Patent Application
20230092770
The presently disclosed subject matter relates generally to methods and devices for transcranial illumination for the therapeutic treatment of neurological conditions. Preferred embodiments can include wearable devices that communicate with mobile devices such as web enabled phones and tablets to facilitate system operation and patient data analysis. This can optionally include cross-modal brain stimulation, diagnostic modalities and, more particularly, provide methods and devices for treating children suffering from autism that can optionally utilize simultaneous audio and light stimulation.
Research indicates that in treating many neurological and psychiatric conditions, a strong combinatory effect of two separate types of treatments exists. For example, in the treatment of depression and anxiety, a combination of both medications and cognitive behavioral therapy (or dialectic behavioral therapy) produces stronger effects than either one of those modalities independently.
Furthermore, music therapy and videos games have been used to treat epilepsy patients. Some of the results indicate that listening to specific musical content in combination with pharmacological treatment reduced both the frequencies of epileptic discharges and frequencies of seizures. Similarly, combining video games with pharmacological treatment has also been shown to modulate the brain neuroplasticity and improve age-related neuronal deficits and enhanced cognitive functions in older adults. Therefore, adding two types of different treatments together has been shown to improve the outcome of the overall treatment of neurological and psychiatric conditions across various domains. Overall, when treating psychiatric or neurological disorder, combinatory effects of brain stimulation through various channels is likely to be stronger than unimodal stimulation.
For children diagnosed with Autism Spectrum Disorder (“ASD”), one of the most common challenges they face is learning language. Studies show that children with ASD struggle with acquiring syntax. As a result, they cannot parse sentences, understand speech, and/or acquire or produce new words. In particular, learning language by the age of five (being able to speak full sentences) is critical for future successful integration with neuro-typical community and independent functioning. In addition, language learning may only occur during the sensitive period (REFS), which ends between 5-7 years of age. If a child does not fully learn language during that period, subsequent learning is highly effortful and achieving fluency is unlikely. Furthermore, being able to comprehend and produce language reduces tantrums and improves behavior in individuals with ASD. Therefore, delays in speech development is one of the most critical symptoms that needs to be alleviated.
Another critical symptom that needs to be alleviated in children with ASD is anxiety. General anxiety is frequently quite debilitating in ASD children and it affects, among other things, children's ability to learn and ability to integrate socially. Children with ASD are frequently prescribed medication to reduce their anxiety, but these medications often have unintended side effects and may be not effective.
In the United States, there are over 1.5 MM children currently diagnosed with ASD, and approximately 80,000 new children are diagnosed with ASD annually. Across the world, approximately 1.5 MM-2 MM new children are annually diagnosed with ASD. Autism services cost Americans approximately $250 billion a year, which includes both medical costs (outpatient care, home care, and drugs) and non-medical costs (special education services, residential services, etc.). In addition to outright costs, there are hidden ones, such as emotional stress as well as the time required to figure out and coordinate care. Research indicates that lifelong care costs can be reduced by almost two thirds with proper early intervention. Further research indicates that ASD is often correlated with mitochondrial dysfunction. Mitochondria in brain cells of autistic individuals does not produce enough adenosine triphosphate (“ATP”). The result of mitochondria dysfunction may be especially pronounced in the brain, since it uses 20% of all the energy generated by the human body, which may lead to neuro-developmental disorders, such as ASD. Encouraging research has shown that infrared and red light may activate a child's mitochondria, and therefore increase ATP production. The metabolic pathways impacting neurological function have been studied in significant detail. See, for example, Naviaux, “Metabolic features and regulation of the healing cycle-A new model for chronic disease pathogenesis and treatment”, Mitochondrion. 2019 May; 46:278-297. doi: 10.1016/j.mito.2018.08.001. Epub 2018 Aug. 9. Note also, Mason et al. “Nitric Oxide inhibition of respiration involves both competitive (heme) and noncompetitive (copper) binding to cytochrome c oxidase”, PNAS, vol. 103, no. 3, Jan. 17, 2006, the entire contents of the above two publications being incorporated herein by reference.
Transcranial photobiomodulation (“tPBM”) of the brain with near infrared and red light has been shown to be beneficial for treating various psychiatric and neurological conditions such as anxiety, stroke and traumatic brain injury. Remarkably, autism spectrum disorder may potentially be treated therapeutically with tPBM as several scientists have recently linked the disorder to mitochondria disbalance and tPBM can potentially affect mitochondria by causing it to produce more ATP. Patients treated with tPBM will absorb near infrared light, which can potentially reduce inflammation, increase oxygen flow to the brain and increase production of ATP. However, devices and methods are needed that will enable additional treatment options for various neurological conditions.
One problem with language acquisition is that many children with ASD cannot focus on the language enough to extract syntactic features of words, to parse sentences, and/or to attend to syntactic and semantic clues of speech. Therefore, their word learning may be delayed.
The problem with anxiety is that ASD children frequently get very stressed and do not know how to calm themselves before a particular learning or social situation. As a result, they are unable to participate in regular activities (such as playdates or classes).
Accordingly, there is a need for improved methods and devices providing treatment of neurological disorders and to specifically provide therapies for the treatment of children.
Preferred embodiments provide devices and methods in which a head wearable device is configured to be worn by a subject that is operated to deliver illuminating wavelengths of light with sufficient energy that are absorbed by a region of brain tissue during a therapeutic period. Transcranial delivery of illuminating light can be performed with a plurality of light emitting devices mounted to the head wearable device that can also preferably include control and processing circuitry. Therefore, providing brain stimulation with one or more of, or combinations of (i) infrared, near infrared and red light to improve operational states of the brain such as by ATP production in the brain, for example, and (ii) provide additional specific linguistic input(s) to learn syntax will improve language acquisition in ASD children. Therefore, providing brain stimulation with a combination of (i) near infrared and red light to reduce anxiety and (ii) specific meditations written for ASD children will reduce anxiety. Reduced anxiety leads to both improved language learning and better social integration. Providing an audio language program specifically designed for ASD children, may focus the attention of the child on the language, provide the child with the information about linguistic markers, and improve the child's ability to communicate. This is likely to reduce lifelong care costs for affected individuals.
Preferred embodiments can use a plurality of laser diodes or light emitting diodes (LEDs) configured to emit sufficient power through the cranium of a patient to provide a therapeutic dose during a therapeutic period. This plurality of light emitting devices can be mounted to circuit boards situated on a head wearable device. For the treatment of children the spacing between light emitters in each array mounted to the head wearable device can be selected to improve penetration depth through the cranium. As the cranium of a child increases in thickness with age, the parameters of light used to penetrate the cranium will change as a function of age. As attenuation of the illuminating light will increase with age, the frequency of light, power density and spot size of each light emitter can be selectively adjusted as a function of age. The system can automatically set the illumination conditions as a function of age of the patient. The thickness of the cranium of an individual patient can also be quantitatively measured by x-ray scan and entered into the system to set the desired illumination parameters needed to deliver the required power density to the selected region of the brain. The density of the cranium can also change as a function of age and can be quantitatively measured by x-ray bone densitometer to generate further data that can be used to control and adjust the level of radiance applied to different regions of the cranium.
Aspects of the disclosed technology include methods and devices for cross-modal stimulation brain stimulation, which may be used to treat ASD children. Consistent with the disclosed embodiments, the systems and methods of their use may include a wearable device (e.g., a bandana) that includes one or more processors, transceivers, microphones, headphones, LED lights (diodes), or power sources (e.g., batteries). One exemplary method may include positioning the wearable device on the head of a patient (an ASD child). The method may further include transmitting, by the wearable device (e.g., the LED lights), a pre-defined amount of light (e.g., red or near infrared light). The method may also include simultaneously outputting, by the headphones of the wearable device or other device that can be heard or seen by the patient, a linguistic input to the patient, for example. The linguistic input may include transparent syntactic structures that facilitate, for example, learning how to parse sentences. Also, the method may include outputting specific meditations written for ASD children, that may help ease anxiety, and thus allowing ASD children to better learn language and more easily integrate socially. In some examples, the method may further include receiving a response to the linguistic input from the patient, that the one or more processors may analyze to determine the accuracy of the response and/or to generate any follow-up linguistic inputs. Further, in some examples, the frequency and/or type of light outputted by the wearable device may be adjusted based on the response received from the patient. Also, in some examples, the wearable device may be paired to a user device (e.g., via Bluetooth®) that determines and sends the linguistic input(s) to the wearable device or other devices including one or more transducer devices, such as speakers, or display devices that can generate auditory or visual signals/images that can be heard and/or seen by the patient.
The battery powered headset can preferably be configured with an onboard power control device that automatically controls optical power output of the device during a therapeutic session. Therapeutic sessions can have preset operating conditions for each patient, or a class of patients, as described herein whereby a power distribution circuit board can independently control current levels through each of a plurality of light sources at a selected frequency and duty cycle. Preferred embodiments provide closed loop control of each light source such that the emitted light signal remains within 10% of a nominal value, and preferably within 5% of the selected nominal value. Safety features can be implemented in preferred embodiments in which a sensor can be used to monitor a selected operating condition of the head mounted device. For example, if a patient alters the position of or removes the headset form his or her head during a session, the sensor can transmit a signal to the system control which can, depending upon the received signal, switch off the power to the headset and/or record the time of the signal reception, and optionally send a signal to a remote device by wireless or wired connection communicating the change in state of the device. In another example, if light being emitted by one of the light sources exceeds a threshold value, or if an operating temperature of a component in the optoelectrical system exceeds a threshold temperature, this will trigger a shutoff of the light sources and cause a signal to be sent to an external device communicating the change in operating condition and record time and cause of the change. As the length of a therapeutic session may vary from patient to patient, the operating conditions can be selected based on a plurality of preset operating parameters stored in a device memory. A therapeutic session may last for at least 5 minutes for treatment of certain conditions, whereas the session may last at least 10 minutes for a further condition, and may last 15 or more minutes for a further distinct condition. Power levels may vary for each of these different treatment modules and different light sources may be controlled differently during one or more sessions.
The head wearable device can comprise rigid, semi-rigid or flexible substrates on which the light emitters and circuit elements are attached. The flexible substrates can include woven fabrics or polymer fibers, molded plastics or machine printed components assembled into a band that extends around the head of the patient. Circuit boards on which electrical and optical components are mounted and interconnected can be standard rigid form or they can be flexible so as to accommodate bending around the curvature of the patient's head. As children and adults have heads in a range of different sizes, it is advantageous to have a conformable material that can adjust to different sizes. More rigid head wearable devices can use foam material to provide a conformable material in contact with the patient's head. The head wearable device can be used in conjunction with diagnostic devices and systems that can be used to select the parameters for the therapeutic use of light as described herein. A computing device such as a tablet or laptop computer can be used to control diagnostic and therapeutic operations of the head worn device and other devices used in conjunction with a therapeutic session. Such computing devices can store and manage patient data and generate electronic health or medical records for storage and further use. The computing device can be programmed with software modules such as a patient data entry module, a system operating module that can include diagnostic and therapeutic submodules, and an electronic medical records module. The system can include a networked server to enable communication with remote devices, web/internet operations and remote monitoring and control by secure communication links. The computing device can include connections to electroencephalogram (EEG) electrodes to monitor brain activity before, during or after therapeutic sessions to generate diagnostic data for the patient. The EEG electrodes can be integrated with the head wearable device and be connected either directly to a processor thereon, or alternatively, can communicate by wired or wireless connection to the external computing device such as a touchscreen operated tablet display device. Light sensors that are optically coupled to the head of the patient can be used to monitor light delivery into the cranium of the patient and/or can measure light returning from the regions of the brain that receive the illuminating light. An array of near infrared sensors can be mounted on the LED panels or circuit boards, for example, that can detect reflected light or other light signals returning from the tissue that can be used to diagnose a condition of the tissue. Diagnostic data generated by the system sensors can be used to monitor the patient during a therapeutic period and can optionally be used to control operating parameters of the system during the therapy session such as by increasing or decreasing the intensity of the light delivered through the cranium or adjusting the time period or areas of the brain being illuminated during the therapy session.
For the treatment of children having an autism spectrum disorder, they often are not responsive to instructions, may exhibit behaviors such as self-injury or attempt to injure others, and may exhibit movements that are not conducive to standard therapeutic treatment. Specifically, it can be necessary with many patients that a device placed on the head must be light in weight and be untethered such as by a wired connection during treatment. Consequently, it is important to have a battery powered device that does not have a wired connection during a therapeutic period or session. Any communication that occurs between the head mounted device and an external device used to control and/or monitor the device during a therapeutic period is preferably performed by wireless connection. Thus, an external computing device such as a mobile communication device such as a mobile phone or a tablet display device can communicate wirelessly with the head mounted device. Such devices can include one or more processors configured to stream data to and from the head mounted device. Such devices are connectable to private or public communications networks to facilitate communication with parents and teachers, for example, that are involved with a child's treatment, medical history and education plan.
Machine learning tools can be employed to process data generated by the devices and methods described herein. Methods such as principal component analysis (PCA), support vector machines (SVM), convolutional and/or recurrent neural networks, clustering and other numeric and quantitative methods can be employed to characterize therapeutic outcomes and generate operational parameters for different classes of patients that exhibit different behavioral and/or medical conditions that can be effectively treated by photobiomodulation therapy. Neurologic conditions can impact sleep patterns and learning capacity of children and such computational methods can be used to improve therapeutic treatment.
Further features of the disclosed design, and the advantages offered thereby, are explained in greater detail hereinafter with reference to specific embodiments illustrated in the accompanying drawings, wherein like elements are indicated be like reference designators.
Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, are incorporated into and constitute a portion of this disclosure, illustrate various implementations and aspects of the disclosed technology, and, together with the description, serve to explain the principles of the disclosed technology. In the drawings:
Some implementations of the disclosed technology will be described more fully with reference to the accompanying drawings. This disclosed technology can be embodied in many different forms, however, and should not be construed as limited to the implementations set forth herein. The components described hereinafter as making up various elements of the disclosed technology are intended to be illustrative and not restrictive. Many suitable components that would perform the same or similar functions as components described herein are intended to be embraced within the scope of the disclosed electronic devices and methods. Such other components not described herein can include, but are not limited to, for example, components developed after development of the disclosed technology.
It is also to be understood that the mention of one or more method steps does not imply that the methods steps must be performed in a particular order or preclude the presence of additional method steps or intervening method steps between the steps expressly identified.
Reference will now be made in detail to exemplary embodiments of the disclosed technology, examples of which are illustrated in the accompanying drawings and disclosed herein. Wherever convenient, the same references numbers will be used throughout the drawings to refer to the same or like parts.
The operating user can change among operational modes of the computing device 150 by interacting with the user-actuatable controls 164 of the GUI 160. Examples of user-actuatable controls include controls to access program control tools, stored data and/or stored data manipulation and visualization tools, audio program tools, assessment tools, and any other suitable control modes or tools known to one of ordinary skill in the art. Upon activation of the program control mode, the GUI 160 displays program control information in the information display area 162. Likewise, activation of other modes using user-actuatable controls 164 can cause the GUI 160 to display relevant mode information in the information display area 162. The system can be programmed to perform therapeutic sessions with variable lengths of between 5 and 30 minutes, for example. The patient's use of language during the session can be recorded by microphone on the head wearable device or used separately and an analysis of language used during the session or stored for later analysis.
In the program control mode, the GUI 160 can display program controls including one or more presets 165. Activation of the preset by the operating user configures the photobiomodulation device 110 to use specific pre-set variables appropriate to light therapy for a particular class of patients or to a specific patient. For example, a specific preset 165 can correspond to a class of patient having a particular age or particular condition. In various embodiments, the pre-set variables that are configured through the preset 165 can include illumination patterns (e.g., spatial patterns, temporal patterns, or both spatial and temporal patterns), illumination wavelengths/frequencies, or illumination power levels.
In some embodiments, the photobiomodulation device 110 can transmit and/or receive data from the computing device 150. For example, the photobiomodulation device 110 can transmit data to log information about a therapy session for a patient. Such data can include, for example, illumination patterns, total length of time, time spent in different phases of a therapy program, electroencephalogram (EEG) readings, and power levels used. The data can be transmitted and logged before, during, and after a therapy session. Similar data can also be received at the computing device 150 from the external EEG system 120′ or the external light sensor array 122′ in embodiments that utilize these components. In the stored data manipulation and/or visualization mode, the operating user can review the data logged from these sources and received at the computing device 150. In some embodiments, the data can include information regarding activities used in conjunction with the therapy session (i.e., information related to tasks presented to the patient during the therapy session such as task identity and scoring). For example, activity data can be input by an operating user on the assessment mode screen as described in greater detail below.
In the audio system mode, the user can control audio information to be delivered to the patient through speakers 116 of the photobiomodulation device 110. Audio information can include instructions to the patient in some embodiments. In other embodiments, audio information can include audio programming for different therapeutic applications.
In the assessment mode, a user can input or review data related to patient assessment such as task identity and scoring. For example,
As described in greater detail below, the computing device 150 and photobiomodulation device 110 can communicate through a variety of methods. In some embodiments, a direct (i.e., wired) connection 117 can be established between the computing device 150 and the photobiomodulation device 110. In some embodiments, the computing device 150 and the photobiomodulation device 110 can communicate directly with one another through a wireless connection 118. In still further embodiments, the computing device 150 and the photobiomodulation device 110 can communication through a communications network 505.
In various embodiments, one or more portions of the communications network 505 can be an ad hoc network, a mesh network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless wide area network (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, a wireless network, a Wi-Fi network, a WiMAX network, an Internet-of-Things (IoT) network established using Bluetooth® or any other protocol, any other type of network, or a combination of two or more such networks.
In exemplary embodiments, the system 100 is configured to treat autistic patients and, in particular, juvenile autistic patients. As such, it is desirable in many embodiments to create a wireless connection between the photobiomodulation device 110 and the computing device 150 as a juvenile patient is less likely to sit still for the length of a therapy session. Wireless connection and use of a battery to power the photobiomodulation device 110 enables uninterrupted transcranial illumination for the entire length of a single therapy session and, further, enables the juvenile patient to move and engage in activities that may, or may not, be associated with the therapy.
Virtualization may be employed in the remote computing device 150 so that infrastructure and resources in the remote computing device 150 may be shared dynamically. A virtual machine 412 may be provided to handle a process running on multiple processors so that the process appears to be using only one computing resource rather than multiple computing resources. Multiple virtual machines may also be used with one processor.
Memory 156 may include a computer system memory or random access memory, such as DRAM, SRAM, EDO RAM, and the like. Memory 156 may include other types of memory as well, or combinations thereof.
A user may interact with the remote computing device 150 through a visual display device 152, such as a computer monitor, which may display one or more graphical user interfaces 160. In exemplary embodiments, the visual display device includes a multi-point touch interface 420 (e.g., touchscreen) that can receive tactile input from an operating user. The operating user may interact with the remote computing device 150 using the multi-point touch interface 420 or a pointing device 418.
The remote computing device 150 may also interact with one or more computer storage devices or databases 401, such as a hard-drive, CD-ROM, or other computer readable media, for storing data and computer-readable instructions and/or software that implement exemplary embodiments of the present disclosure (e.g., applications). For example, exemplary storage device 401 can include modules to execute aspects of the GUI 160 or control presets, audio programs, activity data, or assessment data. The database(s) 401 may be updated manually or automatically at any suitable time to add, delete, and/or update one or more data items in the databases. The remote computing device 150 can send data to or receive data from the database 401 including, for example, patient data, program data, or computer-executable instructions.
The remote computing device 150 can include a communications interface 154 configured to interface via one or more network devices with one or more networks, for example, Local Area Network (LAN), Wide Area Network (WAN) or the Internet through a variety of connections including, but not limited to, standard telephone lines, LAN or WAN links (for example, 802.11, T1, T3, 56 kb, X.25), broadband connections (for example, ISDN, Frame Relay, ATM), wireless connections (for example, WiFi or Bluetooth®), controller area network (CAN), or some combination of any or all of the above. In exemplary embodiments, the remote computing device 150 can include one or more antennas to facilitate wireless communication (e.g., via the network interface) between the remote computing device 150 and a network and/or between the remote computing device 150 and the photobiomodulation device 100. The communications interface 154 may include a built-in network adapter, network interface card, PCMCIA network card, card bus network adapter, wireless network adapter, USB network adapter, modem or any other device suitable for interfacing the remote computing device 150 to any type of network capable of communication and performing the operations described herein.
The remote computing device 150 may run operating system 410, such as versions of the Microsoft® Windows® operating systems, different releases of the Unix and Linux operating systems, versions of the MacOS® for Macintosh computers, embedded operating systems, real-time operating systems, open source operating systems, proprietary operating systems, or other operating system capable of running on the remote computing device 150 and performing the operations described herein. In exemplary embodiments, the operating system 410 may be run in native mode or emulated mode. In an exemplary embodiment, the operating system 410 may be run on one or more cloud machine instances.
The photobiomodulation device 110 can include a processor board 111, one or more light emitter panels 115a-115e, one or more speakers 116, and one or more batteries 118. The photobiomodulation device 110 can optionally include a light sensor array 122 and an EEG sensor system 120. Although five light emitter panels 115a-115e are described with respect to this disclosure, one or ordinary skill in the art would appreciate that a greater or fewer number of panels may be used. In an exemplary embodiment, the light emitter panels 115a-115e hare flexible. In an exemplary embodiment, the light emitter panels 115a-115e are positioned at the front, top, back, and both sides of the user's head. In embodiments wherein the photobiomodulation device 110 does not have a full cap over the user's head (i.e., a headband-style device), the top panel may be omitted.
The power management module 114 can monitor and control use of particular light emitter panels 115a-115e during a therapy session. In some embodiments, the power management module 114 can take action to control or provide feedback to a patient user related to whether light emitter panels 115a- 115e are not used, or are only partially used, during a particular therapy session. By mitigating use of certain panels during a session, longer operation can be achieved. Moreover, different classes of patient (e.g., patients of different ages) can have different cranial thicknesses. As a result, different transmission power (and penetration) may be necessary as a function of patient age. The power management module 114 can control power output to light emitter panels to provide a therapeutically beneficial dose of illumination while still extending battery life. Shown in
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by diminished social functioning, inattentiveness, and linguistic impairment. While autism is likely to be a multi-causal disorder, research indicates that individuals with ASD frequently have mitochondrial disease which results in abnormalities of energy generation from food proteins. However, mitochondria in the brain might be able to produce energy molecules from a different source, such as light.
Using the wearable device 50, certain methods of the present disclosure may perform photobiomodulation (stimulating brain with light) and linguistic training simultaneously to treat children with ASD. The wearable device 50 may include several near infrared and/or red lights to stimulate the language area of the brain. These methods associated with the wearable device 50 may include determining an area of the head to position the wearable device 50 (e.g., the temporal lobe, the prefrontal cortex, and/or the occipital lobe) to output the infrared and/or red lights. The light absorbed by the brain tissue may increase the production of ATP, which may provide the neurons more energy to communicate with each other and provide increased brain connectedness. The wearable device 50 may simultaneously receive linguistic inputs from an application of a user device that is transmitted to the user via the headphones of the wearable device 50. The linguistic inputs may help facilitate language learning. Therefore, by providing these combined mechanisms (photobiomodulation and linguistic input), for example, to children diagnosed with ASD, may significantly improve lifelong outcomes. Further, the wearable device 50 may output meditations that may help reduce anxiety of the patient user (such as an ASD child), which may allow the user to better learn language and integrate socially.
Autism spectrum disorders are associated with brain inflammation, in particular, inflammation characterized by activation of brain macrophages (microglial activation) (see, e.g., Rodriguez et al., Neuron Glia Biol 2011, 7(204):205-213; Suzuki et al., JAMA Psychiatry 2013, 70(1):49-58; and Takano, Dev Neurosci 2015, 37:195-202, the entire contents of each of which are hereby incorporated herein by reference). Brain inflammation can be identified by the presence of delta waves (high voltage slow waves) during wakefulness that can be detected using electroencephalography (EEG). In healthy individuals, delta waves in EEG are detected during the period of restorative sleep, but not during wakefulness. Wakeful delta waves in EEG are associated with pathological conditions that are, in turn, associated with brain injury and inflammation characterized by activation of brain macrophages (microglial activation). Such pathological conditions include, among others, classical mitochondrial diseases like Alpers syndrome, traumatic brain injury and autism spectrum disorders (ASD). The presence of wakeful delta waves in an individual with a pathological condition described above indicates that healing activities normally confined to sleep were not sufficient for inhibiting brain inflammation.
Evidence also indicates that wakeful delta wave power is a reliable marker of brain inflammation and microglial activation. For example, symptomatic improvement in traumatic brain injury and genetic forms of mitochondrial brain disease is accompanied by a decrease in wakeful delta wave power. The presence of wakeful delta waves in an individual with ASD is indicative of brain inflammation.
Activation of microglia during brain inflammation results in synthesis and release of nitric oxide (NO) in the inflamed brain tissues. NO inhibits oxidative phosphorylation in the mitochondria by binding to the iron and copper atoms present in the mitochondrial electron transport chain complex IV cytochrome oxidase and inhibiting its activity (see, e.g., Mason et al., PNAS 2006 103(3):708-713, the entire contents of which are hereby incorporated herein by reference). Decreased levels of oxidative phosphorylation result in the increased levels of dissolved oxygen in the cell which, in turn, results in the increased levels of reactive oxygen species (ROS) and mitochondrial damage and fragmentation.
Near infrared light penetrates biological tissues, including bone structures such as the cranium. It can act to displace NO bound to the complex IV cytochrome oxidase, thereby reversing the effect of elevated NO levels and reversing the inhibition of oxidative phosphorylation. Restoration of mitochondrial oxygen consumption has the effect of stimulating healing of the inflamed tissues. Thus, without being bound by a specific pathway when other physiologic pathways, medications or therapeutic agents may alter the circumstances impacting treatment of a particular patient, it is believed that illuminating brain tissue of a subject with near infrared light can reverse inhibition of oxidative phosphorylation mediated by NO, stimulate oxidative phosphorylation and facilitate healing of inflamed brain tissues, thereby reducing brain inflammation. It is also believed, without being bound by a specific pathway as noted above, that the reduced inflammation of brain tissue resulting from illumination as described in the present application with red, near infrared light and/or infrared portions of the electromagnetic spectrum can cause a decrease in the wakeful delta waves, for example. Indeed, as indicated by the results of the clinical trial described herein, photobiomodulation therapy resulted in a statistically significant decrease in the delta waves in the treatment group as compared to a control group, which, in turn, was associated with a statistically significant reduction in autism symptoms.
Methods for providing cross-modal brain stimulation may include determining the light frequency, location of the LED lights (e.g., areas of the brain needing increased ATP, areas of the brain most likely to respond to light treatments, and/or areas of the brain associated with language (e.g., auditory cortex, Broca area, Wernike area)), whether ATP production increased, and the overall effect of the treatments. Accordingly, based on the determined overall effect on the brain, the wearable device may be dynamically adjusted on a user-specific basis.
The wearable device 50 may be specifically tailored for children with ASD, such that it improves language skills, alleviates anxiety, and/or reduces tantrums. Further, the wearable device 50 may be used on a daily basis, in the convenience of the family's home, without a need for a specially trained therapist. Moreover, the wearable device 50 may be non-invasive, may not require a prescription, and/or may lack side effects.
Methods for using the devices of the present disclosure may further include determining the location(s) of the light emitting diodes that may be used to stimulate specific brain areas responsible for language, comprehension, energy production, and/or for self-regulation (e.g., reducing anxiety). The methods may also include determining total power, power density, pulsing, and/or frequency. The total power may be 400-600 mW (0.4-0.6 W) with 100-150 mW per each of four panels. The power for each panel may be selectively stepped down to the 50-100 mW range, or increased to the 150-200 mW range depending on the age or condition of the patient. Each of these ranges may be further incremented in 10 mW steps during a treatment session or between sessions. The spot size of the light generated by each LED or laser can optionally be controlled by adjusting the spacing between the light emission aperture of the LED or by using a movable lens for one or more LEDs on each circuit board that can be moved between adjustable positions by a MEMS actuator, for example.
Further, the wearable device 50 may be comprised of a comfortable material for prospective patients. For example, the wearable device may be comprised of plastic, fabric (e.g., cotton, polyether, rayon, etc.), and/or the like. Because ASD patients in particular are especially sensitive, the aforementioned materials may be integral in allowing ASD patients to wear it for a sufficient amount of time without being irritable. Of course, the wearable device 50 may need to be both safe and comfortable. The electric components (e.g., processors, microphones, headphones, etc.) may be sewn into the wearable device 50 and may be difficult to reach by children, for example. A cloth or fabric covering can contain the head worn frame and optoelectronic components to the extent possible without interfering with the optical coupling of the LED to the cranium. Further, the weight of the wearable device 100 may be light enough to allow it to be worn comfortably. Moreover, the wearable device 100 may require a power source (e.g., one or more replaceable batteries) that allows it to be portable.
Regarding the linguistic inputs, a patient user device (e.g., a smartphone or tablet) may include an application that 1) performs language acquisition: (e.g., develops and records a vast number of short vignettes specifically designed to make syntactic structure transparent and teaches how to parse sentences); 2) involves a system of specifically designed mediations to alleviate anxiety; and 3) involves a system of musical rewards to keep users (children) interested and engaged.
The application may disambiguate syntactic structure of a language. Present research suggests that word learning spurt occurs after the children learn basic syntax (and it occurs at the syntactic-lexicon interface). Furthermore, without syntax children may not move beyond speaking 10-15 words, which may be used for simple labeling, but not to express their needs, wants and feelings. This means that there may be no ability for proper communication without learning syntax first. In addition, syntax may be necessary to parse the acoustic wave or sound that children hear into sentences and words. Syntax may also be necessary for specific word-learning strategies (e.g., syntactic bootstrapping).
Syntactic bootstrapping is a mechanism which children use to infer meanings of verbs from the syntactic clues. For example, when a child hears “Michael eats soup” this child infers that “eats” is a transitive verb. A classic example used by a famous psycholinguist professor Leila Gleitman is the made-up verb “Derk”. By putting this verb in several syntactic contexts, the meaning of the verb becomes transparent “Derk! Derk up! Derk here! Derk at me! Derk what you did!” Dr. Gleitman argued that children infer the meanings of verbs from hearing them in different syntactic contexts. In addition, Dr. Pinker argued that children also use semantic bootstrapping (contextual clues) to infer meanings of the words. Therefore, there are several mechanisms (most likely innate) available to a typical child while learning language. Overall, there is scientific consensus that typical children learn language by specifically focusing on syntactic and semantic clues of speech.
However, studies suggest that children who are on the autism spectrum cannot always extract syntactic structure and semantic contexts from the imperfect linguistic input they receive. Usual linguistic input is too messy, incomplete and confusing for them. People frequently speak in fragments of sentences, switch between topics, use incorrect words or use words in incorrect forms. Human speech may be too messy to allow for simple learning based on this type of speech alone. Neurotypical children can still extract syntactic structure from this messy input by being predisposed to pay attention to specific syntactic cues (e.g., to look for nouns and verbs in the string of speech). When children grasp syntactic structure of a language, they learn to parse sentences, and therefore, acquire more words. Several studies corroborated this hypothesis that massive word learning happens at this syntax-lexicon interface, including studies with children on the spectrum.
Many children suffering from ASD seem to be unable to move beyond simple labeling, are unable to speak in full sentences, and therefore are unable to communicate effectively. There are many reasons for this difficulty, one of them is that those children do not usually pay enough attention to speech and communication, and therefore they do not pay enough attention to syntactic clues and are not able to parse individual sentences. However, without grasping syntactic structure of the language, word learning beyond simple labeling becomes impossible, specifically, acquisition of verbs may become impossible. Timely acquisition of verbs (not just nouns to label objects around them) may be critical for ASD children, as research shows that the best predictor of future integration with the neuro-typical community (and normal functioning) is speaking full sentences by 5 years of age. Therefore, specifically, the problem is that children with ASD are not focused on the language enough to extract syntactic features of words, to parse sentences and to attend to syntactic and semantic clues of speech. Therefore, their word learning is delayed.
Accordingly, the aforementioned application may calibrate the imperfect linguistic input for ASD children, thus, making syntactic structure as transparent as possible. For example, the child will hear a noun: “dog”, then she will hear “1 dog, 2 dogs, 3 dogs, 4 dogs, 5 dogs”. then she will hear “my dog is brown”, “my brown dog is cute”, “my brown dog is small”, “I have a small, cute, brown dog”, “my dog barks,” “dogs bark” “dogs chase cats”, “dogs eat meat”, “I have a dog,” and so on. By putting the same word in different syntactic contexts over and over again we will flood the child with the information about linguistic markers (syntactic roles in the sentences, countable, noun, animate/inanimate and so on).
Therefore, the application may “wake up” (activate) language learning and make a child pay attention to the syntactic cues of the linguistic input. Further, the application may be refined by observing the behavior of the users and recording their improvements. A method for treatment 450 is described in connection with the process flow diagram of
The following describes an example of a battery powered system as previously described herein where one or two 9 volt batteries are inserted into battery holders in the side and rear views showing the LED case design shown in the figures.
If the LED is uncased, a small tube can be used to ensure that it remains centered and held securely in place. This tube can fit through a hole in the foam band for proper location and is ⅜″ outside diameter. The PCB serves as a backing on the foam and allows clearance for the connecting cable. The same type of construction can be applied to the electronics mounted area, the battery, and the speakers. Sensors used to measure characteristics of the patient during use, such as EEG electrodes, photodetectors and/or temperature sensors can be mounted to the circuit boards carrying the LEDs or laser diodes as described generally herein. Detected electrical signals from the sensors can be routed to the controller board and stored in local memory and can also be transmitted via wireless transmission to the external tablet device so that a user or clinician can monitor the therapeutic session and control changes to the operating parameters of the system during use.
The electronics can comprise three or more separate PCB configurations with the LED PCB having (6) variations for the associated positions on the head. There can be two LED PCB boards on each side (front and rear) with at least one illuminating the temporal lobe on each side and at least one board centered for illuminating the frontal lobe. One or two boards can conform to one or both of the parietal lobe and the occipital lobe.
The system is fitted on the head of a patient and radiates energy via IR LEDs at 40 Hz into the patient's head, for example. The IR LEDs are split into six boards with each containing one IR LED. The LED utilized for preferred embodiments can be the SST-05-IR-B40-K850.
The LED boards can illuminate during the on-time of the 40 Hz signal. The duty cycle of the 40 Hz signal will be equal to the power setting. For example, a power setting of 25% will require a 25% duty cycle for the 40 Hz.
One or more 9V batteries can be the system's source of power. A buck converter reduces the 9V from the battery to 2.5V for the LEDs. One or more batteries of different voltages can be employed particularly where different batteries can be used for the light emitters and powering the circuitry.
In this section, note the calculation of the LED's absolute maximum optical flux output assuming that they are the only components powered by a single 9V battery.
Table 1 shows the current limits of important components. These current limits cannot be violated without the risk of permanent damage to the component.
Conservation of energy dictates that the current sourced by the buck converter will not be the same as the current sourced by the battery. Equation 1 calculates the current drawn from the battery (IBATT).
where VLED_PWR is the LED supply voltage (2.5 V), ILED_PWR is the buck converter output current, η is the efficiency (minimum of 0.85), and VBATT is the battery voltage.
The efficiency of the buck converter changes over the output current range. The minimum efficiency is 0.85 at the maximum current of 2.5 A.
Note that the battery voltage is inversely proportional to the battery draw. For a fixed load, the battery will draw more current as the battery discharges. Therefore, a minimum battery voltage must be specified and observed by the system microcontroller to avoid exceeding the battery's maximum discharge current. Table 2 demonstrates how battery current draw increases as the battery discharges. Each battery draw value is calculated with Equation 1 with the following values: η=0.85, VLED_PWR=2.5V, ILED_PWR=2.5 A, and the battery voltage for VBATT.
Use Equation 2 to calculate the absolute minimum battery voltage, VB_AM. Use the same values as before, but let IB_MAX=1. Battery current draw reaches 1.0 A when the battery voltage discharges to 7.35V, therefore the LEDs must be turned off to avoid exceeding the L522 battery maximum discharge specification of 1.0 A. The buck converter supplying 2.5 A at 2.5V with a battery voltage below 7.35V risks permanent damage to the battery.
Equation 2. Absolute Minimum Batters Current Draw
The absolute minimum battery voltage also affects battery life.
The 2.5 A sourced by the buck converter must be shared amongst six boards (LED). Thus 2.5 A/6=416 mA from the buck converter per LED.
The duty cycle of the 40 Hz will attenuate the optical flux. Equation 3 shows how to calculate the average flux for a single pulsed LED. Ee_pulse is the optical flux during the pulse and D40HZ is the 40 Hz duty cycle. The range of D40HZ is a number between 0 and 1 inclusive.
E
e_average
=D
40HZ
×E
e_pulse
Equation 3. Average irradiance for a single LED.
As an example, Table 3 lists the optical flux for each power setting.
The output optical flux decreases with temperature and must be de-rated accordingly. Sources of heat to take into account are the LEDs' self-heating and the heat from the patient's head. For the purposes of this analysis, assume the patient's head is at body temperature, 37° C.
Table 4 above lists two thermal coefficients. The thermal resistance of the LED can be understood as for every watt consumed by the LED, its temperature will rise by 9.2° C. The third graph below shows normalized V-I characteristics of the LED relative to 350 mA at 2V (at 350 mA, forward voltage ranges between 1.2V and 2.0V, but here we continue to use worst-case value of 2.0 V).
At 416 mA (the maximum current available per LED),
T
Δ
=V
f
×I
f
×D
40HZ×9.2
Equation 4. Temperature Rise due to Self-Heating
The LED can rise to a temperature of TLED=37° C.+7.8° C.=44.8° C. The optical flux vs temperature graph in
PO
Δ%=(TLED−25° C.)×(−0.3%/° C.)
Equation 5. Change in Output Flux due to Temperature.
Note that the system also provides a temperature coefficient for forward voltage. Forward voltage decreases with temperature rise. For a worst-case analysis, the decrease in forward voltage due to temperature can be ignored.
An optical flux of 275 mW is the minimum absolute maximum that can be achieved if the buck converter and the battery are pushed to their limits assuming that the battery is only supplying power to the LEDs.
Since the battery may also be powering the digital logic which includes the microcontroller, the Bluetooth module or other wireless connection, etc. the LEDs cannot draw the 1.0 A maximum from the battery.
The steps below are an effort to summarize the approach described above.
Table 6 gives examples of target LED current and the resulting system specification. Allow a 100 mA margin on the battery draw for supply logic. Values calculated in Table 6 assume worst-case efficiency of 0.85.
The maximum target LED current is 339 mA resulting in a temperature adjusted flux output of 223 mW. Table 7 demonstrates how the 40 Hz duty cycle attenuates the LED output flux.
EEG can be used to augment the use of TPBM to reduce symptoms of autism, for example, and this procedure is described in further detail below.
The head wearable device reduces symptoms of autism by applying tPBM to stabilize functional brain connectivity, while using EEG data as a measure of the efficacy of tPBM and as a guide for continuous applications. The head wearable device can include EEG electrodes situated on one or more of the light emitter printed circuit boards as described herein. Between one and six EEG electrodes can be mounted on one or more of the light emitter panels so that they are interleaved between the light emitters or surround them so as to detect brain wave signals occurring during illumination.
Autism (ASD) is a life-long disorder characterized by repetitive behaviors and deficiencies in verbal and non-verbal communication. Resent research identified early bio-markers of autism, including abnormalities in EEG of ASD infants, toddlers and children as compared to typical children. For example, children diagnosed with ASD present with significantly more epileptiforms (even, when they do not develop seizures), some researchers report as many as 30% of ASD children present with epileptiforms (e.g., Spence and Schneider, Pediatric Research 65, 599-606 (2009). A recent longitudinal study (from 3 to 36 months) detected abnormal developmental trajectory in delta and gamma frequencies, which allow distinguishing children with ASD diagnosis from others (Gabard-Durnam et al 2019). Short-range hyper-connectivity is also reported in ASD children. For example, Orekhova et al (2014). showed that alpha range hyper-connectivity in the frontal area at 14 months (and that it correlates with repetitive behaviors at 3 years old). Wang et al (2013), has indicated that individuals with ASD present with abnormal distribution of various brain waves. Specifically, the researchers argued that individuals with ASD show an excess power displayed in low-frequency (delta, theta) and high-frequency (beta, gamma) bands as shown in
Duffy & Als (2019) argued, based on EEG data, that ASD is not a spectrum but rather a “cluster” disorder (as they identified two separate clusters of ASD population) and Bosl et al Scientific Reports 8, 6828 (2018) used non-linear analyses of infant EEG data to predict autism for babies as young as 3 months. Further details concerning the application of computational methods of Bosl can be found in US Patent publication 2013/0178731 filed on Mar. 25, 2013 with application Ser. No. 13/816,645, from PCT/US2011/047561 filed on Aug. 12, 2011, the entire contents of which is incorporated herein by reference. This application describes the application of machine learning and computational techniques including the use of training data stored over time for numerous patients and conditions that can be used to train the a machine learning system for use with the methods and devices described herein. A neural network can be used for example to tune the parameters employed for transcranial illumination of a child at a certain age range undergoing treatment for autism. An array of 32 or 64 EEG channels can be used with electrodes distributed around the cranium of the child. Overall, the consensus is that ASD is a functional disconnectivity disorder, which has electrophysiological markers, which can be detected through an EEG system. Dickinson et al (2017) showed that at a group level, peak alpha frequency was decreased in ASD compared to TD children.
Transcranial photobiomodulation as described herein is used to treat many neurological conditions (TBI, Alzheimer, Depression, Anxiety), and is uniquely beneficial to autism, as it increases functional connectivity AND affects brain oscillations (Zombordi, et al, 2019; Wang et al 2018). Specifically, Zomonrodi et al Scientific Reports 9(1) 6309 (2019) showed that applying tPBM (LED-based device) to Default Mode Network increases a power of alpha, beta and gamma, while reduces the power of delta and theta (at resting state). Wang et al (2018) also showed significant increases in alpha and beta bands. Finally, Pruitt et all (2019) showed that tPBM increases cerebral metabolism of human brain (increasing ATP production).
Thus, preferred embodiments use a system that correlates continuously collected EEG data with observable symptoms (as reported by the parents) and use EEG to guide application of LED based tPBM. The symptoms provided by parents can provide ranked data can be used to formulate the parameters for a therapy session.
LED based tPBM can be applied to Default Mode Network (avoiding central midline areas) as well as Occipital lobe, and Broca area (left parietal lobe) as well as Wernike area (left temporal lobe).
Stimulating DMN (and simultaneous stimulation of frontal lobe with occipital lobe) increases long-range coherence. Stimulating language producing areas (e.g., Broca and Wernike areas with DMN) has been shown to facilitate language production in aphasic stroke patients (Naeser, 2014).
The device performs EEG measurements in combination with photobiomodulation therapy:
1. Analyze initial EEG data for epileptiforms, long-range coherence and hemispheric dominance.
2. Correlate EEG data with observed symptoms.
3. Based on the observed symptoms and the EEG data, the head wearable device can apply tPBM. For example, for children with severe repetitive behaviors and strong delta and theta power in the prefrontal cortex, the device stimulates prefrontal cortex to increase power within alpha and beta frequency band (and decrease power of delta and theta bands). For children who struggle with language, the device can stimulate DMN and Broca and Wernike areas. For children with various and severe symptoms, the device can stimulate all identified targeted areas (DMN, Broca, Wernike, occipital lobes).
4. The device can adjust power gradually and increasing it until the minimal change in brain oscillation is detected. This thresholding avoids applying too much power to a developing brain. The device operates at the lowest power that achieves the desired oscillation.
5. As the symptoms improve and the measured EEG signal stabilizes, the power level of the device can be gradually reduced. This system can be automated to control each therapy session.
6. Machine learning algorithms analyze EEG data and behavioral data, and the power alterations provided by the algorithm in the form of guidance to parents (and therapists), as well as indicate further improvements in the therapy being given to the patient.
7. As the symptoms sufficiently improve (expected improvement is within 8 weeks based on Leisman et al 2018), the device controls a break from tPBM and collect only EEG and behavioral symptoms to monitor for possible regression.
8. If any regress is detected, the device can instruct that tPBM is gradually resumed.
The device can apply tPBM to DMN, occipital lobe as well as to Broca and Wernike areas. The device collects EEG signals from prefrontal cortex, occipital cortex and temporal cortex (left and right to monitor hemispheric dominance observed in ASD children). The platform connected to the device can conduct initial assessment of behavioral symptoms (to be correlated with EEG data) as well as ongoing collection of symptoms (allowing for continuous correlations with EEG). Therefore the platform will continuously measure the efficacy of tPBM and personalization can be developed. Initially, a baseline is established by two separate measures. First, functional brain connectivity and brain oscillations baselines can be established in targeted brain areas (e.g., F1 & F2, T3 & T4, O1 & O2) prior to using treatment. Second, baseline demographic information (age, gender, race, etc), most concerning symptoms, and medical history (e.g., known genetic mutations, mitochondrial dysfunctions, gastroenterological symptoms, asthma, epilepsy, medications taken on regular basis) of each child is collected from parents. After the initial low-dosage treatment is administered several times (>3), a child's brain oscillations can be measured in order to establish the trend for reduction of delta brain waves, which is needed in order to detect the treatment's effect on the brain's electrophysiological activity and penetration of light through the skull. Separately, data can be collected from parents about the child's behavioral symptoms, including language, responsiveness, aggression, self-injurious behavior, irritability, and sleep disturbances. An AI algorithm, described in further detail below, processes collected data to determine the combination of effectiveness (as marked by behavioral symptoms and EEG data) as well as tolerability (as marked by behavior, reported by parents) to compute an optimal dosage (which include total power, time administered and frequency of pulsing). For example, the device used in some embodiments uses 40 HZ pulsing, which usually increases focus. However, for hyper-active children 10 HZ pulsing or continuous wave administration can be used and can be effective. To further improve personalization features, the system can be programmed to adjust for skin color based on the timing and strength of dosage. Darker skin pigments absorb light more than lighter skin, therefore fewer photons are likely to reach the brain. In the clinical study, children with darker skin showed improvement later than children with lighter skin, thereby indicating a need to adjust dosage based on skin absorption. Therefore, they might need longer usage of the device at a given dosage to detect improvements. The control software for the device can be programmed for such personal characteristics as race and ethnicity.
The process flow diagram in
Thus, an operating module of the software can be programmed to retrieve fields of data or data files from a patient data entry module that can include patient information and other initial observations of parents or clinicians regarding a child's age, condition, medical history including medications that may impact a further diagnostic or therapeutic program.
In an alternative embodiment, the parameters can be set algorithmically or automatedly. The processor of the computing device can process the patient data (including, for example, age and condition data) to determine the first therapeutic dose level or dose level sequence (step 620). For example, the processor 155 of the remote computing device 150 can analyze and process the patient data. Then, the automatically selected illumination and therapy session parameters (as well as other session parameters) can be displayed on the display associated with the computing device (step 622). Optionally, the set of automatically selected parameters can be augmented in this step with additional manual parameters such as an audio or video file used as part of the therapeutic session.
Whether the parameters are determined automatically or manually, the head wearable device can then be positioned on the head of patient (e.g., a child or adult) and the therapy session can be actuated based on the session parameters (step 608). Data related to the patient or device during the session can be monitored and recorded. Then, the patient data (e.g., age or condition data) can be adjusted to optimize session parameters for future (i.e., second, third, or more) therapeutic sessions (step 610).
After concluding the therapeutic session, output data can be exported in a format compatible with standard medical records using a medical records module (step 708). Output data can include the illumination time and/or power for each individual illumination LED, a data distribution of which regions of the brain were illuminated, the cumulative power delivered, or annotations from a user conducting the session such as a medical professional. The data can be time-course data including time stamps that record when observations or other data events occurred within the therapeutic session.
Shown in
Shown in
The head wearable device 5000 is formed at least partially of a soft material with airy open spaces in some embodiments. In some embodiments, a surface of the headband 5006 is formed of a non-porous material to improve sterilizability and cleanability. In some embodiments, the material can include one or more of low-density polyethylene (LDPE), silicone, or ethylene-vinyl acetate (EVA) closed-cell foam. In some embodiments, the headband 5006 can include light, pastel, or bright colors that appeal to children for pediatric therapy applications.
The size adjustment mechanism 5008 can enable adjustment of the headband 5004 for comfort and/or to improve contact or coupling between the user's scalp and the LED modules 5010. The size adjustment mechanism 5008 can include a band or strap that tightens against a patient's skull or tightens below the occipital bone of the skull. In some embodiments, the adjustment mechanism can include a fastener such as a hook-and-loop fastener. For example, the headband 5006 can include two separated straps that fasten with the hook-and-loop fasteners or a single strap that passes through a retaining ring and doubles back upon itself so that hooks on the end of the strap can attach to a separate portion of the strap that includes loops. In some embodiments, the adjustment mechanism can include a snap closure wherein snaps or pegs in one portion of the headband 5006 connected with a variety of snaps or holes at different positions along the headband 5006. Similarly, the head strap 5004 can include a size adjustment mechanism to enable sizing adjustments of the head strap 5004 to improve comfort for a user with a given head size. The same size adjustment mechanisms 5008 described herein for the headband 5006 can be employed in the size adjustment mechanism for the head strap 5004.
In some embodiments, the occipital mount 5002 can include an electronics housing 5014 to accommodate a battery or other electronics or power sources to power elements of the head mounted device 5000 such as the LED modules 5010.
The power PCB 5062 can include a power gauge integrated circuit (IC) 5065, a charging circuit 5067, a voltage regulation module 5069, a USB interface 5066, an enable button 5064, and battery level and BlueTooth® low energy (BLE) connection indicators 5063. The power gauge IC 5065 can monitor the voltage level of the battery 5060 to determine the remaining energy (power) in the battery 5060. The battery level indicators 5068 can indicate visually to the user the level of remaining energy in the battery 5060 as measured by the power gauge IC 5065. The charging circuit 5067 enables wireless charging of the battery 5060 using inductive charging techniques such as those that conform to the Qi® wireless charging standard or other charging standards. The voltage regulation module 5069 can regulate the output voltage provided on the cable 5052 to the other components. The enable button 5064 can include a mechanical or electrical switch operable by the user to turn on or off the electrical systems of the head mounted device 5000. The USB interface 5066 enables wired charging of the battery 5060 and/or provides an interface for re-programming (e.g., flashing) or debugging components of the power PCB or connected PCBs. The USB interface 5066 can also be used for transfer of data such as usage statistics (e.g., recorded or sensed power levels, up-time or down-time, or error statuses).
The occipital PCB 5040 includes a microcontroller unit (MCU) and BlueTooth® low energy (BLE) module 5044, non-volatile memory 5042, and a patient detection module 5046. The MCU/BLE module 5044 can control power output to each individual LED PCB 5035. The MCU/BLE module 5044 enables communication with external devices using the BLE protocol. For example, an external device such as a computer, tablet, or smartphone operated by the user can wirelessly send instructions to the MCU/BLE control module 5044 to adjust individual LEDs to different power settings over time according to a therapeutic program. The memory 5042 can include one or more of logical address information or instructions to control the LED PCBs 5035. The patient detection module 5046 can detect whether or not the head mounted device 5000 is being worn by the patient. If the patient detection module 546 detects that the head mounted device 5000 is not being worn by a patient, it can send signals to the controller 5044 or the power PCB 5062 to disable power to the LEDs to prevent light output. This automatic shutoff when the patient is not present can conserve power in the battery 5060 and provide safety by preventing illumination from being turned when the light could accidentally enter a patient's eyes, for example. This is especially important in pediatric applications where a child may inadvertently remove the head mounted device 5000 and could accidentally aim the light at their eyes if it were not automatically shut off. In some embodiments, the patient detection module 5046 can operate by employing sensors that detect whether LED light is being reflected by a very close object. Alternatively, the patient detection module 5046 can use an accelerometer or inertial position system to determine the orientation of the head mounted device 5000 and disable the device when the position is not consistent with placement on a head of a sitting or standing individual.
The occipital PCB 5040 includes individual connectors 5047 to connect cables 5052 to the LED PCBs 5035. The occipital PCB 5040 also includes a connector 5049 to connect cables 5052 to the frontal PCB 5046 and a connector 5046 to connect the cables 5052 to the power PCB 5062. Any of the connectors 5049, 5047, 5048 can include a flat-flex connector that allows low-profile and flexible connections to reduce the space taken by cables 5052 within the headband 5006 or occipital mount 5002.
Photobiomodulation can be used to treat several ailments including Alzheimer's disease, post-traumatic stress disorder (“PTSD”), cognitive enhancement, cognitive impairment from trauma and/or injury, depression, anxiety, mood disorders, Parkinson's Disease, strokes, Global Ischema, and Autism Spectrum Disorder (“ASD”). In particular, these ailments can be treated with transcranial photobiomodulation, which involves targeted light energy to the brain. The devices associated with performing transcranial photobiomodulation are often applied over the head, such as in certain embodiments described herein. However many such devices, can be cumbersome and in particular, for especially sensitive patients (e.g., children with ASD), it can be difficult to comfortably apply the device for treatment over a meaningful duration of time without the patients attempting to shift or remove the device.
Aspects of the disclosed technology include devices for photobiomodulation, which can be used to treat various patients including ASD children and older adults. Consistent with the disclosed embodiments, the photobiomodulation device may be sized and shaped to fit inside the oral cavity of the human mouth. The photobiomodulation device may include one or more light emitting diode (LED) lights, which may be located in a center portion of the device. Further, the LED emitters may be positioned to point downwards or to other regions, such that light from the device affects blood vessels that flow within the body to regions of the brain. Preferred embodiments of can be used in conjunction with methods and devices that can illuminate blood vessels within the brain or that supply blood directly to the brain such as the internal carotid artery. Further, the LED light emitters may emit light at one or more wavelengths which can be red, infrared, and/or a combination of the two. The LED light emitters may have a material (e.g., latex, silicone, rubber, etc.) surrounding it that allows the light to penetrate tissue within the mouth yet is also difficult to chew. The surrounding material can comprise one or more lenses to couple the emitted light onto the tissue that contacts a surface of the device and wherein the tissue contains regions of vascular flow that is illuminated with the device. The photobiomodulation device may further include an extendable portion that protrudes outwards from the device in a longitudinal direction. In some examples, the extendable portion may include the LED light emitters. The photobiomodulation device may be shaped similarly or substantially similar to a pacifier, for example. Therefore, a wearer of the photobiomodulation device can bite down or suck on the extendable portion while it is inside the mouth. The photobiomodulation device may further include one or more processors, transceivers, or power sources (e.g., batteries). Preferred embodiments can also include a cavity to collect a sample of fluid from within the mouth for further testing and analysis, such as a saliva sample. A surface of the device, or the cavity, can optionally include a sensor to measure further characteristics of the tissue and/or the sample. The sensor can be electronically connected to circuitry for readout of sensor data during use. The sensor can include a light sensor such as a photodetector to measure light from the tissue and/or sample. The device can be configured to communicate with an external portable communication device as previously described herein to store patient data in a memory, and to further process and communicate data as described in the present application.
In some examples, the frequency and/or type of light emitted by the photobiomodulation device may be adjustable. Therefore, the photobiomodulation device may further include a controller that allows the user to adjust the frequency, illumination pattern and/or intensity of light. Also, in some examples, the photobiomodulation device may be paired to a user device (e.g., via Bluetooth®) that can send instructions to adjust the operating parameters of light emitted. In some examples, the position of the LED light emitter may be adjustable, i.e., the LED light emitters can be moved or scanned in another direction (e.g., left, right, up, or down).
Some implementations of the disclosed technology will be described more fully with reference to the accompanying drawing. This disclosed technology can be embodied in many different forms, however, and should not be construed as limited to the implementations set forth herein. The components described hereinafter as making up various elements of the disclosed technology are intended to be illustrative and not restrictive. Many suitable components that would perform the same or similar functions as components described herein are intended to be embraced within the scope of the disclosed electronic devices and methods. Such other components not described herein can include, but are not limited to, for example, components developed after development of the disclosed technology.
It is also to be understood that the mention of one or more method steps does not imply that the methods steps must be performed in a particular order or preclude the presence of additional method steps or intervening method steps between the steps expressly identified.
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Further methods of the invention can include photobiomodulation of lymphatic vessels to improve drainage to treat neurological conditions. See, for example, the publication by Semyachkina-Glushkovskaya et al., “Photobiomodulation of lymphatic drainage and clearance; perspective strategy for augmentation of meningeal lymphatic functions”, Biomedical Optics Express, Vol. 11, No. 2, February 2020, the entire contents of which is incorporated herein by reference. By using PBM to augment the rate of drainage of lymphatic fluid from the brain there are improvements in transport of components that adversely impact neurological condition of the patient. Improved drainage of the lymphatic system has been shown to improve the condition of autistic patients. See Antonucci et al., “Manual Lymphatic Drainage in Autism Treatment”, Madridge Journal of Immunology, Vol. 3, Issue 1, December 2018, the entire contents of which is incorporated herein by reference. Thus, methods of treatment can include transcranial PBM of lymphatic channels in the brain. The LED array elements can be actuated to illuminate lymphatic channels at the energy densities described herein to perform therapeutic treatment of the patient. Imaging technologies including Optical Coherence Tomography (OCT) and ultrasound have been used to monitor lymphatic flow as well as blood flow and perfusion.
Using the photobiomodulation device 1100, certain methods of the present disclosure may perform photobiomodulation (stimulating brain with light). In some examples, photobiomodulation may be performed simultaneously with linguistic training to treat, for example, children with ASD as described previously herein. Preferred photobiomodulation devices and methods may include several near infrared and/or red light emitters to stimulate certain blood vessels within the cranium or that flow directly into the brain. Methods associated with the photobiomodulation device 1100 may include determining a position the LED lights 1102 to output the infrared and/or red lights. The light absorbed by the blood vessels may increase the production of ATP, which may provide the neurons more energy to communicate with each other and provide increased brain connectedness. In some examples, the photobiomodulation device 1100 can determine an amount of change in ATP. Further, the photobiomodulation device 1100 may continue to output light until a desired amount of ATP change is reached. Therefore, in some examples, the photobiomodulation device 1100 may further include one or more sensors that can determine the amount of ATP of cells within a predetermined distance of the device. Also, using a controller, the frequency of transmitted light and/or the type of light emitted by the photobiomodulation device 1100 can be manually or automatically adjusted.
Methods for providing photobiomodulation may include determining the light frequency, location of the LED lights (e.g., blood vessels needing increased ATP), whether ATP production increased, and the overall effect of the treatments. Accordingly, based on the determined overall effect on the brain, the photobiomodulation device 1100 may be dynamically adjusted on a user-specific basis.
The photobiomodulation device 1100 may be specifically tailored for children and/or older adults, such that it alleviates certain ailments (e.g., ASD, Alzheimer's Disease). Further, the photobiomodulation device 1100 may be used on a daily basis, in the convenience of the family's home, without a need for a specially trained therapist. Moreover, the photobiomodulation device 1100 can be non-invasive, not require a prescription, and lack side effects.
Methods of the present disclosure may further include determining the location(s) of the light diodes that may be used to stimulate specific brain areas responsible for language, comprehension, energy production, and/or for self-regulation (e.g., reducing anxiety). Therefore, application of light therapy by the photobiomodulation device 1100 may result in improved sleep, improved language, and/or improved general cognition. The methods may also include determining total power, power density, pulsing, and/or frequency. The total power may be 400-600 mW (0.4-0.6 W) with 100-150 mW per each of four panels.
Further, the photobiomodulation device 1100 may be comprised of a comfortable material for prospective patients. For example, the photobiomodulation device 1100 may be comprised of plastic, latex, silicone, rubber, and/or the like. Because ASD patients in particular are especially sensitive, the aforementioned shape and materials may be integral in allowing ASD patients to wear it for a sufficient amount of time without being irritable. Of course, the photobiomodulation device 1100 is preferably both safe and comfortable. The electric components (e.g., processors, wires, transceivers, etc.) may be included within the interior of the photobiomodulation device 1100 and may be difficult to reach by children, for example. Further, the weight of the photobiomodulation device 1100 may be light enough to allow it to be held in the mouth comfortably. Moreover, the photobiomodulation device 1100 may require a power source (e.g., batteries) that allows it to be portable. The emitter section 1102 can include one or more sensors as described herein to measure a fluid analyte, such as glucose or lactose, in a saliva sample that can be captured by a small port into a cell within the device. A motion sensor or piezoelectric sensor can be included to measure mechanical movements of the device.
As mentioned above, the photobiomodulation device 1100 can be paired to a user device, such that a user can adjust the position of the LED light emitters 1102 as well as the frequency and/or type of light. Further, the photobiomodulation device 1100 may be dynamically adjustable based on the determined ATP levels of the cells near photobiomodulation device 1100 before and after application of the light treatment. For example, the photobiomodulation device 1100 can be configured to treat a predetermined amount of ATP that the cells near the photobiomodulation device 1100 can have. Then, the photobiomodulation device 1100 may determine an amount of the ATP cells before application of the light treatment and during the application of the light treatment. Based on the determined ATP levels, the photobiomodulation device 1100 may continue to apply light treatment until the predetermined amount of ATP is reached.
Shown in the side and perspective views of
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Fetal Alcohol Syndrome (FACS) results from a baby being exposed to alcohol during the neonatal stage of development. The fetal liver cannot metabolize alcohol (ethanol), so when alcohol enters the blood stream of the developing baby it interferes with the delivery of nutrition and oxygen to the developing organs. Therefore, it interferes with cell growth and proliferation. Specifically, ethanol in the developing baby's blood stream can result in permanent and irreversible brain damage. Neurological and behavioral symptoms often reflect the affected brain areas. The most common affected brain areas are the prefrontal cortex, which results in difficulties with focus, decision making and social interactions; the hippocampus can also be affected, which results in difficulties with forming memories; the cerebellum, which results in difficulties controlling movements; and also the corpus callosum, which affects overall brain function and results in mental retardation.
Brain imaging studies have specifically identified these areas (frontal lobe, corpus callosum, hippocampus and cerebellum) as being most likely to be affected by FACS. Other imaging studies showed that FACS results in poor communication between various brain areas (i.e., poor brain connectivity). Children affected by FACS usually have smaller brains. In addition, children affected by FACS may develop physical characteristics like microcephaly, growth retardation, dislocated limbs, certain facial features (e.g., thinner upper lip) and cardiological problems. It should be noted that the physiological features of FACS may or may not be present and a percentage of FACS children are misdiagnosed as having ADHD (due to their difficulties with focus, organization, planning, decision making and memories). It should also be noted that Fetal Alcohol Syndrome disproportionally affects babies in the minority communities (specifically in the Black community). No treatment is currently available for FACS.
tPBM (stimulation of the brain with near-infra red light) has been shown in animal and human studies (in vivo and in vitro) to increase blood oxygenation, cerebral blood flow, and mitochondrial ATP production. In addition, EEG and NIRS data has shown that tPBM improves brain connectivity. Therefore, blood brings more oxygen and nutrition to the brain. In addition, increased ATP production results in more neurogenesis and synaptogenesis. Furthermore, functional brain connectivity has been shown to improve after one session. tPBM has been shown to be beneficial for traumatic brain injury, depression, ischemic stroke, and Parkinson's disorder. In addition, it has been shown to be effective for Down syndrome, autism and ADHD. Similarly, tPBM can be effective for the neurological symptoms of FACS by increasing the amount of oxygen and nutrients delivered to the brain, improving functional brain connectivity, and increasing neurogenesis and synaptogenesis. Specifically, the effect may be most pronounced in cortical structures (frontal lobes), which improves organization, focus, and decision making. The effect on memory and motor functions may be less pronounced since sub-cortical structures are implicated (e.g., hippocampus and cerebellum). However, due to neuroplasticity, the beneficial effect of tPBM may be most pronounced when treatment is administered to young children.
The preferred embodiment of the present invention includes a neuro-biomodulation device, sensors and other measurement instruments, a software platform for personalized treatment recommendation and progress monitoring, and respective child, parent, and therapist interfaces described herein and as shown in
The User Profile Module (UPM) 3002 receives all data related to the child's profile, including demographic data, neuro-developmental assessment data, health data, ongoing device data, treatment history, progress indicators, parental assessments, and behavioral data. The user profile is continuously updated with treatment and progress data and contains both baseline and longitudinal data.
The Reference Population Module (RPM) 3016 is the database containing all user profiles created within the UPM and is used as a calibration and testing sample for the Machine Learning Module 3018.
The neuro-developmental assessment module (NDA) 3006 uses the user profile together with questionnaire data to assess the baseline and continuous performance of the child along attachment, playing, communication and language, and other behavioral factors using a range of metrics and scores the child's current state for each of the measures. As treatments are administered, the NDA scoring is updated and resulting recommendations modified. The NDA assessment together with the UPM data feed into the Personalized Treatment Module (PTM).
The Personalized Treatment Module (PTM) 3004 leverages the cluster-treatment mapping data from the Machine Learning Module 3018 to create personalized plans for the Neuromodulation Treatment Module (NMT) 3008 and the Cognitive Programming Module (CPM) 3010. This includes physical device treatment duration, intensity, and frequency as well as specific cognitive treatment activity portfolios to be administered to the child.
The Neuromodulation Treatment Module (NMT) 3008 leverages the personalized treatment recommendations of the PTM and provides them across the parent and therapist interfaces for administration.
The Cognitive Programming Module (CPM) 3010 leverages the personalized treatment recommendations from the PTM and provides cognitive activity and treatment content to the child via the child interface and/or the parent/therapist interfaces.
The Sensor and Quantitative Data Feedback Module (SQD) 3012 captures data from physical sensors and devices such as EEG, heart rate and pulse wearables, and other devices alongside with performance data of the child on the cognitive programming module (CPM) as well as parental and therapist feedback to measure the impact of the treatments on the NDA metrics of the child.
The Performance Progress Module (PPM) 3014 compares the individual data from the SQD 3012 with expected progress thresholds established for the selected cluster within the RPM 3016 and provides effectiveness scores for administered treatments.
The Machine Learning Module (MLM) 3018 uses an embedding-based vectorization methodology to create user profile vectors that are then mapped into different profile-treatment clusters which match an individual profile background to treatments that have the highest effectiveness scores for individuals with similar user profile vectors.
The Feature Selection and Vectorization Module (FSV) 3020 takes individual data fields from the UPM and vectorizes them into n-dimensional numeric feature vectors that represent the initial UPM data. The FSV 3020 uses a modified tf/idf in the form of a boolean (feature) frequency-inverse boolean vector frequency (BF/IBVF) measure to convert different measurement variables into numeric data indices of a vector. This vectorization of variables can be expressed as:
Wherein bl represents the lth bin, dj represents the jth document, Freq(bl, dj) is the number of times bin l appears in document j, ΣlFreq(bl, dj) represents the total number of bins in document j, Ndoc is the number of users, and Ndoc(bl) is the number of uses with the bin bl.
The FSV 3020 then engages in dimensionality reduction of the vector into a lower-dimensional orthogonal subspace that captures as much of the variation of the UPM and RPM data sets as possible.
The Embedded Cluster Predictor (ECP) 3024 takes the reduced dimensionality vectors from the FSV 3020 and clusters them based on the K-means or LDA, bypassing BF/IBVF and dimensionality reduction) into bi-partite profile-treatment clusters using effectiveness scores from the PPM as a omni-distance measure.
The Deep Learning Module (DLM) 3022 takes data from the SQM and from the PPM for the reference population and adjusts the ECP 3024 clusters continuously with new feedback data from all users. The raw SQM data is augmented with gaussian noise to reduce inter-subject variability. DNN learns features automatically. Further details describing the use of machine learning computational systems and methods, and particularly with respect to the application of neural networks to EEG data and similar data sets is described in Moinnereau et al., “Classification of Auditory Stimuli from EEG with a regulated recurrent neural network reservoir”, arXiv:1804.10322v1 [eess.SP], published 27 Apr. 2018, the entire contents of which is incorporated herein by reference. A regulated recurrent neural network (RNN) to characterize hearing of patients to auditory stimuli or speech which improved on the classification rates over other methods such as a naïve Bayes classifier or support vector machine (SVM). In this method, EEG signals recorded by methods previously described herein, are transformed into spike trains that are accumulated in a reservoir of connected neurons. For example, the process of encoding of signals into spike trains can assume that an analog signal is the result of filtering spike trans with a reconstruction finite impulse response (FIR) filter and uses the FIR to find spikes in the EEG signals according to the following two equations at every given time τ.
where s represents the different EEG signals, h(k) is the reconstruction FIR filter, and M is the order of the filter. This can digitize the output to enable further processing of the data. The readout from the reservoir is classified over time using linear regression. The results of the RNN were compared to a deep neural network (DNN) which can employ, in this example, three convolutional layers where EEG signals were input into the network. This enables EEG measurements to be used to measure the response of system users to auditory stimuli as described above where photobiomodulation is used to treat patients to improve language learning. Thus, changes in language comprehension can be quantified over time during treatment.
In a further example, as described in Chambon et. al., “A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series,” IEEE Neural Systems of Rehabilitation Eng. 26, 758-69 (2017) involving the use of a multivariate time series to classify sleep patterns, and further machine learning techniques to classify and score sleep patterns using a convolutional neural network in Chambon et. al., “A deep learning architecture to detect events in EEG signals during sleep”, arXiv:1807.05981v1 [eess.SP] 11 Jul. 2018, in which the learning problem can be solved by a minimization problem to detect events from EEG signals measured during sleep. This can be expressed as event detector {circumflex over (f)}
{circumflex over (f)}∈ar
min
x∈X[l(E(x)),f(x)]
where an iterative computational process is used to detect events during sleep. As the patient is treated with photobiomodulation therapy, changes in the detected events can be quantified and stored over time in relation to the course of treatment. Note that x denotes input EEG signals while E(x) denotes a “true” or measured event; there are also “default” events used to train the neural network. An event label l can have a zero-value or a selected non-zero value such as 1.
The process detects spindles and K-complexes jointly or severally to detect and score events during sleep. Thus, the network identifies classes of sleep events using EEG sensor data that can be measured during the course of photobiomodulation therapy to characterize and quantify therapeutic outcomes of the treatment.
The architecture of the Deep Neural Network (DNN) contains convolutional neural network (CNN) layers to extract frequency domain features and recurrent neural network (RNN) layers to capture the temporal structure. Thus, the DNN generates quantitative frequency domain and time domain data that are used to characterize the results of the photobiomodulation therapy and can be used to guide modifications of the therapeutic plan for the patient and serve to train the network to treat subsequent patients that are within the same class in the PPM so that the appropriate thresholds are established.
The Cluster-Treatment Mapper (CTM) 3026 takes the individual's UPM vector and maps it into the clusters identified in the ECP to identify the optimal treatment options based on the RPM. It then feeds the identified cluster into the PTM for further processing.
The entire process for an individual is captured in the flowchart in
The reference population treatment cluster analysis process and the respective personalized treatment mapping process are shown in
In
In some embodiments, the photobiomodulation and/or neurobiomodulation devices and methods of use described herein can produce statistically significant improvements in autism symptoms and related indicators. A clinical trial was conducted with the objective of demonstrating that transcranial photobiomodulation (tPBM) is an effective treatment modality to improve language and communication skills in children with ASD. In recent pilot studies, tPBM has been shown to be an effective treatment for certain conditions such as stroke, traumatic brain injury (TBI), and depression (Ando et al., 2011; Cassano et al., 2018; Naeser et al., 2020). Pilot studies have shown that tPBM can reduce symptoms of autism (Ceranouglu et al, 2019; Leisman et al 2018). The above referenced study hypothesized that children with ASD will demonstrate improvement in communication skills and language acquisition with experimental treatment.
The present clinical study examined the effect of tPBM modulation on symptoms of autism in children 2-6 years old. It is a randomized, placebo-controlled, double-blind study. Twenty-nine participants were enrolled and wore the tPBM device (such as the photobiomodulation device(s) of the present disclosure) for 6 minutes, and in which illuminating light delivering an energy in a range of 16-24 joules was administered during each session. Each participant completed 16 sessions during an 8-week course of the study. Data about children's behavior was collected from parents through weekly interviews. Children's therapists are interviewed regarding any observed changes in child's behavior. Before and after treatment scores of Childhood Autism Rating Scales are compared for placebo and experimental conditions. EEG measurements from frontal, occipital and temporal area were collected before and after each treatment.
The results of the trial indicated a statistically significant reduction in autism symptoms as measured using the Childhood Autism Rating Scale (CARS). A CARS assessment was made for each participating child (n=21) before the beginning of the trial and after the trial by a blinded researcher. The CARS is defined such that higher scores indicate worse autism symptoms. As shown in the tables below, treatment using photobiomodulation in accordance with the systems and methods described herein produces a statistically significant improvement in children in the “experimental” group whereas children in the “placebo” group did not have a statistically significant improvement. In the preliminary CARS results Control: before: 40.3 7.5 after 39.8 7.3 Experiment: before: 45.3 5.7 after 35.4 4.7 T-test p-value: 1-sided: p=0.001; 2-sided: p=0.005.
DiD
In the final clinical study results, the CARS scoring showed a statistically significant decrease for overall based on 16 active patients and a slight decrease in the placebo control group of 14 patients:
DiD
Clinical trials also indicate that systems and methods for photobiomodulation as described herein can produce measurable improvements in EEG data. EEG data was gathered before and after treatment from the frontal, occipital, and temporal areas in consenting subjects. (In some cases, it was not permissible or possible to collect EEG data from a child, for example, due to hair interference or sensory issues, and collected data was meaningless for analysis in some cases such as when a child is jumping around.) The data was analyzed by the Pirogov Institute in Moscow. Changes in the EEG data show a decrease in delta waves and increase in alpha, beta, and theta in a few patients which is associated with better focus, implicit learning and faster language acquisition. Compared to placebo, active stimulation using photobiomodulation systems and methods as described herein presented suppression of the increase in the lower frequency bands (delta) and a further increase in power in the higher frequency bands (alpha, beta,). Normalization of alpha activity can represent normalization of DMN functions and is indicative of increased organization in the cortex including language areas. A summary of the trajectory of the alpha and beta is 13.2-15.36-8.78-7.45-21.8-27.7 (increasing). A summary of the trajectory of the theta wave is 49.08-51.04-58.92-71.6-52.9-61.7 (increasing). Note that ASD is associated with lower theta wave values. Consequently, the measured increase in theta waves and the decrease in delta waves serves as a biomarker for improvement of cognitive function of ASD patients. A summary of the trajectory of the delta wave is 0-31.4-27.8-19.2-27.7-0 (decreasing).
Progress of the subjects in the study was also analyzed through qualitative interviews. Specifically, a researcher conducted weekly interviews with parents regarding their observation of the children. In addition, most of the time when the parent came in the researcher collected notes as well. The table below shows averaged data for experimental and control groups for individual question categories and for aggregated “index” scores as described below:
A ‘total improvement score’ was computed by combining the categories that are related to socialization (e.g. eye contact), language (new words), and responsiveness to create the total Benefit Index score.
A further analysis of clinical results can be based on a treatment protocol taking place 2 times a week for up to 60 minutes at a time. During this patient study, the participants first wear the mobile EEG device for approximately 15 minutes, and the EEG signals before treatment are collected. Then the participants wear the tPBM device for up to 15 minutes. Then the participants wear the EEG device again for approximately 15 more minutes. The child can be encouraged to play with toys and interact with the parent (or the experimenter). The children in the active and sham treatment group wear the same devices (the tPBM device won't be turned on for children in the sham group). The total time in the office can be about 40-60 minutes (including playing). An example assessment schedule is shown in the table below
Several Standard tests (scales) can be used for pre-test and post-test, as well as weekly interviews.
1. Childhood Autism Rating Scales, Second Edition (CARS2).
2. Secondary End POINTS: Social Responsiveness Scales (SRS)
3. Receptive-Expressive Language Scales, Third Edition (REELS-3)
4. EXPLORATORY END POINTS: EEG will be collected from each participant.
5. Parental interviews.
6. Therapist interviews
Statistics: There are no universally accepted clinical outcome measures developed for measuring changes in core symptoms in ASD, based on interventions. A recently proposed clinical efficiency benchmark is a 4-4.5 decrease in CARS 2, based on a recent article by Jurek et. al. 2021, who conducted a panel of 5 experts including pediatric and adult psychiatrists who work with patients in Europe and India. Their proposal should be taken with caution, because they do not work with a diverse population, similar to the population in the United States. We propose a reduction of 3 points in CARS, as it is a 10% reduction—based on 30 points being a cutoff Score for ASD. Clinically, 10% reduction of CARS score may mean moving from SEVERE to Moderate or from Moderate to Mild subgroup of the spectrum, which might mean more independent functioning (e.g., improved signaling of their needs, improved focus, responsiveness to language and therapy) and improved quality of life for children and their caregivers.
A sample size of 60 subjects, 30 in each arm is sufficient to detect a clinically important difference of 3 points between treatment groups in reducing symptoms of autism as measured by the CARSII assessment assuming a between treatment standard deviation of 8.02 using a two-tailed t-test of difference between means with 80% power and a 5% level of significance. to reach significance in the primary end point. If we reach statistical and clinical significance with 60 participants, we plan to continue recruiting up to 150 participants to measure the effectiveness in the secondary and exploratory end points.
Analysis Population: Data analysis will be conducted on the intent to treat (ITT) population which includes all subjects that were consented and randomized to either the JelikaLite device or the sham device. Additional analysis will be conducted on a per protocol population which is defined as all participants who have participated in the trial.
Effectiveness Analysis: The following analyses are conducted:
The primary analysis of efficacy includes a comparison of change in mean CARSII scores between Baseline and 12 for the actively treated versus sham arm. The primary analysis will be performed on the ITT population.
A model for repeated measures fitted by restricted maximum likelihood method will be used for the primary analysis. This model takes into account the presence of missing data and yields valid estimates under the assumption of data missing at random (MAR).
Fixed effects will further include treatment, visit, treatment by visit interaction, baseline CARSII score and baseline CARSII by visit interaction. A general (co)variance structure with unconstrained correlations and variances will be used to model the within-subject errors. If this analysis fails to converge, alternative variance-covariance structures will be considered. More specifically, the same mean model will be fitted with following variance-covariance structure (in this order):
The Secondary Analysis of Efficacy: In the first part of the secondary analysis of efficacy, a comparison of change in mean SRS and REEL scores is calculated between Baseline and Week 12 for the actively treated versus sham arm. The primary analysis will be performed on the ITT population. A model for repeated measures fitted by restricted maximum likelihood method will be used for the primary analysis. This model takes into account the presence of missing data and yields valid estimates under the assumption of data missing at random (MAR). Fixed effects can further include treatment, visit, treatment by visit interaction, baseline SRS and REEL score and baseline SRS and REEL scores by visit interaction. A general (co)variance structure with unconstrained correlations and variances can be used to model the within-subject errors. If this analysis fails to converge, alternative variance-covariance structures will be considered.
In a second part of the secondary analysis of efficacy, linear regression analysis can be used to analyze the change in EEG waves distribution throughout the measurement. Pearson correlation of the distribution of brainwaves with time can be computed.
Exploratory Endpoint Analysis: CARS: Synergy of tPBM treatment and ABA therapy: The model will include ABA treatment used in the randomization as covariate (3 levels: 0 hours of ABA, <=10 hours of ABA and >10 hours of ABA.). This analysis will be conducted if there is a statistically significant correlation of the before and after treatment change in CARS scores and the number of ABA hours each participant in receiving. Parental Interviews: Non-parametric statistics (e.g., Wilcoxon Signed Rank test). Therapist Interviews: Non-parametric statistics (e.g., Wilcoxon Signed Rank test).
Additional confounding variables for Post-Trial Analysis:
Throughout the specification and the claims, the following terms take at least the meanings explicitly associated herein, unless the context clearly dictates otherwise. The term “or” is intended to mean an inclusive “or.” Further, the terms “a,” “an,” and “the” are intended to mean one or more unless specified otherwise or clear from the context to be directed to a singular form.
In this description, numerous specific details have been set forth. It is to be understood, however, that implementations of the disclosed technology can be practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description. References to “one embodiment,” “an embodiment,” “some embodiments,” “example embodiment,” “various embodiments,” “one implementation,” “an implementation,” “example implementation,” “various implementations,” “some implementations,” etc., indicate that the implementation(s) of the disclosed technology so described can include a particular feature, structure, or characteristic, but not every implementation necessarily includes the particular feature, structure, or characteristic. Further, repeated use of the phrase “in one implementation” does not necessarily refer to the same implementation, although it can.
As used herein, unless otherwise specified the use of the ordinal adjectives “first,” “second,” “third,” etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.
While certain implementations of the disclosed technology have been described in connection with what is presently considered to be the most practical and various implementations, it is to be understood that the disclosed technology is not to be limited to the disclosed implementations, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.
This written description uses examples to disclose certain implementations of the disclosed technology, including the best mode, and also to enable any person skilled in the art to practice certain implementations of the disclosed technology, including making and using any devices or systems and performing any incorporated methods. The patentable scope of certain implementations of the disclosed technology is defined in the claims, and can include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.
Exemplary flowcharts are provided herein for illustrative purposes and are non-limiting examples of methods. One of ordinary skill in the art will recognize that exemplary methods may include more or fewer steps than those illustrated in the exemplary flowcharts, and that the steps in the exemplary flowcharts may be performed in a different order than the order shown in the illustrative flowcharts.
This application is a continuation-in-part of International Application No. PCT/US2022/020770, filed Mar. 17, 2022, which claims priority to U.S. Provisional Application No. 63/303,384, filed Jan. 26, 2022, and to U.S. Provisional Application No. 63/272,823, filed Oct. 28, 2021, and to U.S. Provisional Application No. 63/250,703, filed Sep. 30, 2021, and to U.S. Provisional Application No. 63/162,484, filed Mar. 17, 2021. International Application No. PCT/US2022/020770 is also a continuation-in-part of U.S. patent application Ser. No. 17/105,313, filed Nov. 25, 2020, which is a continuation-in-part of International Patent Application No. PCT/US2020/055782, filed Oct. 15, 2020, which claims priority to U.S. Provisional Application No. 63/033,756, filed Jun. 2, 2020, U.S. Provisional Application No. 62/940,788, filed Nov. 26, 2019, U.S. Provisional Application No. 62/915,221, filed Oct. 15, 2019, and U.S. Design Application No. 29/728,109, filed Mar. 16, 2020, the entire contents of each of the above-mentioned applications being incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 63303384 | Jan 2022 | US | |
| 63272823 | Oct 2021 | US | |
| 63250703 | Sep 2021 | US | |
| 63162484 | Mar 2021 | US | |
| 63033756 | Jun 2020 | US | |
| 62940788 | Nov 2019 | US | |
| 62915221 | Oct 2019 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US2022/020770 | Mar 2022 | US |
| Child | 17949997 | US | |
| Parent | 17105313 | Nov 2020 | US |
| Child | PCT/US2022/020770 | US | |
| Parent | PCT/US2020/055782 | Oct 2020 | US |
| Child | 17105313 | US | |
| Parent | 29728109 | Mar 2020 | US |
| Child | PCT/US2020/055782 | US |
