Dobromir Rahnev, Ph.D. - US grants

Project Summary / Abstract Metacognition is the ability to reflect on and evaluate one?s behavior. Impaired metacognition has been proposed as a major symptom for a number of psychiatric disorders such as schizophrenia, depression, generalized anxiety disorder, obsessive-compulsive disorder, and even substance abuse. Deficits in metacognition have further been found after brain lesions. Such deficits can have severe detrimental effects on patients; treating them requires insight into the computational and neural substrates of metacognition. However, efforts in this direction have been hampered by a lack of an overarching framework that can inform computational models of confidence, the measurement of metacognitive ability, and the investigation of the neural bases of metacognition. This proposal will advance a novel theoretical framework based on the concept of hierarchical noise architecture. Perceptual decision making will be used as a model system but the architecture is perfectly general and expected to apply across all domains of metacognition. According to the hierarchical noise architecture (1) sensory noise corrupts the decision-level representation of the stimulus thus affecting both the perceptual and confidence judgments, while (2) an additional metacognitive noise corrupts confidence but not the perceptual judgment. The proposed architecture makes a counter-intuitive prediction that is supported by strong preliminary data. More importantly, this architecture can be used to extract a new, model-based measure of metacognitive ability with desirable psychometric properties such as being independent from participants? bias for high or low confidence. This new measure can therefore be used to examine the effectiveness of treatments on patients? metacognitive abilities: for example, if a patient with deficiency in metacognition changes her strategy and starts using high confidence more, the new measure ? but not previous measures ? will remain unchanged. Beyond predicting new behavioral phenomena and leading to an improved measure of metacognitive ability, the hierarchical noise architecture can also elucidate the neural bases of metacognition. Specifically, the functional roles postulated by the hierarchical noise model can be linked directly to the functions of large-scale brain networks and especially the central executive and the salience networks. These links will be established using a variety of techniques such as causally interfering with different nodes of these networks, correlating metacognitive ability with the connectivity within these networks, and examining the dynamics of the inter-network communication during confidence generation. The insights gained by this proposal will have a direct link to work in the clinic by providing researchers a better tool to assess the metacognitive deficits of patients (including the effectiveness of proposed treatments) and link such dysfunction to specific brain circuits. This work is thus expected to benefit patients suffering a range of diseases from schizophrenia to depression to substance abuse.

Project Summary. Transcranial magnetic stimulation (TMS) is a promising tool for the treatment of a large number of neuropsychiatric disorders including depression, obsessive-compulsive disorder, Alzheimer?s disease, and addiction. In fact, TMS has already been approved by the Food and Drug Administration (FDA) for depres- sion treatment, testifying to its effectiveness. However, the effects of TMS are known to vary substantially be- tween subjects, thus reducing its therapeutic efficacy. Traditionally, the large variability in TMS responsiveness has been seen as an inevitable limitation of the technique. On the other hand, computational ?state-based? the- ories postulate that the effects of TMS critically depend on a combination of the pre-TMS state of the targeted network and the strength of stimulation. State-based theories imply that it is possible to reduce the variability of TMS and enhance its therapeutic effectiveness by taking into account the state of the brain right before stimula- tion. However, the notion that the pre-TMS state can qualitatively alter the behavioral effects of TMS has not been examined directly by actual recordings of pre-TMS brain activity. The current proposal will perform the first direct test of these theories by an innovative combination of TMS and concurrent functional magnetic resonance imaging (fMRI). Delivering precisely-targeted TMS inside the MRI environment and obtaining artifact-free fMRI data is difficult but the Georgia Tech research group has already built a setup and collected pilot data from three different protocols demonstrating ability to conduct such experiments and obtain high-quality data. Existing pilot data comes from experiments characterizing the effects of TMS on the area under the coil by applying TMS at rest. To directly test state-based theories, the current proposal will employ concurrent TMS-fMRI with two distinct tasks in a large sample of healthy young adults. Aim 1 will examine how the pre-TMS activity in the targeted area influences the effect of TMS on behavior and whether the level of this activity interacts with the intensity of TMS. Based on state-based theories, it is expected that low pre-TMS activity coupled with low-intensity TMS will lead to performance improvement, whereas high pre-TMS activity coupled with high-intensity TMS will lead to perfor- mance impairment. However, the effectiveness of TMS is likely determined not just by the activity of the area under the TMS coil but also by the state of large brain networks. Therefore, Aim 2 will additionally test how the pattern of connectivity in large brain networks affects the behavioral effects of TMS. Further, it will be examined whether the global brain state is important in determining the effect of TMS and whether particular networks can be identified that influence TMS effectiveness. The proposed research will thus test a long-standing hypothesis regarding the state-dependency of TMS effects by an innovative use of concurrent TMS-fMRI. The findings from this proposal are expected to have immediate applications in already existing efforts to use TMS as a therapeutic device.