Author name cluster

Stefano Magon

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers

1 author row

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.

Details DOI

YNIMG Journal 2023 Journal Article

FIESTA: Autoencoders for accurate fiber segmentation in tractography

Félix Dumais
Jon Haitz Legarreta
Carl Lemaire
Philippe Poulin
François Rheault
Laurent Petit
Muhamed Barakovic
Stefano Magon

White matter bundle segmentation is a cornerstone of modern tractography to study the brain’s structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIbEr Segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate white matter bundles. This pipeline is built upon previous works that demonstrated how autoencoders can be used successfully for streamline filtering, bundle segmentation, and streamline generation in tractography. Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase the spatial coverage of each bundle while remaining anatomically correct. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as RecoBundles, RecoBundlesX, TractSeg, White Matter Analysis and XTRACT. Our framework allows for the transition from one anatomical bundle definition to another with marginal calibration efforts. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundle segmentation framework

Details DOI

YNICL Journal 2023 Journal Article

Impact of follow ups, time interval and study duration in diffusion & myelin MRI clinical study in MS

Manon Edde
Francis Houde
Guillaume Theaud
Matthieu Dumont
Guillaume Gilbert
Jean-Christophe Houde
Loïka Maltais
Antoine Théberge

It is currently unknown how quantitative diffusion and myelin MRI designs affect the results of a longitudinal study. We used two independent datasets containing 6 monthly MRI measurements from 20 healthy controls and 20 relapsing-remitting multiple sclerosis (RR-MS) patients. Six designs were tested, including 3 MRI acquisitions, either over 6 months or over a shorter study duration, with balanced (same interval) or unbalanced (different interval) time intervals between MRI acquisitions. First, we show that in RR-MS patients, the brain changes over time obtained with 3 MRI acquisitions were similar to those observed with 5 MRI acquisitions and that designs with an unbalanced time interval showed the highest similarity, regardless of study duration. No significant brain changes were found in the healthy controls over the same periods. Second, the study duration affects the sample size in the RR-MS dataset; a longer study requires more subjects and vice versa. Third, the number of follow-up acquisitions and study duration affect the sensitivity and specificity of the associations with clinical parameters, and these depend on the white matter bundle and MRI measure considered. Together, this suggests that the optimal design depends on the assumption of the dynamics of change in the target population and the accuracy required to capture these dynamics. Thus, this work provides a better understanding of key factors to consider in a longitudinal study and provides clues for better strategies in clinical trial design.

Details DOI

YNICL Journal 2022 Journal Article

A comparative assessment of myelin-sensitive measures in multiple sclerosis patients and healthy subjects

Reza Rahmanzadeh
Matthias Weigel
Po-Jui Lu
Lester Melie-Garcia
Thanh D. Nguyen
Alessandro Cagol
Francesco La Rosa
Muhamed Barakovic

INTRODUCTION: Multiple Sclerosis (MS) is a common neurological disease primarily characterized by myelin damage in lesions and in normal - appearing white and gray matter (NAWM, NAGM). Several quantitative MRI (qMRI) methods are sensitive to myelin characteristics by measuring specific tissue biophysical properties. However, there are currently few studies assessing the relative reproducibility and sensitivity of qMRI measures to MS pathology in vivo in patients. METHODS: We performed two studies. The first study assessed of the sensitivity of qMRI measures to MS pathology: in this work, we recruited 150 MS and 100 healthy subjects, who underwent brain MRI at 3 T including quantitative T1 mapping (qT1), quantitative susceptibility mapping (QSM), magnetization transfer saturation imaging (MTsat) and myelin water imaging for myelin water fraction (MWF). The sensitivity of qMRIs to MS focal pathology (MS lesions vs peri-plaque white/gray matter (PPWM/PPGM)) was studied lesion-wise; the sensitivity to diffuse normal appearing (NA) pathology was measured using voxel-wise threshold-free cluster enhancement (TFCE) in NAWM and vertex-wise inflated cortex analysis in NAGM. Furthermore, the sensitivity of qMRI to the identification of lesion tissue was investigated using a voxel-wise logistic regression analysis to distinguish MS lesion and PP voxels. The second study assessed the reproducibility of myelin-sensitive qMRI measures in a single scanner. To evaluate the intra-session and inter-session reproducibility of qMRI measures, we have investigated 10 healthy subjects, who underwent two brain 3 T MRIs within the same day (without repositioning), and one after 1-week interval. Five region of interest (ROIs) in white and deep grey matter areas were segmented, and inter- and intra- session reproducibility was studied using the intra-class correlation coefficient (ICC). Further, we also investigated the voxel-wise reproducibility of qMRI measures in NAWM and NAGM. RESULTS: qT1 and QSM showed the highest sensitivity to distinguish MS focal WM and cortical pathology from peri-plaque WM (P < 0.0001), although QSM also showed the highest variance when applied to lesions. MWF and MTsat exhibited the highest sensitivity to NAWM pathology (P < 0.01). On the other hand, qT1 appeared to be the most sensitive measure to NAGM pathology (P < 0.01). All myelin-sensitive qMRI measures exhibited high inter/intra sessional ICCs in various WM and deep GM ROIs, in NAWM and in NAGM (ICC 0.82 ± 0.12). CONCLUSION: This work shows that the applied qT1, MWF, MTsat and QSM are highly reproducible and exhibit differential sensitivity to focal and diffuse WM and GM pathology in MS patients.

Details DOI

YNICL Journal 2022 Journal Article

Longitudinal changes of deep gray matter shape in multiple sclerosis

Charidimos Tsagkas
Emanuel Geiter
Laura Gaetano
Yvonne Naegelin
Michael Amann
Katrin Parmar
Athina Papadopoulou
Jens Wuerfel

OBJECTIVE: This study aimed to investigate longitudinal deep gray matter (DGM) shape changes and their relationship with measures of clinical disability and white matter lesion-load in a large multiple sclerosis (MS) cohort. MATERIALS AND METHODS: A total of 230 MS patients (179 relapsing-remitting, 51 secondary progressive; baseline age 44.5 ± 11.3 years; baseline disease duration 12.99 ± 9.18) underwent annual clinical and MRI examinations over a maximum of 6 years (mean 4.32 ± 2.07 years). The DGM structures were segmented on the T1-weighted images using the "Multiple Automatically Generated Templates" brain algorithm. White matter lesion-load was measured on T2-weighted MRI. Clinical examination included the expanded disability status scale, 9-hole peg test, timed 25-foot walk test, symbol digit modalities test and paced auditory serial addition test. Vertex-wise longitudinal analysis of DGM shapes was performed using linear mixed effect models and evaluated the association between average/temporal changes of DGM shapes with average/temporal changes of clinical measurements, respectively. RESULTS: A significant shrinkage over time of the bilateral ventrolateral pallidal and the left posterolateral striatal surface was observed, whereas no significant shape changes over time were observed at the bilateral thalamic and right striatal surfaces. Higher average lesion-load was associated with an average inwards displacement of the global thalamic surface with relative sparing on the posterior side (slight left-side predominance), the antero-dorso-lateral striatal surfaces bilaterally (symmetric on both sides) and the antero-lateral pallidal surface (left-side predominance). There was also an association between shrinkage of large lateral DGM surfaces with higher clinical motor and cognitive disease severity. However, there was no correlation between any DGM shape changes over time and measurements of clinical progression or lesion-load changes over time. CONCLUSIONS: This study showed specific shape change of DGM structures occurring over time in relapse-onset MS. Although these shape changes over time were not associated with disease progression, we demonstrated a link between DGM shape and the patients' average disease severity as well as white matter lesion-load.

Details DOI

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.

Details DOI

YNIMG Journal 2019 Journal Article

Clinical associations of T2-weighted lesion load and lesion location in small vessel disease: Insights from a large prospective cohort study

Anna Altermatt
Laura Gaetano
Stefano Magon
Lorena Bauer
Regina Feurer
Hans Gnahn
Julia Hartmann
Christian L. Seifert

Background Subcortical T2-weighted (T2w) lesions are very common in older adults and have been associated with dementia. However, little is known about the strategic lesion distribution and how lesion patterns relate to vascular risk factors and cognitive impairment. Aim The aim of this study was to analyze the association between T2w lesion load and location, vascular risk factors, and cognitive impairment in a large cohort of older adults. Methods 1017 patients participating in a large prospective cohort study (INtervention project on cerebroVAscular disease and Dementia in the district of Ebersberg, INVADE II) were analyzed. Cerebral T2w white matter and deep grey matter lesions, the so-called white matter hyperintensities (WMHs), were outlined semi-automatically on fluid attenuated inversion recovery images and normalized to standard stereotaxic space (MNI152) by non-linear registration. Patients were assigned to either a low-risk or a high-risk group. The risk assessment considered ankle brachial index, intima media thickness, carotid artery stenosis, atrial fibrillation, previous cerebro-/cardiovascular events and peripheral artery disease as well as a score based on cholesterol levels, blood pressure and smoking. Separate lesion distributions were obtained for the two risk groups and compared using voxel-based lesion-symptom mapping. Moreover, we assessed the relation between lesion location and cognitive impairment (demographically adjusted z-scores of the Consortium to Establish a Registry for Alzheimer's Disease Neuropsychological Assessment Battery Plus, CERAD-NAB Plus) using voxel-based statistics (α = 0. 05). Results A total of 878 out of 1017 subjects (86%) had evaluable MRI data and were included in the analyses (mean age: 68. 2 ± 7. 6 years, female: 515). Patients in the high-risk group were characterized by a significantly higher age, a higher proportion of men, a higher lesion load (p < 0. 001), and a worse performance in some of the cognitive subdomain scores (p < 0. 05). Voxels with significant associations to the subjects' cerebrovascular risk profiles were mainly found at locations of the corpus callosum, superior corona radiata, superior longitudinal fasciculus, internal and external capsule, and putamen. While several cognitive domains have shown significant associations with the participants’ total lesion burden (p < 0. 05), no focal WMH locations were found to be associated with cognitive impairment. Conclusion Age, gender, several cognitive scores, and WMH lesion load were shown to be significantly associated with vascular risk factors in a population of older, but cognitively preserved adults. Vascular risk factors seem to promote lesion formation most severely at well-defined locations. While lesion load showed weak associations to some cognitive scores, no focal locations causing specific cognitive disturbances were identified in this large cohort of older adults.

Details DOI

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.

Details DOI

YNICL Journal 2015 Journal Article

Addendum to “A white matter lesion-filling approach to improve braintissue volume measurements” [NeuroImage Clin. vol. 6, 2014, pages 86–92]

Stefano Magon
Laura Gaetano
M. Mallar Chakravarty
Till Sprenger

In their interesting paper, Valverde and colleagues (Valverde et al., 2014) have proposed a new method for filling white matter lesions to reduce their impact on brain tissue classification and compared it with several other available tools/approaches. This comparison aimed at including a method we previously presented at the European Committee for Treatment and Research in Multiple Sclerosis meeting (ECTRIMS, Magon et al., 2013; for a detailed description see Magon et al., 2014, published after Valverde's paper). Overall, Valverde et al. (2014) showed that lesion filling is a fundamental step to correctly estimate white and gray matter volumes using magnetic resonance data. Indeed, all tested methods strongly improved the accuracy of tissue volume computation by both FSL and SPM. In the paper, our method is referred to as “MAGON” method. We would like to clarify here that, as applied by Valverde et al. (2014), a crucial step of our method was missed. Specifically the voxels belonging to the gray matter were not excluded from the computation of white matter intensity values. Our method consists of the following steps. First, white matter lesions were semi-automatically delineated on proton density/T2-weighted images in order to obtain binary lesion masks. To determine the signal intensity later applied for filling of the lesions on high-resolution 3D T1-weighted images, the lesion masks were expanded to the neighboring two voxels in each direction. The border voxels were then identified by subtracting the original lesion mask from the expanded lesion mask. Valverde et al. (2014) note in the discussion of their paper that in case of juxtacortical lesions, voxels that belong to the gray matter could be included in the expanded border and may decrease the values, which are later used for filling of white matter lesions. As a consequence, the gray matter/white matter border could shift towards gray matter intensity values leading to overestimation of the white matter volume and underestimation of the gray matter volume. We agree that this could be a potential source of error and therefore, indeed, we have subtracted the gray matter mask generated before the lesion filling to the expanded lesion border. Moreover, to reduce partial volume effects due to the resampling of the low-resolution lesion mask to higher resolution images, we excluded 10% of voxels with the lowest signal intensities from the computation of the mean signal intensity used to fill the lesions. These last two steps of our method are fundamental in order to avoid a biased computation of intensity values used for lesion filling and to avoid a subsequent misclassification due to the influence of gray matter. In conclusion, we congratulate Valverde and colleagues to their very interesting and important paper, however, the method referred to as “MAGON” method in their paper does miss one key step of the methodology, which we have previously proposed for lesion filling (Magon et al., 2013, 2014) and hence the results determined for the “MAGON” method in their paper may not reflect the full potential performance of the method we have previously suggested.

Details DOI

YNICL Journal 2015 Journal Article

Subcortical brain segmentation of two dimensional T1-weighted data sets with FMRIB's Integrated Registration and Segmentation Tool (FIRST)

Michael Amann
Michaela Andělová
Armanda Pfister
Nicole Mueller-Lenke
Stefan Traud
Julia Reinhardt
Stefano Magon
Kerstin Bendfeldt

Brain atrophy has been identified as an important contributing factor to the development of disability in multiple sclerosis (MS). In this respect, more and more interest is focussing on the role of deep grey matter (DGM) areas. Novel data analysis pipelines are available for the automatic segmentation of DGM using three-dimensional (3D) MRI data. However, in clinical trials, often no such high-resolution data are acquired and hence no conclusions regarding the impact of new treatments on DGM atrophy were possible so far. In this work, we used FMRIB's Integrated Registration and Segmentation Tool (FIRST) to evaluate the possibility of segmenting DGM structures using standard two-dimensional (2D) T1-weighted MRI. In a cohort of 70 MS patients, both 2D and 3D T1-weighted data were acquired. The thalamus, putamen, pallidum, nucleus accumbens, and caudate nucleus were bilaterally segmented using FIRST. Volumes were calculated for each structure and for the sum of basal ganglia (BG) as well as for the total DGM. The accuracy and reliability of the 2D data segmentation were compared with the respective results of 3D segmentations using volume difference, volume overlap and intra-class correlation coefficients (ICCs). The mean differences for the individual substructures were between 1.3% (putamen) and -25.2% (nucleus accumbens). The respective values for the BG were -2.7% and for DGM 1.3%. Mean volume overlap was between 89.1% (thalamus) and 61.5% (nucleus accumbens); BG: 84.1%; DGM: 86.3%. Regarding ICC, all structures showed good agreement with the exception of the nucleus accumbens. The results of the segmentation were additionally validated through expert manual delineation of the caudate nucleus and putamen in a subset of the 3D data. In conclusion, we demonstrate that subcortical segmentation of 2D data are feasible using FIRST. The larger subcortical GM structures can be segmented with high consistency. This forms the basis for the application of FIRST in large 2D MRI data sets of clinical trials in order to determine the impact of therapeutic interventions on DGM atrophy in MS.

Details DOI

YNIMG Journal 2009 Journal Article

Reproducibility of BOLD signal change induced by breath holding

Stefano Magon
Gianpaolo Basso
Paolo Farace
Giuseppe Kenneth Ricciardi
Alberto Beltramello
Andrea Sbarbati

Blood oxygen level dependent (BOLD) contrast is influenced by some physiological factors such as blood flow and blood volume that can be a source of variability in fMRI analysis. Previous studies proposed to use the cerebrovascular response data to normalize or calibrate BOLD maps in order to reduce variability of fMRI data both among brain areas in single subject analysis and across subjects. Breath holding is one of the most widely used methods to investigate the vascular reactivity. However, little is known about the robustness and reproducibility of this procedure. In this study we investigated three different breath holding periods. Subjects were asked to hold their breath for 9, 15 or 21 s in three separate runs and the fMRI protocol was repeated after 15 to 20 days. Our data show that the BOLD response to breath holding after inspiration results in a complex shape due to physiological factors that influence the signal variation with a timing that is highly reproducible. Nevertheless, the reproducibility of the magnitude of the cerebrovascular response to CO2, expressed as amplitude of BOLD signal and number of responding voxels, strongly depends on duration of breath holding periods. Breath holding period of 9 s results in high variability of the magnitude of the response while longer breath holding durations produce more robust and reproducible BOLD responses.

Details DOI