Biomedical subjects

Jane Neumann

Publications and source records attributed to Jane Neumann.

11 recordsLinked to original sources

PubMed2006

Neural correlates of error awareness.

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.

Adult↗

PubMed2006

Neural networks in frontotemporal dementia--a meta-analysis.

OBJECTIVE: Frontotemporal dementia is the most common form of frontotemporal lobar degeneration. It is characterized by deep alterations in behavior and personality. We conducted a systematic and quantitative meta-analysis to examine its neural correlates and place the disease in a framework of cognitive neuropsychiatry. METHODS: MedLine and Current Contents search engines were used to identify imaging studies investigating frontotemporal dementia between 1980 and 2005. Nine studies were identified reporting either atrophy or decreases in glucose utilization. Finally, the analysis involved 132 patients and 166 controls. A quantitative meta-analysis was performed. Maxima of the studies resulted in activation likelihood estimates. RESULTS: The meta-analysis revealed a particularly frontomedian network impaired in frontotemporal dementia. Additionally, right anterior insula, and medial thalamus were identified. CONCLUSIONS: Our study specifies frontotemporal dementia as the frontomedian variant of frontotemporal lobar degeneration. The disease affects neural networks enabling self-monitoring, theory of mind capabilities, processing/evaluation of internal mental states, perception of pain and emotions, and sustaining personality and self. Our study contributes to placing frontotemporal dementia in cognitive neuropsychiatry.

Aged↗

PubMed2006

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

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.

Adult↗

PubMed2006

The parcellation of cortical areas using replicator dynamics in fMRI.

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.

Brain Mapping↗

PubMed2006

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

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.

Adult↗

PubMed2005

Involvement of the inferior frontal junction in cognitive control: meta-analyses of switching and Stroop studies.

There is growing evidence that a specific region in the posterior frontolateral cortex is involved intimately in cognitive control processes. This region, located in the vicinity of the junction of the inferior frontal sulcus and the inferior precentral sulcus, was termed the inferior frontal junction (IFJ). The IFJ was shown to be involved in the updating of task representations and to be activated commonly in a within-subject investigation of a task-switching paradigm, the Stroop task, and a verbal n-back task. Here, we investigate the involvement of the IFJ in cognitive control by employing a meta-analytic approach. Two quantitative meta-analyses of functional magnetic resonance imaging (fMRI) studies were conducted. One meta-analysis included frontal activations from task-switching, set-shifting, and stimulus-response (S-R) reversal studies, the other included frontal activations from color-word Stroop studies. Results showed highly significant clustering of activations in the IFJ in both analyses. These results provide strong evidence for the consistent involvement of the IFJ in both switching and Stroop paradigms. Furthermore, they support our concept of areal specialization in the frontolateral cortex, which posits that it is not only the middorsolateral part that plays an important role in cognitive control, but also the IFJ. Finally, our results demonstrate how quantitative meta-analyses can be used to test hypotheses about the involvement of specific brain regions in cognitive control.

Brain Mapping↗

PubMed2005

Meta-analysis of functional imaging data using replicator dynamics.

Despite the rapidly growing number of meta-analyses in functional neuroimaging, the field lacks formal mathematical tools for the quantitative and qualitative evaluation of meta-analytic data. We propose to use replicator dynamics in the meta-analysis of functional imaging data to address an important aspect of neuroimaging research, the search for functional networks of cortical areas that underlie a specific cognitive task. The replicator process requires as input only a list of activation locations, and it results in a network of locations that jointly show significant activation in most studies included in the meta-analysis. These locations are likely to play a critical role in solving the investigated cognitive task. Our method was applied to a meta-analysis of the Stroop interference task using data provided by the publicly accessible database BrainMap DBJ.

Brain↗

PubMed2005

The correlation between blood oxygenation level-dependent signal strength and latency.

PURPOSE: To investigate the relationship between signal strength and latency of the blood oxygenation level-dependent (BOLD) signal. MATERIALS AND METHODS: Several correlation analyses were performed on data obtained in a functional magnetic resonance imaging (fMRI) experiment, where subjects were presented with a simple visual stimulus. The BOLD signal strength was correlated with both the phase shift of the spectral density matrix and time-to-peak calculated from trial-averaged time courses. Correlation coefficients were calculated for visual stimuli of 2, 6, and 15 seconds in duration. RESULTS: Analyzing all functional runs for the same subject separately, i.e., including for each run all significantly activated voxels, we observed that correlations between phase shift and signal strength, as well as between time-to-peak and signal strength, decreased with increasing stimulus length. However, when analyses were restricted to voxels found activated in all functional runs, we observed similar correlations between BOLD signal strength and latency in all runs, independent of the length of stimulation. This result was again obtained for both latency measures: the spectral density phase shift and time-to-peak. CONCLUSION: For both latency measures, phase shift and time-to-peak, a high correlation between BOLD signal strength and latency was observed. We have shown that this correlation is independent of the length of visual stimulation. Thus, the correlation between BOLD signal strength and latency seems to be an inherent property of the BOLD response that is independent of the length of stimulation and can be observed using different methods for determining signal latency.

Adult↗

PubMed2004

Investigating the wavelet coherence phase of the BOLD signal.

PURPOSE: To investigate the temporal behavior of the blood oxygenation-level dependent (BOLD) signal applying the wavelet coherence phase to functional magnetic resonance imaging (fMRI) data. MATERIALS AND METHODS: The wavelet coherence phase was computed for a group of four subjects using three functional runs of different visual stimulation lengths. In order to consider the variability of the wavelet coherence phase, a correlation analysis was performed between the variance of the phase shift and the associated activation strength. In activated brain regions, correlation was performed between the mean wavelet coherence phase and the spectral density phase shift. RESULTS: The scalograms of the wavelet coherence phase show the temporal variability of the phase shift between fMRI time series. For brain regions with weakly stationary behavior, the mean wavelet coherence phase supports the results obtained by spectral analyses. CONCLUSION: The wavelet coherence phase provides a description of the temporal behavior of the BOLD signal even for the nonstationary case. In particular, temporal changes of the phase shift can be investigated. This makes the wavelet coherence phase more suitable for the investigation of BOLD dynamics than an average phase lag obtained by correlation or spectral methods.

Adult↗

PubMed2003

Within-subject variability of BOLD response dynamics.

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.

Adult↗

PubMed2003

Bayesian second-level analysis of functional magnetic resonance images.

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.

Algorithms↗