A large body of work has demonstrated that human cognition depends on the activity in large-scale brain networks. This network activity has been linked to the emergence of consciousness, to a variety of individual traits as diverse as motivation, empathy, neuroticism, extraversion, and IQ and to clinical conditions such as Alzheimer's, stroke, traumatic brain injury, schizophrenia and depression. Thus, characterizing the pattern and dynamics of brain connectivity is of utmost importance for understanding the workings of the human brain in both health and disease. However, large-scale brain networks are typically studied with correlational methods such as functional MRI (fMRI), EEG or MEG, which cannot detect causal relationships. Consequently, the focus to date has been on characterizing the spatial composition of the brain's networks with less emphasis on the dynamics of intra- and inter-network communication. We propose that the method of simultaneous transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) can fill this gap and provide an invaluable tool for understanding network communication. This method allows researchers to observe the spread of an artificially induced neural signal to the rest of the brain in the context of different tasks. The central hypothesis of this proposal is that the effect of TMS will only extend within the targeted region's network during rest or tasks that engage preferentially the said network, but that regions of other networks will also be affected in tasks that engage the two networks simultaneously. Specifically, we will first test whether brain networks emerge spontaneously as a result of the spread of artificially induced neural signals during rest. Then, we will further explore the role o engagement in a task that either preferentially activates the network of the targeted brain region, or a competing brain network. Finally, we have developed a novel task that activates two separate brain networks in order to test whether the coordination between them will result in a change in the spread of neural signals originating in a region belonging to one of the networks. In this way, the proposed project will pave the way for a wide range of studies on the dynamics of neural networks that can lead to fundamental insights of cognition, as well as a deeper understanding of brain dysfunction. Relevant to the NIH mission, identification of brain networks as proposed in these studies can serve as targets for the development of diagnostic biomarkers as well as cognitive therapy interventions for rehabilitation of patients with cognitive deficits de to neurological or psychiatric disorders.
The proposed research is relevant to public health because abnormalities in the functioning of large-scale brain networks have been associated with a large number of neurological and psychiatric diseases such as Alzheimer's, stroke, traumatic brain injury, schizophrenia and depression. These conditions are some of the most highly prevalent neurological and psychiatric disorders and the care of these patients result in significant health care costs. The proposed research is also relevant to NIH's mission because it will lead to basic knowledge about large scale organization of the brain supporting cognitive function which has the potential to provide valuable insights into the understanding, diagnosis and treatment of a wide range of neurological and psychiatric conditions.