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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.
YNICL Journal 2025 Journal Article
YNICL Journal 2023 Journal Article
BACKGROUND AND PURPOSE: Metachromatic leukodystrophy (MLD) is a lysosomal enzyme deficiency disorder leading to demyelination and subsequently to a progressive decline in cognitive and motor function. It affects mainly white matter where changes during the course of the disease can be visualized on T2-weighted MRI as hyperintense areas. Associated changes in brain metabolism can be quantified by MR spectroscopy (MRS) and may give complementary information as biomarkers for disease characterisation and progression. Our study aimed to further investigate the correlation of MRS with clinical parameters for motor and cognitive function by using a model free MRS analysis approach that would be precise and straightforward to implement. MATERIALS AND METHODS: H (proton) nuclear magnetic resonance (NMR) spectroscopy. The applied interval integration method was validated in the control cohort against the standard approach, using spectral profile templates of known metabolites (LCModel). Both methods showed good agreement, with coefficients of variance being slightly lower for our approach compared to the related LCModel results. Moreover, the new approach was able to extract information out of the frequency range around the main peaks of aspartate and glutamine where LCModel showed only few usable values for the respective metabolites. RESULTS: MLD spectra clearly differed from controls. The most pronounced differences were found in white matter (much less in grey matter), with larger values corresponding to main peaks of myo-inositol, choline and aspartate, and smaller values associated with NAA and glutamine. Late-infantile patients had more severe changes compared to later-onset patients, especially in intervals corresponding to NAA, aspartate, myo-inositol, choline and glutamine. There was a high correlation of several intervals in the corticospinal tract region with motor function (with the most relevant interval corresponding to NAA peak with a correlation coefficient of -0.75; p < 0.001), while cognitive function, by means of IQ, was found to be most correlating in frontal white matter corresponding to the NAA peak (r = 0.84, p < 0.001). The post-hoc analysis showed that the main NAA peak interval correlated negatively with the NAA in urine (r = -0.584, p < 0.001). CONCLUSION: The applied model and fitting free interval integration approach to analyse MRS data of a semi-LASER sequence at 3T suits well to detect and quantify pathological changes in MLD patients through the different courses of the disease and correlates well with clinical symptoms while showing smaller dimensions of variation compared to the more sophisticated single metabolite analysis using LCModel. NAA seems the most clinically meaningful biomarker to use in this context. Its correlation with urine measurements further underlines its potential as a clinically and biologically useful parameter of disease progression in MLD.
YNICL Journal 2022 Journal Article
Spinal diffusion tensor imaging (sDTI) is still a challenging technique for selectively evaluating anatomical areas like the pyramidal tracts (PT), dorsal columns (DC), and anterior horns (AH) in clinical routine and for reliably quantifying white matter anisotropy and diffusivity. In neurodegenerative diseases, the value of sDTI is promising but not yet well understood. The objective of this prospective, single-center study was to evaluate the long fiber tract degeneration within the spinal cord in normal aging (n = 125) and to prove its applicability in pathologic conditions as in patients with molecular genetically confirmed hereditary spastic paraplegias (HSP; n = 40), a prototypical disease of the first motor neuron and in some genetic variants with affection of the dorsal columns. An optimized monopolar Stejskal-Tanner sequence for high-resolution, axial sDTI of the cervical spinal cord at 3. 0 T with advanced standardized evaluation methods was developed for a robust DTI value estimation of PT, DC, and AH in both groups. After sDTI measurement at C2, an automatic motion correction and an advanced semi-automatic ROI-based, standardized evaluation of white matter anisotropy and diffusivity was performed to obtain regional diffusivity measures for PT, DC, and AH. Reliable and stable sDTI values were acquired in a healthy population without significant decline between age 20 and 65. Reference values for PT, DC, and AH for fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) were established. In HSP patients, the decline of the long spinal fiber tracts could be demonstrated by diffusivity abnormalities in the pyramidal tracts with significantly reduced PTFA (p < 0. 001), elevated PTRD (p = 0. 002) and reduced PTMD (p = 0. 003) compared to healthy controls. Furthermore, FA was significantly reduced in DCFA (p < 0. 001) with no differences in AH. In a genetically homogeneous subgroup of SPG4 patients (n = 12) with affection of the dorsal columns, DCRD significantly correlated with the overall disease severity as measured by the Spastic Paraplegia Rating Scale (SPRS) (r = − 0. 713, p = 0. 009). With the most extensive sDTI study in vivo to date, we showed that axial sDTI combined with motion correction and advanced data post-processing strategies enables robust measurements and is ready to use, allowing recognition and quantification of disease- and age-related changes of the PT, DC, and AH. These results may also encourage the usage of sDTI in other neurodegenerative diseases with spinal cord involvement to explore its capability as selective biomarkers.
YNICL Journal 2021 Journal Article
Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.
YNICL Journal 2021 Journal Article
BACKGROUND: Studies have previously shown evidence for presymptomatic cortical atrophy in genetic FTD. Whilst initial investigations have also identified early deep grey matter volume loss, little is known about the extent of subcortical involvement, particularly within subregions, and how this differs between genetic groups. METHODS: 480 mutation carriers from the Genetic FTD Initiative (GENFI) were included (198 GRN, 202 C9orf72, 80 MAPT), together with 298 non-carrier cognitively normal controls. Cortical and subcortical volumes of interest were generated using automated parcellation methods on volumetric 3 T T1-weighted MRI scans. Mutation carriers were divided into three disease stages based on their global CDR® plus NACC FTLD score: asymptomatic (0), possibly or mildly symptomatic (0.5) and fully symptomatic (1 or more). RESULTS: In all three groups, subcortical involvement was seen at the CDR 0.5 stage prior to phenoconversion, whereas in the C9orf72 and MAPT mutation carriers there was also involvement at the CDR 0 stage. In the C9orf72 expansion carriers the earliest volume changes were in thalamic subnuclei (particularly pulvinar and lateral geniculate, 9-10%) cerebellum (lobules VIIa-Crus II and VIIIb, 2-3%), hippocampus (particularly presubiculum and CA1, 2-3%), amygdala (all subregions, 2-6%) and hypothalamus (superior tuberal region, 1%). In MAPT mutation carriers changes were seen at CDR 0 in the hippocampus (subiculum, presubiculum and tail, 3-4%) and amygdala (accessory basal and superficial nuclei, 2-4%). GRN mutation carriers showed subcortical differences at CDR 0.5 in the presubiculum of the hippocampus (8%). CONCLUSIONS: C9orf72 expansion carriers show the earliest and most widespread changes including the thalamus, basal ganglia and medial temporal lobe. By investigating individual subregions, changes can also be seen at CDR 0 in MAPT mutation carriers within the limbic system. Our results suggest that subcortical brain volumes may be used as markers of neurodegeneration even prior to the onset of prodromal symptoms.
YNICL Journal 2021 Journal Article
Mutations in the granulin gene (GRN) cause familial frontotemporal dementia. Understanding the structural brain changes in presymptomatic GRN carriers would enforce the use of neuroimaging biomarkers for early diagnosis and monitoring. We studied 100 presymptomatic GRN mutation carriers and 94 noncarriers from the Genetic Frontotemporal dementia initiative (GENFI), with MRI structural images. We analyzed 3T MRI structural images using the FreeSurfer pipeline to calculate the whole brain cortical thickness (CTh) for each subject. We also perform a vertex-wise general linear model to assess differences between groups in the relationship between CTh and diverse covariables as gender, age, the estimated years to onset and education. We also explored differences according to TMEM106B genotype, a possible disease modifier. Whole brain CTh did not differ between carriers and noncarriers. Both groups showed age-related cortical thinning. The group-by-age interaction analysis showed that this age-related cortical thinning was significantly greater in GRN carriers in the left superior frontal cortex. TMEM106B did not significantly influence the age-related cortical thinning. Our results validate and expand previous findings suggesting an increased CTh loss associated with age and estimated proximity to symptoms onset in GRN carriers, even before the disease onset.
YNICL Journal 2019 Journal Article
BACKGROUND AND PURPOSE: O PET/CT with ACZ challenge to assess comparability between both modalities. METHODS: O PET/CT with ACZ challenge in patients with angiographically proven MMD. Images of both modalities were analyzed retrospectively in a blinded, standardized fashion by visual inspection, as well as with a semi-quantitative analysis using stimuli-induced approximated regional perfusion-weighted data and BOLD-signal changes with reference to cerebellum. RESULTS: 20 consecutive patients fulfilled the inclusion criteria, a total of 160 vascular territories were analyzed retrospectively. Visual analysis (4-step visual rating system) of standardized, color-coded cerebrovascular reserve/reactivity maps showed a very strong correlation (Spearman's rho = 0.9, P < 0.001) between both modalities. Likewise, comparison of approximated regional perfusion changes across vascular territories (normalized to cerebellar change) reveal a highly significant correlation between both methods (Pearson's r = 0.71, P < 0.001). CONCLUSIONS: O PET/CT with ACZ challenge. It therefore holds future potential in becoming a routine examination in the pre- and postoperative evaluation of MMD patients after further prospective evaluation.
YNIMG Journal 2019 Journal Article
Frontotemporal Dementia (FTD) is preceded by a long period of subtle brain changes, occurring in the absence of overt cognitive symptoms, that need to be still fully characterized. Dynamic network analysis based on resting-state magnetic resonance imaging (rs-fMRI) is a potentially powerful tool for the study of preclinical FTD. In the present study, we employed a "chronnectome" approach (recurring, time-varying patterns of connectivity) to evaluate measures of dynamic connectivity in 472 at-risk FTD subjects from the Genetic Frontotemporal dementia research Initiative (GENFI) cohort. We considered 249 subjects with FTD-related pathogenetic mutations and 223 mutation non-carriers (HC). Dynamic connectivity was evaluated using independent component analysis and sliding-time window correlation to rs-fMRI data, and meta-state measures of global brain flexibility were extracted. Results show that presymptomatic FTD exhibits diminished dynamic fluidity, visiting less meta-states, shifting less often across them, and travelling through a narrowed meta-state distance, as compared to HC. Dynamic connectivity changes characterize preclinical FTD, arguing for the desynchronization of the inner fluctuations of the brain. These changes antedate clinical symptoms, and might represent an early signature of FTD to be used as a biomarker in clinical trials.
YNICL Journal 2019 Journal Article
Frontotemporal dementia (FTD) is a heterogeneous group of neurodegenerative disorders with both sporadic and genetic forms. Mutations in the progranulin gene (GRN) are a common cause of genetic FTD, causing either a behavioural presentation or, less commonly, language impairment. Presence on T2-weighted images of white matter hyperintensities (WMH) has been previously shown to be more commonly associated with GRN mutations rather than other forms of FTD. The aim of the current study was to investigate the longitudinal change in WMH and the associations of WMH burden with grey matter (GM) loss, markers of neurodegeneration and cognitive function in GRN mutation carriers. 336 participants in the Genetic FTD Initiative (GENFI) study were included in the analysis: 101 presymptomatic and 32 symptomatic GRN mutation carriers, as well as 203 mutation-negative controls. 39 presymptomatic and 12 symptomatic carriers, and 73 controls also had longitudinal data available. Participants underwent MR imaging acquisition including isotropic 1 mm T1-weighted and T2-weighted sequences. WMH were automatically segmented and locally subdivided to enable a more detailed representation of the pathology distribution. Log-transformed WMH volumes were investigated in terms of their global and regional associations with imaging measures (grey matter volumes), biomarker concentrations (plasma neurofilament light chain, NfL, and glial fibrillary acidic protein, GFAP), genetic status (TMEM106B risk genotype) and cognition (tests of executive function). Analyses revealed that WMH load was higher in both symptomatic and presymptomatic groups compared with controls and this load increased over time. In particular, lesions were seen periventricularly in frontal and occipital lobes, progressing to medial layers over time. However, there was variability in the WMH load across GRN mutation carriers - in the symptomatic group 25.0% had none/mild load, 37.5% had medium and 37.5% had a severe load - a difference not fully explained by disease duration. GM atrophy was strongly associated with WMH load both globally and in separate lobes, and increased WMH burden in the frontal, periventricular and medial regions was associated with worse executive function. Furthermore, plasma NfL and to a lesser extent GFAP concentrations were seen to be associated with increased lesion burden. Lastly, the presence of the homozygous TMEM106B rs1990622 TT risk genotypic status was associated with an increased accrual of WMH per year. In summary, WMH occur in GRN mutation carriers and accumulate over time, but are variable in their severity. They are associated with increased GM atrophy and executive dysfunction. Furthermore, their presence is associated with markers of WM damage (NfL) and astrocytosis (GFAP), whilst their accrual is modified by TMEM106B genetic status. WMH load may represent a target marker for trials of disease modifying therapies in individual patients but the variability across the GRN population would prevent use of such markers as a global outcome measure across all participants in a trial.
YNIMG Journal 2018 Journal Article
Purpose The high chemical shift separation at 9. 4 T allows for selective saturation of proton pools in exchange with water protons. For the first time, highly selective and comprehensive chemical exchange saturation transfer (CEST) experiments were performed in the human brain at 9. 4 T. This work provides insight into CEST signals in the human brain in comparison with existing animal studies, as well as with CEST effects in vivo at lower field strengths. Methods A novel snapshot-CEST method for human brain scans at 9. 4 T was optimized and employed for highly-spectrally-resolved (95 offsets) CEST measurements in healthy subjects and one brain tumor patient. Reproducibility and stability between scans was verified in grey and white matter after B0, B1, and motion correction of the acquired 3D CEST volumes. Two-step Lorentzian fitting was used to further improve separation of spectrally discernible signals to create known and novel CEST contrast maps at 9. 4 T. Results At a saturation power of B1 = 0. 5 μT most selective CEST effects could be obtained in the human brain with high inter-scan reproducibility. While contrast behavior of previously measured signals at lower field, namely amide-, guanidyl- and NOE-CEST effects, could be reproduced, novel signals at 2. 7 ppm, and −1. 6 ppm could be verified in healthy subjects and in a brain tumor patient for the first time. Conclusion High spectral resolution chemical exchange saturation transfer at 9. 4 T allows deeper insights into the Z-spectrum structure of the human brain, and provides many different contrasts showing different correlations in healthy tissue and in tumor-affected areas of the brain, generating hypotheses for future investigations of in-vivo-CEST at UHF.
YNIMG Journal 2018 Journal Article
Voxel-based morphometry is still mainly based on T1-weighted MRI scans. Misclassification of vessels and dura mater as gray matter has been previously reported. Goal of the present work was to evaluate the effect of multimodal segmentation methods available in SPM12, and their influence on identification of age related atrophy and lesion detection in epilepsy patients. 3D T1-, T2- and FLAIR-images of 77 healthy adults (mean age 35. 8 years, 19–66 years, 45 females), 7 patients with malformation of cortical development (MCD) (mean age 28. 1 years, 19–40 years, 3 females), and 5 patients with left hippocampal sclerosis (LHS) (mean age 49. 0 years, 25–67 years, 3 females) from a 3T scanner were evaluated. Segmentation based on T1-only, T1+T2, T1+FLAIR, T2+FLAIR, and T1+T2+FLAIR were compared in the healthy subjects. Clinical VBM results based on the different segmentation approaches for MCD and for LHS were compared. T1-only segmentation overestimated total intracranial volume by about 80ml compared to the other segmentation methods. This was due to misclassification of dura mater and vessels as GM and CSF. Significant differences were found for several anatomical regions: the occipital lobe, the basal ganglia/thalamus, the pre- and postcentral gyrus, the cerebellum, and the brainstem. None of the segmentation methods yielded completely satisfying results for the basal ganglia/thalamus and the brainstem. The best correlation with age could be found for the multimodal T1+T2+FLAIR segmentation. Highest T-scores for identification of LHS were found for T1+T2 segmentation, while highest T-scores for MCD were dependent on lesion and anatomical location. Multimodal segmentation is superior to T1-only segmentation and reduces the misclassification of dura mater and vessels as GM and CSF. Depending on the anatomical region and the pathology of interest (atrophy, lesion detection, etc.), different combinations of T1, T2 and FLAIR yield optimal results.
YNIMG Journal 2012 Journal Article
Spatial normalization reshapes an individual's brain to match the shape and size of a template image. This is a crucial step required for group-level statistical analyses. The most popular standard templates are derived from MRI scans of young adults. We introduce specialized templates that allow normalization algorithms to be applied to stroke-aged populations. First, we developed a CT template: while this is the dominant modality for many clinical situations, there are no modern CT templates and popular algorithms fail to successfully normalize CT scans. Importantly, our template was based on healthy individuals with ages similar to what is commonly seen in stroke (mean 65years old). This template allows studies where only CT scans are available. Second, we derived a MRI template that approximately matches the shape of our CT template as well as processing steps that aid the normalization of scans from older individuals (including lesion masking and the ability to generate high quality cortical renderings despite brain injury). The benefit of this strategy is that the resulting templates can be used in studies where mixed modalities are present. We have integrated these templates and processing algorithms into a simple SPM toolbox (http: //www. mricro. com/clinical-toolbox/spm8-scripts).