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Friedemann Paul

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13 papers

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YNICL Journal 2026 Journal Article

Microstructure-informed deep learning improves thalamic atrophy segmentation and clinical associations in multiple sclerosis and related neuroimmunological diseases

Omar Angelo Ibrahim
Henri Trang
Qianlan Chen
Lara Zimmermann
Alexander U. Brandt
Tatiana Usnich
Stefano Magon
Muhamed Barakovic

Thalamic atrophy is a sensitive imaging marker of neurodegeneration in multiple sclerosis (MS) and related disorders, though thalamus segmentation remains method-dependent. Quantitative magnetic resonance imaging (qMRI) may enhance thalamic boundary contrast, particularly in the context of deep learning. We benchmarked thalamic segmentations from two atlas-constrained algorithms, FreeSurfer and FIRST, and two deep learning algorithms, DBSegment and MindGlide (an MS-trained model), against ground truth (GT) labels, tested whether quantitative R1 maps improve performance, and evaluated clinical validity cross-sectionally and longitudinally. We generated thalamus masks using each algorithm from T1-weighted data in a single-scanner cohort (baseline n = 321; 1-year follow-up n = 234) including patients with MS/related disorders and healthy controls. Using MindGlide, we also produced FLAIR- and R1-based masks and ensembles. Manual GT labels were obtained for 50 MS patients using T1w and FLAIR scans. For voxel-wise GT agreement, DBSegment yielded the highest Dice-similarity coefficient; atlas-constrained methods showed the highest sensitivity but lowest precision, while MindGlide balanced both. Volumetrically, MindGlide showed the most accurate estimates; DBSegment and FreeSurfer showed proportional bias, and both atlas-constrained methods overestimated thalamic volumes. Adding R1 input to MindGlide produced modest or no gains in GT agreement. Additionally, MindGlide volumes were most consistently associated with disability and cognitive scores cross-sectionally, and longitudinally showed the largest effects between thalamic volume change and EDSS worsening. Incorporating R1 maps offered no cross-sectional benefit but strengthened longitudinal associations. Higher-resolution qMRI and multi-contrast deep learning architectures may further enhance thalamic segmentation and monitoring in neuroinflammatory diseases.

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YNICL Journal 2022 Journal Article

Brain disconnectome mapping derived from white matter lesions and serum neurofilament light levels in multiple sclerosis: A longitudinal multicenter study

Henning H. Rise
Synne Brune
Claudia Chien
Tone Berge
Steffan D. Bos
Magí Andorrà
Irene Pulido Valdeolivas
Mona K. Beyer

BACKGROUND AND OBJECTIVES: Connectivity-based approaches incorporating the distribution and magnitude of the extended brain network aberrations caused by lesions may offer higher sensitivity for axonal damage in patients with multiple sclerosis (MS) than conventional lesion characteristics. Using individual brain disconnectome mapping, we tested the longitudinal associations between putative imaging-based brain network aberrations and levels of serum neurofilament light chain (NfL) as a neuroaxonal injury biomarker. METHODS: MS patients (n = 312, mean age 42.9 years, 71 % female) and healthy controls (HC) (n = 59, mean age 39.9 years, 78 % female) were prospectively enrolled at four European MS centres, and reassessed after two years (MS, n = 242; HC, n = 30). Post-processing of 3 Tesla (3 T) MRI data was performed at one centre using a harmonized pipeline, and disconnectome maps were calculated using BCBtoolkit based on individual lesion maps. Global disconnectivity (GD) was defined as the average disconnectome probability in each patient's white matter. Serum NfL concentrations were measured by single molecule array (Simoa). Robust linear mixed models (rLMM) with GD or T2-lesion volume (T2LV) as dependent variables, patient as a random factor, serum NfL, age, sex, timepoint for visit, diagnosis, treatment, and center as fixed factors were run. RESULTS: ), and longitudinal changes in NfL (t = -2.29, p = 0.02), but not for sex (t = 0.63, p = 0.53) or treatments (t = 0.80-0.83, p = 0.41-0.42). Voxel-wise analyses revealed significant associations between dysconnectivity in cerebellar and brainstem regions and serum NfL (t = 7.03, p < 0.001). DISCUSSION: In our prospective multi-site MS cohort, rLMMs demonstrated that the extent of global and regional brain disconnectivity is sensitive to a systemic biomarker of axonal damage, serum NfL, in patients with MS. These findings provide a neuroaxonal correlate of advanced disconnectome mapping and provide a platform for further investigations of the functional and potential clinical relevance of brain disconnectome mapping in patients with brain disorders.

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YNICL Journal 2022 Journal Article

Functional connectivity dynamics reflect disability and multi-domain clinical impairment in patients with relapsing-remitting multiple sclerosis

Amy Romanello
Stephan Krohn
Nina von Schwanenflug
Claudia Chien
Judith Bellmann-Strobl
Klemens Ruprecht
Friedemann Paul
Carsten Finke

BACKGROUND & AIM: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system associated with deficits in cognitive and motor functioning. While structural brain changes such as demyelination are an early hallmark of the disease, a characteristic profile of functional brain alterations in early MS is lacking. Functional neuroimaging studies at various disease stages have revealed complex and heterogeneous patterns of aberrant functional connectivity (FC) in MS, with previous studies largely being limited to a static account of FC. Thus, it remains unclear how time-resolved FC relates to variance in clinical disability status in early MS. We here aimed to characterize brain network organization in early MS patients with time-resolved FC analysis and to explore the relationship between disability status, multi-domain clinical outcomes and altered network dynamics. METHODS: Resting-state functional MRI (rs-fMRI) data were acquired from 101 MS patients and 101 age- and sex-matched healthy controls (HC). Based on the Expanded Disability Status Score (EDSS), patients were split into two sub-groups: patients without clinical disability (EDSS ≤ 1, n = 36) and patients with mild to moderate levels of disability (EDSS ≥ 2, n = 39). Five dynamic FC states were extracted from whole-brain rs-fMRI data. Group differences in static and dynamic FC strength, across-state overall connectivity, dwell time, transition frequency, modularity, and global connectivity were assessed. Patients' impairment was quantified as custom clinical outcome z-scores (higher: worse) for the domains depressive symptoms, fatigue, motor, vision, cognition, total brain atrophy, and lesion load. Correlation analyses between functional measures and clinical outcomes were performed with Spearman partial correlation analyses controlling for age. RESULTS: Patients with mild to moderate levels of disability exhibited a more widespread spatiotemporal pattern of altered FC and spent more time in a high-connectivity, low-occurrence state compared to patients without disability and HCs. Worse symptoms in all clinical outcome domains were positively associated with EDSS scores. Furthermore, depressive symptom severity was positively related to functional dynamics as measured by state-specific global connectivity and default mode network connectivity with attention networks, while fatigue and motor impairment were related to reduced frontoparietal network connectivity with the basal ganglia. CONCLUSIONS: Despite comparably low impairment levels in early MS, we identified distinct connectivity alterations between patients with mild to moderate disability and those without disability, and these changes were sensitive to clinical outcomes in multiple domains. Furthermore, time-resolved analysis uncovered alterations in network dynamics and clinical correlations that remained undetected with conventional static analyses, showing that accounting for temporal dynamics helps disentangle the relationship between functional alterations, disability status, and symptoms in early MS.

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YNICL Journal 2021 Journal Article

Lateral geniculate nucleus volume changes after optic neuritis in neuromyelitis optica: A longitudinal study

Athina Papadopoulou
Frederike C. Oertel
Claudia Chien
Joseph Kuchling
Hanna G. Zimmermann
Nadja Siebert
Seyedamirhosein Motamedi
Marcus D' Souza

OBJECTIVES: Lateral geniculate nucleus (LGN) volume is reduced after optic neuritis (ON) in neuromyelitis optica spectrum disorders (NMOSD). We aimed at a longitudinal assessment of LGN volume in NMOSD. METHODS: Twenty-nine patients with aquaporin 4-IgG seropositive NMOSD (age: 47.8 ± 14.6 years (y), female: n = 27, history of ON (NMO-ON): n = 17, median time since ON: 3[1.2-12.1]y) and 18 healthy controls (HC; age: 39.3 ± 15.8y; female: n = 13) were included. Median follow-up was 4.1[1.1-4.7]y for patients and 1.7[0.9-3.2]y for HC. LGN volume was measured using a multi-atlas-based approach of automated segmentation on 3 Tesla magnetic resonance images. Retinal optical coherence tomography and probabilistic tractography of the optic radiations (OR) were also performed. RESULTS: ; t = -3.6, p = 0.036). CONCLUSION: Although LGN volume is reduced after ON in NMOSD, this volume loss is not progressive over longer follow-up or independent of ON. Thus, our findings -at least in this relatively small cohort- do not support occult neurodegeneration of the afferent visual pathway in NMOSD.

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YNIMG Journal 2020 Journal Article

Conduction delays in the visual pathways of progressive multiple sclerosis patients covary with brain structure

Shai Berman
Yael Backner
Ronnie Krupnik
Friedemann Paul
Panayiota Petrou
Dimitrios Karussis
Netta Levin
Aviv A. Mezer

In developed countries, multiple sclerosis (MS) is the leading cause of non-traumatic neurological disability in young adults. MS is a chronic demyelinating disease of the central nervous system, in which myelin is attacked, changing white matter structure and leaving lesions. The demyelination has a direct effect on white matter conductivity. This effect can be examined in the visual system, where damage is highly prevalent in MS, leading to substantial delays in conduction, commonly measured with visual evoked potentials (VEPs). The structural damage to the visual system in MS is often estimated with MRI measurements in the white matter. Recent developments in quantitative MRI (qMRI) provide improved sensitivity to myelin content and new structural methods allow better modeling of the axonal structure, leading researchers to link white matter microstructure to conduction properties of action potentials along fiber tracts. This study attempts to explain the variance in conduction latencies down the visual pathway using structural measurements of both the retina and the optic radiation (OR). Forty-eight progressive MS patients, participants in a longitudinal stem-cell therapy clinical trial, were included in this study, three and six months post final treatment. Twenty-seven patients had no history of optic neuritis, and were the main focus of this study. All participants underwent conventional MRI scans, as well as diffusion MRI and qMRI sequences to account for white matter microstructure. Optical coherence tomography scans were also obtained, and peripapillary retinal nerve fiber layer (pRNFL) thickness and macular volume measurements were extracted. Finally, latencies of recorded VEPs were estimated. Our results show that in non-optic neuritis progressive MS patients there is a relationship between the VEP latency and both retinal damage and OR lesion load. In addition, we find that qMRI values, sampled along the OR, are also correlated with VEP latency. Finally, we show that combining these parameters using PCA we can explain more than 40% of the inter-subject variance in VEP latency. In conclusion, this study contributes to understanding the relationship between the structural properties and conduction in the visual system in disease. We focus on the visual system, where the conduction latencies can be estimated, but the conclusions could be generalized to other brain systems where the white matter structure can be measured. It also highlights the importance of having multiple parameters when assessing the clinical stages of MS patients, which could have major implications for future studies of other white matter diseases.

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YNICL Journal 2020 Journal Article

Transdiagnostic hippocampal damage patterns in neuroimmunological disorders

Josephine Heine
Harald Prüß
Michael Scheel
Alexander U. Brandt
Stefan M. Gold
Thorsten Bartsch
Friedemann Paul
Carsten Finke

Hippocampal damage and associated cognitive deficits are frequently observed in neuroimmunological disorders, but comparative analyses to identify shared hippocampal damage patterns are missing. Here, we adopted a transdiagnostic analytical approach and investigated hippocampal shape deformations and associated cognitive deficits in four neuroimmunological diseases. We studied 120 patients (n = 30 in each group), including patients with multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), anti-NMDAR and anti-LGI1 encephalitis. A control group was matched to each patient sample from a pool of 79 healthy participants. We performed an MRI-based vertex-wise hippocampal shape analysis, extracted hippocampal volume estimates and scalar projection values as a measure of surface displacement. Cognitive testing included assessment of verbal memory and semantic fluency performance. Our cross-sectional analyses revealed characteristic patterns of bilateral inward deformations covering up to 32% of the hippocampal surface in MS, anti-NMDAR encephalitis, and anti-LGI1 encephalitis, whereas NMOSD patients showed no deformations compared to controls. Significant inversions were noted mainly on the hippocampal head, were accompanied by volume loss, and correlated with semantic fluency scores and verbal episodic memory in autoimmune encephalitis and MS. A deformation overlap analysis across disorders revealed a convergence zone on the left anterior hippocampus that corresponds to the CA1 subfield. This convergence zone indicates a shared downstream substrate of immune-mediated damage that appears to be particularly vulnerable to neuroinflammatory processes. Our transdiagnostic morphological view sheds light on mutual pathophysiologic pathways of cognitive deficits in neuroimmunological diseases and stimulates further research into the mechanisms of increased susceptibility of the hippocampus to autoimmunity.

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YNICL Journal 2019 Journal Article

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Fabian Eitel
Emily Soehler
Judith Bellmann-Strobl
Alexander U. Brandt
Klemens Ruprecht
René M. Giess
Joseph Kuchling
Susanna Asseyer

Machine learning-based imaging diagnostics has recently reached or even surpassed the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on 3D convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS), the most widespread autoimmune neuroinflammatory disease. MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients (n = 76) and healthy controls (n = 71). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of CNN models transparent, which could serve to justify classification decisions for clinical review, verify diagnosis-relevant features and potentially gather new disease knowledge.

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YNICL Journal 2018 Journal Article

Comparison of probabilistic tractography and tract-based spatial statistics for assessing optic radiation damage in patients with autoimmune inflammatory disorders of the central nervous system

Joseph Kuchling
Yael Backner
Frederike C. Oertel
Noa Raz
Judith Bellmann-Strobl
Klemens Ruprecht
Friedemann Paul
Netta Levin

Background: Diffusion Tensor Imaging (DTI) can evaluate microstructural tissue damage in the optic radiation (OR) of patients with clinically isolated syndrome (CIS), early relapsing-remitting multiple sclerosis and neuromyelitis optica spectrum disorders (NMOSD). Different post-processing techniques, e.g. tract-based spatial statistics (TBSS) and probabilistic tractography, exist to quantify this damage. Objective: To evaluate the capacity of TBSS-based atlas region-of-interest (ROI) combination with 1) posterior thalamic radiation ROIs from the Johns Hopkins University atlas (JHU-TBSS), 2) Juelich Probabilistic ROIs (JUEL-TBSS) and tractography methods using 3) ConTrack (CON-PROB) and 4) constrained spherical deconvolution tractography (CSD-PROB) to detect OR damage in patients with a) NMOSD with prior ON (NMOSD-ON), b) CIS and early RRMS patients with ON (CIS/RRMS-ON) and c) CIS and early RRMS patients without prior ON (CIS/RRMS-NON) against healthy controls (HCs). Methods: Twenty-three NMOSD-ON, 18 CIS/RRMS-ON, 21 CIS/RRMS-NON, and 26 HCs underwent 3 T MRI. DTI data analysis was carried out using JUEL-TBSS, JHU-TBSS, CON-PROB and CSD-PROB. Optical coherence tomography (OCT) and visual acuity testing was performed in the majority of patients and HCs. Results: = 0.094). No significant differences between CIS/RRMS-ON and HC were detected by any of the methods. Conclusions: All DTI post-processing techniques facilitated the detection of OR damage in patient groups with severe microstructural OR degradation. The comparison of distinct disease groups by use of different methods may lead to different - either false-positive or false-negative - results. Since different DTI post-processing approaches seem to provide complementary information on OR damage, application of distinct methods may depend on the relevant research question.

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YNIMG Journal 2016 Journal Article

Power estimation for non-standardized multisite studies

Anisha Keshavan
Friedemann Paul
Mona K. Beyer
Alyssa H. Zhu
Nico Papinutto
Russell T. Shinohara
William Stern
Michael Amann

A concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based correction entirely. We hypothesized that the bias of estimated regional volumes is scaled between sites due to the contrast and gradient distortion differences between scanners and sequences. Given this assumption, we provide a new statistical framework and derive a power equation to define inclusion criteria for a set of sites based on the variability of their scaling factors. We estimated the scaling factors of 20 scanners with heterogeneous hardware and sequence parameters by scanning a single set of 12 subjects at sites across the United States and Europe. Regional volumes and their scaling factors were estimated for each site using Freesurfer's segmentation algorithm and ordinary least squares, respectively. The scaling factors were validated by comparing the theoretical and simulated power curves, performing a leave-one-out calibration of regional volumes, and evaluating the absolute agreement of all regional volumes between sites before and after calibration. Using our derived power equation, we were able to define the conditions under which harmonization is not necessary to achieve 80% power. This approach can inform choice of processing pipelines and outcome metrics for multisite studies based on scaling factor variability across sites, enabling collaboration between clinical and research institutions.

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YNICL Journal 2013 Journal Article

Cerebral magnetic resonance elastography in supranuclear palsy and idiopathic Parkinson's disease

Axel Lipp
Radmila Trbojevic
Friedemann Paul
Andreas Fehlner
Sebastian Hirsch
Michael Scheel
Cornelia Noack
Jürgen Braun

Detection and discrimination of neurodegenerative Parkinson syndromes are challenging clinical tasks and the use of standard T1- and T2-weighted cerebral magnetic resonance (MR) imaging is limited to exclude symptomatic Parkinsonism. We used a quantitative structural MR-based technique, MR-elastography (MRE), to assess viscoelastic properties of the brain, providing insights into altered tissue architecture in neurodegenerative diseases on a macroscopic level. We measured single-slice multifrequency MRE (MMRE) and three-dimensional MRE (3DMRE) in two neurodegenerative disorders with overlapping clinical presentation but different neuropathology - progressive supranuclear palsy (PSP: N = 16) and idiopathic Parkinson's disease (PD: N = 18) as well as in controls (N = 18). In PSP, both MMRE (Δμ = - 28.8%, Δα = - 4.9%) and 3DMRE (Δ|G*|: - 10.6%, Δφ: - 34.6%) were significantly reduced compared to controls, with a pronounced reduction within the lentiform nucleus (Δμ = - 34.6%, Δα = - 8.1%; Δ|G*|: - 7.8%, Δφ: - 44.8%). MRE in PD showed a comparable pattern, but overall reduction in brain elasticity was less severe reaching significance only in the lentiform nucleus (Δμ n.s., Δα = - 7.4%; Δ|G*|: - 6.9%, Δφ: n.s.). Beyond that, patients showed a close negative correlation between MRE constants and clinical severity. Our data indicate that brain viscoelasticity in PSP and PD is differently affected by the underlying neurodegeneration; whereas in PSP all MRE constants are reduced and changes in brain softness (reduced μ and |G*|) predominate those of viscosity (α and φ) in PD.

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YNICL Journal 2012 Journal Article

Magnetic resonance elastography reveals altered brain viscoelasticity in experimental autoimmune encephalomyelitis

Kerstin Riek
Jason M. Millward
Isabell Hamann
Susanne Mueller
Caspar F. Pfueller
Friedemann Paul
Jürgen Braun
Carmen Infante-Duarte

Cerebral magnetic resonance elastography (MRE) measures the viscoelastic properties of brain tissues in vivo. It was recently shown that brain viscoelasticity is reduced in patients with multiple sclerosis (MS), highlighting the potential of cerebral MRE to detect tissue pathology during neuroinflammation. To further investigate the relationship between inflammation and brain viscoelasticity, we applied MRE to a mouse model of MS, experimental autoimmune encephalomyelitis (EAE). EAE was induced and monitored by MRE in a 7-tesla animal MRI scanner over 4 weeks. At the peak of the disease (day 14 after immunization), we detected a significant decrease in both the storage modulus (G') and the loss modulus (G″), indicating that both the elasticity and the viscosity of the brain are reduced during acute inflammation. Interestingly, these parameters normalized at a later time point (day 28) corresponding to the clinical recovery phase. Consistent with this, we observed a clear correlation between viscoelastic tissue alteration and the magnitude of perivascular T cell infiltration at both day 14 and day 28. Hence, acute neuroinflammation is associated with reduced mechanical cohesion of brain tissues. Moreover, the reduction of brain viscoelasticity appears to be a reversible process, which is restored when inflammation resolves. For the first time, our study has demonstrated the applicability of cerebral MRE in EAE, and showed that this novel imaging technology is highly sensitive to early tissue alterations resulting from the inflammatory processes. Thus, MRE may serve to monitor early stages of perivascular immune infiltration during neuroinflammation.

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YNIMG Journal 2012 Journal Article

Multi-scale classification of disease using structural MRI and wavelet transform

Kerstin Hackmack
Friedemann Paul
Martin Weygandt
Carsten Allefeld
John-Dylan Haynes

Recently, multivariate analysis algorithms have become a popular tool to diagnose neurological diseases based on neuroimaging data. Most studies, however, are biased for one specific scale, namely the scale given by the spatial resolution (i. e. dimension) of the data. In the present study, we propose to use the dual-tree complex wavelet transform to extract information on different spatial scales from structural MRI data and show its relevance for disease classification. Based on the magnitude representation of the complex wavelet coefficients calculated from the MR images, we identified a new class of features taking scale, directionality and potentially local information into account simultaneously. By using a linear support vector machine, these features were shown to discriminate significantly between spatially normalized MR images of 41 patients suffering from multiple sclerosis and 26 healthy controls. Interestingly, the decoding accuracies varied strongly among the different scales and it turned out that scales containing low frequency information were partly superior to scales containing high frequency information. Usually, this type of information is neglected since most decoding studies use only the original scale of the data. In conclusion, our proposed method has not only a high potential to assist in the diagnostic process of multiple sclerosis, but can be applied to other diseases or general decoding problems in structural or functional MRI.

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YNIMG Journal 2010 Journal Article

MR-elastography reveals degradation of tissue integrity in multiple sclerosis

Jens Wuerfel
Friedemann Paul
Bernd Beierbach
Uwe Hamhaber
Dieter Klatt
Sebastian Papazoglou
Frauke Zipp
Peter Martus

In multiple sclerosis (MS), diffuse brain parenchymal damage exceeding focal inflammation is increasingly recognized to be present from the very onset of the disease, and, although occult to conventional imaging techniques, may present a major cause of permanent neurological disability. Subtle tissue alterations significantly influence biomechanical properties given by stiffness and internal friction, that – in more accessible organs than the brain – are traditionally assessed by manual palpation during the clinical exam. The brain, however, is protected from our sense of touch, and thus our current knowledge on cerebral viscoelasticity is very limited. We developed a clinically feasible magnetic resonance elastography setup sensitive to subtle alterations of brain parenchymal biomechanical properties. Investigating 45 MS patients revealed a significant decrease (13%, P <0. 001) of cerebral viscoelasticity compared to matched healthy volunteers, indicating a widespread tissue integrity degradation, while structure-geometry defining parameters remained unchanged. Cerebral viscoelasticity may represent a novel in vivo marker of neuroinflammatory and neurodegenerative pathology.

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