Nikolaus Weiskopf

When fear is near: Threat imminence elicits prefrontal-periaqueductal gray shifts in humans

Humans, like other animals, alter their behavior depending on whether a threat is close or distant. We investigated spatial imminence of threat by developing an active avoidance paradigm in which volunteers were pursued through a maze by a virtual predator endowed with an ability to chase, capture, and inflict pain. Using functional magnetic resonance imaging, we found that as the virtual predator grew closer, brain activity shifted from the ventromedial prefrontal cortex to the periaqueductal gray. This shift showed maximal expression when a high degree of pain was anticipated. Moreover, imminence-driven periaqueductal gray activity correlated with increased subjective degree of dread and decreased confidence of escape. Our findings cast light on the neural dynamics of threat anticipation and have implications for the neurobiology of human anxiety-related disorders.

Evidence of mirror neurons in human inferior frontal gyrus

There is much current debate about the existence of mirror neurons in humans. To identify mirror neurons in the inferior frontal gyrus (IFG) of humans, we used a repetition suppression paradigm while measuring neural activity with functional magnetic resonance imaging. Subjects either executed or observed a series of actions. Here we show that in the IFG, responses were suppressed both when an executed action was followed by the same rather than a different observed action and when an observed action was followed by the same rather than a different executed action. This pattern of responses is consistent with that predicted by mirror neurons and is evidence of mirror neurons in the human IFG.

Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): Methodology and exemplary data

A brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) is presented which allows human subjects to observe and control changes of their own blood oxygen level-dependent (BOLD) response. This BCI performs data preprocessing (including linear trend removal, 3D motion correction) and statistical analysis on-line. Local BOLD signals are continuously fed back to the subject in the magnetic resonance scanner with a delay of less than 2 s from image acquisition. The mean signal of a region of interest is plotted as a time-series superimposed on color-coded stripes which indicate the task, i.e., to increase or decrease the BOLD signal. We exemplify the presented BCI with one volunteer intending to control the signal of the rostral-ventral and dorsal part of the anterior cingulate cortex (ACC). The subject achieved significant changes of local BOLD responses as revealed by region of interest analysis and statistical parametric maps. The percent signal change increased across fMRI-feedback sessions suggesting a learning effect with training. This methodology of fMRI-feedback can assess voluntary control of circumscribed brain areas. As a further extension, behavioral effects of local self-regulation become accessible as a new field of research.

Context-dependent human extinction memory is mediated by a ventromedial prefrontal and hippocampal network.

In fear extinction, an animal learns that a conditioned stimulus (CS) no longer predicts a noxious stimulus [unconditioned stimulus (UCS)] to which it had previously been associated, leading to inhibition of the conditioned response (CR). Extinction creates a new CS–noUCS memory trace, competing with the initial fear (CS–UCS) memory. Recall of extinction memory and, hence, CR inhibition at later CS encounters is facilitated by contextual stimuli present during extinction training. In line with theoretical predictions derived from animal studies, we show that, after extinction, a CS-evoked engagement of human ventromedial prefrontal cortex (VMPFC) and hippocampus is context dependent, being expressed in an extinction, but not a conditioning, context. Likewise, a positive correlation between VMPFC and hippocampal activity is extinction context dependent. Thus, a VMPFC–hippocampal network provides for context-dependent recall of human extinction memory, consistent with a view that hippocampus confers context dependence on VMPFC.

A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging

The morphology of cortical grey matter is commonly assessed using T1-weighted MRI together with automated computerised methods such as voxel-based morphometry (VBM) and cortical thickness measures. In the presented study we investigate how grey matter changes identified using voxel-based cortical thickness (VBCT) measures compare with local grey matter volume changes identified using VBM. We use data from a healthy aging population to perform the comparison, focusing on brain regions where age-related changes have been observed in previous studies. Our results show that overall, in healthy aging, VBCT and VBM yield very consistent results but VBCT provides a more sensitive measure of age-associated decline in grey matter compared with VBM. Our findings suggest that while VBCT selectively investigates cortical thickness, VBM provides a mixed measure of grey matter including cortical surface area or cortical folding, as well as cortical thickness. We therefore propose that used together, these techniques can separate the underlying grey matter changes, highlighting the utility of combining these complementary methods.

Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI)

A brain-computer interface (BCI) based on functional magnetic resonance imaging (fMRI) records noninvasively activity of the entire brain with a high spatial resolution. We present a fMRI-based BCI which performs data processing and feedback of the hemodynamic brain activity within 1.3 s. Using this technique, differential feedback and self-regulation is feasible as exemplified by the supplementary motor area (SMA) and parahippocampal place area (PPA). Technical and experimental aspects are discussed with respect to neurofeedback. The methodology now allows for studying behavioral effects and strategies of local self-regulation in healthy and diseased subjects.

Optimal EPI parameters for reduction of susceptibility-induced BOLD sensitivity losses: a whole-brain analysis at 3 T and 1.5 T.

Most functional magnetic resonance imaging (fMRI) studies record the blood oxygen level-dependent (BOLD) signal using fast gradient-echo echo-planar imaging (GE EPI). However, GE EPI can suffer from substantial signal dropout caused by inhomogeneities in the static magnetic field. These field inhomogeneities occur near air/tissue interfaces, because they are generated by variations in magnetic susceptibilities. Thus, fMRI studies are often limited by a reduced BOLD sensitivity (BS) in inferior brain regions. Recently, a method has been developed which allows for optimizing the BS in dropout regions by specifically adjusting the slice tilt, the direction of the phase-encoding (PE), and the z-shim moment. However, optimal imaging parameters were only reported for the orbitofrontal cortex (OFC) and inferior temporal lobes. The present study determines the optimal slice tilt, PE direction, and z-shim moment at 3 T and 1.5 T, otherwise using standard fMRI acquisition parameters. Results are reported for all brain regions, yielding a whole-brain atlas of optimal parameters. At both field strengths, optimal parameters increase the BS by more than 60% in many voxels in the OFC and by at least 30% in the other dropout regions. BS gains are shown to be more widespread at 3 T, suggesting an increased benefit from the dropout compensation at higher fields. Even the mean BS of a large brain region, e.g., encompassing the medial OFC, can be increased by more than 15%. The maps of optimal parameters allow for assessing the feasibility and improving fMRI of brain regions affected by susceptibility-induced BS losses.

Regulation of emotional responses elicited by threat-related stimuli

The capacity to voluntarily regulate emotions is critical for mental health, especially when coping with aversive events. Several neuroimaging studies of emotion regulation found the amygdala to be a target for downregulation and prefrontal regions to be associated with downregulation. To characterize the role of prefrontal regions in bidirectional emotion regulation and to investigate regulatory influences on amygdala activity and peripheral physiological measures, a functional magnetic resonance imaging (fMRI) study with simultaneous recording of self‐report, startle eyeblink, and skin conductance responses was carried out. Subjects viewed threat‐related pictures and were asked to up‐ and downregulate their emotional responses using reappraisal strategies. While startle eyeblink responses (in successful regulators) and skin conductance responses were amplified during upregulation, but showed no consistent effect during downregulation, amygdala activity was increased and decreased according to the regulation instructions. Trial‐by‐trial ratings of regulation success correlated positively with activity in amygdala during upregulation and orbitofrontal cortex during downregulation. Downregulation was characterized by left‐hemispheric activation peaks in anterior cingulate cortex, dorsolateral prefrontal cortex, and orbitofrontal cortex and upregulation was characterized by a pattern of prefrontal activation not restricted to the left hemisphere. Further analyses showed significant overlap of prefrontal activation across both regulation conditions, possibly reflecting cognitive processes underlying both up‐ and downregulation, but also showed distinct activations in each condition. The present study demonstrates that amygdala responses to threat‐related stimuli can be controlled through the use of cognitive strategies depending on recruitment of prefrontal areas, thereby changing the subjects affective state. Hum Brain Mapp, 2007.

Comparing hemodynamic models with DCM

The classical model of blood oxygen level-dependent (BOLD) responses by Buxton et al. [Buxton, R.B., Wong, E.C., Frank, L.R., 1998. Dynamics of blood flow and oxygenation changes during brain activation: the Balloon model. Magn. Reson. Med. 39, 855–864] has been very important in providing a biophysically plausible framework for explaining different aspects of hemodynamic responses. It also plays an important role in the hemodynamic forward model for dynamic causal modeling (DCM) of fMRI data. A recent study by Obata et al. [Obata, T., Liu, T.T., Miller, K.L., Luh, W.M., Wong, E.C., Frank, L.R., Buxton, R.B., 2004. Discrepancies between BOLD and flow dynamics in primary and supplementary motor areas: application of the Balloon model to the interpretation of BOLD transients. NeuroImage 21, 144–153] linearized the BOLD signal equation and suggested a revised form for the model coefficients. In this paper, we show that the classical and revised models are special cases of a generalized model. The BOLD signal equation of this generalized model can be reduced to that of the classical Buxton model by simplifying the coefficients or can be linearized to give the Obata model. Given the importance of hemodynamic models for investigating BOLD responses and analyses of effective connectivity with DCM, the question arises which formulation is the best model for empirically measured BOLD responses. In this article, we address this question by embedding different variants of the BOLD signal equation in a well-established DCM of functional interactions among visual areas. This allows us to compare the ensuing models using Bayesian model selection. Our model comparison approach had a factorial structure, comparing eight different hemodynamic models based on (i) classical vs. revised forms for the coefficients, (ii) linear vs. non-linear output equations, and (iii) fixed vs. free parameters, ε, for region-specific ratios of intra- and extravascular signals. Using fMRI data from a group of twelve subjects, we demonstrate that the best model is a non-linear model with a revised form for the coefficients, in which ε is treated as a free parameter.

Regulation of anterior insular cortex activity using real-time fMRI

Recent advances in functional magnetic resonance imaging (fMRI) data acquisition and processing techniques have made real-time fMRI (rtfMRI) of localized brain areas feasible, reliable and less susceptible to artefacts. Previous studies have shown that healthy subjects learn to control local brain activity with operant training by using rtfMRI-based neurofeedback. In the present study, we investigated whether healthy subjects could voluntarily gain control over right anterior insular activity. Subjects were provided with continuously updated information of the target ROIs level of activation by visual feedback. All participants were able to successfully regulate BOLD-magnitude in the right anterior insular cortex within three sessions of 4 min each. Training resulted in a significantly increased activation cluster in the anterior portion of the right insula across sessions. An increased activity was also found in the left anterior insula but the percent signal change was lower than in the target ROI. Two different control conditions intended to assess the effects of non-specific feedback and mental imagery demonstrated that the training effect was not due to unspecific activations or non feedback-related cognitive strategies. Both control groups showed no enhanced activation across the sessions, which confirmed our main hypothesis that rtfMRI feedback is area-specific. The increased activity in the right anterior insula during training demonstrates that the effects observed are anatomically specific and self-regulation of right anterior insula only is achievable. This is the first group study investigating the volitional control of emotionally relevant brain region by using rtfMRI training and confirms that self-regulation of local brain activity with rtfMRI is possible.