Mapping the whole-brain response to noninvasive neuromodulation : from rodents to humans
- Because of recent advances in electrophysiology and imaging, we are now capable of recording intracranial neurologic activity at an unprecedented scale in terms of both spatiotemporal resolution and dimensionality. However, there still remains very few clinically translatable technologies for noninvasive neuromodulations to be able to causally perturb map functional networks within the brain, nor analysis techniques to leverage whole-brain intracranial data to gain insight into the relationship between CNS and peripheral physiology. In this dissertation, we present the novel use of ultrasonic drug uncaging for spatiotemporally precise noninvasive neuromodulation, in-human characterization of the whole-brain effects of Transcranial Magnetic Stimulation (TMS), and the application of machine learning to decode peripheral metabolic activity from intracranial electrophysiology. First, we develop biocompatible and clinically-translatable nanoparticles that enable ultrasound-induced uncaging of neuromodulatory drugs noninvasively within millimeter-sized brain regions. Utilizing the anesthetic propofol together with electrophysiological and imaging assays, we show that the neuromodulatory effect of ultrasonic drug uncaging is limited spatially and temporally by the size of the ultrasound focus, the sonication timing, and the pharmacokinetics of the uncaged drug. Moreover, we see secondary effects in brain regions anatomically distinct from and functionally connected to the sonicated region, indicating that ultrasonic drug uncaging could noninvasively map the changes in functional network connectivity associated with pharmacologic action at a particular brain target. We then present the first in-human study of the effect of TMS across the human brain using intracranial electrophysiology. With this technology, we show for the first time that single pulses of TMS delivered to the dorsolateral prefrontal cortex can elicit evoked potentials not only at the direct stimulation site, but also recruits downstream activity within the saliency network via the anterior cingulate cortex. These findings provide mechanistic explanation for TMS's efficacy in treating depression, while also exhibiting the potential for intracranial EEG to map the whole-brain effect of noninvasive neuromodulation in humans. Finally, we show that intracranial electrophysiology can proactively decode peripheral metabolic activity (i.e. serum glucose levels) up to hours into the future. In three human subjects, we simultaneously measured interstitial glucose concentrations and local field potentials from cortical and subcortical regions, including the hypothalamus in one subject. Correlations between high frequency activity (HFA, 70-170 Hz) and peripheral glucose levels were found across multiple brain regions, notably in the hypothalamus. With machine learning, we then show that spectro-spatial features of neural activity enable decoding of peripheral glucose levels both in the present and up to hours in the future. Our findings demonstrate proactive encoding of homeostatic glucose dynamics by the CNS and highlight the possibility of using intracranial electrophysiology to decode peripheral physiologic states. In summary, we show that noninvasive neuromodulation, whether by ultrasonic drug uncaging or TMS, exerts significant effects across entire brain networks in a predictable manner. We also leverage machine learning tools to show that intracranial activity can proactively decode physiologic parameters. Our hope is that these findings can be translated to advance noninvasive neuromodulation for treating both neuropsychiatric conditions and other diseases.
|Type of resource
|electronic resource; remote; computer; online resource
|1 online resource.
|Wang, Jeffrey Bond
|Degree committee member
|Degree committee member
|Stanford University, School of Humanities and Sciences
|Stanford University, Biophysics Program
|Statement of responsibility
|Jeffrey Bond Wang.
|Submitted to the Biophysics Program
|Thesis Ph.D. Stanford University 2023.
- © 2023 by Jeffrey Bond Wang
- This work is licensed under a Creative Commons Attribution Share Alike 3.0 Unported license (CC BY-SA).
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