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Stefan J. Teipel

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11 papers

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YNIMG Journal 2026 Journal Article

Associations between structural neuroimaging markers and neuropathology of Alzheimer’s Disease

Stefan Frenzel
Stefan J. Teipel
Hans J. Grabe

Structural MRI is widely used for assessing the progression of Alzheimer's disease (AD). However, postmortem pathological examination remains the gold standard for confirming the diagnosis. We systematically investigated associations of structural MRI markers with AD neuropathology in well-characterized cohorts from Alzheimer's Disease Research Centres and the Alzheimer's Disease Neuroimaging Initiative. Data of 805 individuals who died between 2006 and 2024 were included. Markers of medial temporal lobe (MTL) and whole brain atrophy were determined from T1-weighted images acquired shortly before death. Two aggregate indices of AD-related brain atrophy (FSAD) and brain ageing (brainageR) were also included. Associations with neuropathology ratings were examined using ordinal logistic regression with proportional odds. At the time of MRI, median age was 78.5 years and 61 % had dementia. The median time between MRI and death was 4.6 years. At autopsy, 52 % had high Alzheimer's disease neuropathologic change (ADNC). In unadjusted analyses, ADNC most strongly correlated with volume of the MTL and the FSAD score (Spearman's ρ=-0.28 and 0.43). In adjusted analyses, the odds ratio associating low MTL volume with ADNC was 5.0 (95 % CI: 3.0-8.3); for a high FSAD score it was 11.0 (95 % CI: 6.6-18.0). MRI markers of AD generally were more strongly associated with tau than with amyloid beta pathology. AD neuropathology was associated with a distinct pattern of atrophy most pronounced in - but not restricted to - the MTL, differing from patterns seen in normal ageing. These results support the use of aggregate indices of AD-related atrophy over single-region morphometric characteristics for sample enrichment or as secondary outcomes in clinical trials.

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YNICL Journal 2021 Journal Article

Association between composite scores of domain-specific cognitive functions and regional patterns of atrophy and functional connectivity in the Alzheimer’s disease spectrum

Chimezie O. Amaefule
Martin Dyrba
Steffen Wolfsgruber
Alexandra Polcher
Anja Schneider
Klaus Fliessbach
Annika Spottke
Dix Meiberth

BACKGROUND: Cognitive decline has been found to be associated with gray matter atrophy and disruption of functional neural networks in Alzheimer's disease (AD) in structural and functional imaging (fMRI) studies. Most previous studies have used single test scores of cognitive performance among monocentric cohorts. However, cognitive domain composite scores could be more reliable than single test scores due to the reduction of measurement error. Adopting a multicentric resting state fMRI (rs-fMRI) and cognitive domain approach, we provide a comprehensive description of the structural and functional correlates of the key cognitive domains of AD. METHOD: We analyzed MRI, rs-fMRI and cognitive domain score data of 490 participants from an interim baseline release of the multicenter DELCODE study cohort, including 54 people with AD, 86 with Mild Cognitive Impairment (MCI), 175 with Subjective Cognitive Decline (SCD), and 175 Healthy Controls (HC) in the AD-spectrum. Resulting cognitive domain composite scores (executive, visuo-spatial, memory, working memory and language) from the DELCODE neuropsychological battery (DELCODE-NP), were previously derived using confirmatory factor analysis. Statistical analyses examined the differences between diagnostic groups, and the association of composite scores with regional atrophy and network-specific functional connectivity among the patient subgroup of SCD, MCI and AD. RESULT: Cognitive performance, atrophy patterns and functional connectivity significantly differed between diagnostic groups in the AD-spectrum. Regional gray matter atrophy was positively associated with visuospatial and other cognitive impairments among the patient subgroup in the AD-spectrum. Except for the visual network, patterns of network-specific resting-state functional connectivity were positively associated with distinct cognitive impairments among the patient subgroup in the AD-spectrum. CONCLUSION: Consistent associations between cognitive domain scores and both regional atrophy and network-specific functional connectivity (except for the visual network), support the utility of a multicentric and cognitive domain approach towards explicating the relationship between imaging markers and cognition in the AD-spectrum.

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YNICL Journal 2017 Journal Article

APPswe/PS1dE9 mice with cortical amyloid pathology show a reduced NAA/Cr ratio without apparent brain atrophy: A MRS and MRI study

Angela Kuhla
Claire Rühlmann
Tobias Lindner
Stefan Polei
Stefan Hadlich
Bernd J. Krause
Brigitte Vollmar
Stefan J. Teipel

Transgenic animal models of Aβ pathology provide mechanistic insight into some aspects of Alzheimer disease (AD) pathology related to Aβ accumulation. Quantitative neuroimaging is a possible aid to improve translation of mechanistic findings in transgenic models to human end phenotypes of brain morphology or function. Therefore, we combined MRI-based morphometry, MRS-based NAA-assessment and quantitative histology of neurons and amyloid plaque load in the APPswe/PS1dE9 mouse model to determine the interrelationship between morphological changes, changes in neuron numbers and amyloid plaque load with reductions of NAA levels as marker of neuronal functional viability. The APPswe/PS1dE9 mouse showed an increase of Aβ plaques, loss of neurons and an impairment of NAA/Cr ratio, which however was not accompanied with brain atrophy. As brain atrophy is one main characteristic in human AD, conclusions from murine to human AD pathology should be drawn with caution.

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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 2015 Journal Article

The relative importance of imaging markers for the prediction of Alzheimer's disease dementia in mild cognitive impairment — Beyond classical regression

Stefan J. Teipel
Jens Kurth
Bernd Krause
Michel J. Grothe

Selecting a set of relevant markers to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has become a challenging task given the wealth of regional pathologic information that can be extracted from multimodal imaging data. Here, we used regularized regression approaches with an elastic net penalty for best subset selection of multiregional information from AV45-PET, FDG-PET and volumetric MRI data to predict conversion from MCI to AD. The study sample consisted of 127 MCI subjects from ADNI-2 who had a clinical follow-up between 6 and 31 months. Additional analyses assessed the effect of partial volume correction on predictive performance of AV45- and FDG-PET data. Predictor variables were highly collinear within and across imaging modalities. Penalized Cox regression yielded more parsimonious prediction models compared to unpenalized Cox regression. Within single modalities, time to conversion was best predicted by increased AV45-PET signal in posterior medial and lateral cortical regions, decreased FDG-PET signal in medial temporal and temporobasal regions, and reduced gray matter volume in medial, basal, and lateral temporal regions. Logistic regression models reached up to 72% cross-validated accuracy for prediction of conversion status, which was comparable to cross-validated accuracy of non-linear support vector machine classification. Regularized regression outperformed unpenalized stepwise regression when number of parameters approached or exceeded the number of training cases. Partial volume correction had a negative effect on the predictive performance of AV45-PET, but slightly improved the predictive value of FDG-PET data. Penalized regression yielded more parsimonious models than unpenalized stepwise regression for the integration of multiregional and multimodal imaging information. The advantage of penalized regression was particularly strong with a high number of collinear predictors.

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YNIMG Journal 2011 Journal Article

A comparison of different automated methods for the detection of white matter lesions in MRI data

Stefan Klöppel
Ahmed Abdulkadir
Stathis Hadjidemetriou
Sabine Issleib
Lars Frings
Thao Nguyen Thanh
Irina Mader
Stefan J. Teipel

White matter hyperintensities (WMH) are the focus of intensive research and have been linked to cognitive impairment and depression in the elderly. Cumbersome manual outlining procedures make research on WMH labour intensive and prone to subjective bias. This study compares fully automated supervised detection methods that learn to identify WMH from manual examples against unsupervised approaches on the combination of FLAIR and T1 weighted images. Data were collected from ten subjects with mild cognitive impairment and another set of ten individuals who fulfilled diagnostic criteria for dementia. Data were split into balanced groups to create a training set used to optimize the different methods. Manual outlining served as gold standard to evaluate performance of the automated methods that identified each voxel either as intact or as part of a WMH. Otsu's approach for multiple thresholds which is based only on voxel intensities of the FLAIR image produced a high number of false positives at grey matter boundaries. Performance on an independent test set was similarly disappointing when simply applying a threshold to the FLAIR that was found from training data. Among the supervised methods, precision–recall curves of support vector machines (SVM) indicated advantages over the performance achieved by K-nearest-neighbor classifiers (KNN). The curves indicated a clear benefit from optimizing the threshold of the SVM decision value and the voting rule of the KNN. Best performance was reached by selecting training voxels according to their distance to the lesion boundary and repeated training after replacing the feature vectors from those voxels that did not form support vectors of the SVM. The study demonstrates advantages of SVM for the problem of detecting WMH at least for studies that include only FLAIR and T1 weighted images. Various optimization strategies are discussed and compared against each other.

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YNIMG Journal 2010 Journal Article

Age-related networks of regional covariance in MRI gray matter: Reproducible multivariate patterns in healthy aging

Kaitlin L. Bergfield
Krista D. Hanson
Kewei Chen
Stefan J. Teipel
Harald Hampel
Stanley I. Rapoport
James R. Moeller
Gene E. Alexander

Healthy aging is associated with brain volume reductions that involve the frontal cortex, but also affect other brain regions. We sought to identify an age-related network pattern of MRI gray matter using a multivariate statistical model of regional covariance, the Scaled Subprofile Model (SSM) with voxel based morphometry (VBM) in 29 healthy adults, 23-84 years of age (Group 1). In addition, we evaluated the reproducibility of the age-related gray matter pattern derived from a prior SSM VBM study of 26 healthy adults, 22-77 years of age (Group 2; Alexander et al. , 2006) in relation to the current sample and tested the ability of the network analysis to extract an age-related pattern from both cohorts combined. The SSM VBM analysis of Group 1 identified a regional pattern of gray matter atrophy associated with healthy aging (R2 =0. 64, p<0. 000001) that included extensive reductions in bilateral dorsolateral and medial frontal, anterior cingulate, insula/perisylvian, precuneus, parietotemporal, and caudate regions with areas of relative preservation in bilateral cerebellum, thalamus, putamen, mid cingulate, and temporal pole regions. The age-related SSM VBM gray matter pattern, previously reported for Group 2, was highly expressed in Group 1 (R2 =0. 52, p<0. 00002). SSM analysis of the combined cohorts extracted a common age-related pattern of gray matter showing reductions involving bilateral medial frontal, insula/perisylvian, anterior cingulate and, to a lesser extent, bilateral dorsolateral prefrontal, lateral temporal, parietal, and caudate brain regions with relative preservation in bilateral cerebellum, temporal pole, and right thalamic regions. The results suggest that healthy aging is associated with a regionally distributed pattern of gray matter atrophy that has reproducible regional features. Whereas the network patterns of atrophy included parietal, temporal, and subcortical regions, involvement of the frontal brain regions showed the most consistently extensive and reliable reductions across samples. Network analysis with SSM VBM can help detect reproducible age-related MRI patterns, assisting efforts in the study of healthy and pathological aging.

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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 2010 Journal Article

White matter microstructure underlying default mode network connectivity in the human brain

Stefan J. Teipel
Arun L.W. Bokde
Thomas Meindl
Edson Amaro
Jasmin Soldner
Maximilian F. Reiser
Sabine C. Herpertz
Hans-Jürgen Möller

Resting state functional magnetic resonance imaging (fMRI) reveals a distinct network of correlated brain function representing a default mode state of the human brain. The underlying structural basis of this functional connectivity pattern is still widely unexplored. We combined fractional anisotropy measures of fiber tract integrity derived from diffusion tensor imaging (DTI) and resting state fMRI data obtained at 3 Tesla from 20 healthy elderly subjects (56 to 83 years of age) to determine white matter microstructure underlying default mode connectivity. We hypothesized that the functional connectivity between the posterior cingulate and hippocampus from resting state fMRI data would be associated with the white matter microstructure in the cingulate bundle and fiber tracts connecting posterior cingulate gyrus with lateral temporal lobes, medial temporal lobes, and precuneus. This was demonstrated at the p <0. 001 level using a voxel-based multivariate analysis of covariance (MANCOVA) approach. In addition, we used a data-driven technique of joint independent component analysis (ICA) that uncovers spatial pattern that are linked across modalities. It revealed a pattern of white matter tracts including cingulate bundle and associated fiber tracts resembling the findings from the hypothesis-driven analysis and was linked to the pattern of default mode network (DMN) connectivity in the resting state fMRI data. Our findings support the notion that the functional connectivity between the posterior cingulate and hippocampus and the functional connectivity across the entire DMN is based on distinct pattern of anatomical connectivity within the cerebral white matter.

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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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YNIMG Journal 2007 Journal Article

Multivariate network analysis of fiber tract integrity in Alzheimer’s disease

Stefan J. Teipel
Robert Stahl
Olaf Dietrich
Stefan O. Schoenberg
Robert Perneczky
Arun L.W. Bokde
Maximilian F. Reiser
Hans-Jürgen Möller

Axonal and dendritic integrity is affected early in Alzheimer’s disease (AD). Studies using region of interest or voxel-based analysis of diffusion tensor imaging data found significant decline of fractional anisotropy, a marker of fiber tract integrity, in selected white matter areas. We applied a multivariate network analysis based on principal component analysis to fractional anisotropy maps derived from diffusion-weighted scans from 15 AD patients, and 14 elderly healthy controls. Fractional anisotropy maps were obtained from an EPI diffusion sequence using parallel imaging to reduce distortion artifacts. We used high-dimensional image warping to control for partial volume effects due to white matter atrophy in AD. We found a significant regional pattern of fiber changes (p <0. 01) indicating that the integrity of intracortical projecting fiber tracts (including corpus callosum, cingulum and fornix, and frontal, temporal and occipital lobe white matter areas) was reduced, whereas extracortical projecting fiber tracts, including the pyramidal and extrapyramidal systems and somatosensory projections, were relatively preserved in AD. Effects of a univariate analysis were almost entirely contained within the multivariate effect. Our findings illustrate the use of a multivariate approach to fractional anisotropy data that takes advantage of the highly organized structure of anisotropy maps, and is independent of multiple comparison correction and partial volume effects. In agreement with post-mortem evidence, our study demonstrates dissociation between intracortical and extracortical projecting fiber systems in AD in the living human brain.

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