COGNITIVE CONTROL & HIGHER COGNITION

Cognitive control refers to mental operations that allow for the adaptation of goal-oriented behavior according to environmental demands. The control of interferences in information processing and inhibitory control are major topics in this field. A major goal of my research is to identify a genuine neural marker of inhibition and to specify the connectivity patterns of neurocognitive mechanisms that guide inhibitory efficiency. Understanding the mechanisms facilitating effective inhibition and identifying a neural marker of this process evident in non-invasive recordings of brain activity is of crucial importance for clinical diagnostics.

BRAIN STRUCTURE AND FUNCTION

An interesting question is whether differences in physiological reactivity and behavior can be predicted from brain morphology. In previous work, I showed that a stronger folding of the left midcingulate cortex is associated with increased behavioral performance and augmented neural reactivity. Still, the diagnosis of impairments of neural systems underlying cognitive control, or the prognosis of treatment outcome, are relatively unreliable. Therefore, I develop an innovative framework that directly binds profiles of neural cognitive control networks to profiles of behavioral performance measures. This integrative multi-level and multi-systems approach concurrently considers data from several measurement modalities and tasks.

MODULATION OF COGNITION

Here, techniques for neuromodulation are applied to successfully augment cognitive performance. By using neurofeedback of frontal-midline theta oscillations (4-8 Hz), an EEG phenomenon associated with frontal-lobe functions, we showed that participants not only learn to increase brain activity of this frequency band at will, but results further indicate concurrent behavioral improvements in tasks measuring cognitive control processes. Similarly, we apply transcranial electrical stimulation during performance of working memory tasks. We found that the memory span indeed could be increased, but only under conditions that require minimal mental operations on memory content in addition to item maintenance.

METHODS IN COMPUTATIONAL NEUROSCIENCE

Most of my research relies on advanced computational methods for signal processing and machine learning; consequently, I also actively contribute to the advancement of these methods. For example, we designed and implemented our own software for neurofeedback studies, NeuroFeedback Suite 1.0, which we made freely available on sourceforge. We also work on unsupervised learning procedures to uncover the latent structure of multi-subject EEG data sets to overcome problems associated with induced responses and inter-subject differences in scalp topographies.