BOLD fMRI Data Research Articles

Error-related anterior cingulate cortex activity and the prediction of conscious error awareness

Research examining the neural mechanisms associated with error awareness has consistently identified dorsal anterior cingulate cortex (ACC) activity as necessary but not predictive of conscious error detection. Two recent studies (Steinhauser and Yeung, 2010; Wessel et al., 2011) have found a contrary pattern of greater dorsal ACC (dACC) activity [in the form of the error-related negativity (ERN)] during detected errors, but suggested that the greater activity may instead reflect task influences (e.g., response conflict, error probability) and or individual variability (e.g., statistical power). We re-analyzed fMRI BOLD data from 56 healthy participants who had previously been administered the Error Awareness Task (EAT), a motor Go/No-go response inhibition task in which subjects make errors of commission of which they are aware (Aware errors), or unaware (Unaware errors). Consistent with previous data, the activity in a number of cortical regions was predictive of error awareness, including bilateral inferior parietal and insula cortices, however, in contrast to previous studies, including our own smaller sample studies using the same task, error-related dACC activity was significantly greater during aware errors when compared to unaware errors. While the significantly faster RT for aware errors (compared to unaware) was consistent with the hypothesis of higher response conflict increasing ACC activity, we could find no relationship between dACC activity and the error RT difference. The data suggests that error awareness is associated with error-related dACC activity but that the role of this activity is probably best understood in relation to the activity in other regions. Activity in the dACC may be important to conscious error detection, but it remains unclear what task and individual factors influence error awareness.

The utility of near-infrared spectroscopy in the regression of low-frequency physiological noise from functional magnetic resonance imaging data

Near-infrared spectroscopy (NIRS) signals have been shown to correlate with resting-state BOLD-fMRI data across the whole brain volume, particularly at frequencies below 0.1Hz. While the physiological origins of this correlation remain unclear, its existence may have a practical application in minimizing the background physiological noise present in BOLD-fMRI recordings. We performed simultaneous, resting-state fMRI and 28-channel NIRS in seven adult subjects in order to assess the utility of NIRS signals in the regression of physiological noise from fMRI data. We calculated the variance of the residual error in a general linear model of the baseline fMRI signal, and the reduction of this variance achieved by including NIRS signals in the model. In addition, we introduced a sequence of simulated hemodynamic response functions (HRFs) into the resting-state fMRI data of each subject in order to quantify the effectiveness of NIRS signals in optimizing the recovery of that HRF. For comparison, these calculations were also performed using a pulse and respiration RETROICOR model. Our results show that the use of 10 or more NIRS channels can reduce variance in the residual error by as much as 36% on average across the whole cortex. However the same number of low-pass filtered white noise regressors is shown to produce a reduction of 19%. The RETROICOR model obtained a variance reduction of 6.4%. Our HRF simulation showed that the mean-squared error (MSE) between the recovered and true HRFs is reduced by 21% on average when 10 NIRS channels are applied and by introducing an optimized time lag between the NIRS and fMRI time series, a single NIRS channel can provide an average MSE reduction of 14%. The RETROICOR model did not provide a significant change in MSE. By each of the metrics calculated, NIRS recording is shown to be of significant benefit to the regression of low-frequency physiological noise from fMRI data.

Network modulation during complex syntactic processing

Complex sentence processing is supported by a left-lateralized neural network including inferior frontal cortex and posterior superior temporal cortex. This study investigates the pattern of connectivity and information flow within this network. We used fMRI BOLD data derived from 12 healthy participants reported in an earlier study (Thompson, C. K., Den Ouden, D. B., Bonakdarpour, B., Garibaldi, K., & Parrish, T. B. (2010b). Neural plasticity and treatment-induced recovery of sentence processing in agrammatism. Neuropsychologia, 48(11), 3211-3227) to identify activation peaks associated with object-cleft over syntactically less complex subject-cleft processing. Directed Partial Correlation Analysis was conducted on time series extracted from participant-specific activation peaks and showed evidence of functional connectivity between four regions, linearly between premotor cortex, inferior frontal gyrus, posterior superior temporal sulcus and anterior middle temporal gyrus. This pattern served as the basis for Dynamic Causal Modeling of networks with a driving input to posterior superior temporal cortex, which likely supports thematic role assignment, and networks with a driving input to inferior frontal cortex, a core region associated with syntactic computation. The optimal model was determined through both frequentist and Bayesian Model Selection and turned out to reflect a network with a primary drive from inferior frontal cortex and modulation of the connection between inferior frontal cortex and posterior superior temporal cortex by complex sentence processing. The winning model also showed a substantive role for a feedback mechanism from posterior superior temporal cortex back to inferior frontal cortex. We suggest that complex syntactic processing is driven by word-order analysis, supported by inferior frontal cortex, in an interactive relation with posterior superior temporal cortex, which supports verb argument structure processing.

Complex and magnitude-only preprocessing of 2D and 3D BOLD fMRI data at 7 T.

A challenge to ultra high field functional magnetic resonance imaging is the predominance of noise associated with physiological processes unrelated to tasks of interest. This degradation in data quality may be partially reversed using a series of preprocessing algorithms designed to retrospectively estimate and remove the effects of these noise sources. However, such algorithms are routinely validated only in isolation, and thus consideration of their efficacies within realistic preprocessing pipelines and on different data sets is often overlooked. We investigate the application of eight possible combinations of three pseudo-complementary preprocessing algorithms - phase regression, Stockwell transform filtering, and retrospective image correction - to suppress physiological noise in 2D and 3D functional data at 7 T. The performance of each preprocessing pipeline was evaluated using data-driven metrics of reproducibility and prediction. The optimal preprocessing pipeline for both 2D and 3D functional data included phase regression, Stockwell transform filtering, and retrospective image correction. This result supports the hypothesis that a complex preprocessing pipeline is preferable to a magnitude-only pipeline, and suggests that functional magnetic resonance imaging studies should retain complex images and externally monitor subjects' respiratory and cardiac cycles so that these supplementary data may be used to retrospectively reduce noise and enhance overall data quality.

Handedness-dependent and -independent cerebral asymmetries in the anterior intraparietal sulcus and ventral premotor cortex during grasp planning

When planning grasping actions, right-handers show left-lateralized responses in the anterior intraparietal sulcus (aIPS) and ventral premotor cortex (vPMC), two areas that are also implicated in sensorimotor control of grasp. We investigated whether a similar cerebral asymmetry is evident in strongly left-handed individuals. Fourteen participants were trained to grasp an object appearing in a variety of orientations with their left and right hands and with a novel mechanical tool (operated with either hand). BOLD fMRI data were then acquired while they decided prospectively whether an over- or under-hand grip would be most comfortable for grasping the same stimulus set while remaining still. Behavioral performances were equivalent to those recorded previously in right-handers and indicated reliance on effector-specific internal representations. In left-handers, however, grip selection decisions for both sides (left, right) and effectors (hand, tool) were associated with bilateral increases in activity within aIPS and vPMC. A direct comparison between left- and right-handers did reveal equivalent increases in left vPMC regardless of hand dominance. By contrast, aIPS and right vPMC activity were dependent on handedness, showing greater activity in the motor-dominant hemisphere. Though showing bilateral increases in both left- and right-handers, greater increases in the motor dominant hemisphere were also detected in the caudal IPS (cIPS), superior parietal lobule (SPL) and dorsal premotor cortex (dPMC). These findings provide further evidence that regions involved in the sensorimotor control of grasp also participate in grasp planning, and that for certain areas hand dominance is a predictor of the cerebral organization of motor cognitive functions.

Contrast Response Functions for Single Gabor Patches: ROI-Based Analysis Over-Represents Low-Contrast Patches for GE BOLD

Important for the interpretation of BOLD fMRI data is a linear relationship between the BOLD response and the underlying neural activity: increased BOLD responses should reflect proportionate increases in the underlying neural activity. While previous studies have demonstrated a linear relationship between the peak amplitude of the BOLD response and neural activity in primary visual cortex (V1), these studies have used stimuli that excite large areas of cortex, and the linearity of the BOLD response has not been demonstrated when only a small patch of cortex is stimulated. The BOLD response to isolated Gabor patches of increasing contrast was measured with gradient echo (GE) BOLD and spin echo (SE) BOLD at 7 T. Our primary finding is notable spatial heterogeneity of the BOLD contrast response, particularly for the GE BOLD data, resulting in a more reliably linear relationship between BOLD data and estimated neural responses in the center of the cortical representations of the individual Gabor patches than near the edges. A control experiment with larger sinusoidal grating patches confirms that the observed sensitivity to voxel selection in the regions of interest-based analysis is unique to the small stimuli.

Spatial normalization of BOLD fMRI data in cases of qualitative gross neuroanatomical changes resulting from pathology

Spatial normalization of BOLD fMRI data in cases of qualitative gross neuroanatomical changes resulting from pathology

Increased activation in superior temporal gyri as a function of increment in phonetic features

A common assumption is that phonetic sounds initiate unique processing in the superior temporal gyri and sulci (STG/STS). The anatomical areas subserving these processes are also implicated in the processing of non-phonetic stimuli such as music instrument sounds. The differential processing of phonetic and non-phonetic sounds was investigated in this study by applying a “sound-morphing” paradigm, where the presence of phonetic features were parametrically varied, creating a step-wise transition from a non-phonetic sound into a phonetic sound. The stimuli were presented in an event-related fMRI design. The fMRI-BOLD data were analysed using parametric contrasts. The results showed a higher sensitivity for sounds containing phonetic features compared to non-phonetic sounds in the middle part of STG, and in the anterior part of the planum temporale (PT) bilaterally. Although the same areas were involved in the processing of non-phonetic sounds, a difference in activation was evident in the STG, with an increase in activation related to increment of phonetic features in the sounds. The results indicate a stimulus-driven, bottom-up process that utilizes general auditory resources in the secondary auditory cortex, depending on specific phonetic features in the sounds.

Differentiation of somatosensory cortices by high-resolution fMRI at 7 T

This study aimed to evaluate the ability of BOLD signals at high MRI field (7T) to map fine-scale single-digit activations in subdivisions (areas 3b and 1) of the human primary somatosensory cortex (SI) in individual subjects. We acquired BOLD fMRI data from cortical areas around the central suclus in six healthy human subjects while stimulating individual finger pads with 2-Hz air puffs. Discrete, single-digit responses were identified in an area along the posterior bank of the central sulcus corresponding to area 3b and in an area along the crest of the postcentral gyrus corresponding to area 1. In single subjects, activations of digits 1 to 4 in both areas 3b and 1 were organized in a somatotopic manner. The separation of digit representations was measured for adjacent digits and was approximately 1.6 times greater in area 3b than in area 1. Within individual subjects, the cortical responses to single-digit stimulations and the magnitude of the BOLD signals were reproducible across imaging runs and were comparable across subjects. Our findings demonstrate that BOLD fMRI at 7T is capable of revealing the somatotopic organization of single-digit activations with good within-subject reliability and reproducibility, and activation maps can be acquired within a reasonably short time window, which are essential characteristics for several neurological applications within patient populations.

Dimensionality estimation for optimal detection of functional networks in BOLD fMRI data

Estimation of the intrinsic dimensionality of fMRI data is an important part of data analysis that helps to separate the signal of interest from noise. We have studied multiple methods of dimensionality estimation proposed in the literature and used these estimates to select a subset of principal components that was subsequently processed by linear discriminant analysis (LDA). Using simulated multivariate Gaussian data, we show that the dimensionality that optimizes signal detection (in terms of the receiver operating characteristic (ROC) metric) goes through a transition from many dimensions to a single dimension as a function of the signal-to-noise ratio. This transition happens when the loci of activation are organized into a spatial network and the variance of the networked, task-related signals is high enough for the signal to be easily detected in the data. We show that reproducibility of activation maps is a metric that captures this switch in intrinsic dimensionality. Except for reproducibility, all of the methods of dimensionality estimation we considered failed to capture this transition: optimization of Bayesian evidence, minimum description length, supervised and unsupervised LDA prediction, and Stein's unbiased risk estimator. This failure results in sub-optimal ROC performance of LDA in the presence of a spatially distributed network, and may have caused LDA to underperform in many of the reported comparisons in the literature. Using real fMRI data sets, including multi-subject group and within-subject longitudinal analysis we demonstrate the existence of these dimensionality transitions in real data.

Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans

Most studies involving spontaneous fluctuations in the BOLD signal extract connectivity patterns that show relationships between brain areas that are maintained over the length of the scanning session. In this study, however, we examine the spatiotemporal dynamics of the BOLD fluctuations to identify common patterns of propagation within a scan. A novel pattern finding algorithm was developed for detecting repeated spatiotemporal patterns in BOLD fMRI data. The algorithm was applied to high temporal resolution T2*-weighted multislice images obtained from rats and humans in the absence of any task or stimulation. In rats, the primary pattern consisted of waves of high signal intensity, propagating in a lateral to medial direction across the cortex, replicating our previous findings (Majeed et al., 2009a). These waves were observed primarily in sensorimotor cortex, but also extended to visual and parietal association areas. A secondary pattern, confined to subcortical regions consisted of an initial increase and subsequent decrease in signal intensity in the caudate–putamen. In humans, the most common spatiotemporal pattern consisted of an alteration between activation of areas comprising the “default-mode” (e.g., posterior cingulate and anterior medial prefrontal cortices) and the “task-positive” (e.g., superior parietal and premotor cortices) networks. Signal propagation from focal starting points was also observed. The pattern finding algorithm was shown to be reasonably insensitive to the variation in user-defined parameters, and the results were consistent within and between subjects. This novel approach for probing the spontaneous network activity of the brain has implications for the interpretation of conventional functional connectivity studies, and may increase the amount of information that can be obtained from neuroimaging data.

Robustly measuring vascular reactivity differences with breath-hold: Normalising stimulus-evoked and resting state BOLD fMRI data

Inter-subject differences in local cerebral blood flow (CBF) and cerebral blood volume (CBV) contribute to differences in BOLD signal reactivity and, therefore, unmodelled variance in group level fMRI analyses. A simple way of elevating blood CO2 concentrations to characterise subject differences in vascular reactivity is through breath-holds but two aspects of this measure are often neglected: (1) breath-holds are usually modelled as blocks even though CO2 accumulates over time and (2) increases in CO2 differ between subjects. This study demonstrates that the BOLD breath-hold response is best modelled by convolving the end-tidal CO2 trace with a standard haemodynamic response function and including its temporal derivative. Inclusion of the BOLD breath-hold response as a voxel-dependent covariate in a group level analysis increases the spatial extent of activation in stimulus evoked and resting state datasets. By expressing the BOLD breath-hold response as a percentage signal increase with respect to an absolute change in the partial pressure of CO2 (expressed in mmHg), the spatial extent of stimulus-evoked activation is further improved. This demonstrates that individual end-tidal CO2 increases to breath-hold should be accounted for to provide an accurate measure of vascular reactivity resulting in more statistically active voxels in group level analyses.

Mapping Genetic Influences on Brain Activation during the N-Back Working Memory Task: An fMRI Study of 315 Twins

Methods: BOLD fMRI data were acquired during the 0-back and 2-back conditions of a spatial N-back working memory task (see Fig. 1 in [1]) in 366 twins on a 4T Bruker Medspec MRI scanner. Echo Planar Images of all twins were realigned, coregistered with the twin’s 3D T1-weighted scan, spatially normalised in MNI atlas space, smoothed with an 8 mm FWHM Gaussian kernel, and detrended, using SPM5. We excluded 41 twins with 0-back contrast images generated at the single-subject level in each of 40,136 voxels comprising a brain mask created from a group random effects analysis irrespective of zygosity. We calculated voxel-by-voxel intra-class correlations (ICCs) and corresponding significance values for MZ and DZ groups. In addition, we computed Falconer’s heritability estimate (h2 = 2(rMZ – rDZ); [2]). Finally, we assessed fMRI reproducibility, using a paired-samples t-test, in a sub-sample of 20 twin pairs (5 MZF, 5 MZM, 5 DZF, 5 DZM) rescanned 120±55 (M±SD) days after their initial scan.

Biophysical modeling of phase changes in BOLD fMRI

In BOLD fMRI, stimulus related phase changes have been repeatedly observed in humans. However, virtually all fMRI processing utilizes the magnitude information only, while ignoring the phase. This results in an unnecessary loss of physiological information and signal-to-noise efficiency. A widely held view is that the BOLD phase change is zero for a voxel containing randomly orientated blood vessels and that phase changes are only due to the presence of large vessels. Based on a previously developed theoretical model, we show through simulations and experimental human BOLD fMRI data that a non-zero phase change can be present in a region with randomly oriented vessels. Using simulations of the model, we first demonstrate that a spatially distributed susceptibility results in a non-zero phase distribution. Next, experimental data in a finger-tapping experiment show consistent bipolar phase distribution across multiple subjects. This model is then used to show that in theory a bipolar phase distribution can also be produced by the model. Finally, we show that the model can produce a bipolar phase pattern consistent with that observed in the experimental data. Understanding of the mechanisms behind the experimentally observed phase changes in BOLD fMRI would be an important step forward and will enable biophysical model based methods for integrating the phase and magnitude information in BOLD fMRI experiments.

An additive-factors design to disambiguate neuronal and areal convergence: measuring multisensory interactions between audio, visual, and haptic sensory streams using fMRI

It can be shown empirically and theoretically that inferences based on established metrics used to assess multisensory integration with BOLD fMRI data, such as superadditivity, are dependent on the particular experimental situation. For example, the law of inverse effectiveness shows that the likelihood of finding superadditivity in a known multisensory region increases with decreasing stimulus discriminability. In this paper, we suggest that Sternberg's additive-factors design allows for an unbiased assessment of multisensory integration. Through the manipulation of signal-to-noise ratio as an additive factor, we have identified networks of cortical regions that show properties of audio-visual or visuo-haptic neuronal convergence. These networks contained previously identified multisensory regions and also many new regions, for example, the caudate nucleus for audio-visual integration, and the fusiform gyrus for visuo-haptic integration. A comparison of integrative networks across audio-visual and visuo-haptic conditions showed very little overlap, suggesting that neural mechanisms of integration are unique to particular sensory pairings. Our results provide evidence for the utility of the additive-factors approach by demonstrating its effectiveness across modality (vision, audition, and haptics), stimulus type (speech and non-speech), experimental design (blocked and event-related), method of analysis (SPM and ROI), and experimenter-chosen baseline. The additive-factors approach provides a method for investigating multisensory interactions that goes beyond what can be achieved with more established metric-based, subtraction-type methods.

Spatial and temporal reproducibility-based ranking of the independent components of BOLD fMRI data

Independent component analysis (ICA) decomposes fMRI data into spatially independent maps and their corresponding time courses. However, distinguishing the neurobiologically and biophysically reasonable components from those representing noise and artifacts is not trivial. We present a simple method for the ranking of independent components, by assessing the resemblance between components estimated from all the data, and components estimated from only the odd- (or even-) numbered time points. We show that the meaningful independent components of fMRI data resemble independent components estimated from downsampled data, and thus tend to be highly ranked by the method.

The Impact of Age-Related Changes on Working Memory Functional Activity

This work investigated associations of age-related brain atrophy and functional neural networks identified using multivariate analyses of BOLD fMRI data in young and elder participants (young, N=37; mean age=25; elders, N=15; mean age=74). Two networks were involved in retaining increasing loads of verbal information in working memory. Network utilizations were used to test associations between function and indices of grey matter volume changes using voxel based morphometry. Global changes in brain volume were not associated with the secondary network. Lower regional grey matter volume in the left pre-central gyrus within the primary network was associated with increased secondary network utilization independent of age group. Decreased regional grey matter volume was associated with increased age only in the elders. Increased secondary network expression was associated with increased slope of reaction times across memory load, in the elders. These results support the theory of neural compensation, that elder participants recruit additional neural resources to maintain task performance in the face of age-related decreases in regional grey matter volume.

Chronically Deafferented Sensory Cortex Recovers a Grossly Typical Organization after Allogenic Hand Transplantation

Amputation induces substantial reorganization of the body part somatotopy in primary sensory cortex (S1 complex, hereafter S1) [1, 2], and these effects of deafferentiation increase with time [3]. Determining whether these changes are reversible is critical for understanding the potential to recover from deafferenting injuries. Earlier BOLD fMRI data demonstrate increased S1 activity in response to stimulation of an allogenically transplanted hand [4]. Here, we report the first evidence that the representation of a transplanted hand can actually recapture the pre-amputation S1 hand territory. A 54-year-old male received a unilateral hand transplant 35 years after traumatic amputation of his right hand. Despite limited sensation, palmar tactile stimulation delivered 4 months post-transplant evoked contralateral S1 responses that were indistinguishable in location and amplitude from those detected in healthy matched controls. We find no evidence for persistent intrusion of representations of the face within the representation of the transplanted hand, although such intrusions are commonly reported in amputees [5, 6]. Our results suggest that even decades after complete deafferentiation, restoring afferent input to S1 leads to re-establishment of the gross hand representation within its original territory. Unexpectedly, large ipsilateral S1 responses accompanied sensory stimulation of the patient's intact hand. These may reflect a change in interhemispheric inhibition that could contribute to maintaining latent hand representations during the period of amputation.

Comparison of spatial integration and surround suppression characteristics in spiking activity and the local field potential in macaque V1

Neurons in primary visual cortex exhibit well documented centre-surround receptive field organization, whereby the centre is dominated by excitatory influences and the surround is generally dominated by inhibitory influences. These effects have largely been established by measuring the output of neurons, i.e. their spiking activity. How excitation and inhibition are reflected in the local field potential (LFP) is little understood. As this can bear on the interpretation of human fMRI BOLD data and on our understanding of the mechanisms of local field potential oscillations, we measured spatial integration and centre-surround properties in single- and multiunit recordings of V1 in the awake fixating macaque monkey, and compared these to spectral power in different frequency bands of simultaneously recorded LFPs. We quantified centre-surround organization by determining the size of the summation and suppression area in spiking activity as well as in different frequency bands of the LFP, with the main focus on the gamma band. Gratings extending beyond the summation area usually inhibited spiking activity while the LFP gamma-band activity increased monotonically for all grating sizes. This increase was maximal for stimuli infringing upon the near classical receptive field surround, where suppression started to dominate spiking activity. Thus, suppressive influences in primary cortex can be inferred from spiking activity, but they also seem to affect specific features of gamma-band LFP activity.

Prediction of human errors by maladaptive changes in brain networks

Event Abstract Back to Event Prediction of human errors by maladaptive changes in brain networks Tom Eichele1*, S. Debener2, VD Calhoun3, 4, 5, Karsten Specht1, 6, A.K Engel7, Kenneth Hugdahl1, 6, D.Y Von Cramon8 and Markus Ullsperger8, 9 1 University of Bergen, Norway 2 MRC Institute of Hearing Research, United Kingdom 3 MIND Institute, United States 4 University of New Mexico, United States 5 Yale University School of Medicine, United States 6 Haukeland University Hospital, Norway 7 University of Hamburg, Germany 8 Max Planck Institute for Human Cognitive and Brain Sciences, Germany 9 Max Planck Institute for Neurological Research, Germany Humans engaged in monotonous tasks are susceptible to occasional errors which may lead to serious consequences, but little is known about patterns of brain activity preceding errors. While it is conceivable that brain activity preceding action execution is causally responsible for the outcome, previous research on performance monitoring focused on the activity evoked by, and occurring after behavioral errors, and the ensuing adaptive effects. We investigated the possibility that some errors may be caused by a systematic maladjustment of cognitive control that develops more slowly over time, and which thus affords a prediction about the accuracy of future behavior from analyzing trends in the recent history of brain activity. To extract such error-preceding activity, we analyzed data from participants who performed a speeded visual flanker task while BOLD-fMRI data were collected. We have previously demonstrated the role of the medial frontal cortex in monitoring errors with an EEG-informed fMRI analysis of these data (Debener et al, J Neuroscience, 2005). By using independent component analysis in combination with deconvolution of hemodynamic responses and single trial estimation we found a set of brain regions in which the temporal evolution of activation predicted performance errors (Eichele et al, PNAS 2008). In particular, a coincident decrease of event-related deactivation in the default mode’s precuneus, together with a decline of activation in regions associated with maintaining task effort in the right lateral and medial superior frontal lobe, raised the probability of future errors. These maladaptive brain activity changes started as early as 30 seconds prior to the error. Our findings provide new insights into the trial-to-trial dynamics of performance monitoring, and suggest that understanding and monitoring these precursors may help avoiding human errors in real-world situations. Conference: 10th International Conference on Cognitive Neuroscience, Bodrum, Turkey, 1 Sep - 5 Sep, 2008. Presentation Type: Oral Presentation Topic: Symposium 6: Adaptive and maladaptive changes in brain activity prior and subsequent to erroneous behavior Citation: Eichele T, Debener S, Calhoun V, Specht K, Engel A, Hugdahl K, Von Cramon D and Ullsperger M (2008). Prediction of human errors by maladaptive changes in brain networks. Conference Abstract: 10th International Conference on Cognitive Neuroscience. doi: 10.3389/conf.neuro.09.2009.01.030 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 27 Nov 2008; Published Online: 27 Nov 2008. * Correspondence: Tom Eichele, University of Bergen, Bergen, Norway, tom.eichele@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Tom Eichele S. Debener VD Calhoun Karsten Specht A.K Engel Kenneth Hugdahl D.Y Von Cramon Markus Ullsperger Google Tom Eichele S. Debener VD Calhoun Karsten Specht A.K Engel Kenneth Hugdahl D.Y Von Cramon Markus Ullsperger Google Scholar Tom Eichele S. Debener VD Calhoun Karsten Specht A.K Engel Kenneth Hugdahl D.Y Von Cramon Markus Ullsperger PubMed Tom Eichele S. Debener VD Calhoun Karsten Specht A.K Engel Kenneth Hugdahl D.Y Von Cramon Markus Ullsperger Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.