RII Track-4:NSF:Multimodal imaging of large-scale neural networks for optimized neurostimulation

University Of Oklahoma Norman Campus, Norman OK

Investigators

Abstract

Mental illnesses, including depression and anxiety, are chronic, disabling, and devastating conditions. However, many people are not receiving the care they need to fully recover from their illnesses because of a shortage of health services and a lack of state-of-the-art treatment options. Studies have documented that up to two-thirds of patients who seek standard pharmacological and/or psychological interventions to emotional disorders will not respond. Noninvasive neurostimulation methods, such as transcranial magnetic stimulation (TMS), are emerging techniques to treat patients who have failed multiple attempts of standard interventions. However, issues of variable response effects and inter-subject variability have arisen in numerous investigations of treating depression and other mental illnesses, which have prevented the broad application of noninvasive neurostimulation for clinical use. This EPSCoR Research Fellows RII Track-4:NSF fellowship will enable the PI from the University of Oklahoma, to partner with psychiatrists and scientists at the Medical University of South Carolina to develop a novel neurostimulation technology that is integrated with neuroimaging in a closed-loop design. This research and the associated partnerships will pave the way for developing individualized treatment with much improved outcomes in people with mental illness. This success can be further generalized to the neurostimulation treatment of other disorders. Noninvasive neurostimulation is an emerging technology that is rapidly booming in the recent decade for treating many neurological and neuropsychiatric disorders. However, the responses to a standard protocol varied greatly among individuals. Addressing the issue of heterogeneous responses will be an important step forward to exploit the full potential of neurostimulation as the treatment option. The key innovation of the project is to leverage the large-scale neural networks as biomarkers, using the algorithms previously established by the PI, to individually optimize the therapeutic outcomes of neurostimulations. A high-density, whole-head montage of multimodal electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) will be used in the project for recording in the human participants. The computational analysis of the multimodal imaging data will primarily focus on identifying the functional connectivity of neural networks as biomarkers in response to neurostimulation treatment. Addressing these research questions will be important steps towards the next-generation neurostimulation methods via a biomarker-based, closed-loop approach to optimize individual treatment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.