Jane Neumann

YNICL Journal 2019 Journal Article

Atrophy in midbrain & cerebral/cerebellar pedunculi is characteristic for progressive supranuclear palsy – A double-validation whole-brain meta-analysis

• Franziska Albrecht
• Sandrine Bisenius
• Jane Neumann
• Jennifer Whitwell
• Matthias L. Schroeter

OBJECTIVE: Progressive supranuclear palsy (PSP) is an atypical parkinsonian syndrome characterized by vertical gaze palsy and postural instability. Midbrain atrophy is suggested as a hallmark, but it has not been validated systematically in whole-brain imaging. METHODS: We conducted whole-brain meta-analyses identifying disease-related atrophy in structural MRI. Eighteen studies were identified (N = 315 PSP, 393 controls) and separated into gray or white matter analyses (15/12). All patients were diagnosed according to the National Institute of Neurological Disorders and Stroke and the Society for PSP (NINDS-SPSP criteria, Litvan et al. (1996a)), which are now considered as PSP-Richardson syndrome (Höglinger et al., 2017). With overlay analyses, we double-validated two meta-analytical algorithms: anatomical likelihood estimation and seed-based D mapping. Additionally, we conducted region-of-interest effect size meta-analyses on radiological biomarkers and subtraction analyses differentiating PSP from Parkinson's disease. RESULTS: Whole brain meta-analyses revealed consistent gray matter atrophy in bilateral thalamus, anterior insulae, midbrain, and left caudate nucleus. White matter alterations were consistently detected in bilateral superior/middle cerebellar pedunculi, cerebral pedunculi, and midbrain atrophy. Region-of-interest meta-analyses demonstrated that midbrain metrics generally perform very well in distinguishing PSP from other parkinsonian syndromes with strong effect sizes. Subtraction analyses identified the midbrain as differentiating between PSP and Parkinson's disease. CONCLUSIONS: Our meta-analyses identify gray matter atrophy of the midbrain and white matter atrophy of the cerebral/cerebellar pedunculi and midbrain as characteristic for PSP. Results support the incorporation of structural MRI data, and particularly these structures, into the revised PSP diagnostic criteria.

YNICL Journal 2019 Journal Article

FDG-PET hypometabolism is more sensitive than MRI atrophy in Parkinson's disease: A whole-brain multimodal imaging meta-analysis

• Franziska Albrecht
• Tommaso Ballarini
• Jane Neumann
• Matthias L. Schroeter

Recently, revised diagnostic criteria for Parkinson's disease (PD) were introduced (Postuma et al., 2015). Yet, except for well-established dopaminergic imaging, validated imaging biomarkers for PD are still missing, though they could improve diagnostic accuracy. We conducted systematic meta-analyses to identify PD-specific markers in whole-brain structural magnetic resonance imaging (MRI), [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET) and diffusion tensor imaging (DTI) studies. Overall, 74 studies were identified including 2323 patients and 1767 healthy controls. Studies were first grouped according to imaging modalities (MRI 50; PET 14; DTI 10) and then into subcohorts based on clinical phenotypes. To ensure reliable results, we combined established meta-analytical algorithms - anatomical likelihood estimation and seed-based D mapping - and cross-validated them in a conjunction analysis. Glucose hypometabolism was found using FDG-PET extensively in bilateral inferior parietal cortex and left caudate nucleus with both meta-analytic methods. This hypometabolism pattern was confirmed in subcohort analyses and related to cognitive deficits (inferior parietal cortex) and motor symptoms (caudate nucleus). Structural MRI showed only small focal gray matter atrophy in the middle occipital gyrus that was not confirmed in subcohort analyses. DTI revealed fractional anisotropy reductions in the cingulate bundle near the orbital and anterior cingulate gyri in PD. Our results suggest that FDG-PET reliably identifies consistent functional brain abnormalities in PD, whereas structural MRI and DTI show only focal alterations and rather inconsistent results. In conclusion, FDG-PET hypometabolism outperforms structural MRI in PD, although both imaging methods do not offer disease-specific imaging biomarkers for PD.

YNIMG Journal 2015 Journal Article

Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data

• Claudia Grellmann
• Sebastian Bitzer
• Jane Neumann
• Lars T. Westlye
• Ole A. Andreassen
• Arno Villringer
• Annette Horstmann

The standard analysis approach in neuroimaging genetics studies is the mass-univariate linear modeling (MULM) approach. From a statistical view, however, this approach is disadvantageous, as it is computationally intensive, cannot account for complex multivariate relationships, and has to be corrected for multiple testing. In contrast, multivariate methods offer the opportunity to include combined information from multiple variants to discover meaningful associations between genetic and brain imaging data. We assessed three multivariate techniques, partial least squares correlation (PLSC), sparse canonical correlation analysis (sparse CCA) and Bayesian inter-battery factor analysis (Bayesian IBFA), with respect to their ability to detect multivariate genotype–phenotype associations. Our goal was to systematically compare these three approaches with respect to their performance and to assess their suitability for high-dimensional and multi-collinearly dependent data as is the case in neuroimaging genetics studies. In a series of simulations using both linearly independent and multi-collinear data, we show that sparse CCA and PLSC are suitable even for very high-dimensional collinear imaging data sets. Among those two, the predictive power was higher for sparse CCA when voxel numbers were below 400 times sample size and candidate SNPs were considered. Accordingly, we recommend Sparse CCA for candidate phenotype, candidate SNP studies. When voxel numbers exceeded 500 times sample size, the predictive power was the highest for PLSC. Therefore, PLSC can be considered a promising technique for multivariate modeling of high-dimensional brain–SNP-associations. In contrast, Bayesian IBFA cannot be recommended, since additional post-processing steps were necessary to detect causal relations. To verify the applicability of sparse CCA and PLSC, we applied them to an experimental imaging genetics data set provided for us. Most importantly, application of both methods replicated the findings of this data set.

YNIMG Journal 2015 Journal Article

Identifying neural correlates of visual consciousness with ALE meta-analyses

• Sandrine Bisenius
• Sabrina Trapp
• Jane Neumann
• Matthias L. Schroeter

Neural correlates of consciousness (NCC) have been a topic of study for nearly two decades. In functional imaging studies, several regions have been proposed to constitute possible candidates for NCC, but as of yet, no quantitative summary of the literature on NCC has been done. The question whether single (striate or extrastriate) regions or a network consisting of extrastriate areas that project directly to fronto-parietal regions are necessary and sufficient neural correlates for visual consciousness is still highly debated [e. g. , Rees et al. , 2002, Nat Rev. Neurosci 3, 261–270; Tong, 2003, Nat Rev. Neurosci 4, 219–229]. The aim of this work was to elucidate this issue and give a synopsis of the present state of the art by conducting systematic and quantitative meta-analyses across functional magnetic resonance imaging (fMRI) studies using several standard paradigms for conscious visual perception. In these paradigms, consciousness is operationalized via perceptual changes, while the visual stimulus remains invariant. An activation likelihood estimation (ALE) meta-analysis was performed, representing the best approach for voxel-wise meta-analyses to date. In addition to computing a meta-analysis across all paradigms, separate meta-analyses on bistable perception and masking paradigms were conducted to assess whether these paradigms show common or different NCC. For the overall meta-analysis, we found significant clusters of activation in inferior and middle occipital gyrus; fusiform gyrus; inferior temporal gyrus; caudate nucleus; insula; inferior, middle, and superior frontal gyri; precuneus; as well as in inferior and superior parietal lobules. These results suggest a subcortical-extrastriate-fronto-parietal network rather than a single region that constitutes the necessary NCC. The results of our exploratory paradigm-specific meta-analyses suggest that this subcortical-extrastriate-fronto-parietal network might be differentially activated as a function of the paradigms used to probe for NCC.

YNIMG Journal 2014 Journal Article

Response inhibition and its relation to multidimensional impulsivity

• Tilmann Wilbertz
• Lorenz Deserno
• Annette Horstmann
• Jane Neumann
• Arno Villringer
• Hans-Jochen Heinze
• Carsten N. Boehler
• Florian Schlagenhauf

Impulsivity is a multidimensional construct that has been suggested as a vulnerability factor for several psychiatric disorders, especially addiction disorders. Poor response inhibition may constitute one facet of impulsivity. Trait impulsivity can be assessed by self-report questionnaires such as the widely used Barratt Impulsiveness Scale (BIS-11). However, regarding the multidimensionality of impulsivity different concepts have been proposed, in particular the UPPS self-report questionnaire (‘Urgency’, ‘Lack of Premeditation’, ‘Lack of Perseverance’, ‘Sensation Seeking’) that is based on a factor analytic approach. The question as to which aspects of trait impulsivity map on individual differences of the behavioral and neural correlates of response inhibition so far remains unclear. In the present study, we investigated 52 healthy individuals that scored either very high or low on the BIS-11 and underwent a reward-modulated Stop-signal task during fMRI. Neither behavioral nor neural differences were observed with respect to high- and low-BIS groups. In contrast, UPPS subdomain Urgency best explained inter-individual variability in SSRT scores and was further negatively correlated to right IFG/aI activation in 'Stop>Go’ trials — a key region for response inhibition. Successful response inhibition in rewarded compared to nonrewarded stop trials yielded ventral striatal (VS) activation which might represent a feedback signal. Interestingly, only participants with low Urgency scores were able to use this VS feedback signal for better response inhibition. Our findings indicate that the relationship of impulsivity and response inhibition has to be treated carefully. We propose Urgency as an important subdomain that might be linked to response inhibition as well as to the use of reward-based neural signals. Based on the present results, further studies examining the influence of impulsivity on psychiatric disorders should take into account Urgency as an important modulator of behavioral adaptation.

YNIMG Journal 2013 Journal Article

Corrigendum to “Continuous theta-burst stimulation (cTBS) over the lateral prefrontal cortex alters reinforcement learning bias” [Neuroimage 57 (2) (2011) 617–623]

• Derek V.M. Ott
• Markus Ullsperger
• Gerhard Jocham
• Jane Neumann
• Tilmann A. Klein

YNIMG Journal 2012 Journal Article

Cortico-subthalamic white matter tract strength predicts interindividual efficacy in stopping a motor response

• Birte U. Forstmann
• Max C. Keuken
• Sara Jahfari
• Pierre-Louis Bazin
• Jane Neumann
• Andreas Schäfer
• Alfred Anwander
• Robert Turner

The subthalamic nucleus (STN) is a small but vitally important structure in the basal ganglia. Because of its small volume, and its localization in the basal ganglia, the STN can best be visualized using ultra-high resolution 7 Tesla (T) magnetic resonance imaging (MRI). In the present study, first we individually segmented 7T MRI STN masks to generate atlas probability maps. Secondly, the individually segmented STN masks and the probability maps were used to derive cortico-subthalamic white matter tract strength. Tract strength measures were then taken to test two functional STN hypotheses which account for the efficiency in stopping a motor response: the right inferior fronto-subthalamic (rIFC-STN) hypothesis and the posterior medial frontal cortex-subthalamic (pMFC-STN) hypothesis. Results of two independent experiments show that increased white matter tract strength between the pMFC and STN results in better stopping behaviour.

YNIMG Journal 2011 Journal Article

Continuous theta-burst stimulation (cTBS) over the lateral prefrontal cortex alters reinforcement learning bias

• Derek V.M. Ott
• Markus Ullsperger
• Gerhard Jocham
• Jane Neumann
• Tilmann A. Klein

The prefrontal cortex is known to play a key role in higher-order cognitive functions. Recently, we showed that this brain region is active in reinforcement learning, during which subjects constantly have to integrate trial outcomes in order to optimize performance. To further elucidate the role of the dorsolateral prefrontal cortex (DLPFC) in reinforcement learning, we applied continuous theta-burst stimulation (cTBS) either to the left or right DLPFC, or to the vertex as a control region, respectively, prior to the performance of a probabilistic learning task in an fMRI environment. While there was no influence of cTBS on learning performance per se, we observed a stimulation-dependent modulation of reward vs. punishment sensitivity: Left-hemispherical DLPFC stimulation led to a more reward-guided performance, while right-hemispherical cTBS induced a more avoidance-guided behavior. FMRI results showed enhanced prediction error coding in the ventral striatum in subjects stimulated over the left as compared to the right DLPFC. Both behavioral and imaging results are in line with recent findings that left, but not right-hemispherical stimulation can trigger a release of dopamine in the ventral striatum, which has been suggested to increase the relative impact of rewards rather than punishment on behavior.

YNIMG Journal 2011 Journal Article

Exploring functional relations between brain regions from fMRI meta-analysis data: Comments on Ramsey, Spirtes, and Glymour

• Jane Neumann
• Robert Turner
• Peter T. Fox
• Gabriele Lohmann

In this paper, we address the critical assessment of Ramsey et al. of our method for learning partially directed graphs from meta-analysis imaging data (Neumann et al. , 2010). We argue that our method provides valid and interpretable results when applied to data representing a single experimental paradigm. Simulations further suggest that, despite theoretical limitations, the application of our method to mixed probability distributions yields reliable results with error rates at acceptable levels. Finally, we discuss the nature of meta-analysis data and the notion of causality in the context of functional neuroimaging.

YNIMG Journal 2010 Journal Article

Learning partially directed functional networks from meta-analysis imaging data

• Jane Neumann
• Peter T. Fox
• Robert Turner
• Gabriele Lohmann

We propose a new exploratory method for the discovery of partially directed functional networks from fMRI meta-analysis data. The method performs structure learning of Bayesian networks in search of directed probabilistic dependencies between brain regions. Learning is based on the co-activation of brain regions observed across several independent imaging experiments. In a series of simulations, we first demonstrate the reliability of the method. We then present the application of our approach in an extensive meta-analysis including several thousand activation coordinates from more than 500 imaging studies. Results show that our method is able to automatically infer Bayesian networks that capture both directed and undirected probabilistic dependencies between a number of brain regions, including regions that are frequently observed in motor-related and cognitive control tasks.

YNIMG Journal 2009 Journal Article

Neural correlates of Alzheimer's disease and mild cognitive impairment: A systematic and quantitative meta-analysis involving 1351 patients

• Matthias L. Schroeter
• Timo Stein
• Nina Maslowski
• Jane Neumann

Alzheimer's disease is the most common form of dementia. Its prodromal stage amnestic mild cognitive impairment is characterized by deficits of anterograde episodic memory. The development of standardized imaging inclusion criteria has to be regarded as a prerequisite for future diagnostic systems. Moreover, successful treatment requires isolating imaging markers predicting the disease. Accordingly, we conducted a systematic and quantitative meta-analysis to reveal the prototypical neural correlates of Alzheimer's disease and its prodromal stage. To prevent any a priori assumptions and enable a data-driven approach only studies applying quantitative automated whole brain analysis were included. Finally, 40 studies were identified involving 1351 patients and 1097 healthy control subjects reporting either atrophy or decreases in glucose utilization and perfusion. The currently most sophisticated and best-validated of coordinate-based voxel-wise meta-analyses was applied (anatomical likelihood estimates). The meta-analysis reveals that early Alzheimer's disease affects structurally the (trans-)entorhinal and hippocampal regions, functionally the inferior parietal lobules and precuneus. Results further may suggest that atrophy in the (trans-)entorhinal area/hippocampus and hypometabolism/hypoperfusion in the inferior parietal lobules predicts most reliably the progression from amnestic mild cognitive impairment to Alzheimer's disease, whereas changes in the posterior cingulate cortex and precuneus are unspecific. Fully developed Alzheimer's disease involved additionally a frontomedian-thalamic network. In conclusion, the meta-analysis characterizes the prototypical neural substrates of Alzheimer's disease and its prodromal stage amnestic mild cognitive impairment. By isolating predictive markers it enables successful treatment strategies in the future and contributes to standardized imaging inclusion criteria for Alzheimer's disease as suggested for future diagnostic systems.

YNIMG Journal 2008 Journal Article

Temporal dynamics of perisylvian activation during language processing in children and adults

• Jens Brauer
• Jane Neumann
• Angela D. Friederici

The perisylvian region of the human cortex is known to play a major role in language processing. Especially the superior temporal cortex (STC) and the inferior frontal cortex (IFC) have been investigated with respect to their particular involvement in language comprehension. In the present research, the timing of recruitment of these language-related brain areas in both hemispheres was examined as a function of age using functional imaging data of 6-year-old children and adults with a special focus on blood oxygenation level dependent (BOLD) response time courses. The results show that children's activation time courses differ from that of adults. First, children show an overall later peak of BOLD responses. Second, children's IFC responds much later than their STC, while in adults the difference between both regions is less pronounced. Within the STC, both groups show similar regionally U-shaped activation patterns with fastest peaks in voxels at the STC's mid-portion around Heschl's gyrus and longer latencies in anterior and posterior directions, suggesting a coarsely similar information flow in adults and children in the temporal region. Finally, children in contrast to adults, display a temporal primacy of right over left hemispheric activation. The observed overall latency differences between children and adults are in line with the assumption of ongoing maturation in perisylvian brain regions and the connections between them. A functional perspective on BOLD timing argues for a developmental change from higher processing costs in children compared to adults due to slower and less automatic language processes, in particular those located in the IFC. The observed hemispheric differences are discussed in the context of developmental models assuming a high reliance on right-hemisphere-based suprasegmental information processing during language comprehension in childhood.

YNIMG Journal 2007 Journal Article

Neural correlates of error awareness

• Tilmann A. Klein
• Tanja Endrass
• Norbert Kathmann
• Jane Neumann
• D. Yves von Cramon
• Markus Ullsperger

Error processing results in a number of consequences on multiple levels. The posterior frontomedian cortex (pFMC) is involved in performance monitoring and signalling the need for adjustments, which can be observed as post-error speed-accuracy shifts at the behavioural level. Furthermore autonomic reactions to an error have been reported. The role of conscious error awareness for this processing cascade has received little attention of researchers so far. We examined the neural correlates of conscious error perception in a functional magnetic resonance imaging (fMRI) study. An antisaccade task known to yield sufficient numbers of aware and unaware errors was used. Results from a metaanalysis were used to guide a region of interest (ROI) analysis of the fMRI data. Consistent with previous reports, error-related activity in the rostral cingulate zone (RCZ), the pre-supplementary motor area (pre-SMA) and the insular cortex bilaterally was found. Whereas the RCZ activity did not differentiate between aware and unaware errors, activity in the left anterior inferior insular cortex was stronger for aware as compared to unaware errors. This could be due to increased awareness of the autonomic reaction to an error, or the increased autonomic reaction itself. Furthermore, post-error adjustments were only observed after aware errors and a correlation between post-error slowing and the hemodynamic activity in the RCZ was revealed. The data suggest that the RCZ activity alone is insufficient to drive error awareness. Its signal appears to be useful for post-error speed-accuracy adjustments only when the error is consciously perceived.

YNIMG Journal 2007 Journal Article

Towards a nosology for frontotemporal lobar degenerations—A meta-analysis involving 267 subjects

• Matthias L. Schroeter
• Karolina Raczka
• Jane Neumann
• D. Yves von Cramon

Frontotemporal lobar degeneration is the second most common diagnosis of dementia in individuals younger than 65 years. We conducted a systematic and quantitative meta-analysis to examine neural correlates of frontotemporal lobar degeneration and its subtypes and to place the disease in a framework of cognitive neuropsychiatry. MedLine and Current Contents search engines were used to identify functional and anatomical imaging studies investigating frontotemporal lobar degeneration between 1980 and 2005. Studies were included, if they were peer-reviewed, applied internationally recognized diagnostic criteria, were original studies, and had results normalized to a stereotactic space. 19 studies were identified reporting either atrophy or decreases in glucose utilization. Finally, the analysis involved 267 subjects suffering from frontotemporal lobar degeneration and 351 control subjects. A quantitative meta-analysis was performed. Maxima of the studies resulted in activation likelihood estimates. The meta-analysis revealed specific neural networks for each of the three clinically defined subtypes of frontotemporal lobar degeneration, namely frontotemporal dementia, semantic dementia, and progressive non-fluent aphasia. Networks did not overlap as shown by a conjunction analysis, and they corresponded to clinical characteristics. The study relates the clinical features of each subtype of frontotemporal lobar degeneration specifically to its neural substrate. By ‘triple dissociating’ frontotemporal lobar degenerations into three clinicoanatomical prototypes, the study contributes to placing these disorders in cognitive neuropsychiatry and suggests a respective nosology.

YNIMG Journal 2006 Journal Article

The neural implementation of multi-attribute decision making: A parametric fMRI study with human subjects

• Stefan Zysset
• Cornelia S. Wendt
• Kirsten G. Volz
• Jane Neumann
• Oswald Huber
• D. Yves von Cramon

Decision making is not a unitary entity but involves rather a series of interdependent processes. Decisions entail a choice between two or more alternatives. Within the complex series of decisional processes, at least two levels can be differentiated: a first level of information integration (process level) and a second level of information interpretation (control level), leading to a subsequent motor response or cognitive process. The aim of this study was to investigate the neural network of these decisional processes. In a single trial fMRI study, we implemented a simple decision-making task, where subjects had to decide between two alternatives represented on five attributes. The similarity between the two alternatives was varied systematically in order to achieve a parametric variation of decisional effort. For easy trials, the two alternatives differed significantly in several attributes, whereas for difficult trials, the two alternatives differed only in small details. The results show a distributed neural network related to decisional effort. By means of time course analysis different subprocesses within this network could be differentiated: regions subserving the integration of the presented information (premotor areas and superior parietal lobe) and regions subserving the interpretation of this information (frontolateral and frontomedial cortex, anterior insula, and caudate) as well as a region in the inferior frontal junction updating task rules.

YNIMG Journal 2006 Journal Article

The parcellation of cortical areas using replicator dynamics in fMRI

• Jane Neumann
• D. Yves von Cramon
• Birte U. Forstmann
• Stefan Zysset
• Gabriele Lohmann

In this paper, we show that replicator dynamics can be used as an exploratory analysis tool to detect subregions of cortical areas on the basis of the similarity between fMRI time series. As similarity measure, we propose to use canonical correlation, a multivariate extension to the typically employed Pearson's correlation coefficient. We applied the replicator process to data obtained from two different experimental paradigms in the search for subregions within the left lateral frontal cortex (LFC). In both cases, the replicator process resulted in a parcellation that corresponds to a recently suggested subdivision of the LFC in anterior–posterior direction. Most notably, these results were very consistent when compared across different measurements of a single subject and across a group of subjects.

YNIMG Journal 2006 Journal Article

Variability of the BOLD response over time: An examination of within-session differences

• Mareike M. Menz
• Jane Neumann
• Karsten Müller
• Stefan Zysset

Model-based analysis methods for fMRI data assume a priori knowledge of the time course of the hemodynamic response (HR) in reaction to experimental stimuli or events. This knowledge is incorporated into the hemodynamic response function (HRF), which is a common model of the HR. Although it is already known that the HR varies across individuals and brain regions, few studies have investigated how variations within one session affect the results of statistical analysis using the general linear model (GLM). In this study, we formally tested for a possible variation of the BOLD response during prolonged functional measurement (120 min). To provoke performance of simple visual, motor, and cognitive tasks, we opted for a combination of a variant of the Stroop task and rotating L’s. In selected regions of interest, time courses were extracted and compared with regard to mean and maximum amplitudes throughout the time of functional measurement. Additionally, parameter estimates derived from the GLM were tested for differences over time. Although differences between conditions were found to be significant, results did not show significant variance due to a within-factor time. Similarly, a temporal change in the relation between conditions, in terms of an interaction between the within-factor time and the within-factor condition, was not detectable by a repeated measures ANOVA. Similar results were obtained for analysis of mean and maximum amplitudes as well as for the analyses of parameter estimates.

YNIMG Journal 2003 Journal Article

Bayesian second-level analysis of functional magnetic resonance images

• Jane Neumann
• Gabriele Lohmann

We propose a new method for the second-level analysis of functional MRI data based on Bayesian statistics. Our method does not require a computationally costly Bayesian model on the first level of analysis. Rather, modeling for single subjects is realized by means of the commonly applied General Linear Model. On the basis of the resulting parameter estimates for single subjects we calculate posterior probability maps and maps of the effect size for effects of interest in groups of subjects. A comparison of this method with the conventional analysis based on t statistics shows that the new approach is more robust against outliers. Moreover, our method overcomes some of the severe problems of null hypothesis significance tests such as the need to correct for multiple comparisons and facilitates inferences which are hard to formulate in terms of classical inferences.

YNIMG Journal 2003 Journal Article

Within-subject variability of BOLD response dynamics

• Jane Neumann
• Gabriele Lohmann
• Stefan Zysset
• D.Yves von Cramon

In this paper we investigate the within-subject variability of dynamical aspects of the BOLD response obtained in a series of fMRI sessions several days apart. Five different parameters describing the temporal behavior of trial-averaged time courses, such as time-to-peak and time-to-onset, were estimated and analyzed with respect to their variability across nine sessions. Results show that small variances of the estimated parameters can be found, provided that the analysis is restricted to voxels activated in all individual sessions. Among the investigated parameters, time-to-peak shows the most stable behavior. These results were obtained using two different analysis methods, the estimation of the parameters directly from trial-averaged time courses and fitting trial-averaged time courses to an assumed hemodynamic response function. Both methods yield comparable results.