation in the MS cohort compared with healthy controls. It decreased in the MS cohort with increasing lesion load in OR and with disease duration in the splenium. All of this suggests demyelination in normal appearing white matter. However, the apparent MSA observed in the SLF pointed to potential systematic issues that require further exploration to realize the full potential of the presented approach. Despite the limitations of such single-orientation ROI-specific estimation, we believe that our clinically feasible approach to study degenerative changes in WM is worthy of further investigation.

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YNICL Journal 2022 Journal Article

Periventricular magnetisation transfer abnormalities in early multiple sclerosis

Lukas Pirpamer
Bálint Kincses
Zsigmond Tamás Kincses
Christian Kiss
Anna Damulina
Michael Khalil
Rudolf Stollberger
Reinhold Schmidt

OBJECTIVE: Recent studies suggested that CSF-mediated factors contribute to periventricular (PV) T2-hyperintense lesion formation in multiple sclerosis (MS) and this in turn correlates with cortical damage. We thus investigated if such PV-changes are observable microstructurally in early-MS and if they correlate with cortical damage. METHODS: We assessed the magnetisation transfer ratio (MTR) in PV normal-appearing white matter (NAWM) and in MS lesions in 44 patients with a clinically isolated syndrome (CIS) suggestive of MS and 73 relapsing-remitting MS (RRMS) patients. Band-wise MTR values were related to cortical mean thickness (CMT) and compared with 49 healthy controls (HCs). For each band, MTR changes were assessed relative to the average MTR values of all HCs. RESULTS: Relative to HCs, PV-MTR was significantly reduced up to 2.63% in CIS and 5.37% in RRMS (p < 0.0001). The MTR decreased towards the lateral ventricles with 0.18%/mm in CIS and 0.31%/mm in RRMS patients, relative to HCs. In RRMS, MTR-values adjacent to the ventricle and in PV-lesions correlated positively with CMT and negatively with EDSS. CONCLUSION: PV-MTR gradients are present from the earliest stage of MS, consistent with more pronounced microstructural WM-damage closer to the ventricles. The positive association between reduced CMT and lower MTR in PV-NAWM suggests a common pathophysiologic mechanism. Together, these findings indicate the potential use of multimodal MRI as refined marker for MS-related tissue changes.

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YNICL Journal 2021 Journal Article

Development and evaluation of a manual segmentation protocol for deep grey matter in multiple sclerosis: Towards accelerated semi-automated references

Alexandra de Sitter
Jessica Burggraaff
Fabian Bartel
Miklos Palotai
Yaou Liu
Jorge Simoes
Serena Ruggieri
Katharina Schregel

BACKGROUND: Deep grey matter (dGM) structures, particularly the thalamus, are clinically relevant in multiple sclerosis (MS). However, segmentation of dGM in MS is challenging; labeled MS-specific reference sets are needed for objective evaluation and training of new methods. OBJECTIVES: This study aimed to (i) create a standardized protocol for manual delineations of dGM; (ii) evaluate the reliability of the protocol with multiple raters; and (iii) evaluate the accuracy of a fast-semi-automated segmentation approach (FASTSURF). METHODS: A standardized manual segmentation protocol for caudate nucleus, putamen, and thalamus was created, and applied by three raters on multi-center 3D T1-weighted MRI scans of 23 MS patients and 12 controls. Intra- and inter-rater agreement was assessed through intra-class correlation coefficient (ICC); spatial overlap through Jaccard Index (JI) and generalized conformity index (CIgen). From sparse delineations, FASTSURF reconstructed full segmentations; accuracy was assessed both volumetrically and spatially. RESULTS: All structures showed excellent agreement on expert manual outlines: intra-rater JI > 0.83; inter-rater ICC ≥ 0.76 and CIgen ≥ 0.74. FASTSURF reproduced manual references excellently, with ICC ≥ 0.97 and JI ≥ 0.92. CONCLUSIONS: The manual dGM segmentation protocol showed excellent reproducibility within and between raters. Moreover, combined with FASTSURF a reliable reference set of dGM segmentations can be produced with lower workload.

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YNIMG Journal 2021 Journal Article

Improved susceptibility weighted imaging at ultra-high field using bipolar multi-echo acquisition and optimized image processing: CLEAR-SWI

Korbinian Eckstein
Beata Bachrata
Gilbert Hangel
Georg Widhalm
Christian Enzinger
Markus Barth
Siegfried Trattnig
Simon Daniel Robinson

PURPOSE: inhomogeneities at ultra-high field to generate homogeneous images with increased contrast and free of common artefacts. All steps in SWI processing have been addressed - coil combination, phase unwrapping, image combination over echoes, phase filtering and homogeneity correction - and applied to an efficient bipolar multi-echo acquisition to substantially improve the quality of SWI. PRINCIPAL RESULTS: Our findings regarding the optimal individual processing steps lead us to propose a Contrast-weighted, Laplace-unwrapped, bipolar multi-Echo, ASPIRE-combined, homogeneous, improved Resolution SWI, or CLEAR-SWI. CLEAR-SWI was compared to two other multi-echo SWI methods and standard, single-echo SWI with the same acquisition time at 7 T in 10 healthy volunteers and with single-echo SWI in 13 patients with brain tumors. CLEAR-SWI had improved contrast-to-noise and homogeneity, reduced signal dropout and was not compromised by the artefacts which affected standard SWI in 10 out of 13 cases close to tumors (as assessed by expert raters), as well as generating T2* maps and phase images which can be used for Quantitative Susceptibility Mapping. In a comparison with other multi-echo SWI methods, CLEAR-SWI had the fewest artefacts, highest SNR and generally higher contrast-to-noise. MAJOR CONCLUSIONS: CLEAR-SWI eliminates the artefacts common in standard, single-echo SWI, reduces signal dropouts and improves image homogeneity and contrast-to-noise. Applied clinically, in a study of brain tumor patients, CLEAR-SWI was free of the artefacts which affected standard, single-echo SWI.

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YNICL Journal 2021 Journal Article

Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study

Jessica Burggraaff
Yao Liu
Juan C. Prieto
Jorge Simoes
Alexandra de Sitter
Serena Ruggieri
Iman Brouwer
Birgit I. Lissenberg-Witte

BACKGROUND AND RATIONALE: Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. METHODS: Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. RESULTS: In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings. CONCLUSION: Automated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings.

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YNICL Journal 2019 Journal Article

SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis

Viktor Wottschel
Declan T. Chard
Christian Enzinger
Massimo Filippi
Jette L. Frederiksen
Claudio Gasperini
Antonio Giorgio
Maria A. Rocca

Machine learning classification is an attractive approach to automatically differentiate patients from healthy subjects, and to predict future disease outcomes. A clinically isolated syndrome (CIS) is often the first presentation of multiple sclerosis (MS), but it is difficult at onset to predict who will have a second relapse and hence convert to clinically definite MS. In this study, we thus aimed to distinguish CIS converters from non-converters at onset of a CIS, using recursive feature elimination and weight averaging with support vector machines. We also sought to assess the influence of cohort size and cross-validation methods on the accuracy estimate of the classification. We retrospectively collected 400 patients with CIS from six European MAGNIMS MS centres. Patients underwent brain MRI at onset of a CIS according to local standard-of-care protocols. The diagnosis of clinically definite MS at one-year follow-up was the standard against which the accuracy of the model was tested. For each patient, we derived MRI-based features, such as grey matter probability, white matter lesion load, cortical thickness, and volume of specific cortical and white matter regions. Features with little contribution to the classification model were removed iteratively through an interleaved sample bootstrapping and feature averaging approach. Classification of CIS outcome at one-year follow-up was performed with 2-fold, 5-fold, 10-fold and leave-one-out cross-validation for each centre cohort independently and in all patients together. The estimated classification accuracy across centres ranged from 64.9% to 88.1% using 2-fold cross-validation and from 73% to 92.9% using leave-one-out cross-validation. The classification accuracy estimate was higher in single-centre, smaller data sets than in combinations of data sets, being the lowest when all patients were merged together. Regional MRI features such as WM lesions, grey matter probability in the thalamus and the precuneus or cortical thickness in the cuneus and inferior temporal gyrus predicted the occurrence of a second relapse in patients at onset of a CIS using support vector machines. The increased accuracy estimate of the classification achieved with smaller and single-centre samples may indicate a model bias (overfitting) when data points were limited, but also more homogeneous. We provide an overview of classifier performance from a range of cross-validation schemes to give insight into the variability across schemes. The proposed recursive feature elimination approach with weight averaging can be used both in single- and multi-centre data sets in order to bridge the gap between group-level comparisons and making predictions for individual patients.

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YNICL Journal 2018 Journal Article

Urgent challenges in quantification and interpretation of brain grey matter atrophy in individual MS patients using MRI

Houshang Amiri
Alexandra de Sitter
Kerstin Bendfeldt
Marco Battaglini
Claudia A.M. Gandini Wheeler-Kingshott
Massimiliano Calabrese
Jeroen J.G. Geurts
Maria A. Rocca

Atrophy of the brain grey matter (GM) is an accepted and important feature of multiple sclerosis (MS). However, its accurate measurement is hampered by various technical, pathological and physiological factors. As a consequence, it is challenging to investigate the role of GM atrophy in the disease process as well as the effect of treatments that aim to reduce neurodegeneration. In this paper we discuss the most important challenges currently hampering the measurement and interpretation of GM atrophy in MS. The focus is on measurements that are obtained in individual patients rather than on group analysis methods, because of their importance in clinical trials and ultimately in clinical care. We discuss the sources and possible solutions of the current challenges, and provide recommendations to achieve reliable measurement and interpretation of brain GM atrophy in MS.

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YNIMG Journal 2017 Journal Article

Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study

Alexandra de Sitter
Martijn D. Steenwijk
Aurélie Ruet
Adriaan Versteeg
Yaou Liu
Ronald A. van Schijndel
Petra J.W. Pouwels
Iris D. Kilsdonk

Background and Purpose In vivoidentification of white matter lesions plays a key-role in evaluation of patients with multiple sclerosis (MS). Automated lesion segmentation methods have been developed to substitute manual outlining, but evidence of their performance in multi-center investigations is lacking. In this work, five research-domain automated segmentation methods were evaluated using a multi-center MS dataset. Methods 70 MS patients (median EDSS of 2. 0 [range 0. 0–6. 5]) were included from a six-center dataset of the MAGNIMS Study Group (www. magnims. eu) which included 2D FLAIR and 3D T1 images with manual lesion segmentation as a reference. Automated lesion segmentations were produced using five algorithms: Cascade; Lesion Segmentation Toolbox (LST) with both the Lesion growth algorithm (LGA) and the Lesion prediction algorithm (LPA); Lesion-Topology preserving Anatomical Segmentation (Lesion-TOADS); and k-Nearest Neighbor with Tissue Type Priors (kNN-TTP). Main software parameters were optimized using a training set (N = 18), and formal testing was performed on the remaining patients (N = 52). To evaluate volumetric agreement with the reference segmentations, intraclass correlation coefficient (ICC) as well as mean difference in lesion volumes between the automated and reference segmentations were calculated. The Similarity Index (SI), False Positive (FP) volumes and False Negative (FN) volumes were used to examine spatial agreement. All analyses were repeated using a leave-one-center-out design to exclude the center of interest from the training phase to evaluate the performance of the method on ‘unseen’ center. Results Compared to the reference mean lesion volume (4. 85 ± 7. 29 mL), the methods displayed a mean difference of 1. 60 ± 4. 83 (Cascade), 2. 31 ± 7. 66 (LGA), 0. 44 ± 4. 68 (LPA), 1. 76 ± 4. 17 (Lesion-TOADS) and −1. 39 ± 4. 10 mL (kNN-TTP). The ICCs were 0. 755, 0. 713, 0. 851, 0. 806 and 0. 723, respectively. Spatial agreement with reference segmentations was higher for LPA (SI = 0. 37 ± 0. 23), Lesion-TOADS (SI = 0. 35 ± 0. 18) and kNN-TTP (SI = 0. 44 ± 0. 14) than for Cascade (SI = 0. 26 ± 0. 17) or LGA (SI = 0. 31 ± 0. 23). All methods showed highly similar results when used on data from a center not used in software parameter optimization. Conclusion The performance of the methods in this multi-center MS dataset was moderate, but appeared to be robust even with new datasets from centers not included in training the automated methods.

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YNICL Journal 2015 Journal Article

Predictive value of different conventional and non-conventional MRI-parameters for specific domains of cognitive function in multiple sclerosis

Daniela Pinter
Michael Khalil
Alexander Pichler
Christian Langkammer
Stefan Ropele
Peter B. Marschik
Siegrid Fuchs
Franz Fazekas

OBJECTIVE: While many studies correlated cognitive function with changes in brain morphology in multiple sclerosis (MS), few of them used a multi-parametric approach in a single dataset so far. We thus here assessed the predictive value of different conventional and quantitative MRI-parameters both for overall and domain-specific cognitive performance in MS patients from a single center. METHODS: 69 patients (17 clinically isolated syndrome, 47 relapsing-remitting MS, 5 secondary-progressive MS) underwent the "Brief Repeatable Battery of Neuropsychological Tests" assessing overall cognition, cognitive efficiency and memory function as well as MRI at 3 Tesla to obtain T2-lesion load (T2-LL), normalized brain volume (global brain volume loss), normalized cortical volume (NCV), normalized thalamic volume (NTV), normalized hippocampal volume (NHV), normalized caudate nuclei volume (NCNV), basal ganglia R2* values (iron deposition) and magnetization transfer ratios (MTRs) for cortex and normal appearing brain tissue (NABT). RESULTS: Regression models including clinical, demographic variables and MRI-parameters explained 22-27% of variance of overall cognition, 17-26% of cognitive efficiency and 22-23% of memory. NCV, T2-LL and MTR of NABT were the strongest predictors of overall cognitive function. Cognitive efficiency was best predicted by NCV, T2-LL and iron deposition in the basal ganglia. NTV was the strongest predictor for memory function and NHV was particularly related to memory function. CONCLUSIONS: The predictive value of distinct MRI-parameters differs for specific domains of cognitive function, with a greater impact of cortical volume, focal and diffuse white matter abnormalities on overall cognitive function, an additional role of basal ganglia iron deposition on cognitive efficiency, and thalamic and hippocampal volume on memory function. This suggests the usefulness of using multiparametric MRI to assess (micro)structural correlates of different cognitive constructs.

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