SYSTEM AND SIGNATURES FOR A MULTI-MODAL PHYSIOLOGICAL PERIODIC BIOMARKER ASSESSMENT

Methods for diagnosing Autism and/or Autism Spectrum Disorder (ASD) of a subject include establishing baseline brain wave patterns of the subject by having the subject perform a series of task and measuring brain waves during the tasks using an EEG measurement device, applying a light stimulus or images to the subject's eyes and capturing eye movements and/or changes in facial expression in response to the light stimulus or images, and giving a neuropsychological and cognition battery of tasks to the subject to generate a provoked cognitive assessment of the subject. A processing device correlates the baseline brain wave patterns, eye movements and/or facial expressions, and provoked cognitive assessment of the subject to profile data indicative of Autism and/or ASD. The corresponding system may also include an auditory testing device that tests the subject's sensitivity to sound and records the subject's speech in response to verbal tasks. The processing device performs language processing of the recorded speech and correlates the processed language to the profile data indicative of Autism and/or ASD.

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Description
CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority benefit of U.S. Provisional Patent Application No. 62/019,291 filed Jun. 30, 2014. The content of that patent application is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The invention relates to diagnosis and analysis of brain health through the use of activated tasks and stimuli in a system to dynamically assess one's brain state and function in a periodic biomarker assessment.

BACKGROUND

Normal functioning of the brain and central nervous system is critical to a healthy, enjoyable and productive life. Disorders of the brain and central nervous system are among the most dreaded of diseases. Many neurological disorders such as stroke, Alzheimer's disease, and Parkinson's disease are insidious and progressive, becoming more common with increasing age. Others such as schizophrenia, depression, multiple sclerosis and epilepsy arise at younger age and can persist and progress throughout an individual's lifetime. Sudden catastrophic damage to the nervous system, such as brain trauma, infections and intoxications can also affect any individual of any age at any time.

Most nervous system dysfunction arises from complex interactions between an individual's genotype, environment and personal habits and thus often presents in highly personalized ways. However, despite the emerging importance of preventative health care, convenient means for objectively assessing the health of one's own nervous system have not been widely available. Therefore, new ways to monitor the health status of the brain and nervous system are needed for normal health surveillance, early diagnosis of dysfunction, tracking of disease progression and the discovery and optimization of treatments and new therapies.

Unlike cardiovascular and metabolic disorders, where personalized health monitoring biomarkers such as blood pressure, cholesterol, and blood glucose have long become household terms, no such convenient biomarkers of brain and nervous system health exist. Quantitative neurophysiological assessment approaches such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI) and neuropsychiatric or cognition testing involve significant operator expertise, inpatient or clinic-based testing and significant time and expense. One potential technique that may be adapted to serve a broader role as a facile biomarker of nervous system function is a multi-modal assessment of the brain from a number of different forms of data, including (i) electroencephalography (EEG), which measures the electrical activity of the brain resulting from the generation and transmission of information between different regions of the brain; (ii) eye tracking, which measures the position of the eye (x,y) as a function of time t (x,y,t); (iii) accelerometer based measures for postural stability and balance, both static and dynamic; (iv) visual probes that test the subject include how they think cognitively as well as (v) other non-limiting biosensor based measurements.

Alternate and innovative biomarker approaches are needed to provide quantitative measurements of personal brain health that could greatly improve the prevention, diagnosis and treatment of neurological and psychiatric disorders, in particular, Autism Spectrum Disorder (ASD). Unique multimodal devices and tests that lead to biomarkers of Parkinson's disease, Alzheimer's disease, concussion and other neurological and neuropsychiatric conditions are a pressing need.

SUMMARY

Systems and methods are provided for diagnosing Autism, ASD, and/or other neurological and neuropsychiatric conditions of a subject. The method includes taking periodic baseline assessments by performing the steps of:

establishing baseline brain wave patterns of the subject by having the subject perform a series of task and measuring brain waves during the tasks using an EEG measurement device;

applying a light stimulus or images to the subject's eyes and capturing eye movements and/or changes in facial expression in response to the light stimulus or images;

giving a neuropsychological and cognition battery of tasks to the subject to generate a provoked cognitive assessment of the subject; and

correlating the baseline brain wave patterns, eye movements and/or facial expressions, and provoked cognitive assessment of the subject to profile data indicative of Autism and/or ASD.

These steps are repeated periodically to determine changes over time.

In exemplary embodiments, the step of establishing baseline brain wave patterns includes performing saccade and/or anti-saccade tests on the subject, while the step of applying light stimulus or images to the subject's eyes include showing the subject static images to evoke emotional responses and/or showing the subject dynamic images to evoke emotional responses. The step of giving a neuropsychological and cognition battery of tasks to the subject includes testing of the subject's memory, attention, and/or executive function. Other data may be collected and correlated to Autism and/or ASD as well. For example, the subject's speech in response to verbal tasks may be recorded and language processing of the recorded speech may be performed by the processor. Heart rate variability of the subject during the battery of tasks may also be measured.

In addition to an EEG measurement device and a device adapted to apply a light stimulus or images to the subject's eyes and to capture eye movements and/or changes in facial expression in response to the light stimulus or images, the system of the invention may also include an auditory testing device that tests the subject's sensitivity to sound and records the subject's speech in response to verbal tasks. The processing device is programmed to perform language processing of the recorded speech and to correlate the baseline brain wave pattern, eye movements and/or facial expression, provoked cognitive assessment of the subject, and processed language to a profile data indicative of Autism and/or ASD. The device adapted to apply a light stimulus or images to the subject's eyes and to capture eye movements and/or changes in facial expression in response to the light stimulus or images may comprise an eye tracking camera or biosensors that track eye gaze position and duration and pupillary response. Static probes may also be used to measure parts of the subject's face and dynamic probes may be used to apply dynamic stimulus to the subject relating to an emotionally evocative event and to measure the subject's response to the dynamic stimulus. Other biosensors may measure the subject's heart rate variability during the neuropsychological and cognition battery of tasks given to the subject.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention can be better understood with reference to the following drawings, of which:

FIG. 1 is a schematic diagram illustrating a human body outfitted with multiple REM modules as well as a nearby peripheral microprocessor (MCU) with direct or wireless access to electronic medical records.

FIG. 2 is a schematic diagram illustrating the flow of data from the human subject wearing a headset to the laptop, tablet or smartphone where it is encrypted and transmitted to the cloud.

FIG. 3 is a schematic diagram illustrating a basic periodic biosensor assessment including five elements.

FIG. 4 is a schematic diagram illustrating a more complicated periodic biosensor assessment including multiple biosensors and several more tasks and/or probes.

FIG. 5 is a schematic diagram illustrating a complicated periodic biosensor assessment including many biosensors and many tasks and/or probes.

 DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The invention will be described in detail below with reference to FIGS. 1-5. Those skilled in the art will appreciate that the description given herein with respect to those figures is for exemplary purposes only and is not intended in any way to limit the scope of the invention. All questions regarding the scope of the invention may be resolved by referring to the appended claims.

Definitions

By “electrode to the scalp” we mean to include, without limitation, those electrodes requiring gel, dry electrode sensors, contactless sensors and any other means of measuring the electrical potential or apparent electrical induced potential by electromagnetic means.

By “monitor the brain and nervous system” we mean to include, without limitation, surveillance of normal health and aging, the early detection and monitoring of brain dysfunction, monitoring of brain injury and recovery, monitoring disease onset, progression and response to therapy, for the discovery and optimization of treatment and drug therapies, including without limitation, monitoring investigational compounds and registered pharmaceutical agents, as well as the monitoring of illegal substances and their presence or influence on an individual while driving, playing sports, or engaged in other regulated behaviors.

A “medical therapy” as used herein is intended to encompass any form of therapy with potential medical effect, including, without limitation, any pharmaceutical agent or treatment, compounds, biologics, medical device therapy, exercise, biofeedback or combinations thereof.

By “EEG data” we mean to include without limitation the raw time series, any spectral properties determined after Fourier or other transformation into the frequency domain, any nonlinear properties after non-linear analysis, any wavelet properties, any summary biometric variables and any combinations thereof.

A “sensory and cognitive challenge” as used herein is intended to encompass any form of sensory stimuli (to the five senses), cognitive challenges (to the mind), and other challenges (such as a respiratory CO2 challenge, virtual reality balance challenge, hammer to knee reflex challenge, etc.).

A “sensory and cognitive challenge state” as used herein is intended to encompass any state of the brain and nervous system during the exposure to sensory stimuli and cognitive load challenge.

An “electronic system” as used herein is intended to encompass, without limitation, hardware, software, firmware, analog circuits, DC-coupled or AC-coupled circuits, digital circuits, optical circuits, FPGA, ASICS, visual displays, audio transducers, temperature transducers, olfactory and odor generators, or any combination of the above.

By “spectral bands” we mean without limitation the generally accepted definitions in the standard literature conventions such that the bands of the PSD are often separated into the Delta band (f<4 Hz), the Theta band (4<f<7 Hz), the Alpha band (8<f<12 Hz), the Beta band (12<f<30 Hz), and the Gamma band (30<f<100 Hz). The exact boundaries of these bands are subject to some interpretation and are not considered hard and fast to all practitioners in the field.

By “calibrating” we mean the process of inputting known signals into the system and adjusting internal gain, offset or other adjustable parameters in order to bring the system to a quantitative state of reproducibility.

By “conducting quality control” we mean conducting assessments of the system with known input signals and verifying that the output of the system is as expected. Moreover, verifying the output to known input reference signals constitutes a form of quality control which assures that the system was in good working order either before or just after a block of data was collected on a human subject.

By “biomarker” we mean an objective measure of a biological or physiological function or process.

By “biomarker features or metrics” we mean a variable, biomarker, metric or feature which characterizes some aspect of the raw underlying time series data. These terms are equivalent for a biomarker as an objective measure and can be used interchangeably.

By “non-invasively” we mean lacking the need to penetrate the skin or tissue of a human subject.

By “diagnosis” we mean any one of the multiple intended use of a diagnostic including to classify subjects in categorical groups, to aid in the diagnosis when used with other additional information, to screen at a high level where no a priori reason exists, to be used as a prognostic marker, to be used as a disease or injury progression marker, to be used as a treatment response marker or even as a treatment monitoring endpoint.

By “electronics module” or “EM” or “reusable electronic module” or “REM” or “multi-functional biosensor” or “MFB” we mean an electronics module or device that can be used to record biological signals from the same subject or multiple subjects at different times. By the same terms, we also mean a disposable electronics module that can be used once and thrown away which may be part of the future as miniaturization becomes more common place and costs of production are reduced. The electronics module can have only one sensing function or a multitude (more than one), where the latter (more than one) is more common. All of these terms are equivalent and do not limit the scope of the invention.

By “biosignals” or “bio signals” or “bio-signals” we mean any direct or indirect biological signal measurement data streams which either directly derives from the human subject under assessment or indirectly derives from the human subject. Non-limiting examples for illustration purposes include EEG brainwave data recorded either directly from the scalp or contactless from the scalp, core temperature, physical motion or balance derived from body worn accelerometers, gyrometers, and magnetic compasses, the acoustic sound from a microphone to capture the voice of the individual, the stream of camera images from a front facing camera, the heart rate, heart rate variability and arterial oxygen from a pulse oximeter, the skin conductance measured along the skin (Galvonic Skin Conductance/Resistance, also called Electrodermal Activity), the cognitive task information recorded as keyboard strokes, mouse clicks or touch screen events. There are many other biosignals to be recorded as well.

A System of Multiple Transducers to Both Stimulate and Record Physiological and Brain Response as a Periodic Biosensor Assessment for Autism and Autism Spectrum Disorder (ASD)

The systems and methods of the invention comprise multiple transducers to both stimulate and record the physiological response of the brain and the body in order to assess its health and function. Central to the system is the ability to directly record brainwave activity from an electrode placed non-invasively on or near the scalp. Moreover, additional information on brain health and function can be derived from transducers that measure position and motion, temperature, cardiovascular properties like heart rate, heart rate variability, and arterial oxygen, as well as cognitive information, speech, eye movement, and surface skin conductance to name a few non-limiting additional biological signal measurement data stream examples. It is often necessary to bring the system to the human subject, getting out of the hospital or doctor's office and enabling data collection in the home on or near the sports field or combat theater, thus providing accessibility to the brain health and function assessment from a lightweight and portable form factor. Moreover, it would be advantageous to have a minimal cost associated with the system so that it can be used around the globe to help those in need of brain health and function assessments.

A solution to these problems includes the creation of a system of body worn or body proximal electronic modules (EMs or REMs) with the ability to both record biological signal measurement data streams as well as present stimuli to the human subject in the form of various sensory and cognitive challenges and tasks. In particular, one such electronic module (EM) or reusable electronic module (REM) can be placed in the vicinity of the head and be either reused over and over if it does not touch the human body or disposed of if it comes in direct contact with the human body.

In one embodiment of the system, as illustrated in FIG. 1, a human subject 3 is outfitted on their head 4 with an electronic module (EM) or reusable electronic module (REM) 5, which has several sensors and transducers within it to both stimulate the human subject and to record biological signal measurement data streams (“bio signals”) in a precise fashion driven via software either embedded within the REM on a local microprocessor control unit or microcontroller unit (MCU) or running on a nearby peripheral MCU. In this system, limb 6 in the form of an arm or limb 7 in the form of a leg can hold additional REM modules 8 or 10 for additional readout and acquisition of additional biological signals. As desired, an REM module 9 is placed on the trunk of the human subject or up by the chest or around the neck. Nearby, typically connected via wireless interface, a peripheral MCU 11 would both control the standardized application of sensory and cognitive stimuli as well as coordinate the extensive data acquisition of the biological signals derived from the human subject 3. The peripheral MCU 11 is either a laptop, tablet PC or smartphone, or perhaps it may be sitting in a separate location altogether from where a human subject is immersed in an audio-video like home theater of image, sound, and other sensory stimuli, so-called virtual reality. It is contemplated that the REM modules could eventually interface with each other via newer RF technology which enables long distance communication with large bandwidth. Importantly, peripheral MCU 11 may have database access either locally via a hard wire 12 to a mass storage device like a hard drive 13 or, alternatively, it may be connected via a wired or wireless network interface 14 (e.g. ethernet cable, Wi-Fi, cellular data modem, satellite data modem to name a few non-limiting examples) to a remote mass storage device 15 with remote MCU capability. The purpose of the access to a database is to enable the system of the present invention to access and pull down additional information about a human subject from electronic records that may exist in some other location and where either downloaded locally to the peripheral MCU 11 or available remotely through network connectivity 14 to remote data base 15 (for instance to pull genetic information or other lab results into the system to make predictive signatures more accurate or precise with the inclusion of blood type, last recorded blood pressure, or ApoE genotype status as non-limiting examples). In either case, once a unique patient identification number has been entered and proper security clearance made (such as two factor authentication), then many additional variables of data can be pulled out of the data base records stored on mass storage device 13 and/or 15.

Another embodiment of the invention includes a data recording and analysis system that includes at least one REM placed on the head of a human subject 3 to record brain related biological health signals, a peripheral MCU, and a cloud based enterprise information technology infrastructure to process and report the data that has been collected. In particular, FIG. 2 illustrates an electronic REM module 306 on a subject's head transmitting wireless data to peripheral MCU (in the form of a tablet PC) 304. While the data is being collected through the Bluetooth port in the MCU 304, the camera 300 is recording a movie of images of the subject as he/she performs tasks to not only verify their identity but also to analyze their eye and facial movement for features of interest (including saccade and emotional state). Microphone 312 records the voice of the subject for voice recognition analysis, while built-in accelerometer and gyrometer 302 measure the stability or lack thereof of the subject, while touch screen 304 of the peripheral MCU records events at precise times and spatial (x,y) locations on the touch screen. Finally, when all the various data streams are complete, along with demographic and personal health information, the entire package of information is encrypted locally using AES-128 or AES-256 bit encryption (or equivalent security measures) 308 before being transmitted at 310 to the virtual or remote based servers through an internet connection 314 which could be Wi-Fi, Ethernet, cellular, satellite, or other technology in nature.

Need for Early Diagnosis in Autism and Autism Spectrum Disorder

An area of particular interest and challenge is in the early diagnosis and management of subjects who either have Autism or Autism Spectrum Disorder or are being managed after having been clinically diagnosed. Present theory holds that there is a critical window before a child reaches the age of four or five years old. During when therapeutic approaches have been shown to produce excellent results in enhancing the cognitive and social function of a child along the spectrum. That is the Cognitive Behavioral Therapy (CBT) has been shown to significant shift patients towards the normal end of the spectrum if they can get CBT when the child's brain is still highly plastic, neural connections are actively being tested and created. Thus, if an infant or toddler is properly diagnosed in the first or second year of life, then CBT and other therapy has been shown to shift individuals who are initially believed moderate to mild, or in the case of a mild case towards Asperger's, or potentially even towards normal developmental status. Thus, there is a great opportunity to have a major health impact in these individual's lives for many decades if they can be diagnosed early.

Moreover, large pharmaceutical companies are actively investigating drug and biologic interventions and therapies for Autism and ASD. Thus, by improving our ability to diagnose an individual early in life, one can dramatically shift their prognosis by getting them the proper behavioral therapy, medical therapy, and other possible therapeutic interventions including medical technology such as neuro-feedback and other medical device or medical technology related interventions.

Thus, if one can utilize the methods and systems of the present invention, then patients identified with Autism or ASD or Asperger's (even milder along the ASD spectrum) can promptly receive the best that therapy has to offer and enhance the quality of their lives for the next 50 to 70 years. Moreover, in the case of those who are diagnosed later in the golden window, at age 2, 3, or 4 years old, can also benefit from promptly receiving the best therapy that is available. They may not experience as dramatic as shift as may have been possible at an earlier age but can still nevertheless improve their cognitive and social skills thereby reducing their symptoms and enhancing their quality of life.

Additionally, in school aged children, pre-adolescent and adolescent teenagers, a more objective diagnosis and monitoring of the physiological biomarkers enabled by the biosensors of the present invention, permit a more quantitative tracking of how any given individual is progressing using much more objective biomarkers than are presently available today. One could envision that at some point, the methods and system of the present invention could be part of an at-home diagnostic toolkit which would permit parents to monitor on a weekly, bi-weekly, every third week or monthly basis. This would allow a much more precise means of measuring which therapeutic approaches are working and which are not working. Moreover, it would enable a much better understanding of the impact of nutrition and environment on the individual. One example of this can be found in the use of the present invention during the drug development process in order to help researchers understand the effects, both positive and potentially negative, on the clinical trial participants who are receiving a non-FDA approved drug or active pharmaceutical agent. In this case, the biomarkers extracted from the methods and system of the present invention could enable at home measurements during the course of an investigational clinical study, thereby significantly enhancing the signal to noise ratio.

As a concrete example, if a pharmaceutical company or biotechnology company is developing a therapy for Autism, ASD, or Asperger's Syndrome (collectively “Autism”), then they would typically see the patient after enrollment in the trial on a quarterly or every 6 months basis. By utilizing the methods and systems of the present invention, a therapy developer could train the parents how to acquire the biosensor data and send the equipment home with them to collect data on a much more regular basis. According to the central limit theorem, for each independent observation made, they are typically distributed equally according to a “normal” or Gaussian distribution (the so-called bell curve) such that in the limit of large numbers of observations, the sample mean approaches the true value. Further mathematical manipulation can show that the signal to noise ratio typical scales with the square root of the number of observations N in the sample for that subject or patient. Thus if one could measure every other week over the course of a 6 month trial, then that would amount to N=13 observations, rather than one at the beginning and the end. Thus, bi-weekly assessments over a six month trial represents a square root of 13=3.65 fold increase in the signal to noise ratio, thus enabling a therapy developer to understand with much better clarity if a given intervention is working or not. This is an enormous benefit that the systems and methods of the present invention provide via the neuroscience biomarkers gleaned from the present invention.

Development of Diagnostic Algorithms, Predictive Models, and Biomarkers

The methods and system of the present invention can also be put to use in the diagnostic classification of individuals based on the biosensor data collected and the analytic tools built into the present invention. In particular, the system and methods provide the means to synchronize raw streams of biosensor data and accurately synchronize this to probe stimuli otherwise jittery in timestamp. This should enable enhanced data processing algorithms whereby one channel of data is used to gate in or out another channel of data. Other methods can include but are not limited to the construction of continuous two point correlations of well synchronized data streams to create new biomarker and biomarker streams heretofore not possible.

Moreover, any and all the extracted features can be worked into statistical predictive models which are designed to either classify an unknown subject into category (so called categorical classification) or regress to a continuous number in a regression model with an index as the output variable. Standard techniques well known in the field include logistic regression/classification (including stepwise techniques which select candidate features based on their classification accuracy and performance), discriminant techniques (including linear discriminant analysis and quadratic discriminant analysis), tree based methods (including decision trees, as well as bagged and boosted techniques like Random Forest), neural nets (with or without hidden layers and various weighting functions), support vector machines and other machine learning algorithms.

The idea amongst all of these techniques is first signal pre-process the data to remove artifacts. Then, one runs signal processing algorithms like the fast Fourier transform to extract features from the raw times series data. Then from the extracted features, on builds discovery predictive models on known subjects and “trains” the model with known information in a “training” set of data. The second step after discovery models have been built and assessed is to test the performance or accuracy of the predictive discovery model on subject data which was not used to build or train the model. This test of the model with independent data is termed model “verification” and comes from a second and different verification set of data than that used to train the model. This is then followed by the third step which consists of model validation whereby the locked model with no adjustable parameters is evaluated prospectively in another cohort of subjects, typically moving forward in time. It is these sorts of algorithms and models that are most interesting to the clinical and medical community because they can supplement the subjective observations of a clinician with objective biosensor data through a locked algorithm. The clinical performance of a diagnostic algorithm or device is often characterized by the clinical sensitivity and specificity.

Recall that the sensitivity is the probability that the diagnostic test correctly identifies a truly positive case. The clinical specificity is the probability for the diagnostic tool to properly identify a negative case as negative. Thus, the most desired outcome is 100% sensitivity and 100% specificity. In neuroscience, it would be great to be above 80% in either category let alone both. One can quickly see the clinical utility in using the methods and system of the present invention to create synchronized biosensor data streams with probe stimuli and to enable advanced analytics to extract biomarkers and features for predictive modelling.

Specific Embodiments of an Autism Periodic Biosensor Assessment

As illustrated in FIG. 3, a primary objective of such a system in accordance with an exemplary embodiment is to make a periodic biosensor assessment in school age ASD patients (6-17 yrs. old) that fall on the mild-to-moderate level of severity as well as adults. The system will demonstrate multiple recordings throughout behavioral therapy to see changes within a given subject (looking for a tendency in the biomarkers to move in the direction of Developmental Normal subjects).

The detailed objectives around Periodic Biosensor Assessment (PBA) is to develop objective, independent measures of core and associated symptoms of ASDs to better define sub-populations for research and to provide robust, objective assessment of treatment response. The biology that the biosensors are designed to pick up includes robust objective measures of core ASD symptoms, including communication and social interaction, restrictive repetitive behaviors (RRB), and anxiety. Generally, biomarkers fall into various classes including (i) emotional, (ii) affect, (iii) neural and (iv) relational information.

Biosensors fall into two major categories, continuous and periodic. Continuous biosensors include measurement of native activity, including fluctuations over the time course of a day. They include actigraphy (typically from three dimensional accelerometer measurements), Galvanic Skin Response (also called electro dermal activity or EDA), Heart Rate Variability (HRV), temperature (both ambient and skin), and vocalizations or language use (if possible). Attributes of continuous biosensors include waterproof, long battery life, co-registers data/time stamp for linking and synchronizing data, Bluetooth or wireless upload of data, balance of high measurement precision (accuracy)/sample rate frequency, access to an application programming interface (API) and the ability to record raw data, and can possibly possess pre-existing normative data (or can be easily generated). In addition, for children and adolescents one would like to see features such as easy to wear, low footprint, and comfortable form factor.

On the other hand, as shown in FIG. 3, PBA biosensors include but are not limited to (i) EEG (ii) eye gaze or eye tracking (either via an integrated sensor like a web cam or an external device, (iii) cognition (neuropsychological tasks or events), (iv) facial affect expression/recognition (typically via webcam), and (v) language processing (via microphone). In one particular embodiment of the present invention, the PBA includes:

    • Single lead EEG headset
    • Eye tracking capability, either through external hardware (e.g. Tobii, GazePoint, etc.) or through web-cam API (e.g. Sticky) with sample rates of 30 Hz or less to capture eye movements in response to light stimulus
    • Cognitive battery tasks
    • Suitable probes
    • Explore the addition of other components, such as electrodermal activity (EDA) and actigraphy.
    • Image Presentation (direct vs averted gaze, faces vs houses, cropped eye regions with emotional choice selection)
    • Movie Presentation (social interaction of mom with school aged kid)
    • Embedded Figure Task—to observe hyper-performance (superlative) relative to Developmental Normal (DN) kids

The PBA includes the following features: (i) at home use by school aged ASD subjects with supervision by their parents; (2) a prospective intended use population to include eventually both pre-schoolers and adults; (3) hardware that is fit for its purpose; (4) easy to use; (5) has a robust GUI that is adaptable to the form factor; (6) the ability to swap tasks; (7) enjoyable to the extent possible (e.g. leverage gamification where possible); and (8) a hardware form factor which is a laptop or Android or iOS or Windows tablet. A particularly preferred embodiment includes a Windows laptop, Cerora MindReader™ EEG headset, and eye tracker. The software would include a data acquisition or DAQ component, a cloud based analysis code, and a data transfer/integration capability to other databases and APIs.

Table I below illustrates the relationship between desired clinical biology objective, literature supported biomarkers, and chosen biosensor and biometric. The biology that the biosensors are designed to assess includes robust objective measures of core symptoms: (1) Communication and social interaction; (2) restrictive repetitive behaviors (RRB); and (3) anxiety.

Behavioral Number ClinicalObjective Biomarker Probe/Task Biosensor& biometric 1 Establish baseline Eyes open, eyes closed, EEG standard tasks brain wave patterns saccade and anti-saccade 2 Communication/ Display of static and ET (including both gaze- Social Interaction dynamic images of mother tracking and pupillometry, the specific tasks like people engaged in two measure of the diameter of the classes: averted (usually to pupil); EEG (with select the side) vs direct gaze parameters from the literature) (directly towards the participant) 3 Static display and ET; EEG observation of images of houses vs human faces 4 Static display and ET; EEG; NPT observation of cropped images of the eyes region of a human face; subject asked to select the emotion in the image from amongst 4 choices in each quadrant outside the image 5 Dynamic display on the ET; EEG, GSR video monitor and observe movies of mother-child interactions with zones of engagement such as passing a ball or toy back and forth 6 Neuropsychological and NPT cognition battery of tasks such as reaction time, choice serial reaction time, one card back, one card learning 7 Embedded Figures Test to EEG, ET, NPT assess the superlative behavior of ASD subjects to find embedded figures within a complex graphic 8 Verbal tasks such as reading LP passages of text, naming numbers off saccade cards (such as the Pierce, King- Devick, Developmental Eye Movement or Cerora Saccade Tests) 9 Anxiety, RRB Static display on the video GSR, ET, EEG, Actigraphy monitor of two classes of (RRB) images; averted vs direct gaze photos of parent like people 10 Display and presentation of GSR, ET, EEG, Actigraphy dynamic movies of moms in (RRB) direct vs averted gaze towards the participant 11 Cognitive measures Attention, memory, and Cognitive tasks as well as design executive function of cognitive tasks integrated into paradigms above Abbreviation Key: ET = Eye Tracking EEG = Electroencephalography NPT = Neuropsychological testing LP = language processing or automated speech analysis (from the microphone recording) GSR = Galvanic Skin Response (also called Electrodermal Activity or EDA)

A Second (2nd) Embodiment of the Periodic Biosensor Assessment (PBA) for Autism or ASD

An alternate or second embodiment of the PBA for Autism or ASD would allow for the administration of a discrete set of home-based, simple-to-use, periodically administered biosensors that will help capture information that is correlated with or sensitive to one or more ASD symptoms (core or associated), that can be used as a surrogate measure for change in a subject, either over time naturally or due to a possible therapeutic intervention, including cognitive behavioral, pharmacodynamics and others. The system of this embodiment can be seen in FIG. 4 including the following non-limiting periodic biosensor assessments: (1) eye gaze position (x,y,t) for each of the left and right eye, duration (eye tracking, head position); (2) pupillary response (radius or diameter as function of time); (3) EEG (waveform data, ERP or evoked response potentials); (4) facial affect recognition; (5) cognition; and (6) facial emotion expression.

A Provisional Task List would include non-limiting tasks such as: (1) saccade and anti-saccade tasks for baseline EEG patterns; (2) alternate saccade and anti-saccade tasks for baseline Eye Tracking performance; (3) static display of houses vs faces photos or images; (4) static display of cropped images of human faces with the eyes visible where the subject is asked to select the correct emotional response of the eyes shown from among the four choices presented, one in each quadrant outside the cropped facial photo showing only the eyes; (5) dynamic presentation of short movies of social interaction (mother-child) where the duration ranges from as short as 15-30 seconds up to 5 to 10 minutes, give or take; (6) dynamic presentation of short movies of mother-like actresses in direct vs averted gaze towards the test subject; (7) static display of Embedded Figures Test (from the Autism published literature where someone tries to find embedded figures within a complicated graphical visual display); (8) verbal tasks for language processing including reading of passages of text, reading of numbers, letters and elements within saccade tests; and (9) neuropsychological testing or cognition testing to include non-limiting assessment of brief attention, working memory, and/or executive function.

The system of this present embodiment would include use of control data from unaffected siblings where available or an independent sample of non-related demographically matched developmentally normal (DN) comparator subjects. The system would establish tasks across various co-variate dimensions including: (1) age where groups of age ranges for each task are established and developing equivalent tasks across age ranges of: (a) 3-5 years; (b) 6-12 years; (c) 13-17 years; (d) 18+ years. Moreover, the system should assess and document the functional level of the test subject, their severity of communication deficit and any possible IQ impairment. Furthermore, levels of engagement of required tasks should be considered and designed into the test battery.

Literature supported biomarker findings in the Autism or ASD subjects motivate the selection of tasks and measurements across several different task/probe and biosensor modalities.

In particular, several EEG findings have been reported in the literature including: 1) EEG paroxysmal abnormalities in patients with ASDs with and without seizures, mainly temporal and central (Parmagianni 2010), Yasuhara (2010)) with findings in frontal and central regions in ASD subjects with and without epilepsy; 2) qEEG-increased delta-theta activity in frontal region (Pop-Jordanova, 2010); 3) measurement of Event Related Potentials (ERP) during face processing of infants show differential pattern versus controls in infants (Webb, 2010); 4) ERP different for inverted faces in control adults versus those with ASDs (Webb, 2009), despite poorer behavioral performance in ASD subjects, both groups had comparable P1 and N170 responses; 5) adults with ASD>alpha power (eyes-open) versus controls, controls>occipital alpha suppression (eyes-open) versus ASD, finding ASD eyes-open alpha power and coherence in posterior brain regions were inversely correlated with attention to detail (Mathewson, 2012); 6) High-frequency (gamma) spectral oscillations with sustained visual attention in ASD boys 3-8 years (vs. controls), correlated with developmental delay (Orekhova, 2012); and 7) ERP (oddball task) of detection of eye gaze direction in children ASD. The detection of a change in eye direction elicited occipito-temporal negativity, which had two major differences between children with and without autism. Occipito-temporal negativity was right-lateral dominant and more pronounced in direct gaze in typical children, while bilaterally distributed and less pronounced (lower amplitude) to direct gaze in ASD (Senju, 2005).

Moreover, several eye tracking published findings motivate the present approach and system including emotion recognition ability, eye direction detection, decreased focus on eyes during face scanning, greater focus on other parts of face and irrelevant areas like feet or background objects.

Moreover, paradigms or outcome variables utilized in the present embodiment include: 1) cognition; 2) performance on attention, memory, and executive function tasks; 3) measure of efficiency, coherence, and pattern of EEG during cognitive tasks; 4) measures of EEG to be included; 5) emotional processing and expression, typically assessed using prototypic facial emotion discrimination where the test subject is asked to classify or accurately identify the emotion or facial expression; 6) also facial emotion expression which measures the reactivity to emotional scenes (e.g. fighting and arguments) or in response to prompting for the expression of emotions (such as the Computer Emotion Recognition Toolkit (CERT)).

Moreover, eye movements and gaze direction measures could include: 1) pupil position using camera: tracking of focal vision—gaze position and duration; 2) pupillary light reflect: autonomic response, regulation of pupil size (infrared pupillometer); 3) electro-oculography (EOG): resting potential of retina to record eye movement); 4) corneal-reflexion photography: (camera detects corneal reflection of infrared light to identify eye movements); and 5) position-sensing system: account for head movements to provide a steady stream of eye position data (acoustic, or web-camera based).

A Third (3rd) Embodiment of the Periodic Biosensor Assessment (PBA) for Autism or ASD

A third embodiment of the PBA for Autism or ASD would allow for the elements and features of the second embodiment described above plus the following differences and enhancements. In particular, the use of static versus dynamic stimuli and probes would be altered, as shown in FIG. 5.

In particular, the static probes would include, but not be limited to: 1) parts of faces (e.g. the cropped eyes but could also include the nose or forehead in addition); 2) stable faces where a comparison of social images which involve humans/people compared to non-social images which involve personified inanimate objects, e.g. such as a talking coffee cup; 3) Morph picture animation where strange evolution of images in time leads to a reaction from the test subject; 4) point light displays; 5) simple caregiver dyad videos; and 6) simulated social interactions between peer aged kids or a peer aged subject and a parental figure.

In particular, the dynamic probes would include, but not be limited to: 1) social versus non-social dyads or movies between a parent and peer aged child actor compared to a talking coffee cup or pencil and eraser and movies or dyads of emotionally pleasant play versus emotionally charged interaction such as fighting, bullying, humiliation or enormous pain. The elements of the present embodiment include but are not limited to: 1) simple attention engagement—social versus non-social dynamic stimuli; 2) dynamic stimulus that has an emotionally evocative event (where distress is shown by the actor in the autonomic and attentional response literature as a measure of response to others' distress or the ability to show/express empathy); and 3) response to social threat (anxiety domain) such as angry or fearful faces.

Additional tasks of interest within the present embodiment include but are not limited to: 1) repetitive behavior—cognitive flexibility, inhibit a pre-potent response; 2) executive function or Posner—disengagement task (e.g. a gap task) which is a classic task used widely in ASD with several variations; 3) delayed non-match-to-sample: both immediate and long term memory; 4) task that measures rate of learning and neuroplasticity; and 5) several auditory tasks. In particular, auditory tasks can include an auditory list learning task as a language-based test where one tends to see some decrement in language processing, fluency, auditory memory span or general auditory memory. Understanding semantic or pragmatic, gist or nuance, emotional prosody in the voice (emotional sound embedded in language). The auditory tasks can also include sensory sensitivity (auditory) and integration of auditory and visual information including language that is being presented. For example, for two speakers on the screen, does the subject look at the matching face that is pronouncing the words that the sound card is generating.

The system of the present embodiment would make measurements using the following biosensors: 1) EEG; 2) autonomic responses; 3) visual attention; 4) pupillometry; and 5) Heart Rate Variability (HRV). In addition to the biosensors of the above description, or their equivalents, the system should control or monitor the effect of environment of the test subject.

Moreover, the system should have as many features as possible which would document the effect of other covariates on PBA biosensor data. As a non-limiting example, it would be advantageous to measure and record movement artifact on EEG alpha sub-band relative power as the test subject enters a repetitive behavior phase. As one non-limiting embodiment, one could include a video monitor recording behavior during the situation—post-hoc, one can remove epochs or parts of the EEG where the test subject is fidgeting or talking or not attending, or alternatively incorporate a multi-axis accelerometer embedded in the REM or its skull supported form factor (e.g. headband or eye glass frame).

Alternatively, one could build a customized chair for the purpose of controlling behavior of the individual (e.g. reclined, forcing the test subject to relax based on gravity and support). Thus, by monitoring the video, through automated or through manually examined video tape, one could in principal determine when a test subject is not paying attention and then off-line gate on attention to remove irrelevant or confounding EEG data.

An alternate approach to attention is sclera tracking with an eye tracking camera. One can automate the detection of eyes forward and eyes averted using video of faces. In fact, one could prefer to do this from a web-cam obtained video of the test subject. Additional embodiments include types of chairs that would minimize movement, ones that keep the legs of the test subject touching the ground (so as to not swing), has chair arms to support the test subject's arms, etc.

Moreover, one can attempt to control the background in addition to the foreground of the test subject's visual field. What sounds are being isolated from the test subject and controlled for? What is the ambient temperature in the test room? Is it warm and thus inviting sleep or, alternatively, chilly, keeping the subject awake and attentive? In particular, on could create a set of instructions to optimize the environment for testing. In one embodiment, one could create a puppet show theater stage or photo booth. Alternatively, one could utilize a live web-cam based observation, watching the test subject remotely. If the test subject was not paying attention, one could use that signal to toggle off and on the EEG or other signal of interest based on when they were and when they were not paying attention.

In addition, the present embodiment includes the process improvement of conducting training sessions in a controlled environment before allowing practice sessions at home, to minimize artifactual movement. One could establish a precise set of criteria whereby a preferred entertainment video was only played when the test subject sat still, using the entertainment video as a source of motivation to behave well. This approach could be extended further by conducting practice after training in the lab setting, then practicing at home, with the motivation of an entertaining stimulus. Entertaining, but also habituate, first in the lab, and then in the home, before one proceeds to collect at home data. The test subject is being asked to earn something for their participation. Controls could be built into the system that measure confounding variables that could be utilized to control for spurious behavior.

A 4th PBA Embodiment for Autism or ASD

An alternate embodiment of the present invention includes the following features and elements as described in Table 2.

TABLE 2 Features and elements as part of a Periodic Biosensor Assessment for Autism. Discussion Action Use of Static vs Dynamic Stimuli In addition to some novel tasks require sets of stimuli Pursue whether there are existing which have already been tested in the target population stimuli sets available and shown to influence the variables to be measured. Possible contacts: Focus on 3 types of dynamic stimuli: → Theta, alpha, gamma EEG 1. Simple attention engagement - social vs. non-social absolute power, look at  dynamic stimuli, can also include baseline condition differences between social & non-  with an abstract stimuli. social interaction before and after 2. Dynamic stimulus that has an emotionally evocative therapy Rx  event (where distress is shown by the actor such as  crying or pretend cut finger with distress) - robust  literature with live people (not video). Look at  autonomic and attentional response. Measure of  response to others' distress. 3. Response to social threat (anxiety domain). Angry or  fearful faces; also consider female actor with face  away and not interact vs face toward audience and  interact socially; threat cues. Additional tasks which may be of interest: 1. Repetitive Restrictive Behavior - cognitive flexibility - Particular focus on stimuli sets inhibition of a prepotent response 2. Executive function disengagement of attention or “GAP” task, anti-saccade away from the target after the background cue with delay 3. Rate of Learning - May be impacted Therefore need a good task to capture rate of learning - neuroplasticity 4. Auditory tasks  a. Auditory list learning task. Language-based   test. Tend to see some decrement in language   processing, fluency, auditory memory span or   general auditory memory. Understanding   semantic or pragmatic, gist or nuance,   emotional prosody in the voice (emotional   sound embedded in language).  b. Sensory sensitivity (auditory).  c. Integration of auditory and visual information.   Language that is being presented. Two   speakers on the screen, do they look at the   matching speaker (vs mismatch between   shape of mouth and sounds/phonemes   emerging). 5. Memory tasks Delayed match to sample Extra matrix shift - categories Neuropsych testing - definition of parallel versions for different ages What is being measured: EEG Autonomic responses Visual attention Pupillometry Heart Rate Variability Consider practicalities of testing at home and insure integrity of data Consider what are sources of error? How can these What is he measuring and how is be controlled? he controlling for behaviors? Means to mitigate artifacts from at home testing fMRI/DTI artifact reduction with 1. Monitor - automated or through examined statistical control video tape removing EEG data when child is tracking with eye tracking camera, moving or not attending sclera of the eye 2. Design tasks that produce measures of child Set of instructions/criteria to paying attention, e.g. use eye tracking to detect optimize environment for testing eyes forward or averted, gate one channel on Sources of entertaining/novel another video which will be played as a reward for sitting still Discuss Environmental Variables Control environmental factors like chairs that Use additional equipment which minimize movement will allow more isolated Background sound testing/less distractions Temperature/humidity (sweating or not) Background EMF/RF noise Have puppet show around the laptop to cut out visual distractions “Easier than come to study site” for the parent as the alternative Video monitor setup live via webcast, could have lab person toggle switch that shows up on EEG record Site selection for PBA: clinic 1st, then go home? Compliance Procedures Operant Conditioning works well in ASD Use of practice sessions - in lab first before go home, then establish performance at home in practice sessions before go live with study data collection Video which will only be played when a child sits still; start with 100% entertainment, then insert more and more testing within the entertainment to keep it both fun and informative

A 5th PBA Embodiment for Autism or ASD

An alternate embodiment of the present invention includes the following features and elements as described in Table 3. One methodological aspect of the present invention includes working in a staged or sequential fashion such that one starts in the clinic, then go to analysis for high runners, then to next in home based devices, which could be later ruggedized and put into the home directly in the future. In particular, some wearable continuous biosensor data can get correlated with a symptoms report or neuropsychological report within an EMR/EHR system. Other components, labs or a home based system with more tasked stimuli, can assess and measure shifting behavior and attention.

One aspect of the present invention is the cross gating of one modality of data within another so one only evaluates relevant portions of a particular biosensor data stream. This will thus increase the signal to noise ratio by cutting out irrelevant noisy data epochs. One can easily use a mobile app to manually create synchronization with the biosensors. For instance, a mother could create a key stroke or mouse click or touch screen event as a temporal book mark. Methods can automatically identify what is normal and what is not normal but using rolling time window statistical analysis, such as rolling 3 sigma gates that flag anything that crosses outside of 3 sigma of the past N hour mean and standard deviation sigma.

The fifth embodiment of the present invention is designed to assess: 1) broad symptoms and cognitive function over a broad spectrum in ASD; 2) target anxiety and hyper-reactivity; and 3) pro-social, decrease social anxiety and increase social approach. With children or adults, benign adverse events (AEs) can be used to generate anxiety-related responses. It is suggested to move away from strictly using static stimuli and instead to use dynamic probes much more extensively.

If a later goal is to conduct the periodic biosensor assessment in the home, then maybe small wearable biosensors can be employed in order to correlate to a more tightly controlled periodic biosensor assessment in the laboratory. One may need to develop procedures to help parents utilize sensors and children to wear the sensors. Those in greatest need may have largest aversion. Thus, one embodiment suggests to videotape the procedures for training parents. Another suggests use of the gaming paradigm like mock-MRI scanner as well.

Biosensor modalities of this embodiment include: 1) EEG, 2) eye gaze, 3) autonomic response, 4) facial expression detection (assuming video record). One may also measure affect response to social images versus images of their own circumscribed interest. One may also use biosensors as means of visual exploration of social and non-social information. For example, for ASD do a visual search and correlate with RRBs using an eye tracking paradigm.

In one nonlimiting example, EEG data may be used to determine whether brain activity is suppressed during social versus non-social tasks and behavior; one may also determine an increase in brain activity during emotion processing. In a second non-limiting example, emotion processing of a subject during the PBA may be used to assess the affective and cognitive perceptual response to emotional stimuli (Expression, rows 6, 7, 8). If one wants to examine the physiologic response to emotional stimuli, one may also look at facial movements in response to stimuli.

The biosensor output may also be correlated with use of well validated anxiety measures in ASD and anxiety without autism, with intentional bias towards threats stimuli (passive viewing of pairs of emotional faces). Some have bias towards threat stimuli with disengagement of attention. For example, people have trouble disengaging from threat if they have high anxiety. Such a test may also be used with infants.

The biosensor data captured in accordance with the methods of the invention is stored and provided to a processing device for assessment. For example, the captured data is compared to previously captured data from the same patient to identify changes over time and/or is correlated to profile data indicative of autism, ASD, an/or other neurological disorders.

Those skilled in the art will also appreciate that the invention may be applied to other applications and may be modified without departing from the scope of the invention. For example, the signal processing described herein may be performed on a server, in the cloud, in the electronics module, or on a local PC, tablet PC, smartphone, or custom hand held device. Accordingly, the scope of the invention is not intended to be limited to the exemplary embodiments described above, but only by the appended claims.

Claims

1. A method of diagnosing Autism and/or Autism Spectrum Disorder (ASD) of a subject, comprising:

a) establishing baseline brain wave patterns of the subject by having the subject perform a series of task and measuring brain waves during said tasks using an EEG measurement device;
b) applying a light stimulus or images to the subject's eyes and capturing eye movements and/or changes in facial expression in response to the light stimulus or images;
c) giving a neuropsychological and cognition battery of tasks to the subject to generate a provoked cognitive assessment of the subject; and
d) correlating the baseline brain wave patterns, eye movements and/or facial expressions, and provoked cognitive assessment of the subject to profile data indicative of Autism and/or ASD.

2. A method as in claim 1, further comprising repeating steps a) through d) periodically.

3. A method as in claim 1, wherein said step of establishing baseline brain wave patterns includes performing saccade and/or anti-saccade tests on the subject.

4. A method as in claim 1, wherein said step of applying light stimulus or images to the subject's eyes include showing the subject static images to evoke emotional responses.

5. A method as in claim 1, wherein said step of applying light stimulus or images to the subject's eyes include showing the subject dynamic images to evoke emotional responses.

6. A method as in claim 1, wherein said step of giving a neuropsychological and cognition battery of tasks to the subject includes testing of the subject's memory, attention, and/or executive function.

7. A method as in claim 1, further comprising recording the subject's speech in response to verbal tasks and performing language processing of the recorded speech.

8. A method as in claim 1, further comprising measuring heart rate variability of the subject during said battery of tasks.

9. A system used to diagnose Autism and/or Autism Spectrum Disorder (ASD) of a subject, comprising:

an EEG measurement device that measures brain wave patterns of the subject while performing a series of tasks to establish a baseline brain wave pattern;
a device adapted to apply a light stimulus or images to the subject's eyes and to capture eye movements and/or changes in facial expression in response to the light stimulus or images;
an auditory testing device that tests the subject's sensitivity to sound and records the subject's speech in response to verbal tasks; and
a processing device that performs language processing of the recorded speech and correlates the baseline brain wave pattern, eye movements and/or facial expression, and processed language to a profile data indicative of Autism and/or ASD.

10. A system as in claim 9, wherein the device adapted to apply a light stimulus or images to the subject's eyes and to capture eye movements and/or changes in facial expression in response to the light stimulus or images comprises an eye tracking camera that tracks the subjects eye movements and/or tracks changes in facial expression of the subject in response to light stimulus or images.

11. A system as in claim 9, wherein the device adapted to apply a light stimulus or images to the subject's eyes and to capture eye movements and/or changes in facial expression in response to the light stimulus or images comprises biosensors that track eye gaze position and duration and pupillary response.

12. A system as in claim 9, further comprising static probes that measure parts of the subject's face.

13. A system as in claim 9, further comprising dynamic probes that apply dynamic stimulus to the subject relating to an emotionally evocative event and measure the subject's response to the dynamic stimulus.

14. A system as in claim 9, further comprising a biosensor adapted to measure the subject's heart rate variability during a neuropsychological and cognition battery of tasks given to the subject.

Patent History
Publication number: 20180184964
Type: Application
Filed: Jun 30, 2015
Publication Date: Jul 5, 2018
Inventors: Adam J. SIMON (Yardley, PA), David M. Devilbiss (Madison, WI)
Application Number: 15/323,238
Classifications
International Classification: A61B 5/16 (20060101); G16H 50/20 (20060101); A61B 5/00 (20060101); A61B 5/0484 (20060101); A61B 5/1171 (20060101); A61B 5/12 (20060101); A61B 5/024 (20060101); A61B 5/053 (20060101);