Non-invasive assessment of glymphatic flow and neurodegeneration from a wearable device
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.
1. A method implemented with one or more computer processors of a computing device, the method comprising:
recording during sleep, with a wearable device worn by a patient, neurophysiological data and neurovascular data, the wearable device comprising left ear canal electrodes multiplexed to make electroencephalogram and transcranial impedance measurements and right ear canal electrodes multiplexed to make electroencephalogram and transcranial impedance measurements, wherein the neurophysiological data comprises electroencephalogram measurements from both the left ear canal electrodes and the right ear canal electrodes, and wherein the neurovascular data comprises transcranial impedance measurements from both the left ear canal electrodes and the right ear canal electrodes;
transmitting, by a transmission module of the wearable device, the neurophysiological data and the neurovascular data to the computing device;
accessing, by the one or more computer processors, the neurophysiological data and the neurovascular data recorded during sleep by the wearable device;
executing, by the one or more computer processors, a function mapping from the neurophysiological data and the neurovascular data to a target that is a marker of glymphatic flow; and
outputting, by the one or more computer processors, a target prediction model based on the function mapping.
2. The method of claim 1 , further comprising:
executing, by the one or more computer processors, a second function mapping from the marker of glymphatic flow to a target that is one of a molecular analysis marker of neurodegeneration or a neuroimaging marker of neurodegeneration; and
outputting, by the one or more computer processors, a target prediction model based on the second function mapping.
3. The method of claim 1 , wherein sleep macrostructure and sleep microstructure features are extracted from the electroencephalogram measurements.
4. The method of claim 1 , wherein the neurovascular data are used to compute sleep cerebral fluid volume changes, cerebral perfusion pulsation, heart rate variability, resting heart rate, pulse transit time, pulse wave velocity and respiratory rate.
5. The method of claim 1 , wherein the function mapping is derived by the one or more computer processors from wearable devices comprising left ear canal electrodes and right ear canal electrodes recording left and right neurophysiological and neurovascular data on a population of individuals with data that is the marker of glymphatic flow.
6. The method of claim 1 , wherein the neurovascular data comprises data recorded by a photoplethysmograph and an inertial measurement unit.
7. The method of claim 1 , wherein the wearable device comprises: a left ear bladder that is pressurized inside a left ear canal of the patient to increase interfacial surface contact of the left ear canal electrodes with a wall of the left ear canal; and a right ear bladder that is pressurized inside a right ear canal of the patient to increase interfacial surface contact of the right ear canal electrodes with a wall of the right ear canal.
8. The method of claim 2 , wherein the molecular analysis marker of neurodegeneration is a cerebral spinal fluid or blood plasma assay of one of β-amyloid, tau, p-tau, α-synuclein, and neurofilament light.
9. The method of claim 2 , wherein the neuroimaging marker of neurodegeneration is a positron emission tomography scan with a radiotracer that binds to one of β-amyloid, tau, or glucose.
10. The method of claim 2 , wherein the neuroimaging marker of neurodegeneration is a magnetic resonance imaging scan.
11. The method of claim 1 , further comprising:
inputting new neurophysiological data and new neurovascular data into the target prediction model; and
outputting a predicted marker of glymphatic flow.
12. The method of claim 1 , further comprising:
inputting intervention data into the target prediction model;
inputting new neurophysiological data and new neurovascular data into the target prediction model;
outputting a predicted target marker of glymphatic flow; and
determining an effect of the intervention data on the predicted target marker of glymphatic flow.