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Stefan Klöppel

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YNIMG Journal 2024 Journal Article

Simultaneous multi-region detection of GABA+ and Glx using 3D spatially resolved SLOW-editing and EPSI-readout at 7T

  • Guodong Weng
  • Johannes Slotboom
  • Philippe Schucht
  • Ekin Ermiş
  • Roland Wiest
  • Stefan Klöppel
  • Jessica Peter
  • Irena Zubak

GABA+ and Glx (glutamate and glutamine) are widely studied metabolites, yet the commonly used magnetic resonance spectroscopy (MRS) techniques have significant limitations, including sensitivity to B 0 and B 1 +-inhomogeneities, limited bandwidth of MEGA-pulses, high SAR which is accentuated at 7T. To address these limitations, we propose SLOW-EPSI method, employing a large 3D MRSI coverage and achieving a high resolution down to 0. 26 ml. Simulation results demonstrate the robustness of SLOW-editing for both GABA+ and Glx against B 0 and B 1 +-inhomogeneities within the range of [-0. 3, +0. 3] ppm and [40 %, 250 %], respectively. Two protocols, both utilizing a 70 mm thick FOV slab, were employed to target distinct brain regions in vivo, differentiated by their orientation: transverse and tilted. Protocol 1 (n = 11) encompassed 5 locations (cortical gray matter, white matter, frontal lobe, parietal lobe, and cingulate gyrus). Protocol 2 (n = 5) involved 9 locations (cortical gray matter, white matter, frontal lobe, occipital lobe, cingulate gyrus, caudate nucleus, hippocampus, putamen, and inferior thalamus). Quantitative analysis of GABA+ and Glx was conducted in a stepwise manner. First, B 1 + /B 1 –-inhomogeneities were corrected using water reference data. Next, GABA+ and Glx values were calculated employing spectral fitting. Finally, the GABA+ level for each selected region was compared to the global Glx within the same subject, generating the GABA+/Glx_global ratio. Our findings from two protocols indicate that the GABA+/Glx_global level in cortical gray matter was approximately 16 % higher than in white matter. Elevated GABA+/Glx_global levels acquired with protocol 2 were observed in specific regions such as the caudate nucleus (0. 118±0. 067), putamen (0. 108±0. 023), thalamus (0. 092±0. 036), and occipital cortex (0. 091±0. 010), when compared to the cortical gray matter (0. 079±0. 012). Overall, our results highlight the effectiveness of SLOW-EPSI as a robust and efficient technique for accurate measurements of GABA+ and Glx at 7T. In contrast to previous SVS and 2D-MRSI based editing sequences with which only one or a limited number of brain regions can be measured simultaneously, the method presented here measures GABA+ and Glx from any brain area and any arbitrarily shaped volume that can be flexibly selected after the examination. Quantification of GABA+ and Glx across multiple brain regions through spectral fitting is achievable with a 9-minute acquisition. Additionally, acquisition times of 18–27 min (GABA+) and 9–18 min (Glx) are required to generate 3D maps, which are constructed using Gaussian fitting and peak integration.

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YNICL Journal 2018 Journal Article

Cross-sectional and longitudinal voxel-based grey matter asymmetries in Huntington's disease

  • Lora Minkova
  • Sarah Gregory
  • Rachael I. Scahill
  • Ahmed Abdulkadir
  • Christoph P. Kaller
  • Jessica Peter
  • Jeffrey D. Long
  • Julie C. Stout

Huntington's disease (HD) is a progressive neurodegenerative disorder that can be genetically confirmed with certainty decades before clinical onset. This allows the investigation of functional and structural changes in HD many years prior to disease onset, which may reveal important mechanistic insights into brain function, structure and organization in general. While regional atrophy is present at early stages of HD, it is still unclear if both hemispheres are equally affected by neurodegeneration and how the extent of asymmetry affects domain-specific functional decline. Here, we used whole-brain voxel-based analysis to investigate cross-sectional and longitudinal hemispheric asymmetries in grey matter (GM) volume in 56 manifest HD (mHD), 83 pre-manifest HD (preHD), and 80 healthy controls (HC). Furthermore, a regression analysis was used to assess the relationship between neuroanatomical asymmetries and decline in motor and cognitive measures across the disease spectrum. The cross-sectional analysis showed striatal leftward-biased GM atrophy in mHD, but not in preHD, relative to HC. Longitudinally, no net 36-month change in GM asymmetries was found in any of the groups. In the regression analysis, HD-related decline in quantitative-motor (Q-Motor) performance was linked to lower GM volume in the left superior parietal cortex. These findings suggest a stronger disease effect targeting the left hemisphere, especially in those with declining motor performance. This effect did not change over a period of three years and may indicate a compensatory role of the right hemisphere in line with recent functional imaging studies.

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YNICL Journal 2018 Journal Article

Voxel-wise deviations from healthy aging for the detection of region-specific atrophy

  • Stefan Klöppel
  • Shan Yang
  • Elias Kellner
  • Marco Reisert
  • Bernhard Heimbach
  • Horst Urbach
  • Jennifer Linn
  • Stefan Weidauer

The identification of pathological atrophy in MRI scans requires specialized training, which is scarce outside dedicated centers. We sought to investigate the clinical usefulness of computer-generated representations of local grey matter (GM) loss or increased volume of cerebral fluids (CSF) as normalized deviations (z-scores) from healthy aging to either aid human visual readings or directly detect pathological atrophy. Two experienced neuroradiologists rated atrophy in 30 patients with Alzheimer's disease (AD), 30 patients with frontotemporal dementia (FTD), 30 with dementia due to Lewy-body disease (LBD) and 30 healthy controls (HC) on a three-point scale in 10 anatomical regions as reference gold standard. Seven raters, varying in their experience with MRI diagnostics rated all cases on the same scale once with and once without computer-generated volume deviation maps that were overlaid on anatomical slices. In addition, we investigated the predictive value of the computer generated deviation maps on their own for the detection of atrophy as identified by the gold standard raters. Inter and intra-rater agreements of the two gold standard raters were substantial (Cohen's kappa κ > 0.62). The intra-rater agreement of the other raters ranged from fair (κ = 0.37) to substantial (κ = 0.72) and improved on average by 0.13 (0.57 < κ < 0.87) when volume deviation maps were displayed. The seven other raters showed good agreement with the gold standard in regions including the hippocampus but agreement was substantially lower in e.g. the parietal cortex and did not improve with the display of atrophy scores. Rating speed increased over the course of the study and irrespective of the presentation of voxel-wise deviations. Automatically detected large deviations of local volume were consistently associated with gold standard atrophy reading as shown by an area under the receiver operator characteristic of up to 0.95 for the hippocampus region. When applying these test characteristics to prevalences typically found in a memory clinic, we observed a positive or negative predictive value close to or above 0.9 in the hippocampus for almost all of the expected cases. The volume deviation maps derived from CSF volume increase were generally better in detecting atrophy. Our study demonstrates an agreement of visual ratings among non-experts not further increased by displaying, region-specific deviations of volume. The high predictive value of computer generated local deviations independent from human interaction and the consistent advantages of CSF-over GM-based estimations should be considered in the development of diagnostic tools and indicate clinical utility well beyond aiding visual assessments.

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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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YNIMG Journal 2017 Journal Article

The European DTI Study on Dementia — A multicenter DTI and MRI study on Alzheimer's disease and Mild Cognitive Impairment

  • Katharina Brueggen
  • Michel J. Grothe
  • Martin Dyrba
  • Andreas Fellgiebel
  • Florian Fischer
  • Massimo Filippi
  • Federica Agosta
  • Peter Nestor

The European DTI Study on Dementia (EDSD) is a multicenter framework created to study the diagnostic accuracy and inter-site variability of DTI-derived markers in patients with manifest and prodromal Alzheimer's disease (AD). The dynamically growing database presently includes 493 DTI, 512 T1-weighted MRI, and 300 FLAIR scans from patients with AD dementia, patients with Mild Cognitive Impairment (MCI) and matched Healthy Controls, acquired on 13 different scanner platforms. The imaging data is publicly available, along with the subjects' demographic and clinical characterization. Detailed neuropsychological information, cerebrospinal fluid information on biomarkers and clinical follow-up diagnoses are included for a subset of subjects. This paper describes the rationale and structure of the EDSD, summarizes the available data, and explains how to gain access to the database. The EDSD is a useful database for researchers seeking to investigate the contribution of DTI to dementia diagnostics.

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YNICL Journal 2015 Journal Article

An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease

  • Daniel Schmitter
  • Alexis Roche
  • Bénédicte Maréchal
  • Delphine Ribes
  • Ahmed Abdulkadir
  • Meritxell Bach-Cuadra
  • Alessandro Daducci
  • Cristina Granziera

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease.

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YNICL Journal 2015 Journal Article

Classifying individuals at high-risk for psychosis based on functional brain activity during working memory processing

  • Kerstin Bendfeldt
  • Renata Smieskova
  • Nikolaos Koutsouleris
  • Stefan Klöppel
  • André Schmidt
  • Anna Walter
  • Fabienne Harrisberger
  • Johannes Wrege

The psychosis high-risk state is accompanied by alterations in functional brain activity during working memory processing. We used binary automatic pattern-classification to discriminate between the at-risk mental state (ARMS), first episode psychosis (FEP) and healthy controls (HCs) based on n-back WM-induced brain activity. Linear support vector machines and leave-one-out-cross-validation were applied to fMRI data of matched ARMS, FEP and HC (19 subjects/group). The HC and ARMS were correctly classified, with an accuracy of 76.2% (sensitivity 89.5%, specificity 63.2%, p = 0.01) using a verbal working memory network mask. Only 50% and 47.4% of individuals were classified correctly for HC vs. FEP (p = 0.46) or ARMS vs. FEP (p = 0.62), respectively. Without mask, accuracy was 65.8% for HC vs. ARMS (p = 0.03) and 65.8% for HC vs. FEP (p = 0.0047), and 57.9% for ARMS vs. FEP (p = 0.18). Regions in the medial frontal, paracingulate, cingulate, inferior frontal and superior frontal gyri, inferior and superior parietal lobules, and precuneus were particularly important for group separation. These results suggest that FEP and HC or FEP and ARMS cannot be accurately separated in small samples under these conditions. However, ARMS can be identified with very high sensitivity in comparison to HC. This might aid classification and help to predict transition in the ARMS.

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YNIMG Journal 2014 Journal Article

Correction of inter-scanner and within-subject variance in structural MRI based automated diagnosing

  • Daniel Kostro
  • Ahmed Abdulkadir
  • Alexandra Durr
  • Raymund Roos
  • Blair R. Leavitt
  • Hans Johnson
  • David Cash
  • Sarah J. Tabrizi

Automated analysis of structural magnetic resonance images is a promising way to improve early detection of neurodegenerative brain diseases. Clinical applications of such methods involve multiple scanners with potentially different hardware and/or acquisition sequences and demographically heterogeneous groups. To improve classification performance, we propose to correct effects of subject-specific covariates (such as age, total intracranial volume, and sex) as well as effects of scanner by using a non-linear Gaussian process model. To test the efficacy of the correction, we performed classification of carriers of the genetic mutation leading to Huntington's disease (HD) versus healthy controls. Half of the HD carriers were free of typical HD symptoms and had an estimated 5 to 20years before onset of clinical symptoms, thus providing a model for preclinical diagnosis of a neurodegenerative disease. Structural magnetic resonance brain images were acquired at four sites with pairs of sites which had the identical scanner type, equipment, and acquisition parameters. For automatic classification, we used spatially normalized probabilistic maps of gray matter, then removed confounding effects by Gaussian process regression, and then performed classification with a support vector machine. Voxel-based morphometry of gray matter maps showed disease effects that were spatially wider spread than effects of scanner, but no significant interactions between scanner and disease were found. A model trained with data from a single scanner generalized well to data from a different scanner. When confounding diagnostics groups and scanner during training, e. g. by using controls from one scanner and gene carriers from another, classification accuracy dropped significantly in many cases. By regressing out confounds with Gaussian process regression, the performance levels were comparable to those obtained in scenarios without confound. We conclude that models trained on data acquired with a single scanner generalized well to data acquired with a different same-generation scanner even when the vendor differed. When confounding grouping and scanner during training is unavoidable to gather training data, regressing out inter-scanner and between-subject variability can reduce the loss in accuracy due to the confound.

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YNIMG Journal 2013 Journal Article

Interregional compensatory mechanisms of motor functioning in progressing preclinical neurodegeneration

  • Elisa Scheller
  • Ahmed Abdulkadir
  • Jessica Peter
  • Sarah J. Tabrizi
  • Richard S.J. Frackowiak
  • Stefan Klöppel

Understanding brain reserve in preclinical stages of neurodegenerative disorders allows determination of which brain regions contribute to normal functioning despite accelerated neuronal loss. Besides the recruitment of additional regions, a reorganisation and shift of relevance between normally engaged regions are a suggested key mechanism. Thus, network analysis methods seem critical for investigation of changes in directed causal interactions between such candidate brain regions. To identify core compensatory regions, fifteen preclinical patients carrying the genetic mutation leading to Huntington's disease and twelve controls underwent fMRI scanning. They accomplished an auditory paced finger sequence tapping task, which challenged cognitive as well as executive aspects of motor functioning by varying speed and complexity of movements. To investigate causal interactions among brain regions a single Dynamic Causal Model (DCM) was constructed and fitted to the data from each subject. The DCM parameters were analysed using statistical methods to assess group differences in connectivity, and the relationship between connectivity patterns and predicted years to clinical onset was assessed in gene carriers. In preclinical patients, we found indications for neural reserve mechanisms predominantly driven by bilateral dorsal premotor cortex, which increasingly activated superior parietal cortices the closer individuals were to estimated clinical onset. This compensatory mechanism was restricted to complex movements characterised by high cognitive demand. Additionally, we identified task-induced connectivity changes in both groups of subjects towards pre- and caudal supplementary motor areas, which were linked to either faster or more complex task conditions. Interestingly, coupling of dorsal premotor cortex and supplementary motor area was more negative in controls compared to gene mutation carriers. Furthermore, changes in the connectivity pattern of gene carriers allowed prediction of the years to estimated disease onset in individuals. Our study characterises the connectivity pattern of core cortical regions maintaining motor function in relation to varying task demand. We identified connections of bilateral dorsal premotor cortex as critical for compensation as well as task-dependent recruitment of pre- and caudal supplementary motor area. The latter finding nicely mirrors a previously published general linear model-based analysis of the same data. Such knowledge about disease specific inter-regional effective connectivity may help identify foci for interventions based on transcranial magnetic stimulation designed to stimulate functioning and also to predict their impact on other regions in motor-associated networks.

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YNIMG Journal 2012 Journal Article

Diagnostic neuroimaging across diseases

  • Stefan Klöppel
  • Ahmed Abdulkadir
  • Clifford R. Jack
  • Nikolaos Koutsouleris
  • Janaina Mourão-Miranda
  • Prashanthi Vemuri

Fully automated classification algorithms have been successfully applied to diagnose a wide range of neurological and psychiatric diseases. They are sufficiently robust to handle data from different scanners for many applications and in specific cases outperform radiologists. This article provides an overview of current applications taking structural imaging in Alzheimer's disease and schizophrenia as well as functional imaging to diagnose depression as examples. In this context, we also report studies aiming to predict the future course of the disease and the response to treatment for the individual. This has obvious clinical relevance but is also important for the design of treatment studies that may aim to include a cohort with a predicted fast disease progression to be more sensitive to detect treatment effects. In the second part, we present our own opinions on i) the role these classification methods can play in the clinical setting; ii) where their limitations are at the moment and iii) how those can be overcome. Specifically, we discuss strategies to deal with disease heterogeneity, diagnostic uncertainties, a probabilistic framework for classification and multi-class classification approaches.

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YNIMG Journal 2012 Journal Article

Multivariate pattern classification of gray matter pathology in multiple sclerosis

  • Kerstin Bendfeldt
  • Stefan Klöppel
  • Thomas E. Nichols
  • Renata Smieskova
  • Pascal Kuster
  • Stefan Traud
  • Nicole Mueller-Lenke
  • Yvonne Naegelin

Univariate analyses have identified gray matter (GM) alterations in different groups of MS patients. While these methods detect differences on the basis of the single voxel or cluster, multivariate methods like support vector machines (SVM) identify the complex neuroanatomical patterns of GM differences. Using multivariate linear SVM analysis and leave-one-out cross-validation, we aimed at identifying neuroanatomical GM patterns relevant for individual classification of MS patients. We used SVM to separate GM segmentations of T1-weighted three-dimensional magnetic resonance (MR) imaging scans within different age- and sex-matched groups of MS patients with either early (n=17) or late MS (n=17) (contrast I), low (n=20) or high (n=20) white matter lesion load (contrast II), and benign MS (BMS, n=13) or non-benign MS (NBMS, n=13) (contrast III) scanned on a single 1. 5T MR scanner. GM patterns most relevant for individual separation of MS patients comprised cortical areas of all the cerebral lobes as well as deep GM structures, including the thalamus and caudate. The patterns detected were sufficiently informative to separate individuals of the respective groups with high sensitivity and specificity in 85% (contrast I), 83% (contrast II) and 77% (contrast III) of cases. The study demonstrates that neuroanatomical spatial patterns of GM segmentations contain information sufficient for correct classification of MS patients at the single case level, thus making multivariate SVM analysis a promising clinical application.

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YNIMG Journal 2012 Journal Article

The effect of handedness on the shape of the central sulcus

  • Zhong Yi Sun
  • Stefan Klöppel
  • Denis Rivière
  • Matthieu Perrot
  • Richard Frackowiak
  • Hartwig Siebner
  • Jean-François Mangin

Sinistrals differ from dextrals in the size of certain cortical folds. For instance, handedness has an impact on central sulcus surface area: the sulcus is larger in the dominant left hemisphere of dextrals and vice versa for sinistrals. However, the impact of handedness on the shape of the central sulcus is largely unexplored. In this paper, we propose first an original strategy based on manifold learning to quantify the shape of the central sulcus. Using this approach we show that the “hand knob”, a major landmark of the hand motor representation, is sited more dorsally in the left hemisphere in dextrals than in sinistrals. Sinistrals forced to write with their non-preferred right hand display a pattern of central sulcus size asymmetry which is typical of dextrals, yet forced dextrality does not shift the handedness-specific location of the “hand knob”. Hence, cortical morphology in adults holds an accumulated record of both innate biases and early developmental experience. Characterizing normal variation of cortical morphology provides a means of systematically correlating behavior with cortical development.

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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 2011 Journal Article

Effects of hardware heterogeneity on the performance of SVM Alzheimer's disease classifier

  • Ahmed Abdulkadir
  • Bénédicte Mortamet
  • Prashanthi Vemuri
  • Clifford R. Jack
  • Gunnar Krueger
  • Stefan Klöppel

Fully automated machine learning methods based on structural magnetic resonance imaging (MRI) data can assist radiologists in the diagnosis of Alzheimer's disease (AD). These algorithms require large data sets to learn the separation of subjects with and without AD. Training and test data may come from heterogeneous hardware settings, which can potentially affect the performance of disease classification. A total of 518 MRI sessions from 226 healthy controls and 191 individuals with probable AD from the multicenter Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to investigate whether grouping data by acquisition hardware (i. e. vendor, field strength, coil system) is beneficial for the performance of a support vector machine (SVM) classifier, compared to the case where data from different hardware is mixed. We compared the change of the SVM decision value resulting from (a) changes in hardware against the effect of disease and (b) changes resulting simply from rescanning the same subject on the same machine. Maximum accuracy of 87% was obtained with a training set of all 417 subjects. Classifiers trained with 95 subjects in each diagnostic group and acquired with heterogeneous scanner settings had an empirical detection accuracy of 84. 2±2. 4% when tested on an independent set of the same size. These results mirror the accuracy reported in recent studies. Encouragingly, classifiers trained on images acquired with homogenous and heterogeneous hardware settings had equivalent cross-validation performances. Two scans of the same subject acquired on the same machine had very similar decision values and were generally classified into the same group. Higher variation was introduced when two acquisitions of the same subject were performed on two scanners with different field strengths. The variation was unbiased and similar for both diagnostic groups. The findings of the study encourage the pooling of data from different sites to increase the number of training samples and thereby improving performance of disease classifiers. Although small, a change in hardware could lead to a change of the decision value and thus diagnostic grouping. The findings of this study provide estimators for diagnostic accuracy of an automated disease diagnosis method involving scans acquired with different sets of hardware. Furthermore, we show that the level of confidence in the performance estimation significantly depends on the size of the training sample, and hence should be taken into account in a clinical setting.

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YNIMG Journal 2011 Journal Article

Multivariate models of inter-subject anatomical variability

  • John Ashburner
  • Stefan Klöppel

This paper presents a very selective review of some of the approaches for multivariate modelling of inter-subject variability among brain images. It focusses on applying probabilistic kernel-based pattern recognition approaches to pre-processed anatomical MRI, with the aim of most accurately modelling the difference between populations of subjects. Some of the principles underlying the pattern recognition approaches of Gaussian process classification and regression are briefly described, although the reader is advised to look elsewhere for full implementational details. Kernel pattern recognition methods require matrices that encode the degree of similarity between the images of each pair of subjects. This review focusses on similarity measures derived from the relative shapes of the subjects' brains. Pre-processing is viewed as generative modelling of anatomical variability, and there is a special emphasis on the diffeomorphic image registration framework, which provides a very parsimonious representation of relative shapes. Although the review is largely methodological, excessive mathematical notation is avoided as far as possible, as the paper attempts to convey a more intuitive understanding of various concepts. The paper should be of interest to readers wishing to apply pattern recognition methods to MRI data, with the aim of clinical diagnosis or biomarker development. It also tries to explain that the best models are those that most accurately predict, so similar approaches should also be relevant to basic science. Knowledge of some basic linear algebra and probability theory should make the review easier to follow, although it may still have something to offer to those readers whose mathematics may be more limited.

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YNIMG Journal 2010 Journal Article

Combining functional and anatomical connectivity reveals brain networks for auditory language comprehension

  • Dorothee Saur
  • Björn Schelter
  • Susanne Schnell
  • David Kratochvil
  • Hanna Küpper
  • Philipp Kellmeyer
  • Dorothee Kümmerer
  • Stefan Klöppel

Cognitive functions are organized in distributed, overlapping, and interacting brain networks. Investigation of those large-scale brain networks is a major task in neuroimaging research. Here, we introduce a novel combination of functional and anatomical connectivity to study the network topology subserving a cognitive function of interest. (i) In a given network, direct interactions between network nodes are identified by analyzing functional MRI time series with the multivariate method of directed partial correlation (dPC). This method provides important improvements over shortcomings that are typical for ordinary (partial) correlation techniques. (ii) For directly interacting pairs of nodes, a region-to-region probabilistic fiber tracking on diffusion tensor imaging data is performed to identify the most probable anatomical white matter fiber tracts mediating the functional interactions. This combined approach is applied to the language domain to investigate the network topology of two levels of auditory comprehension: lower-level speech perception (i. e. , phonological processing) and higher-level speech recognition (i. e. , semantic processing). For both processing levels, dPC analyses revealed the functional network topology and identified central network nodes by the number of direct interactions with other nodes. Tractography showed that these interactions are mediated by distinct ventral (via the extreme capsule) and dorsal (via the arcuate/superior longitudinal fascicle fiber system) long- and short-distance association tracts as well as commissural fibers. Our findings demonstrate how both processing routines are segregated in the brain on a large-scale network level. Combining dPC with probabilistic tractography is a promising approach to unveil how cognitive functions emerge through interaction of functionally interacting and anatomically interconnected brain regions.

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YNIMG Journal 2010 Journal Article

Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters

  • Katja Franke
  • Gabriel Ziegler
  • Stefan Klöppel
  • Christian Gaser

The early identification of brain anatomy deviating from the normal pattern of growth and atrophy, such as in Alzheimer's disease (AD), has the potential to improve clinical outcomes through early intervention. Recently, Davatzikos et al. (2009) supported the hypothesis that pathologic atrophy in AD is an accelerated aging process, implying accelerated brain atrophy. In order to recognize faster brain atrophy, a model of healthy brain aging is needed first. Here, we introduce a framework for automatically and efficiently estimating the age of healthy subjects from their T1-weighted MRI scans using a kernel method for regression. This method was tested on over 650 healthy subjects, aged 19–86 years, and collected from four different scanners. Furthermore, the influence of various parameters on estimation accuracy was analyzed. Our age estimation framework included automatic preprocessing of the T1-weighted images, dimension reduction via principal component analysis, training of a relevance vector machine (RVM; Tipping, 2000) for regression, and finally estimating the age of the subjects from the test samples. The framework proved to be a reliable, scanner-independent, and efficient method for age estimation in healthy subjects, yielding a correlation of r =0. 92 between the estimated and the real age in the test samples and a mean absolute error of 5 years. The results indicated favorable performance of the RVM and identified the number of training samples as the critical factor for prediction accuracy. Applying the framework to people with mild AD resulted in a mean brain age gap estimate (BrainAGE) score of +10 years.

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YNIMG Journal 2010 Journal Article

Intra- and interscanner variability of automated voxel-based volumetry based on a 3D probabilistic atlas of human cerebral structures

  • Hans-Jürgen Huppertz
  • Judith Kröll-Seger
  • Stefan Klöppel
  • Reinhard E. Ganz
  • Jan Kassubek

The intra- and inter-scanner variability of an automated method for MRI-based volumetry was investigated. Using SPM5 algorithms and predefined masks derived from a probabilistic whole-brain atlas, this method allows to determine the volumes of various brain structures (e. g. , hemispheres, lobes, cerebellum, basal ganglia, grey and white matter etc.) in single subjects in an observer-independent fashion. A healthy volunteer was scanned three times at six different MRI scanners (including different vendors and field strengths) to calculate intra- and inter-scanner volumetric coefficients of variation (CV). The mean intra-scanner CV values per brain structure ranged from 0. 50% to 4. 4% (median, 0. 89%), while the inter-scanner CV results varied between 0. 66% and 14. 7% (median, 4. 74%). The overall (=combined intra- and inter-scanner) variability of measurements was only marginally higher, with CV results of 0. 87–15. 1% (median, 4. 80%). Furthermore, the minimum percentage volume difference for detecting a significant volume change between two volume measurements in the same subject was calculated for each substructure. For example, for the total brain volume, mean intra-scanner, inter-scanner, and overall CV results were 0. 50%, 3. 78%, and 3. 80%, respectively, and the cut-offs for significant volume changes between two measurements in the same subject amounted to 1. 4% for measurements on the same scanner and 10. 5% on different scanners. These findings may be useful for planning and assessing volumetric studies in neurological diseases, for the differentiation of certain patterns of atrophy, or for longitudinal studies monitoring the course of a disease and potential therapeutic effects.

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YNIMG Journal 2010 Journal Article

Predicting clinical scores from magnetic resonance scans in Alzheimer's disease

  • Cynthia M. Stonnington
  • Carlton Chu
  • Stefan Klöppel
  • Clifford R. Jack
  • John Ashburner
  • Richard S.J. Frackowiak

Machine learning and pattern recognition methods have been used to diagnose Alzheimer's disease (AD) and mild cognitive impairment (MCI) from individual MRI scans. Another application of such methods is to predict clinical scores from individual scans. Using relevance vector regression (RVR), we predicted individuals' performances on established tests from their MRI T1 weighted image in two independent data sets. From Mayo Clinic, 73 probable AD patients and 91 cognitively normal (CN) controls completed the Mini-Mental State Examination (MMSE), Dementia Rating Scale (DRS), and Auditory Verbal Learning Test (AVLT) within 3months of their scan. Baseline MRI's from the Alzheimer's disease Neuroimaging Initiative (ADNI) comprised the other data set; 113 AD, 351 MCI, and 122 CN subjects completed the MMSE and Alzheimer's Disease Assessment Scale—Cognitive subtest (ADAS-cog) and 39 AD, 92 MCI, and 32 CN ADNI subjects completed MMSE, ADAS-cog, and AVLT. Predicted and actual clinical scores were highly correlated for the MMSE, DRS, and ADAS-cog tests (P <0. 0001). Training with one data set and testing with another demonstrated stability between data sets. DRS, MMSE, and ADAS-Cog correlated better than AVLT with whole brain grey matter changes associated with AD. This result underscores their utility for screening and tracking disease. RVR offers a novel way to measure interactions between structural changes and neuropsychological tests beyond that of univariate methods. In clinical practice, we envision using RVR to aid in diagnosis and predict clinical outcome.

Details DOI

YNIMG Journal 2008 Journal Article

Interpreting scan data acquired from multiple scanners: A study with Alzheimer's disease

  • Cynthia M. Stonnington
  • Geoffrey Tan
  • Stefan Klöppel
  • Carlton Chu
  • Bogdan Draganski
  • Clifford R. Jack
  • Kewei Chen
  • John Ashburner

Large, multi-site studies utilizing MRI-derived measures from multiple scanners present an opportunity to advance research by pooling data. On the other hand, it remains unclear whether or not the potential confound introduced by different scanners and upgrades will devalue the integrity of any results. Although there are studies of scanner differences for the purpose of calibration and quality control, the current literature is devoid of studies that describe the analysis of multi-scanner data with regard to the interaction of scanner(s) with effects of interest. We investigated a data-set of 136 subjects, 62 patients with mild to moderate Alzheimer's disease and 74 cognitively normal elderly controls, with MRI scans from one center that were acquired over 10 years with 6 different scanners and multiple upgrades over time. We used a whole-brain voxel-wise analysis to evaluate the effect of scanner, effect of disease, and the interaction of scanner and disease for the 6 different scanners. The effect of disease in patients showed the expected significant reduction of grey matter in the medial temporal lobe. Scanner differences were substantially less than the group differences and only significant in the thalamus. There was no significant interaction of scanner with disease group. We describe the rationale for concluding that our results were not confounded by scanner differences. Similar analyses in other multi-scanner data-sets could be used to justify the pooling of data when needed, such as in studies of rare disorders or in multi-center designs.

Details DOI

YNIMG Journal 2008 Journal Article

The cortical motor threshold reflects microstructural properties of cerebral white matter

  • Stefan Klöppel
  • Tobias Bäumer
  • Johan Kroeger
  • Martin A. Koch
  • Christian Büchel
  • Alexander Münchau
  • Hartwig R. Siebner

Transcranial magnetic stimulation (TMS) can be used to probe distinct aspects of excitability of the primary motor hand area (M1Hand). The motor threshold (MT) reflects the trans-synaptic excitability of corticospinal output neurons. The MT corresponds to the minimal intensity at which TMS evokes a contralateral motor response. Here, we employed diffusion-weighted imaging (DWI) to examine whether inter-individual differences in MT of the left and right M1Hand, an index of cortical excitability, are associated with variations in fractional anisotropy (FA), an index of white matter microstructure. Resting and active MT showed an inverse linear relationship with regional FA values in large bihemispheric clusters, including the white matter underlying primary motor, premotor and posterior prefrontal cortices, as well as the genu of the internal capsule, cerebral peduncles and corpus callosum. The linear increase in FA with cortical excitability as indexed by the MT remained significant after controlling for differences in handedness or coil–cortex distance. The posterior limb of the internal capsule, where fast-conducting corticospinal fibres from M1Hand pass through, showed only a weak linear relationship between FA and MT. The FA measurements show that a high level of corticospinal excitability is associated with a higher fibre coherence in large parts of cerebral white matter. The higher FA values in the white matter beneath premotor and motor cortices may reflect a structural property of cortico-cortical connections that renders M1Hand more susceptible to TMS-induced trans-synaptic excitation of the corticospinal fibres and may account for the inverse linear relationship between MT and FA.

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YNIMG Journal 2007 Journal Article

Dynamic causal modeling: A generative model of slice timing in fMRI

  • Stefan J. Kiebel
  • Stefan Klöppel
  • Nikolaus Weiskopf
  • Karl J. Friston

Dynamic causal modeling (DCM) of functional magnetic resonance imaging (fMRI) data allows one to make inferences about the architecture of distributed networks in the brain, in terms of effective connectivity. fMRI data are usually acquired using echo planar imaging (EPI). EPI sequences typically acquire slices at different times over a few seconds. DCM, in its original inception, was not informed about these slice timings and assumed that all slices were acquired simultaneously. It has been shown that DCM can cope with slice timing differences of up to 1 s. However, many fMRI studies employ a repetition time (TR) of 3 to 5 s, which precludes a straightforward DCM of these data. We show that this limitation can be overcome easily by including slice timing in the DCM. Using synthetic data we show that the extended DCM furnishes veridical posterior means, even if there are large slice-timing differences. Model comparisons show that, in general, the extended DCM out-performs the original model. We contrast the modeling of slice timing, in the context of DCM, with the less effective approach of ‘slice-timing correction’, prior to modeling. We apply our procedure to real data and show that slice timings are important parameters. We conclude that, generally, one should use DCM with slice timing.

Details DOI

YNIMG Journal 2007 Journal Article

The effect of handedness on cortical motor activation during simple bilateral movements

  • Stefan Klöppel
  • Thilo van Eimeren
  • Volkmar Glauche
  • Anna Vongerichten
  • Alexander Münchau
  • Richard S.J. Frackowiak
  • Christian Büchel
  • Cornelius Weiller

The neuronal correlates of handedness are still poorly understood. Here we used event-related functional magnetic resonance imaging to investigate the impact of handedness on neuronal activation of the primary sensorimotor cortex, supplementary motor area and dorsal premotor cortex during simple unilateral and bilateral finger movements. In 16 right-handed and 16 left-handed individuals, we mapped changes in regional neuronal activity while participants responded to four symbolic cues presented in a pseudorandom order. According to pre-specified cues, they pressed a button with their right, left or both index fingers or withheld a response. For unilateral right index finger button presses, reaction times, motor and premotor activity were the same for both right- and left-handers. Compared with right-handers, left-handers had shorter reaction times with unilateral left index finger button presses, along with greater activation of the supplementary motor area and right frontal opercular cortex. Simultaneous bilateral compared with unilateral button presses led to a relative increase of activity in the right and left dorsal premotor cortex and the right primary sensorimotor cortex in right but not left-handers. Neither right nor left-handers showed any tendency during bilateral button presses towards faster responses with the dominant hand and the reaction times were equal in the two groups. Therefore, we conclude that the relative increase of activity in dorsal premotor and right primary sensorimotor cortices in right-handers represents a genuine difference in bimanual motor control related to handedness.

Details DOI
v2026.09.13