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WO2025049707A1 - Systèmes et procédés de détermination de paramètres de mémoire - Google Patents

Systèmes et procédés de détermination de paramètres de mémoire Download PDF

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Publication number
WO2025049707A1
WO2025049707A1 PCT/US2024/044374 US2024044374W WO2025049707A1 WO 2025049707 A1 WO2025049707 A1 WO 2025049707A1 US 2024044374 W US2024044374 W US 2024044374W WO 2025049707 A1 WO2025049707 A1 WO 2025049707A1
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WIPO (PCT)
Prior art keywords
memory
stimuli
examples
metric
stimulus
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PCT/US2024/044374
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English (en)
Inventor
Andrea Stocco
Thomas J. GRABOWSKI
Holly Sue HAKE
Dirk Hendrik VAN RIJN
Sarah Caroline MAASS
Maarten Alexander VAN DER VELDE
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Rijksuniversiteit Groningen
University of Washington
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Rijksuniversiteit Groningen
University of Washington
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Publication of WO2025049707A1 publication Critical patent/WO2025049707A1/fr
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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4088—Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient; User input means
    • A61B5/7475—User input or interface means, e.g. keyboard, pointing device, joystick
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/08—Elderly
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient; User input means
    • A61B5/742—Details of notification to user or communication with user or patient; User input means using visual displays
    • A61B5/743—Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • Examples described herein relate to human memory, including systems and methods for determining memory metrics. Examples include the use of computational models to determine memory metrics.
  • test content is fixed and cannot be repeated, because the repetition has been shown to affect results.
  • Translation and standardization are necessary for different languages, each with its own norms (e.g., culture, etc.). Expert professionals are required to administer the tests due to their specialist nature. Lastly, financial and societal barriers hinder widespread population screening using these tests.
  • An example method includes presenting a plurality of stimuli to a patient; collecting behavioral responses from the patient to the plurality of stimuli; extracting signature, using at least one mathematical model, from the behavioral responses related to memory processes; estimating, using a first computational model, a performance metric for each particular stimulus based on the extracted signature, the performance metric relates to speed of forgetting; and estimating, using a second computational model, a memory metric of the patient based on the performance metrics of the stimuli.
  • extracting the signature includes removing at least one portion of the behavioral responses due to non-memory processes.
  • the at least one portion of the behavioral responses may be related to a perception retrieval.
  • the at least one portion of the behavioral responses may be related to a motor response.
  • adjusting a next stimulus of the plurality of stimuli based on the memory metric.
  • adjusting the next stimulus of the plurality of stimuli may be to increase an information gain in updating the memory metric associated with memory capacity of the patient.
  • adjusting the next stimulus of the plurality of stimuli may include at least one of adjusting a difficulty level of a task including the plurality of stimuli or a speed of presenting the next stimulus.
  • An example system includes at least one processor and one or more memory devices.
  • the one or more memory devices storing instructions that, when executed by the processor, configure the system to: present a plurality of stimuli to a patient; collect behavioral responses from the patient to the plurality of stimuli; extract a signature related to memory processes, using at least one mathematical model, from the behavioral responses; estimate, using a first computational model, a performance metric for each particular stimulus based on the extracted signature, the performance metric relating to speed of forgetting; and estimate, using a second computational model, of a memory metric of the patient based on the performance metrics of the plurality of stimuli.
  • the extract the signature includes remove at least one portion of the behavioral responses due to non-memory processes.
  • the at least one portion of the behavioral responses may be related to a perception retrieval.
  • the at least one portion of the behavioral responses may be related to a motor response.
  • the instructions further configure the system to adjust a next stimulus of the plurality of stimuli based on the memory metric.
  • said adjust the next stimulus of the plurality of stimuli may be to increase an information gain in updating the memory metric associated with memory capacity of the patient.
  • said adjust the next stimulus of the plurality of stimuli may include at least one of adjusting a difficulty level of a task including the plurality of stimuli or a speed of presenting the next stimulus.
  • the instructions further configure the system to display diagnostic-related information.
  • An example non-transitory computer-readable medium including instructions that when executed by a computer cause the computer to: present a plurality of stimuli to a patient; collect behavioral responses from the patient to the plurality of stimuli; extract a signature related to memory processes, using at least one mathematical model, from the behavioral responses; estimate, using a first computational model, a performance metric for each particular stimulus based on the extracted signature, the performance metric relating to speed of forgetting; and estimate, using a second computational model, of a memory metric of the patient based on the performance metrics of the plurality of stimuli.
  • the extract the signature includes remove at least one portion of the behavioral responses due to non-memory processes.
  • the at least one portion of the behavioral responses may be related to a perception retrieval.
  • the at least one portion of the behavioral responses may be related to a motor response.
  • the instructions further configure the computer to adjust a next stimulus of the plurality of stimuli based on the memory metric.
  • said adjust the next stimulus of the plurality of stimuli is to increase an information gain in updating the memory metric associated with memory capacity of the patient.
  • said adjust the next stimulus of the plurality of stimuli comprises at least one of adjusting a difficulty level of a task including the plurality of stimuli or a speed of presenting the next stimulus.
  • the instructions further configure the computer to display diagnostic-related information.
  • FIG. 1 is a schematic diagram of a system for determining memory metrics in accordance with examples described herein.
  • FIG. 2 is a block diagram of an example system for determining memory metrics in accordance with examples described herein.
  • FIG. 3 shows an equation representing log odds of retrieving a memory in accordance with examples described herein.
  • FIG. 4 shows an equation representing a trace-specific decay rate in accordance with examples described herein.
  • FIG. 5 shows an equation representing a probability of a correct response to a stimulus related to the activation in accordance with examples described herein.
  • FIG. 6 shows an equation representing a response time associated with a correct response to a stimulus related to the activation in accordance with examples described herein.
  • FIG. 7 shows a diagram of predicted forgetting associated with a specific value of the memory metric in accordance with examples described herein.
  • FIG. 8 shows examples of a software interface in accordance with examples described herein.
  • FIG. 9 includes diagrams showing a distribution of speed of forgetting (SoF) values across all lessons and patients in accordance with examples described herein.
  • FIG. 10 is a diagram showing correlations between memory metrics measured across lessons of different materials.
  • FIG. 11 includes diagrams showing a distribution of memory metrics across patients affected by mild cognitive impairment (MCI) and age-matched healthy controls in accordance with examples described herein.
  • FIG. 12A shows receiver operating characteristic (ROC) classification performances for SoF and for response accuracy from a single session in accordance with examples described herein.
  • ROC receiver operating characteristic
  • FIG. 12B shows average ROC classification performances for SoF and for response accuracy across sessions in accordance with examples described herein.
  • FIG. 13 shows probability of MCI by different levels of memory metrics in accordance with examples described herein.
  • FIG. 14 shows age-related changes in memory metric over time in MCI patients and healthy controls in accordance with examples described herein.
  • FIG. 15 shows a distribution of memory metric values across different groups and types of stimuli in accordance with examples described herein.
  • FIG. 16 shows a detailed view of memory metrics across materials and patient subgroups in accordance with examples described herein.
  • Example methods may assess memory function of the individual using a memory metric.
  • the memory metric may be a quantitative and/or precise measure of memory function.
  • Example methods include collecting data through an automated digital session with a subject. Examples of the digital session may not utilize any human supervision. Each session may be administered remotely on affordable digital devices (e.g., desktop computers, laptop computers, mobile smartphones, wearable devices, and/or tablets).
  • the digital device used by the subject to perform the automated digital session may be connected with other electronic devices via a peer-to-peer connection or a network (e.g., the Internet, Wi-Fi, a local area network “LAN,” a wide area network “WAN,” Bluetooth “BT,” near-field communication “NFC,” etc.) in constant or intermittent communication.
  • a network e.g., the Internet, Wi-Fi, a local area network “LAN,” a wide area network “WAN,” Bluetooth “BT,” near-field communication “NFC,” etc.
  • Example methods use a collection of responses from a subject to a series of stimuli.
  • the responses may include, for example, inputs to an electronic device, such as keystrokes, mouse clicks, spoken responses, and/or touchscreen gestures from the subject and corresponding times elapsed for providing such inputs (e.g., response time).
  • These responses are collected for a subject throughout a session.
  • a session may include an open-ended, continuous series of responses by a subject to a series of corresponding experimental stimuli presented over a software interface (e.g., displayed on a digital device, sound produced from the digital device, etc.).
  • the digital device may transmit the collected responses to a computer device.
  • the computer device may add the collected responses to temporary record of all responses through the session in association with the presented stimuli.
  • the stimuli may be, for example, questions regarding a meaning of one or more words, a prompt to identify one or more locations on a map, or a prompt for a recall of factual information.
  • a particular stimulus e.g., a particular question
  • the recorded responses are examined by a computer program executed on a computer.
  • the computer program uses these responses to estimate a performance metric.
  • the performance metric may include, for example, a speed of forgetting (SoF) for each stimulus.
  • SoF speed of forgetting
  • multiple responses to the same stimulus over time in a session may be used to estimate a performance metric associated with the stimulus.
  • the subject may initially provide an incorrect response to the stimulus, but over time the subject may begin to provide a correct response.
  • the times at which responses are provided may be related to the performance metric (e.g., the SoF) for the stimulus.
  • a current estimated value of SoF of the subject for the current stimulus may be adjusted upwards or downwards to reflect a performance of the subject. If the response is correct and fast, the SoF may be revised downwards. Conversely, if the response is slow and/or incorrect, the SoF may be revised upwards.
  • a memory metric of a subject may be provided by combining the SoFs for all stimuli in a session.
  • Examples described herein employ a computational cognitive model that simulates encoding and passive forgetting based on established cognitive and biological principles, providing a framework for understanding the underlying mechanisms of memory decline in aging and neurodegenerative conditions. Therefore, the memory metric determination technique may be used for predictions about the progression of memory declination and/or identification of potential therapeutic targets.
  • FIG. 1 is a schematic diagram of a system 100 for determining memory metrics in accordance with examples described herein.
  • the system 100 includes a computing device 102 and a digital device 130.
  • the digital device 130 may be coupled to the computing device 102.
  • the digital device 130 and the computing device 102 may be an integrated device.
  • the digital device 130 may function as an interface to a subject 150.
  • the digital device 130 may be implemented, for example, using one or more smartphones, cell phones, tablets, medical devices, wearable devices, computers such as laptop computers, desktop computers, servers, automobiles, appliances, or other electronic devices.
  • Examples of digital devices described herein may include components, such as one or more processors 136, one or more memory devices 138, and communication interface 148.
  • Examples of processors, such as processor 136 may be implemented using one or more central processing units (CPUs), graphics processing units (GPUs), field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), controllers, microcontrollers, or other circuitry.
  • processors described herein may be used to implement software and/or firmware systems.
  • the processor 136 may be used to execute one or more executable instructions encoded on computer-readable media (e.g., one or more memory devices 138). While a single processor 136 is shown in FIG. 1, it is to be understood that any number of processors may be used, and multiple processors may be in communication with each other to perform functions of a processor described herein.
  • computer-readable media e.g., one or more memory devices 138.
  • Examples of computing devices described herein may include memory, such as the memory device 138 of FIG. 1.
  • memory such as the memory device 138 of FIG. 1.
  • any number or kind of memory may be used including random access memory (RAM), read only memory (ROM), solid state drive (SSD), hard disk drives (HDDs), flash memory, etc., or other computer-readable media.
  • RAM random access memory
  • ROM read only memory
  • SSD solid state drive
  • HDDs hard disk drives
  • flash memory etc., or other computer-readable media.
  • RAM random access memory
  • ROM read only memory
  • SSD solid state drive
  • HDDs hard disk drives
  • flash memory etc.
  • the single memory device 138 is shown in FIG. 1, it is to be understood that any number of memory devices may be used, and the executable instructions and/or data may be distributed across multiple memories accessible to the processor 136.
  • the memory devices 138 may include data memory 140 and program memory 142.
  • the program memory 142 may be a non-transitory computer-readable medium storing computer-executable instructions, including executable instructions for presenting stimuli 144 and executable instructions for collecting responses 146.
  • the program memory 142 may be encoded with instructions which, when executed, may cause the digital device 130 to provide stimuli and receive responses, such as behavioral responses related to memory processes, as described herein.
  • the data to be stored in the data memory 140 may include, for example, data for performing instructions encoded in the program memory 142.
  • the data may include a set of stimuli to be presented, such as images, sounds, and/or strings.
  • the set of stimuli may be provided from the computing device 102.
  • the set of stimuli may be stored in the data memory 140 associated with a set of corresponding identifiers, such as numbers, characters, or a combination thereof.
  • the data may include sets of response and response time for providing the corresponding response provided by each subject. Each set of response and response time is further stored in association with a presented stimulus or an identifier of the presented stimulus that caused the related response.
  • Examples of digital devices described herein may include stimuli presentation circuitry, such as a display 132.
  • the executable instructions for presenting stimuli 144 performed by the processor 136 may cause the stimuli presentation circuitry, such as the display 132, to present the stimuli.
  • the display 132 may be integrated into the digital device 130.
  • the display 132 may be external to the digital device 130 and coupled to the digital device 130 via one or more wires (e.g., a data cable, such as a universal serial bus “USB” cable, an unshielded twisted pair “UTP” cable, or a video cable such as a Hi-definition multimedia interface “HDMI” cable, a DisplayPort “DP” cable, or other standardized or proprietary cables) or wirelessly (e.g., the Internet, Wi-Fi, LAN, WAN, BT, NFC, or other standardized or proprietary wireless communication).
  • the stimuli may be provided to the subject 150 through another type of output devices, such as a sound producing device (e.g., speaker, headphone, etc.).
  • Examples of digital devices described herein may include data acquisition circuitry, such as an input interface 134.
  • the executable instructions for collecting responses 146 performed by the processor 136 may cause the input interface 134 to collect the responses.
  • the input interface 134 may receive one or more responses through one or more entries of the subject 150.
  • the input interface 134 may receive an entry of the subject 150.
  • the one or more input interfaces 134 may include an electronic device (e.g., keys, buttons, keyboard, mouse, touchscreen, microphone, etc.) integrated into the digital device 130 placed in proximity to the subject 150.
  • the display 132 and one or more input interfaces 134 may be integrated as one device (e.g., a touchscreen).
  • the one or more input interfaces 134 may provide a communication interface with an electronic peripheral device (e.g., keyboard, mouse, touchscreen, microphone) that may be coupled to the digital device 130 via a cable or wirelessly.
  • the responses provided by the subject 150 using the input interface 134 may be, for example, in the form of keystrokes, mouse clicks, spoken responses, and/or touchscreen gestures, etc.
  • each behavioral response may correspond to each preceding stimulus; thus each stimulus and each behavioral response may be paired.
  • the digital device 130 may store the collected responses in the data memory 140, and provide the collected responses to the computing device 102 from the communication interface 148.
  • the computing device 102 may be implemented, for example, using one or more smartphones, cell phones, tablets, medical devices, wearable devices, computers such as laptop computers, desktop computers or servers, automobiles, appliances, or other electronic devices.
  • Examples of computing devices described herein may include components, such as one or more processors 104, one or more memory devices 106, a communication interface 128, a display 126, and an internal bus 124.
  • Examples of computing devices described herein may generally include one or more processors, such as a processor 104 of FIG. 1.
  • Processors, such as one or more processors 104 may be implemented using one or more CPUs, GPUs, FPGAs, ASICs, controllers, microcontrollers, or other circuitry.
  • processors described herein may be used to implement software and/or firmware systems. While a single processor 104 is shown in FIG. 1, it is to be understood that any number of processors may be used, and multiple processors may be in communication with each other to perform functions of a processor described herein.
  • the processor 104 may be coupled to (e.g., in communication with) a memory device 106, a communication interface 128, and/or one or more displays 126 of FIG. 1 via an internal bus 124.
  • the processor 104 may be used to execute one or more executable instructions encoded on computer-readable media (e.g., memory).
  • Examples of computing devices described herein may include memory, such as the memory device 106 of FIG. 1.
  • memory such as the memory device 106 of FIG. 1.
  • any number or kind of memory may be used including ROM, RAM, flash memory, one or more SSDs, one or more HDDs, or other computer-readable media. While the single memory device 106 is shown in FIG. 1, it is to be understood that any number of memory devices may be used, and the executable instructions and/or data may be distributed across multiple memories accessible to the processor 104.
  • the memory device 106 may include program memory 110 and data memory 108. In some examples, the program memory 110 and the data memory 108 may be implemented as separate segments of the memory device 106 as one or more integrated memory devices.
  • the program memory 110 and the data memory 108 may be implemented as separate memory devices of the same kind or different kinds. In some examples, any of the program memory 110 and/or the data memory 108 may be fixed in the computing device 102. In some examples, any of the program memory 110 and/or the data memory 108 may be attachable to and detachable from the computing device 102.
  • the program memory 110 may be implemented as a non-transitory computer-readable medium storing computer-executable instructions, including executable instructions for determining memory metrics 112.
  • the executable instructions for determining memory metrics 112 may include executable instructions for receiving and storing responses 114, executable instructions for extracting signature from responses 116, executable instructions for estimating a performance metric related to speed of forgetting 118, executable instructions for estimating a memory metric based on performance metrics 120 and/or executable instructions for determining stimulus 122.
  • the data memory 108 described herein may store data.
  • the data to be stored in the data memory 108 may include, for example, data for performing instructions encoded in the program memory 110.
  • the data stored in the data memory 108 may include data to be exchanged with external devices, such as the digital device 130.
  • the data to be exchanged may include information about a stimulus to be presented.
  • the data memory 108 may include a stimuli database that may store a set of stimuli to be presented, such as images, sounds, and/or strings.
  • the data memory 108 may include a stimuli database that may store a set of identifiers, such as numbers, characters, or a combination thereof, that may be associated with the set of stimuli that may be included in the data memory 140 of the digital device 130.
  • the data may include a level of information associated with each stimulus.
  • the data may include information about a difficulty level of a task including the plurality of stimuli or a speed of presenting the next stimulus associated with a memory state, a threshold of memory state, or a range of memory state.
  • the data may include sets of response and response time for providing the corresponding response of each subject collected by the digital device 130.
  • the data may include at least one mathematical model.
  • the data may include extracted signature from the responses using at least one mathematical model, from the responses related to memory processes.
  • the data may include a computational model for estimating a performance metric.
  • the data may include a performance metric for each particular stimulus estimated using the computational model based on the extracted signature.
  • the performance metric may relate to SoF.
  • the data may include a computational model for estimating a memory metric.
  • a memory metric generally refers to a quantitative measure of how quickly a piece of information would be forgotten by a patient.
  • the memory metric of each subject may be estimated based on the performance metrics of the stimuli. While a single memory device 106 is shown in FIG. 1, it is to be understood that any number of memory devices may be used, and the executable instructions and/or data may be distributed across multiple memories accessible to the processor 104.
  • Examples of computing devices described herein may include additional components.
  • the computing device 102 may include or be coupled to output devices.
  • the output devices may be one or more display(s), such as a display 126 of FIG. 1, and/or speakers. While FIG. 1 shows the display 126 integrated into the computing device 102, the display 126 or any output devices may be external devices coupled to the computing device 102.
  • the computing device 102 may include or be coupled to input devices.
  • the input device(s) may include keys, buttons, keyboards, mice, touchscreens, microphones, etc.
  • the additional components in the computing device 102 may communicate with the processor 104 and/or the memory device 106 of FIG. 1 via the internal bus 124.
  • the computing device 102 may further include communication interface 128 (e.g., cellular antenna, Wi-Fi, network interface such as the Internet, Wi-Fi, LAN, WAN, BT, NFC) of FIG. 1 that may communicate wirelessly or via wire(s) such as USB cables, ether cables, HDMI cables, or other standardized or proprietary cables.
  • the additional components and/or the digital device 130 coupled to the computing device 102 may communicate with the computing device 102 via the communication interface 128.
  • the communication interface 128 may handle communications between the computing device 102 and external devices, including the digital device 130.
  • a set of stimuli or each stimulus in a set of stimuli may be selected.
  • the executable instructions for determining stimulus 122 in the executable instructions for determining memory metrics 112 may cause the processor 104 of the computing device 102 to select a stimulus and/or a course of stimuli during a session.
  • the stimuli may be, for example, questions regarding a meaning of one or more words, a prompt to identify one or more locations on a map, or a prompt for a recall of factual information.
  • the computing device 102 may provide the digital device 130 with the selected stimulus and/or the course of stimuli during the session in a form of images, sounds, and/or strings.
  • the computing device 102 may provide the digital device 130 with a corresponding identifier, such as numeric, characters, or a combination thereof, of the selected stimulus and/or a set of identifiers that represents a corresponding course of stimuli.
  • the set of stimuli may be stored in the data memory 140 associated with a set of corresponding identifiers.
  • the executable instructions for presenting stimuli 144 may cause the processor 136 of the digital device 130 to prepare a next stimulus or a course of stimuli based on the received identifier or set of identifiers.
  • executable instructions for determining stimulus 122 may be included in the executable instructions for presenting stimuli 144 stored in the digital device 130.
  • the executable instructions for presenting stimuli 144 may cause the processor 136 of the digital device 130 to select a next stimulus or a course of stimuli independently.
  • a stimulus or a course of stimuli may be selected by either the computing devices 102 or the digital device 130.
  • a session may include an open-ended, continuous series of behavioral responses by a subject to a series of corresponding experimental stimuli presented over a software interface (e.g., displayed on a digital device, sound produced from the digital device, etc.).
  • a software interface e.g., displayed on a digital device, sound produced from the digital device, etc.
  • each selected stimulus may be presented once or multiple times.
  • the executable instructions for presenting stimuli 144 performed by the processor 136 may cause the stimuli presentation circuitry, such as the display 132, to present the selected stimulus.
  • the executable instructions for collecting responses 146 stored in the digital device 130 may cause the processor 136 of the digital device 130 to collect each corresponding response to each stimulus.
  • the digital device 130 may wait for one or more inputs, such as keystrokes, mouse clicks, spoken responses, and/or touchscreen gestures of the subject 150 from data acquisition circuitry, such the input interface 134.
  • the executable instructions for collecting responses 146 may cause the processor 136 to run a timer to measure a response time (e.g. the time passed from the presentation of the stimulus to the response).
  • the executable instructions for collecting responses 146 may cause the processor 136 of the digital device 130 to record the corresponding time measured as the response time.
  • behavioral responses including a response to the stimulus selected by the subject and its associated response time may be collected.
  • Sets of responses and response times by each subject responsive to the presented set of stimuli may be collected for each subject throughout a session.
  • the digital device 130 may transmit the collected sets of response and response time to the computing device 102.
  • the digital device 130 may provide the responses to the communication interface 148, and the communication interface 148 may transmit the sets of the response and response time to the communication interface 128 either wired connection or wireless connection described herein.
  • the computing device 102 may add the received sets of response and response time to the temporary record of all sets stored in the data memory 108 through the session in association with the presented stimuli.
  • a signature related to one or more memory processes may be extracted from the responses.
  • the executable instructions for extracting signature from responses 116 may cause the processor 104 to extract signature from the behavioral responses related to memory processes.
  • the processor 104 may separate non-memory components and memory retrieval components of a particular response.
  • the subject 150 may provide a behavioral response to a stimulus with a particular amount of time (e.g., a response time). Some portion of the response time may be due to perception response time to recognize or sense (e.g., see, hear) the stimulus and motor response time to physically provide a response (e.g., locate the input device and make an action with respect to the input device).
  • the response time may include time for memory retrieval components, such as time for retrieval of a behavioral response to the stimulus.
  • the processor 104 may extract signature from the behavioral responses related to memory processes, such as the time for retrieval of an answer, using at least one mathematical model stored in the data memory 108.
  • the at least one mathematical model may be trained to separate aspects of the behavioral response attributable to a memory retrieval phase from aspects attributable to other phases, such as the perception response time and motor response time. That mathematical model could be trained on training data including earlier collected likelihood distributions of relative phase duration.
  • the mathematical model may include a Linear Ballistic Accumulator (LBA). The LBA may be fit to the collected behavioral responses to output the signature.
  • LBA Linear Ballistic Accumulator
  • the mathematical model may include Drift-Diffusion models and/or any derived variants.
  • the mathematical model may apply Bayesian estimation techniques to balance prior knowledge of likely phase duration against currently observed durations, MLE-based estimation of stimulus-specific parameters, etc.
  • the processor 104 may separate times for non-memory processes of the subject 150 from a time for memory retrieval in the response time.
  • the non-memory processes may include the perception and motor components, such as time for visually recognizing the stimuli prior to memory retrieval and time for physically providing the response after the memory retrieval.
  • the processor 104 may provide a response time estimated to be due to memory retrieval upon filtering perception and motor response time, etc., with the response of the subject 150.
  • the processor 104 may store the extracted signature from the responses in the data memory 108. While the example of extraction of signature described herein may be performed by the processor 104, the execution of signature extraction may be performed by another processor and may not be limited to the processor 104 of the computing device 102. In some examples, the executable instructions for extracting signature from responses 116 and the mathematical model may be stored in the memory device 138 of the digital device 130 that cause the processor 136 to perform the signature extraction. These pre-memory retrieval and post-memory retrieval times may help effective comparison of memory retrieval performance across subjects, because subjectdependent perception and motor response times, irrelevant to memory retrieval, may often be salient and obscure analysis of memory retrieval performance.
  • a performance metric for each stimulus may be calculated and/or estimated using a computational model based on the extracted signature during the session.
  • the performance metric may relate to SoF.
  • the executable instructions for estimating a performance metric related to speed of forgetting 118 may cause the processor 104 to estimate a performance metric for each stimulus based on the extracted signature using at least one computational model stored in the data memory 108.
  • the processor 104 may store the performance metric in the data memory 108. While the performance metric estimation described herein may be performed by the processor 104, the execution of performance metric estimation may be performed by another processor, and may not be limited to the processor 104 of the computing device 102.
  • the executable instructions for estimating a performance metric related to speed of forgetting 118 and the computational model may be stored in the memory device 138 of the digital device 130 that cause the processor 136 to perform the performance metric estimation.
  • a memory metric of the subject 150 may be estimated based on performance metrics obtained from the subject 150, using a computational model.
  • the executable instructions for estimating a memory metric based on performance metrics 120 may cause the processor 104 to estimate a memory metric for each subject 150 based on the performance metrics using at least one computational model stored in the data memory 108.
  • the processor 104 may store the memory metric in the data memory 108. While the memory metric estimation described herein may be performed by the processor 104, the execution of memory metric estimation may be performed by another processor and may not be limited to the processor 104 of the computing device 102.
  • the executable instructions for estimating a memory metric based on performance metrics 120 and the computational model may be stored in the memory device 138 of the digital device 130 that cause the processor 136 to perform the memory metrics estimation.
  • a next stimulus of a set of stimuli to be presented may be adjusted.
  • the executable instructions for determining memory metrics 112 may cause the processor 104 to adjust either selection a next stimulus or to a manner of presentation of a stimulus. In some examples, such adjustment may be performed to increase an information gain in updating the memory metric associated with memory capacity of the subject 150.
  • such adjustment may include adjustment of a difficulty level of a task, including a set of stimuli, that may result in selecting a different set of stimuli to be presented. In some examples, such adjustment may include adjusting a speed of presenting the next stimulus which may have been selected.
  • the memory metric obtained with diagnostic-related information may be provided to a medical practitioner and/or a subject.
  • the executable instructions for determining memory metrics 112 may cause the display 126 to present the memory metric and the diagnostic-related information.
  • the diagnostic-related information may be obtained by the processor 104.
  • the diagnostic-related information may be provided by one or more medical practitioners prior to determining the memory metric or concurrently by examining the memory metric and other information.
  • the digital device 130 may cause the display 132 to present the memory metric and the diagnostic-related information.
  • FIG. 2 is a block diagram of an example system 200 for determining memory metrics in accordance with examples described herein.
  • determining memory metrics may be performed as a portion of quantifying a patient-specific long-term memory function.
  • Example operations of the system 200 for determining memory metrics to support the functionality and relevant design decisions are described herein. Examples described herein are related to the method used to estimate an individual’s memory metric automatically, rapidly, efficiently, and without human supervision.
  • the example system 200 may include a smartphone 202 and a computing device 212.
  • the components of FIG. 2 are exemplary. Additional, fewer, and/or different components may be used in other examples.
  • the smartphone 202 may be wholly and/or partially implemented using the digital device 130 of FIG. 1.
  • the computing device 212 may be wholly and/or partially implemented using the computing device 102 of FIG. 1.
  • the computing device 212 may perform executable instructions 214.
  • the executable instructions 214 may be wholly and/or partially implemented as the executable instructions for determining memory metrics 112 of FIG. 1.
  • the smartphone 202 and the computing device 212 may be different devices.
  • the smartphone 202 and the computing device 212 may be integrated as one device.
  • the smartphone 202 may include an input/output interface 204 which may present a stimulus 206 and receive a response 208.
  • the input/output interface 204 may be implemented wholly and/or partially implemented using the display 132 and the input interface 134 of FIG. 1.
  • the input/output interface 204 may include separate components, such as a display, a touch pad, and/or a speaker, or an audio/visual interface coupled to external video/audio interface.
  • the input/output interface 204 may include a touchscreen for receiving a response 208.
  • the response 208 may include, for example, touchscreen gestures.
  • the input/output interface 204 may be implemented using another input interface 134 and the response 208 may include keystrokes, mouse clicks, spoken responses, etc.
  • the executable instructions for presenting stimuli 144 of FIG. 1 may be performed by the smartphone 202.
  • the executable instructions for presenting stimuli 144 may cause the stimuli presentation circuitry, such as the input/output interface 204, to present the selected stimulus 206.
  • Examples of a stimulus may include an item that a subject is asked to remember during a session.
  • a set of stimuli may include items (e.g., words, images, objects, sounds, etc.) that are not overly familiar to the subject.
  • the set of stimuli may be visual, textual, verbal, or auditory.
  • the stimulus may be presented through the input/output interface 204.
  • the input/output interface 204 may include, for example, a screen display for presenting the stimulus including visual or textual information or a sound output speaker for presenting the stimulus including verbal or auditory information.
  • the input/output interface 204 may collect the behavioral response 208 following presentation of the stimulus.
  • the executable instructions for collecting responses 146 of FIG. 1 may be performed by the smartphone 202.
  • the executable instructions for collecting responses 146 may cause data acquisition circuitry, such as the input/output interface 204, to collect each corresponding behavioral response 208 to each stimulus 206 provided by a subject.
  • the smartphone 202 may wait for one or more inputs, such as keystrokes, mouse clicks, spoken responses, touchscreen gestures, from the input/output interface 204.
  • the response 208 may be recorded as one or more keystrokes from a keyboard, a gesture performed by pressing a finger on a touchscreen, a click or a movement on a pointing or tracking device, and/or a vocal response to a microphone.
  • the executable instructions for collecting responses 146 may cause the smartphone 202 to run a timer to measure a corresponding time elapsed for such inputs (e.g., response time).
  • the response 208 and the corresponding response time may be combined as a set 210 of response 208 and the corresponding response time and provided to the computing device 212.
  • Sets 210 of the response 208 and corresponding response time provided by each subject responsive to the presented stimuli may be collected for each subject throughout a session by the smartphone 202 and transmitted to the computing device 212.
  • the smartphone 202 may provide the computing device 212 the responses 208 using either wired connection (e.g., USB cables, ether cables using TCP/IP protocol, HDMI cables, or other standardized or proprietary cables) or wireless connection (e.g., cellular antenna, Wi-Fi, network interface such as the Internet, Wi-Fi, LAN, WAN, BT, NFC) described herein.
  • wired connection e.g., USB cables, ether cables using TCP/IP protocol, HDMI cables, or other standardized or proprietary cables
  • wireless connection e.g., cellular antenna, Wi-Fi, network interface such as the Internet, Wi-Fi, LAN, WAN,
  • the smartphone 202 and computing device 212 may be integrated and transmission may be performed using an internal bus. In some examples, the smartphone 202 and computing device 212 may communicate the responses 208 via a memory storage device, either within one of the smartphone 202 and computing device 212 or located outside of the smartphone 202 and computing device 212.
  • the computing device 212 may perform executable instructions 214.
  • the executable instructions 214 may include, for example, the executable instructions for determining memory metrics 112.
  • the executable instructions for determining memory metrics 112 may determine memory metrics 224 using one or more computational models 222.
  • FIG. 3 shows an equation 300 representing log odds of retrieving a memory in accordance with examples described herein.
  • the log odd of retrieving a memory m at time t is proportional to its activation A(m, f).
  • the equation 300 may compute the “activation” of a memory m at time /, denoted as A(m, f).
  • the activation may change as a function of both time and the responses given by a subject to each presentation of a stimulus.
  • ti is the time associated with the presentation of the stimulus
  • a is stimulus- and subject-specific SoF
  • c is a fixed parameter.
  • the stimulus- and subject-specific SoF a captures the subject’s ability to memorize said stimulus.
  • the SoF value that is characteristic of a subject might be estimated by presenting multiple stimuli.
  • the fixed parameter c may have been predetermined by analyzing large amounts of data from published or unpublished studies to represent average human behavior. In some examples, c is 0.25. In some examples, the fixed parameters c may have a different value.
  • f is the creation time of the i-th trace
  • d(i) is a characteristic power decay rate of the i-th trace.
  • This trace-specific decay rate may depend on the residual activation of the memory at the time of creation of the trace.
  • the decay rate of each trace in the equation 400 may depend on the memory’s activation, the equation 400 may provide an explanation for a spacing effect, where traces closer in time may have higher decay rates due to the greater activation A (m, /) of the memory at time t(i).
  • the one or more computational models may depend on the stimulus-specific SoF “a.”
  • the stimulus-specific SoF “a” may represent the relationship between the history of a memory and the likelihood of being able to retrieve the memory in the future.
  • FIG. 5 shows an equation 500 representing a probability of a correct response to a stimulus related to the activation in accordance with examples described herein.
  • r is an individual parameter, known as a speed-accuracy tradeoff, that may determine the degree of certainty to be achieved in responses expected by a subject.
  • the fixed parameter r may have been predetermined by analyzing large amounts of data from published or unpublished studies and finding to represent average human behavior. In some examples, r is 0.80. In some examples, the fixed parameter r may have different values.
  • FIG. 6 shows an equation 600 representing a response time associated with a correct response to a stimulus related to the activation in accordance with examples described herein.
  • time to is a combination of time associated with perceiving a stimulus and general motor agility of a subject.
  • the parameter to may be estimated for a single session or a portion of the session of a single patient.
  • the time associated with perceiving the stimulus may be affected by, for example, dyslexia when the stimulus is among a set of textual stimuli.
  • the subject may have higher values of to. For example, elderly individuals may have less motor agility, and therefore higher values of to, than younger individuals.
  • the fixed parameter F might scale the time it takes to give a response based on the retrieved memory. For example, elderly individuals might take longer times to access memories, or be more cautious in responding, even when the activation of a memory is the same.
  • the value of F may have been predetermined by analyzing large amounts of data from published studies and finding to represent average human behavior.
  • F is 1.0.
  • the fixed parameter F may have different values.
  • the parameter F might be different for every subject.
  • the average SoF across a set of stimuli may be a characteristic of a subject representing a memory metric of their memory function.
  • the memory metric is highly stable (r > 0.7) across times and materials.
  • the SoF may represent individual differences in long-term memory function.
  • the SoF may correlate with, and can be decoded from, spontaneous brain activity at rest from a subject.
  • the model contains several parameters. The parameters c, F, and r may have been predetermined by analyzing large amounts of data from published or unpublished studies and finding the values that, when included in equations 300-600, represent average human behavior.
  • the computing device 212 may add the collected sets 210 of response 208 and response time in association with the presented stimuli 206 to the temporary record of sets of response 208 and response time upon receiving responses 216.
  • an initial default value of the SoF a in the equation 400 may be used. The initial value might or might not be informed by other information available about the subject (e.g., age, sex, level of education, previous clinical history). If the record of sets 210 of response 208 and response time for that particular stimulus includes any data, then an existing estimate of the SoF a of the stimulus may have been computed. Based on the response 208, the SoF a for the stimulus is then updated, either upwards or downwards.
  • the contribution of the parameter to may be separated from the contribution of the A(m, t) to the response times.
  • the computing device 212 may extract a signature related to one or more memory processes from the set 210 of response 208 and response time.
  • non-memory components and memory retrieval components of a response time may be separated using at least one mathematical model stored, such as a mathematical model 218.
  • the at least one mathematical model 218 may be trained to separate aspects of the behavioral response attributable to a memory retrieval phase from aspects attributable to other phases, such as the perception response time and motor response time.
  • the mathematical model 218 may be based on the equation 600.
  • the mathematical model could be trained on training data including earlier collected likelihood distributions of relative phase duration.
  • the mathematical model may include an LBA.
  • the LBA may be fit to the collected behavioral responses to output the signature.
  • the mathematical model may include Drift-Diffusion models and/or any derived variants.
  • the mathematical model may apply Bayesian estimation techniques to balance prior knowledge of likely phase duration against currently observed durations, MLE-based estimation of stimulus-specific parameters, etc.
  • the processor 104 may segment the time to of the equation 600 for non-memory processes and a time for memory retrieval in the response time in each set 210, and extracting signature 220 may be performed by separating (e.g., removing, subtracting, segmenting) the response time for non-memory processes from the response time.
  • the time to is then separated (e.g., removed, subtracted, segmented out) from affected response times in the record of sets 210 of the responses 208 and their associated times for the set of corresponding stimuli.
  • the execution of signature extraction may be performed by another processor, and may not be limited to the computing device 212.
  • the mathematical model 218 may be stored in the smartphone 202 to perform the signature extraction.
  • a re-evaluation of the patient’s memory metric using computational models 222 may be performed through numerical simulations of the sets of responses and their associated time and the corresponding stimuli through the session.
  • the updating of the SoF a for the stimulus may be performed through numerical simulations of the expected behavioral responses 208 using the computational models 222.
  • a performance metric for each stimulus may be estimated using a computational model in the computational models 222 based on the extracted signature during the session.
  • the performance metric may relate to SoF.
  • the stimulusspecific SoF a may be estimated for each stimulus presented to a patient within a single session based on the equation 400.
  • the executable instructions for estimating a performance metric related to speed of forgetting 118 may cause the computing device 212 to estimate a performance metric for each stimulus based on the extracted signature using at least one computational model of the computational models 222.
  • the computing device 212 may store the performance metric. While the performance metric estimation described herein may be performed by the computing device 212, the execution of performance metric estimation may be performed by another processor, and may not be limited to the computing device 212.
  • the executable instructions for estimating a performance metric related to speed of forgetting 118 and the computational model may be stored in the smartphone 202 to cause the smartphone 202 perform the performance metric estimation.
  • a memory metric of each subject may be estimated, using a computational model, based on performance metrics obtained from the subject in the computational models 222.
  • the executable instructions for estimating a memory metric based on performance metrics 120 may cause the computing device 212 to estimate a memory metric 224 for each subject based on the performance metrics using at least one computational model in the computational models 222.
  • the computing device 212 may store the memory metric 224.
  • one or more SoF a may be aggregated together into a single, representative memory metric A(m, t) of a subject based on the equation 300.
  • the distributions of correct and incorrect responses 208 and their associated response times are modeled as an evidence accumulation process whose accumulation rate is proportional to the memory’s activation A(m, t) based on the equation 300.
  • the memory metric estimation described herein may be performed by the computing device 212, the execution of memory metric estimation may be performed by another processor, and may not be limited to the computing device 212.
  • the executable instructions for estimating a memory metric based on performance metrics 120 and the computational model may be stored the smartphone 202 may cause the smartphone 202 to perform the memory metrics estimation.
  • a next stimulus of a set of stimuli to be presented may be adjusted.
  • the executable instructions 214 may cause the computing device 212 to adjust either selection a next stimulus or a manner of presentation of a stimulus.
  • such adjustment may be performed to increase an information gain in updating the memory metric associated with memory capacity of the subject 150.
  • such adjustment may include adjustment of a difficulty level of a task, including a set of stimuli, that may result in selecting a different set of stimuli to be presented.
  • such adjustment may include adjusting the interval at which to present the next stimulus which may have been selected.
  • a next stimulus 228 to be presented may be one of the previously used stimuli or a new stimulus that has never been previously used in the session.
  • a next stimulus 228 may be selected from a stimuli database 226.
  • the stimulus may be selected based on its utility in reducing the uncertainty around the value of the memory metric. For example, if the SoF a for one of the previously used stimuli is associated with unusually high or low a values, the method might present the stimulus again to collect more responses that may aid in estimating a correct value by detecting abnormal a value.
  • the executable instructions 214 may select a new stimulus. If no new stimulus (e.g., has not been presented) exists in the stimuli database 226 or time elapsed since the beginning of the session has exceeded a predefined time limit, the session may terminate.
  • a final estimate for a memory metric 224 of the subject may be saved and provided.
  • the memory metric 224 obtained with diagnostic-related information may be provided to a medical practitioner and/or a subject.
  • a display of the computer computing device 212 may present the memory metric and the diagnostic-related information.
  • the input/output interface 204 e.g., a touchscreen of the smartphone 202 may present the memory metric and the diagnostic-related information.
  • the diagnostic-related information may be provided by one or more medical practitioners prior to determining the memory metric or concurrently by examining the memory metric and other information.
  • the returned memory metric 224 may be used by a physician to make a number of health-related decisions about the patient. For example, a physician may compare a patient’s memory metric against a threshold that is agreed upon as being indicative of AD or other (sub-)clinical conditions and use the memory metric to reach a diagnosis.
  • a physician might compare a patient’s memory metric to other patients of similar age and condition, and, if the memory metric appears suspiciously higher than the other patients’, may decide to recommend further neurological examinations (e.g., screening/scanning using computed axial tomography or magnetic resonance imaging).
  • a physician may use a patient’s memory metric to assess the impact of a procedure (e.g., brain surgery or medication) on the cognitive function of a patient. The physician may collect memory metrics of a patient before and after the procedure and compare the two. If the procedure was intended to alleviate conditions that had affected a patient’s memory, the physician may expect the memory metric to decrease after surgery.
  • the physician may expect the patient’s memory metric to increase.
  • a physician might use the memory metric to evaluate whether the impact of a clinical event (e.g., a mild traumatic brain injury) or a cognitive state (e.g., a depressive episode) has subsided. Even when no prior metric of a patient is available, observing a relatively high SoF followed by a decrease and then a new plateau at a lower level might indicate recovery.
  • a clinical event e.g., a mild traumatic brain injury
  • a cognitive state e.g., a depressive episode
  • Examples described herein include systems and methods that may quantitatively assess a patient’s long-term memory function. Unlike traditional questionnaires, examples described herein may be automatic, without human supervision, language or cultural standardization, and less prone to practice effects. Instead of using an arbitrary measure of memory performance and comparing it to a standardized sample, examples described herein quantify the speed at which processes of forgetting occur in the brain.
  • Example methods may quantify the SoF by fitting parameters of a mathematical model of passive forgetting to data collected from a subject. The data is collected from a digital device that interacts with a subject for a brief amount of time and provides responses to stimuli. The one or more models may be fitted online and interactively, refining its parameters as responses come in. In some examples, example methods use the current estimates of a patient’s memory function to select the most appropriate stimulus to present next.
  • FIG. 7 shows a diagram 702 of predicted forgetting associated with a specific value of the memory metric in accordance with examples described herein.
  • FIG. 7 may be obtained through the procedure described with the system 200 of FIG. 2, and may utilize the system shown in FIG. 1 and/or FIG. 2.
  • the diagram 702 four curves are traced, each curve corresponding to the predicted forgetting associated with a specific value of the memory metric. Any point on a curve represents the probability of forgetting a fact presented after a certain amount of time elapsed. The four points represent times at which a fact presented is forgotten with 95% probability for four different values of the memory metric.
  • examples of a memory metric described herein are directly interpretable in terms of the changes in the probability of forgetting over time. Different memory metrics correspond to different curves on a plot that tracks the probability of forgetting against time. These memory metrics can be plotted against any reference data (for example, data from a cohort of individuals with the same age, educational background, and gender as the patient) or historical data from the same patient. Memory metrics are stable across repeated tests and materials. Unlike other measures of memory function, examples of the memory metric described herein are directly interpretable and do not require standardization and normative metrics. As implied by the equations 300-500, the memory metric is indicative of how quickly the probability of remembering declines over time for a particular patient.
  • FIGS. 8-16 may be obtained through the procedure described with the system 200 of FIG. 2, and may utilize the system shown in FIG. 1 and/or FIG. 2. Results of the test demonstrated that, on average, the correlation between different values of the memory metric, obtained from two different sessions with different materials and separated by an interval between one and 26 weeks, is 0.71.
  • the average SoF successfully represents the different biological processes of passive forgetting at a computational level. These processes include loss of context clues, retrieval interference from other similar memories, and “natural” biological decay. Some of these processes are accelerated in aging and abnormally elevated in amnestic dementias, such as AD. Thus, the model described herein may distinguish between abnormal memory impairments and normal aging controls. Accordingly, this model could become a useful tool in the clinical assessment of memory function. Furthermore, the model-based assessment may provide a new, highly detailed view of memory decay trajectories in normal and abnormal aging, and of the effects of interventions.
  • the scenarios include: (1) individuals with MCI may exhibit greater SoF values than healthy controls; (2) SoF values would be reliable across repeated assessments; (3) SoF values may have clinical validity (e.g., possibility to identify differences in abnormal memory function from an individual SoF); and (4) SoF may increase over a period of months, capturing a trajectory of abnormal and healthy aging.
  • MCI can be defined as a decline in cognitive abilities that is greater than what is typical for a person’s age and educational background, but does not meet the criteria for a diagnosis of dementia.
  • FIG. 8 shows examples of a software interface in accordance with examples described herein.
  • the software interface may be a screen 804 on a smartphone 802.
  • the components of FIG. 8 are exemplary. Additional, fewer, and/or different components may be used in other examples.
  • the smartphone 802 may be wholly and/or partially implemented using the digital device 130 of FIG. 1 and/or the smartphone 202 of FIG. 2.
  • the smartphone 802 may present a stimulus on its screen 804, showing an image of pasta and a text string “Conchiglie,” and then present a test showing multiple choices in text, including the text string “Conchiglie” for a subject to select. After a while, a next stimulus may be presented on the screen 804, showing an image of another type of pasta and a text string “Fusilli .”
  • FIG. 9 includes diagrams 902 and 904 showing a distribution of SoF values across all lessons and patients in accordance with examples described herein.
  • the diagram 902 shows individual SoF on a horizontal axis and a number of observations on a vertical axis for stimuli across topics and materials. For all the stimuli, individual SoFs varied between 0.29 and 0.58 and were normally distributed.
  • the diagram 904 shows ranges and means of SoF across lessons ranged from 0.29 to 0.55 with a mean of 0.4.
  • FIG. 11 includes diagrams 1102 and 1104 showing a distribution of memory metrics across patients affected by MCI and age- matched healthy controls in accordance with examples described herein. The diagram 1102 shows individual SoF on a horizontal axis and a number of observations on a vertical axis for MCI and healthy controls subject groups.
  • ROC curve assesses the sensitivity (true positive rate) and specificity (true negative rate) of a classifier for varying thresholds of the SoFs.
  • the overall accuracy of the classifier is then measured as an area under the curve (AUC) of the sensitivity and specificity obtained for different thresholds.
  • FIG. 12A shows ROC classification performances for SoF and for response accuracy from a single session in accordance with examples described herein.
  • the ROC curves in FIG. 12A for a single eight-minute session of data including probability of correctly identifying group members (MCI or controls) by the SoF of a single test were examined.
  • FIG. 12B shows average ROC classification performances for SoF and for response accuracy across sessions in accordance with examples described herein. As expected, the classifier showed an improved accuracy of 83.6% in FIG. 12B. Because of the high test-retest reliability of the SoF, classification using a single session is almost as accurate as when averaging over 30+ sessions.
  • FIG. 13 shows probability of MCI by different levels of memory metrics in accordance with examples described herein.
  • individual SoF values for each lesson were binned in increments of 0.01, and the probability of MCI diagnosis was computed as the proportion of individuals with an MCI diagnosis for each bin.
  • the logistic curve provided a clear visualization of the relationship between the predictor SoF and a diagnosis of MCI.
  • FIG. 14 shows age-related changes in memory metric over time in MCI patients and healthy controls in accordance with examples described herein.
  • memory function worsens in MCI patients, speeds of forgetting should steadily increase over time. While participants were only halfway through the year-long experiment, the subtle changes in the longitudinal trajectory of MCI patients can already be seen.
  • FIG. 15 shows a distribution of memory metric values across different groups and types of stimuli in accordance with examples described herein.
  • this longitudinal study also offered the opportunity to examine the effects of different types of materials (e.g., verbal vs. visual) on memory function.
  • FIG. 16 shows a detailed view of memory metrics across materials and patient subgroups in accordance with examples described herein.
  • MCI subtypes including amnestic single and multiple domain (aMCI S, aMCI M) and nonamnestic MCI (naMCI), may be accurately distinguished.
  • the aMCI subtype is characterized by a specific memory impairment, while the naMCI subtype is characterized by a more general cognitive decline.
  • the results in FIG. 16 revealed that the cognitive profile of the naMCI participant more closely resembled that of the healthy control group.
  • naMCI is characterized by cognitive decline in domains other than memory, such as executive function (e.g., speed of processing, problem solving, set shifting, inhibition). Therefore, it is expected that the data would be more comparable to the control group as there is no memory loss present in naMCI.
  • executive function e.g., speed of processing, problem solving, set shifting, inhibition.
  • SoF proved to be a purer assessment of memory impairment by avoiding confounds with retrieval strategy and executive function - which may be a problem in clinical assessment of mild memory impairment.
  • the ability to track memory over time as early detection of MCI is likely to be useful in therapies to delay AD and related conditions, and the brief, user- friendly online format makes passive data assessments remarkably convenient.
  • the SoF is demonstrated as having a potential for diagnostic use with repeatability/stability to test the efficacy of interventions like neuromodulation or cognitive enhancers.

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Abstract

Des modes de réalisation de la présente divulgation concernent des procédés, des systèmes et des supports lisibles par ordinateur non transitoires de détermination de paramètres de mémoire. Un procédé donné à titre d'exemple consiste à : présenter une pluralité de stimuli à un patient; collecter des réponses comportementales du patient à la pluralité de stimuli; extraire une signature, à l'aide d'au moins un modèle mathématique, à partir des réponses comportementales associées à des processus de mémoire; estimer, à l'aide d'un premier modèle de calcul, un paramètre de performance pour chaque stimulus particulier sur la base de la signature extraite, le paramètre de performance concernant la vitesse d'oubli; et estimer, à l'aide d'un second modèle de calcul, un paramètre de mémoire du patient sur la base des paramètres de performance des stimuli.
PCT/US2024/044374 2023-08-31 2024-08-29 Systèmes et procédés de détermination de paramètres de mémoire Pending WO2025049707A1 (fr)

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