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Michael Ewers

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9 papers

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YNICL Journal 2024 Journal Article

Tau-network mapping of domain-specific cognitive impairment in Alzheimer’s disease

Ying Luan
Anna Rubinski
Davina Biel
Diana Otero Svaldi
Ixavier Alonzo Higgins
Sergey Shcherbinin
Michael Pontecorvo
Nicolai Franzmeier

Fibrillar tau gradually progresses in the brain during the course of Alzheimer's disease (AD). However, the contribution of tau accumulation in a given brain region to decline in different cognitive domains and thus phenotypic heterogeneity in AD remains unclear. Here, we leveraged the functional connectome to link the locality of tau accumulation to domain-specific cognitive impairment. In the current study, we mapped regional tau-PET accumulation onto the normative functional connectome. Subsequently, we cross-validated in two samples of AD-patients the associations between the tau-connectivity profiles and cognitive domains (episodic memory, executive function, or language). Lastly, we tested the effect of local tau-PET accumulation on the domain-specific tau-lesion networks and cognition. We identified cognitive-domain-specific tau-lesion networks, where closer topological proximity of tau-PET locations to a network was predictive of worse impairment in that domain. Higher tau-PET was associated with decreased domain-specific network connectivity, and the decrease in connectivity was associated with lower domain-specific cognition. The tau locations' connectivity profile explained domain-specific cognitive impairment, where disrupted connectivity may underlie the effect of tau on cognitive impairment.

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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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YNICL Journal 2021 Journal Article

Cognitive reserve hypothesis in frontotemporal dementia: A FDG-PET study

Leonie Beyer
Johanna Meyer-Wilmes
Sonja Schönecker
Jonas Schnabel
Julia Sauerbeck
Maximilian Scheifele
Catharina Prix
Marcus Unterrainer

BACKGROUND AND OBJECTIVE: F]-fluorodeoxyglucose positron-emission-tomography (FDG-PET) hypometabolism in FTD. METHODS: Sixty-six FTD subjects (age 67 ± 8 years) and twenty-four cognitively healthy controls (HC) were evaluated. Brain regions with FTD-related glucose hypometabolism in the contrast against HC and brain regions that correlate with the cognitive function were defined by a voxel-based analysis and individual FDG-PET values were extracted from all frontotemporal brain areas. Linear regression analysis served to test if education is associated with residualized cognitive performance and regional FDG-PET hypometabolism after controlling for global cognition. RESULTS: Compared to healthy controls, patients with FTD showed glucose hypometabolism in bilateral frontal and temporal brain areas whereas cognition was only associated with deteriorated glucose metabolism in the left temporal lobe. The education level was significantly correlated with the residualized cognitive performance (residuals from regression analysis between hypometabolism and cognitive function as a quantitative index of reserve) and also negatively correlated with left temporal FDG-PET hypometabolism after controlling for cognition. CONCLUSIONS: In patients with FTD, the education level predicts the existing left temporal FDG-PET hypometabolism at the same cognition level, supporting the cognitive reserve hypothesis in FTD.

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YNIMG Journal 2021 Journal Article

Microglial activation in the right amygdala-entorhinal-hippocampal complex is associated with preserved spatial learning in App mice

Gloria Biechele
Karin Wind
Tanja Blume
Christian Sacher
Leonie Beyer
Florian Eckenweber
Nicolai Franzmeier
Michael Ewers

BACKGROUND: In Alzheimer`s disease (AD), regional heterogeneity of β-amyloid burden and microglial activation of individual patients is a well-known phenomenon. Recently, we described a high incidence of inter-individual regional heterogeneity in terms of asymmetry of plaque burden and microglial activation in β-amyloid mouse models of AD as assessed by positron-emission-tomography (PET). We now investigate the regional associations between amyloid plaque burden, microglial activation, and impaired spatial learning performance in transgenic mice in vivo. METHODS: mice, controlled for age and sex. In order to assess the effects of hemispheric asymmetry, we also analyzed correlations of spatial learning performance with tracer uptake in bilateral regions of interest for frontal cortex, entorhinal/piriform cortex, amygdala, and hippocampus, using a regression model. We tested the correlation between regional asymmetry of PET biomarkers with individual spatial learning performance. RESULTS: mice revealed that higher TSPO-PET signal in the amygdala, entorhinal and piriform cortices, the hippocampus and the hypothalamus correlated with spatial learning performance. Region-based analysis showed significant correlations between TSPO expression in the right entorhinal/piriform cortex and the right amygdala and spatial learning performance, whereas there were no such correlations in the left hemisphere. Right lateralized TSPO expression in the amygdala predicted better performance in the Morris water maze (β = -0.470, p = 0.013), irrespective of the global microglial activation and amyloid level. Region-based results for amyloid-PET showed no significant associations with spatial learning. CONCLUSION: mice is associated with better spatial learning. Our findings support a protective role of microglia on cognitive function when they highly express TSPO in specific brain regions involved in spatial memory.

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YNICL Journal 2019 Journal Article

Neuronal injury biomarkers for assessment of the individual cognitive reserve in clinically suspected Alzheimer's disease

Leonie Beyer
Jonas Schnabel
Philipp Kazmierczak
Michael Ewers
Sonja Schönecker
Catharina Prix
Johanna Meyer-Wilmes
Marcus Unterrainer

OBJECTIVES: ), all according to the A/T/N-classification. The aim of this study was to calculate residual cognitive performance based on neuronal injury biomarkers as a surrogate of cognitive reserve, and to test the predictive value of this index for the individual clinical course. METHODS: . All neuronal injury markers were tested for an association with clinical MMSE and the resulting residuals were correlated with years of education. We used multiple regression analysis to calculate the expected MMSE score based on neuronal injury biomarkers and covariates. The residuals of the partial correlation for each biomarker and the predicted residualized memory function were correlated with individual cognitive changes measured during clinical follow-up (27 ± 13 months). RESULTS: (R = -0.12, p = .22) showed only weak correlations. Residuals of all neuronal injury biomarker regressions correlated significantly with education level, indicating them to be surrogates of cognitive reserve. A positive residual was associated with faster cognitive deterioration at follow-up for the residuals of stand-alone FDG-PET (R = -0.36, p = .01) and the combined residualized memory function model (R = -0.35, p = .02). CONCLUSIONS: These findings suggest that subjects with higher cognitive reserve had accumulated more pathology, which subsequently caused a faster cognitive decline over time. Together with previous findings suggesting that higher reserve is associated with slower cognitive decline, we propose a biphasic reserve effect, with an initially protective phase followed by more rapid decompensation once the protection is overwhelmed.

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YNICL Journal 2017 Journal Article

Multicenter stability of resting state fMRI in the detection of Alzheimer's disease and amnestic MCI

Stefan J. Teipel
Alexandra Wohlert
Coraline Metzger
Timo Grimmer
Christian Sorg
Michael Ewers
Eva Meisenzahl
Stefan Klöppel

BACKGROUND: In monocentric studies, patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia exhibited alterations of functional cortical connectivity in resting-state functional MRI (rs-fMRI) analyses. Multicenter studies provide access to large sample sizes, but rs-fMRI may be particularly sensitive to multiscanner effects. METHODS: We used data from five centers of the "German resting-state initiative for diagnostic biomarkers" (psymri.org), comprising 367 cases, including AD patients, MCI patients and healthy older controls, to assess the influence of the distributed acquisition on the group effects. We calculated accuracy of group discrimination based on whole brain functional connectivity of the posterior cingulate cortex (PCC) using pooled samples as well as second-level analyses across site-specific group contrast maps. RESULTS: We found decreased functional connectivity in AD patients vs. controls, including clusters in the precuneus, inferior parietal cortex, lateral temporal cortex and medial prefrontal cortex. MCI subjects showed spatially similar, but less pronounced, differences in PCC connectivity when compared to controls. Group discrimination accuracy for AD vs. controls (MCI vs. controls) in the test data was below 76% (72%) based on the pooled analysis, and even lower based on the second level analysis stratified according to scanner. Only a subset of quality measures was useful to detect relevant scanner effects. CONCLUSIONS: Multicenter rs-fMRI analysis needs to employ strict quality measures, including visual inspection of all the data, to avoid seriously confounded group effects. While pending further confirmation in biomarker stratified samples, these findings suggest that multicenter acquisition limits the use of rs-fMRI in AD and MCI diagnosis.

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YNICL Journal 2014 Journal Article

Reduced FDG-PET brain metabolism and executive function predict clinical progression in elderly healthy subjects

Michael Ewers
Matthias Brendel
Angela Rizk-Jackson
Axel Rominger
Peter Bartenstein
Norbert Schuff
Michael W. Weiner

Brain changes reminiscent of Alzheimer disease (AD) have been previously reported in a substantial portion of elderly cognitive healthy (HC) subjects. The major aim was to evaluate the accuracy of MRI assessed regional gray matter (GM) volume, 18F-fluorodeoxyglucose positron emission tomography (FDG-PET), and neuropsychological test scores to identify those HC subjects who subsequently convert to mild cognitive impairment (MCI) or AD dementia. We obtained in 54 healthy control (HC) subjects a priori defined region of interest (ROI) values of medial temporal and parietal FDG-PET and medial temporal GM volume. In logistic regression analyses, these ROI values were tested together with neuropsychological test scores (free recall, trail making test B (TMT-B)) as predictors of HC conversion during a clinical follow-up between 3 and 4 years. In voxel-based analyses, FDG-PET and MRI GM maps were compared between HC converters and HC non-converters. Out of the 54 HC subjects, 11 subjects converted to MCI or AD dementia. Lower FDG-PET ROI values were associated with higher likelihood of conversion (p = 0.004), with the area under the curve (AUC) yielding 82.0% (95% CI = (95.5%, 68.5%)). The GM volume ROI was not a significant predictor (p = 0.07). TMT-B but not the free recall tests were a significant predictor (AUC = 71% (95% CI = 50.4%, 91.7%)). For the combination of FDG-PET and TMT-B, the AUC was 93.4% (sensitivity = 82%, specificity = 93%). Voxel-based group comparison showed reduced FDG-PET metabolism within the temporo-parietal and prefrontal cortex in HC converters. In conclusion, medial temporal and-parietal FDG-PET and executive function show a clinically acceptable accuracy for predicting clinical progression in elderly HC subjects.

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YNIMG Journal 2010 Journal Article

Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease

Claudia Plant
Stefan J. Teipel
Annahita Oswald
Christian Böhm
Thomas Meindl
Janaina Mourao-Miranda
Arun W. Bokde
Harald Hampel

Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimer's disease (AD). Voxel-based MRI studies have demonstrated that widely distributed cortical and subcortical brain areas show atrophic changes in MCI, preceding the onset of AD-type dementia. Here we developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. MRI was collected in 32 AD patients, 24 MCI subjects and 18 healthy controls (HC). Nine out of 24 MCI subjects converted to AD after an average follow-up interval of 2. 5 years. Using feature selection algorithms, brain regions showing the highest accuracy for the discrimination between AD and HC were identified, reaching a classification accuracy of up to 92%. The extracted AD clusters were used as a search region to extract those brain areas that are predictive of conversion to AD within MCI subjects. The most predictive brain areas included the anterior cingulate gyrus and orbitofrontal cortex. The best prediction accuracy, which was cross-validated via train-and-test, was 75% for the prediction of the conversion from MCI to AD. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

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YNIMG Journal 2007 Journal Article

Multivariate deformation-based analysis of brain atrophy to predict Alzheimer's disease in mild cognitive impairment

Stefan J. Teipel
Christine Born
Michael Ewers
Arun L.W. Bokde
Maximilian F. Reiser
Hans-Jürgen Möller
Harald Hampel

Automated deformation-based analysis of MRI scans can be used to detect specific pattern of brain atrophy in Alzheimer's disease (AD), but it still lacks an established model to derive the individual risk of AD in at-risk subjects, such as patients with mild cognitive impairment (MCI). We applied principal component analysis to deformation maps derived from MRI scans of 32 AD patients, 18 elderly healthy controls and 24 MCI patients. Principal component scores were used to discriminate between AD patients and controls and between MCI converters and MCI non-converters. We found a significant regional pattern of atrophy (p <0. 001) in medial temporal lobes, neocortical association areas, thalamus and basal ganglia and corresponding widening of cerebrospinal fluid (CSF) spaces (p <0. 001) in AD patients compared to controls. Accuracy was 81% for CSF- and 83% for brain-based deformation maps to separate AD patients from controls. Nine out of 24 MCI patients converted to AD during clinical follow-up. Discrimination between MCI converters and non-converters reached 80% accuracy based on CSF maps and 73% accuracy based on brain maps. In a logistic regression model, principal component scores based on CSF maps predicted clinical outcome in MCI patients even after controlling for age, gender, MMSE score and time of follow-up. Our findings indicate that multivariate network analysis of deformation maps detects typical features of AD pathology and provides a powerful tool to predict conversion into AD in non-demented at risk patients.

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