Abstract: The present disclosure relates to compositions, formulations, and associated methods for treating neurodegenerative diseases, wherein the compositions include an alpha-1 adrenergic agonist, such as midodrine, and an alpha-2A adrenergic agonist, such as dexmedetomidine. Upon administering to a human subject, the alpha-2A adrenergic agonist crosses the subject's blood-brain barrier thereby acting upon the subject's central nervous system, whereas the alpha-1 adrenergic agonist does not cross the subject's blood-brain barrier. The alpha-1 adrenergic agonist minimizes or eliminates the systemic vascular effects induced by the alpha-2A adrenergic agonist that causes the negative cerebral autoregulatory response, thereby enabling the alpha-2A adrenergic agonist to increase glymphatic flow in the subject's brain.

Abstract: A computer-implemented method and system includes accessing neurophysiological and neurovascular data recorded during sleep. A function mapping is executed from said neurophysiological and neurovascular data to a target that is one of a glymphatic flow marker, a molecular analysis marker of neurodegeneration, or a neuroimaging marker of neurodegeneration. A target prediction model is output based on the function mapping. The target prediction model can receive new neurophysiological and neurovascular data and output a predicted marker of neurodegeneration.

Abstract: A computer-implemented method and system includes a head-mounted wearable device with two current electrodes that provide input alternating currents over a range of frequencies to a subject during at least one intervention. The head-mounted wearable device includes at least two sense electrodes that collect voltage difference measurements in response to the input alternating currents. A measurement validation module applies a Hilbert transform or a Kramer-Kronig test to the voltage difference measurements to generate validated time-series impedance spectra. A cranial fluid dynamics module fits the validated time-series impedance spectra to an electrical circuit model to determine a glymphatic flow response to the at least one intervention.

Abstract: A computer-implemented method and system includes accessing neurophysiological and neurovascular data recorded during sleep. A device mounted on a subject's head during sleep can gather neurovascular data by measuring transcranial impedance. The device can be in the form of a single band, multiple bands, or a headcap that are worn on the subject's head. Electrodes located along an inner surface of the wearable device contact the subject's head and measure transcranial impedance. Transcranial impedance measurements are gathered from multiple locations on the subject's head and over multiple frequencies to obtain a more complete assessment of glymphatic flow.

Abstract: A computer-implemented method and system includes a head-mounted wearable device with two current electrodes that provide input alternating currents over a range of frequencies to a subject during at least one intervention. The head-mounted wearable device includes at least two sense electrodes that collect voltage difference measurements in response to the input alternating currents. A measurement validation module applies a Hilbert transform or a Kramer-Kronig test to the voltage difference measurements to generate validated time-series impedance spectra. A cranial fluid dynamics module fits the validated time-series impedance spectra to an electrical circuit model to determine a glymphatic flow response to the at least one intervention.

Abstract: A computer-implemented method and system includes accessing neurophysiological and neurovascular data recorded during sleep. A function mapping is executed from said neurophysiological and neurovascular data to a target that is one of a glymphatic flow marker, a molecular analysis marker of neurodegeneration, or a neuroimaging marker of neurodegeneration. A target prediction model is output based on the function mapping. The target prediction model can receive new neurophysiological and neurovascular data and output a predicted marker of neurodegeneration.

Abstract: A computer-implemented method and system includes accessing neurophysiological and neurovascular data recorded during sleep. A function mapping is executed from said neurophysiological and neurovascular data to a target that is one of a glymphatic flow marker, a molecular analysis marker of neurodegeneration, or a neuroimaging marker of neurodegeneration. A target prediction model is output based on the function mapping. The target prediction model can receive new neurophysiological and neurovascular data and output a predicted marker of neurodegeneration.