System, device and method for determining and/or assessing brain related conditions based on pupil light response
View Patent ↗Provided herein are systems, devices and methods for monitoring the progression of, determining and/or assessing brain related conditions in a subject based on pupil light responses (PLRs) to focal central and peripheral chromatic light stimuli, in particular, by classifying the PLR based on one or more PLR parameter values, wherein the classifying allows monitoring the progression of, determining and/or assessing the brain related condition.
1 . A non-invasive method for monitoring the progression of, determining and/or assessing a brain related condition of a subject, based on pupil light response (PLR) to chromatic light stimuli, under mesopic light conditions, the method comprising:
determining, for each stimuli, a baseline pupil size of an eye of the subject;
applying high intensity blue light, low intensity blue light and red light stimuli to one or more regions of the visual field of the eye, the light stimuli configured to induce a response in the pupil;
obtaining a value for parameters related to induced changes in the pupil size in response to the light stimuli, said parameters comprise Maximal Contraction Velocity (MCV) of the pupil, Latency of MCV (LMCV) of the pupil and Percentage of Pupil Relaxation (PPR);
normalizing, based on the determined baseline pupil size, the value of the parameters; and
classifying the PLR based on the parameters values;
wherein said classifying results in monitoring the progression of, determining and/or assessing the brain related condition.
2 . The method of claim 1 , wherein the brain related condition comprises: brain tumor, optic neuritis, neurodegenerative conditions, traumatic brain injury, stroke, intracranial lesions, intracranial pressure or pseudotumor cerebri.
3 . The method of claim 2 , wherein the neurodegenerative conditions are selected from the group consisting of: Alzheimer's disease (AD), Multiple Sclerosis (MS), Parkinson disease (PD), and fragile-X related cognitive decline.
4 . The method of claim 1 , wherein the parameters further comprise one or more of: Percent of Pupil Contraction (PPC), Pupil Response Latency (PRL), Maximal Relaxation Velocity (MRV), Latency of MRV (LMRV), Maximal Contraction Acceleration (MCA), Latency of MCA (LMCA), Maximal Relaxation Acceleration (MRA), Latency of MRA (LMRA), Maximal Relaxation Deceleration (MRD), Latency of Maximal Relaxation Deceleration (LMRD), Area of Curve (AC), Latency of Maximal Pupil Contraction (LMP), Maximal Contraction Deceleration (MCD), Latency of MCD (LMCD), Max Pupil Size (Max_PS), or Min Pupil Size (Min_PS).
5 . The method of claim 1 , further comprising applying curve fitting to data associated with pupil size in response to light stimulation.
6 . The method of claim 1 , wherein the classification comprises applying at least one algorithm to one or more selected parameter values and obtaining a brain related condition.
7 . The method of claim 1 , wherein each light stimuli comprises between 1 and 228 individual light stimuli, each applied to a different location of the visual field.
8 . The method of claim 1 , wherein the light stimuli are presented for a period of time of between about 0.1 to 10 seconds.
9 . The method of claim 1 , wherein the regions of the visual field comprise a central visual field ranging between about 0-10 degrees; and/or wherein the regions of the visual field comprise a peripheral visual field greater than about 10 degrees.
10 . The method of claim 1 , further comprising providing an initial pre-determined light stimuli at an initial illumination, duration and visual field locations, configured to determine a possibility of the subject having a brain related condition, prior to applying the blue and red light stimuli, wherein applying the blue and red light stimuli to one or more regions of visual field of the eye is based, at least in part, on the determined possibility of the subject having a brain related condition.
11 . The method of claim 1 , wherein applying the blue and/or red light stimuli to one or more regions of the visual field of the eye comprises selecting a subset of light stimuli based on a location of the light stimuli in relation to the visual field; and/or one or more of: selecting the wavelength of each individual light of the light stimuli, selecting the intensity of each individual light of the light stimuli, selecting the ratio of blue to red light stimuli, or selecting the duration of illumination of each individual light of the light stimuli.
12 . The method of claim 1 , wherein applying the blue and/or red light stimuli to one or more regions of visual field of the eye comprises applying blue and/or red light stimuli in at least two intervals, said at least two intervals are about 2 to 120 seconds apart.
13 . The method of claim 12 , wherein each interval comprises a different subset of light stimuli, different wavelengths of light stimuli, and/or different intensities of light stimuli.
14 . The method of claim 1 , wherein the method comprises determining a risk for developing Alzheimer's disease, and the risk is determined based on at least one of: the MCV parameter in the central region of the visual field in response to a high intensity blue light stimuli, the PRL parameter in response to low intensity blue light in the periphery, the PRL parameter in response to blue light, the PRL parameter in response to red light, the LMCA parameter in response to blue light, the LMCA parameter in response to red light, the LMCD parameter in response to blue light, the LMCD parameter in response to red light, the LMP parameter in response to blue light, the LMP parameter in response to red light, or the MCV parameter in response to blue light.
15 . The method of claim 1 , further comprising inputting one or more selected values of at least one of the parameters to a machine learning algorithm(s) configured to classify the subject as having a brain related condition or not having a brain related condition.
16 . The method of claim 15 , further comprising classifying, using the machine learning algorithm, the brain related condition into types and/or levels of severity and/or progression of the condition based, at least in part, on the parameters and selected values thereof.
17 . A pupillometer device for monitoring the progression of, determining and/or assessing a brain related condition of a subject, based on pupil light response to chromatic light stimuli, under mesopic light conditions, the pupilometer device comprising:
a plurality of chromatic beam emitters configured to generate red and/or blue light stimuli at predetermined locations of the visual field;
at least one camera configured to detect pupil response; and
a control unit in communication with the plurality of chromatic beam emitters and the at least one camera, wherein the control unit is configured to
apply high intensity blue light, low intensity blue light and red light stimuli to one or more regions of the visual field of the eye;
for each stimuli, determine a baseline pupil size of an eye of the subject;
determine a value for parameters related to induced changes in the pupil size in response to the light stimuli, said parameters comprise Maximal Contraction Velocity (MCV) of the pupil, Latency of MCV (LMCV) of the pupil and Percentage of Pupil Relaxation (PPR);
normalize, based on the determined baseline pupil size, the value of the parameters; and
classify the PLR based on the parameter values, wherein said classifying allows monitoring the progression of, determining and/or assessing the brain related condition.
18 . The device of claim 17 , wherein the brain related condition comprises: brain tumor, optic neuritis, neurodegenerative conditions, traumatic brain injury, stroke, intracranial lesions, intracranial pressure or pseudotumor cerebri.
19 . The device of claim 17 , wherein the parameters further comprise: Percent of Pupil Contraction (PPC), Pupil Response Latency (PRL), Maximal Relaxation Velocity (MRV), Latency of MRV (LMRV), Maximal Contraction Acceleration (MCA), Latency of MCA (LMCA), Maximal Relaxation Acceleration (MRA), Latency of MRA (LMRA), Maximal Relaxation Deceleration (MRD), Latency of Maximal Relaxation Deceleration (LMRD), Area of Curve (AC), Latency of Maximal Pupil Contraction (LMP), Maximal Contraction Deceleration (MCD), Latency of MCD (LMCD), Max Pupil Size (Max_PS), Min Pupil Size (Min_PS), or any combination thereof.
20 . The device of claim 17 , wherein each light stimuli comprises between 1 and 228 individual light stimuli, each applied to a different location of the visual field.
21 . The device of claim 17 , wherein the regions of the visual field comprise a central visual field ranging between about 0-10 degrees and/or a peripheral visual field is greater than about 10 degrees.
22 . The device of claim 17 , wherein the control unit is further configured to select a subset of light stimuli based on at least the location of the light stimuli in relation to the visual field.
23 . The device of claim 17 , wherein the control unit is further configured to select the wavelength of each individual light of the light stimuli, the intensity of each individual light of the light stimuli, the ratio of blue to red light stimuli, and/or the duration of illumination of each individual light of the light stimuli.
24 . The device of claim 17 , wherein the control unit is in communication with a server or memory module comprising instructions for identifying, assessing and/or monitoring the progression of a brain related condition.
25 . The device of claim 17 , wherein the control unit is configured to classify, based on a machine learning algorithm, one or more selected values of at least one of the parameters as being associated with a brain related condition and/or a progression of a brain related condition.
26 . A system for monitoring the progression of, determining and/or assessing a brain related condition of a subject, based on pupil light response to chromatic light stimuli under mesopic light conditions, the system comprising:
at least one hardware processor; and
a non-transitory computer-readable storage medium having stored thereon program code, the program code executable by the at least one hardware processor to:
receive data associated with red and/or blue light stimuli generated at predetermined locations of the visual field of the subject, wherein the red and/or blue light stimuli comprises high intensity blue light, low intensity blue light and red light stimuli applied to one or more regions of the visual field of the eye;
receive data associated with the pupil size of the subject, for each stimuli;
determine a value for parameters related to induced changes in the pupil size in response to the light stimuli, the parameters comprise Maximal Contraction Velocity (MCV) of the pupil, Latency of MCV (LMCV) of the pupil and Percentage of Pupil Relaxation (PPR);
normalize, based on the base line pupil size, a value of the parameters; and
input one or more of selected values of at least one of the parameters to an algorithm configured to classify the subject as having a brain related condition or not having a brain related condition based, at least in part, on at least one value of parameters.
27 . The system according to claim 26 , wherein the algorithm is a machine learning algorithm configured to classify the brain related condition into types and/or levels of severity and/or progression of the condition based, at least in part, on the value of the parameters.