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.

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.

Abstract: The present invention extends to methods, systems, and computer program products for forecasting mood changes from digital biomarkers and more generally for forecasting changes in a neuropsychological clinical assessment. User interaction data indicative of a user's interaction with a mobile device is passively captured over a period of time. A function mapping (modeling a neuropsychological test for a brain health metric) is executed to compute a digital biomarker for a brain health metric from the captured user interaction data. A prior digital biomarker for the brain health metric (computed from previously captured user interaction data by executing the function mapping) is accessed. A difference is detected between the digital biomarker and the prior digital biomarker. A change in a score of the neuropsychological test score is forecast to occur within a specified time range in the future based on the detected differences. The forecast change is output.

Abstract: The present invention extends to methods, systems, and computer program products for forecasting mood changes from digital biomarkers and more generally for forecasting changes in a neuropsychological clinical assessment. User interaction data indicative of a user's interaction with a mobile device is passively captured over a period of time and can include keyboard timings. A function mapping is executed to compute a digital biomarker for a brain health metric from the captured user interaction data. A prior digital biomarker for the brain health metric (computed from previously captured user interaction data by executing the function mapping) is accessed. A difference is detected between the digital biomarker and the prior digital biomarker. A change in a score of the neuropsychological test score is forecast to occur within a specified time range in the future based on the detected differences. The forecast change is output.

Abstract: A system and method that enables a person to unobtrusively assess their cognitive function from mobile device usage. The method records on the mobile device the occurrence and timing of user events comprising the opening and closing of applications resident on the device, the characters inputted, touch-screen gestures made, and voice inputs used on those applications, performs the step of learning a function mapping from the mobile device recordings to measurements of cognitive function that uses a loss function to determine relevant features in the recording, identifies a set of optimal weights that produce a minimum of the loss function, creates a function mapping using the optimal weights, and performs the step of applying the learned function mapping to a new recording on the mobile device to compute new cognitive function values.

Abstract: A system and method that enables a person to unobtrusively quantify the effect of mobility, physical activity, learning, social interaction and diet on cognitive function.

Abstract: A system and method that enables a person to unobtrusively quantify the effect of mobility, physical activity, learning, social interaction and diet on cognitive function.

Abstract: A system and method that enables a person to unobtrusively assess their cognitive function from mobile device usage. The method records on the mobile device the occurrence and timing of user events comprising the opening and closing of applications resident on the device, the characters inputted, touch-screen gestures made, and voice inputs used on those applications, performs the step of learning a function mapping from the mobile device recordings to measurements of cognitive function that uses a loss function to determine relevant features in the recording, identifies a set of optimal weights that produce a minimum of the loss function, creates a function mapping using the optimal weights, and performs the step of applying the learned function mapping to a new recording on the mobile device to compute new cognitive function values.

Abstract: A system and method that enables a person to unobtrusively assess their cognitive function from mobile device usage. The method records on the mobile device the occurrence and timing of user events comprising the opening and closing of applications resident on the device, the characters inputted, touch-screen gestures made, and voice inputs used on those applications, performs the step of learning a function mapping from the mobile device recordings to measurements of cognitive function that uses a loss function to determine relevant features in the recording, identifies a set of optimal weights that produce a minimum of the loss function, creates a function mapping using the optimal weights, and performs the step of applying the learned function mapping to a new recording on the mobile device to compute new cognitive function values.

Abstract: A system and method that enables a person to unobtrusively quantify the effect of mobility, physical activity, learning, social interaction and diet on cognitive function.

Abstract: A system and method that enables a person to unobtrusively assess their cognitive function from mobile device usage. The method records on the mobile device the occurrence and timing of user events comprising the opening and closing of applications resident on the device, the characters inputted, touch-screen gestures made, and voice inputs used on those applications, performs the step of learning a function mapping from the mobile device recordings to measurements of cognitive function that uses a loss function to determine relevant features in the recording, identifies a set of optimal weights that produce a minimum of the loss function, creates a function mapping using the optimal weights, and performs the step of applying the learned function mapping to a new recording on the mobile device to compute new cognitive function values.

Abstract: A system and method that enables a person to unobtrusively assess their cognitive function from mobile device usage. The method records on the mobile device the occurrence and timing of user events comprising the opening and closing of applications resident on the device, the characters inputted, touch-screen gestures made, and voice inputs used on those applications, performs the step of learning a function mapping from the mobile device recordings to measurements of cognitive function that uses a loss function to determine relevant features in the recording, identifies a set of optimal weights that produce a minimum of the loss function, creates a function mapping using the optimal weights, and performs the step of applying the learned function mapping to a new recording on the mobile device to compute new cognitive function values.

Abstract: A system and method that enables a person to unobtrusively quantify the effect of mobility, physical activity, learning, social interaction and diet on cognitive function.

Abstract: A system and method that enables a person to unobtrusively assess their cognitive function from mobile device usage. The method records on the mobile device the occurrence and timing of user events comprising the opening and closing of applications resident on the device, the characters inputted, touch-screen gestures made, and voice inputs used on those applications, performs the step of learning a function mapping from the mobile device recordings to measurements of cognitive function that uses a loss function to determine relevant features in the recording, identifies a set of optimal weights that produce a minimum of the loss function, creates a function mapping using the optimal weights, and performs the step of applying the learned function mapping to a new recording on the mobile device to compute new cognitive function values.

Abstract: A system and method that enables a person to unobtrusively quantify the effect of mobility, physical activity, learning, social interaction and diet on cognitive function.

Abstract: A system and method that enables a person to unobtrusively assess their cognitive function from mobile device usage. The method records on the mobile device the occurrence and timing of user events comprising the opening and closing of applications resident on the device, the characters inputted, touch-screen gestures made, and voice inputs used on those applications, performs the step of learning a function mapping from the mobile device recordings to measurements of cognitive function that uses a loss function to determine relevant features in the recording, identifies a set of optimal weights that produce a minimum of the loss function, creates a function mapping using the optimal weights, and performs the step of applying the learned function mapping to a new recording on the mobile device to compute new cognitive function values.