Mindstrong, LLC

Applied Cognition, Inc.

Paul Dagum

Laurent B Giovangrandi

Jonathan I Kaplan

Gregory T A Kovacs

Joerg Christoph Student

Carl J Weber

Nathan Whipple

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.

Non-invasive topographic localization of glymphatic flow

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.

Wearable device and method for non-invasive assessment of glymphatic flow

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.

Non-invasive assessment of glymphatic flow and neurodegeneration from a wearable device

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. The method records on the electronic device one of global positioning system longitude and latitude coordinates, accelerometer coordinates, and gyroscope coordinates, one of outgoing and incoming phone calls, outgoing and incoming emails, and outgoing and incoming text messages, one of URLs visited on an internet browser application, books read on an e-reader application, games played on game applications, and the nutritional content of food consumed, performs the step of learning a function mapping from those recordings to measurements of cognitive function using a loss function to identify a set of optimal weights that produce a minimum for the loss function, uses those optimal weights to create the function mapping, and performs the step of computing the variance of the cognitive function measurements that is explained by the function mapping to assign an attribution to the effect of physical activity on measured changes in cognitive function.

Method and system for assessment of cognitive function based on electronic device usage

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.

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Los Altos Hills, CA, United States of America

Paul Dagum