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WO2024145603A1 - Prédiction du résultat de traitement d'interventions focalisées sur le traumatisme dans le tspt - Google Patents

Prédiction du résultat de traitement d'interventions focalisées sur le traumatisme dans le tspt Download PDF

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WO2024145603A1
WO2024145603A1 PCT/US2023/086469 US2023086469W WO2024145603A1 WO 2024145603 A1 WO2024145603 A1 WO 2024145603A1 US 2023086469 W US2023086469 W US 2023086469W WO 2024145603 A1 WO2024145603 A1 WO 2024145603A1
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electroencephalogram
subject
cognitive
task
therapy
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Inventor
Amit Etkin
Jackob Nimrod KEYNAN
Adi Maron-Katz
Sharon NAPARSTEK
Mallissa WATTS
Emmanuel SHPIGEL
Joseph Gordon
Dawlat EL-SAID
Faizan S. BADAMI
Michelle L. EISENBERG
Madeleine S. GOODKIND
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Cohen Veterans Bioscience Inc
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Cohen Veterans Bioscience Inc
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    • 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
    • A61B5/4058—Detecting, measuring or recording for evaluating the nervous system for evaluating the central nervous system
    • A61B5/4064—Evaluating the brain
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/162—Testing reaction times
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
    • A61B5/291—Bioelectric electrodes therefor specially adapted for particular uses for electroencephalography [EEG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/377—Electroencephalography [EEG] using evoked responses
    • A61B5/378—Visual stimuli
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/377—Electroencephalography [EEG] using evoked responses
    • A61B5/38—Acoustic or auditory stimuli
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4848—Monitoring or testing the effects of treatment, e.g. of medication
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • 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
    • 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

Definitions

  • the present invention relates generally to systems and methods for evaluating whether a subject is amendable to post-traumatic stress disorder therapy.
  • Trauma-focused interventions such as prolonged exposure (PE) and cognitive processing therapy (CPT) are considered first-line treatments for posttraumatic stress disorder (PTSD) (1,2). Both approaches involve reframing or re-processing of the traumatic memory, and thereby reduce consequent distress and maladaptive behaviors.
  • CPT achieves this by addressing cognitive and emotional biases that arise in daily life, while PE does so by imaginary and in-vivo exposure. While both therapies are effective across various populations and trauma types (3), they are also characterized by substantial individual differences in treatment outcome, high dropout rates (4) and require patient effort. Therefore, revealing neurobiological factors that may predict who will benefit from treatment is greatly needed to reduce patient burden.
  • PTSD patients show impairments in cognition and emotion regulation (5). This includes reduced memory encoding and recall (6), slower processing speed (7), and abnormalities in emotion regulation and emotional reactivity (8).
  • impairments in these processes were found to predict treatment response to trauma-focused interventions. For example lower pre-treatment verbal memory capacity was found to be predictive of response to trauma-focused interventions (9,10).
  • fMRI magnetic resonance imaging
  • an electroencephalogram of the subject undergoing a cognitive-emotional task comprising a stimulus.
  • the electroencephalogram is obtained at a sampling rate of between 0.5 kHz and 1.5 kHz with between 32 electrodes and 512 electrodes.
  • the cognitive-emotional task is a color identification task.
  • the cognitive-emotional task is an emotional conflict task.
  • the electroencephalogram of the subject includes between 300 milliseconds prior to a time of application of the stimulus to 1000 milliseconds after the time of application of the stimulus.
  • the electroencephalogram of the subject includes between 300 milliseconds prior to a time of application of the stimulus to 1000 milliseconds after the time of application of the stimulus.
  • the using the electroencephalogram to obtain an electroencephalogram event-related potential comprises clustering an amplitude time course of each electrode in a plurality of electrodes represented by the electroencephalogram thereby obtaining a plurality of clusters.
  • Each such cluster in the plurality of clusters includes a plurality of time intervals.
  • a first time interval in a first cluster in the plurality of clusters is used to compute the first electroencephalogram event-related potential, where the first cluster corresponds to a late frontal region of the subject and the first time interval falls within a period of time that is between 300 milliseconds and 600 milliseconds after the time of application of the stimulus.
  • the first time interval has a duration of 40 milliseconds.
  • FIGS. 2 A, 2B, and 2C collectively provide a flow chart of processes and features of example methods for evaluating whether a subject is amendable to a therapy for post- traumatic stress disorder, in which optional elements are indicated by dashed boxes, in accordance with various embodiments of the present disclosure.
  • Fig. 4 illustrates baseline demographics and clinical scales by therapy arm, with mean (SD), in accordance with an embodiment of the present disclosure.
  • FIG. 6 illustrates accuracy and reaction times differences between PTSD and HC in the Color Identification task in accordance with an embodiment of the present disclosure.
  • FIG. 8 illustrates differences in ERP amplitudes between healthy-controls and PTSD patients were observed on multiple time-points following stim onset, with three noticeable peaks ( ⁇ 192ms, ⁇ 260ms, ⁇ 350ms-420ms) in accordance with an embodiment of the present disclosure.
  • the y-axis shows t-values of the differences in amplitude (PTSD - healthy) for each electrode (lines) starting at +100ms relative to stim onset up to +900ms (x- axis).
  • FIG. 12 illustrates Time/Cluster instances in which the interaction TP * ERP passed significance threshold (p(FDR) ⁇ 0.05)] in accordance with an embodiment of the present disclosure.
  • FIG. 14 illustrates mediation analysis showing that frontal ERP (540ms to 580ms) mediated the correlation between behavioral performance and treatment response (CAPS change) in accordance with an embodiment of the present disclosure.
  • FIG. 17 illustrates that, similar to the color identification task, in the emotional conflict task, PTSD showed lower accuracy and slower reaction-times relative to healthy- controls, in accordance with an embodiment of the present disclosure.
  • FIG. 18 illustrates replication of ERP differences between PTSD and healthy - controls found in the color identification set in accordance with an embodiment of the present disclosure.
  • TimeZElectrode instances in which PTSD showed higher or lower ERP in the color identification showed the same differences in the emotional conflict task, p values for the validation set (emotional conflict) are one-tailed.
  • FIG. 22 illustrates average ERP and behavioral performance composite scores of responders and non-responders in accordance with an embodiment of the present disclosure. Overall, the figure shows generalization of the results of the color identification task indicating that the participants most likely to be treatment resistant are those who show impairment in both neural and behavioral indices of cognitive functioning. DETAILED DESCRIPTION
  • Poorer behavioral performance along with reduced frontal cortical engagement may indicate an impairment in information processing that prevents individuals from updating the contextual meaning of re-occurring stimuli and therefore dampens response to trauma-focused interventions.
  • the present disclosure may allow identification of patients unlikely to respond to current standard-of-care treatment - a procedure that could take place at the point of care.
  • a computational modeling architecture with predictive capabilities is used to determine a treatment-predictive EEG signature using machine learning models.
  • the complexity of a machine learning model includes time complexity (running time, or the measure of the speed of an algorithm for a given input size n), space complexity (space requirements, or the amount of computing power or memory needed to execute an algorithm for a given input size n), or both. Complexity (and subsequent computational burden) applies to both training of and prediction by a given model.
  • computational complexity is impacted by implementation, incorporation of additional algorithms or cross-validation methods, and/or one or more parameters (e.g., weights and/or hyperparameters).
  • computational complexity is expressed as a function of input size zz, where input data is the number of instances (e.g., the number of training samples), dimensions p (e.g., the number of features), the number of trees nt rees (e.g., for methods based on trees), the number of support vectors n sv e.g., for methods based on support vectors), the number of neighbors k (e.g., for k nearest neighbor models), the number of classes c, and/or the number of neurons nt at a layer z (e.g., for neural networks).
  • an approximation of computational complexity denotes how running time and/or space requirements increase as input size increases. Functions can increase in complexity at slower or faster rates relative to an increase in input size.
  • the term “if’ will be construed to mean “when” or “upon” or “in response to determining” or “in accordance with a determination” or “in response to detecting,” that a stated condition precedent is true, depending on the context.
  • the phrase “if it is determined (that a stated condition precedent is true)” or “if (a stated condition precedent is true)” or “when (a stated condition precedent is true)” will be construed to mean “upon determining” or “in response to determining” or “in accordance with a determination” or “upon detecting” or “in response to detecting” that the stated condition precedent is true, depending on the context.
  • the term “classification” refers to any number(s) or other characters(s) that are associated with a particular property of a sample or input (e.g., one or more chemical structures, one or more abundance values, or a portion or representation thereof).
  • the term “classification” refers to an association of a test chemical compound with a reference compound, such as a prediction of similarity between a predicted perturbational effect of the test chemical compound and a measured perturbational effect of the reference compound.
  • the term “classification” refers to a score that indicates a match between a perturbational effect of a training compound and a perturbational effect of a reference compound.
  • a model includes unsupervised machine learning.
  • One example of an unsupervised machine learning is cluster analysis.
  • a model includes supervised machine learning.
  • Nonlimiting examples of supervised machine learning include, but are not limited to, logistic regression, neural networks, support vector machines, Naive Bayes algorithms, nearest neighbors, random forests, decision trees, boosted trees, multinomial logistic regression, linear models, linear regression, Gradient Boosting, mixture models, hidden Markov models, Gaussian NB, linear discriminant analysis, or any combinations thereof.
  • a model is a multinomial classifier.
  • a model is a 2-stage stochastic gradient descent (SGD) model.
  • a model is a deep neural network (e.g., a deep-and-wide sample-level model).
  • the model is a neural network (e.g., a convolutional neural network and/or a residual neural network).
  • Neural networks also known as artificial neural networks (ANNs), include convolutional and/or residual neural networks (deep learning).
  • ANNs artificial neural networks
  • neural networks are machine learning models that are trained to map an input dataset to an output dataset, where the neural network includes an interconnected group of nodes organized into multiple layers of nodes.
  • the neural network architecture includes at least an input layer, one or more hidden layers, and an output layer.
  • the neural network includes any total number of layers, and any number of hidden layers, where the hidden layers function as trainable feature extractors that allow mapping of a set of input data to an output value or set of output values.
  • a deep learning model or deep neural network is a neural network including a plurality of hidden layers, e.g., two or more hidden layers.
  • each layer of the neural network includes a number of nodes (or “neurons”).
  • a node receives input that comes either directly from the input data or the output of nodes in previous layers, and performs a specific operation, e.g., a summation operation.
  • the activation function is, for example, a rectified linear unit (ReLU) activation function, a Leaky ReLU activation function, or other function such as a saturating hyperbolic tangent, identity, binary step, logistic, arcTan, softsign, parametric rectified linear unit, exponential linear unit, softPlus, bent identity, softExponential, Sinusoid, Sine, Gaussian, or sigmoid function, or any combination thereof.
  • ReLU rectified linear unit
  • Leaky ReLU activation function or other function such as a saturating hyperbolic tangent, identity, binary step, logistic, arcTan, softsign, parametric rectified linear unit, exponential linear unit, softPlus, bent identity, softExponential, Sinusoid, Sine, Gaussian, or sigmoid function, or any combination thereof.
  • the weighting factors, bias values, and threshold values, or other computational parameters of the neural network are “taught” or “learned” in a training phase using one or more sets of training data.
  • the parameters are trained using the input data from a training dataset and a gradient descent or backward propagation method so that the output value(s) that the ANN computes are consistent with the examples included in the training dataset.
  • the parameters are obtained from a back propagation neural network training process.
  • any of a variety of neural networks are suitable for use in accordance with the present disclosure. Examples include, but are not limited to, feedforward neural networks, radial basis function networks, recurrent neural networks, residual neural networks, convolutional neural networks, residual convolutional neural networks, and the like, or any combination thereof.
  • the machine learning makes use of a pre-trained and/or transfer-learned ANN or deep learning architecture.
  • convolutional and/or residual neural networks are used, in accordance with the present disclosure.
  • a deep neural network model includes an input layer, a plurality of individually parameterized (e.g., weighted) convolutional layers, and an output scorer.
  • the parameters (e.g., weights) of each of the convolutional layers as well as the input layer contribute to the plurality of parameters (e.g., weights) associated with the deep neural network model.
  • at least 100 parameters, at least 1000 parameters, at least 2000 parameters or at least 5000 parameters are associated with the deep neural network model.
  • deep neural network models require a computer to be used because they cannot be mentally solved. In other words, given an input to the model, the model output needs to be determined using a computer rather than mentally in such embodiments.
  • Neural networks including convolutional neural networks, suitable for use as models are disclosed in, for example, Vincent et al., 2010, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” J Mach Learn Res 11, pp. 3371-3408; Larochelle et al., 2009, “Exploring strategies for training deep neural networks,” J Mach Learn Res 10, pp. 1-40; and Hassoun, 1995, Fundamentals of Artificial Neural Networks, Massachusetts Institute of Technology, each of which is hereby incorporated by reference.
  • model is a support vector machine (SVM).
  • SVMs suitable for use as models are described in, for example, Cristianini and Shawe-Taylor, 2000, “An Introduction to Support Vector Machines,” Cambridge University Press, Cambridge; Boser et al., 1992, “A training algorithm for optimal margin classifiers,” in Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, ACM Press, Pittsburgh, Pa., pp. 142-152; Vapnik, 1998, Statistical Learning Theory, Wiley, New York; Mount, 2001, Bioinformatics: sequence and genome analysis, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.; Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc., pp.
  • the model is a decision tree.
  • Decision trees suitable for use as models are described generally by Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 395-396, which is hereby incorporated by reference. Tree-based methods partition the feature space into a set of rectangles, and then fit a model (like a constant) in each one.
  • the decision tree is random forest regression.
  • one specific model is a classification and regression tree (CART).
  • Other specific decision tree models include, but are not limited to, ID3, C4.5, MART, and Random Forests.
  • the term “parameter” refers to any coefficient or, similarly, any value of an internal or external element (e.g., a weight and/or a hyperparameter) in an algorithm, model, regressor, and/or classifier that affects (e.g., modify, tailor, and/or adjust) one or more inputs, outputs, and/or functions in the algorithm, model, regressor and/or classifier.
  • a parameter refers to any coefficient, weight, and/or hyperparameter used to control, modify, tailor, and/or adjust the behavior, learning, and/or performance of an algorithm, model, regressor, and/or classifier.
  • a value of a parameter is manually and/or automatically adjustable.
  • a value of a parameter is modified by a validation and/or training process for an algorithm, model, regressor, and/or classifier (e.g., by error minimization and/or backpropagation methods).
  • an algorithm, model, regressor, and/or classifier of the present disclosure includes a plurality of parameters.
  • the term “untrained model” refers to a machine learning model or algorithm, such as a classifier, that has not been trained on a target dataset.
  • “training a model” refers to the process of training an untrained or partially trained model.
  • the term “untrained model” does not exclude the possibility that transfer learning techniques are used in such training of the untrained or partially trained model.
  • Examples of networks include the World Wide Web (WWW), an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices by wireless communication.
  • WWW World Wide Web
  • LAN wireless local area network
  • MAN metropolitan area network
  • an electroencephalogram 36 of the subject that has been clustered, each cluster including a subset of the plurality of electrodes used to generate the electroencephalogram and including a plurality of time increments 40 that collectively represent the period of time over which the electroencephalogram 36 was taken;
  • a model 42 comprising a plurality of parameters 44 (e.g., weights) for prediction of whether the subject is amendable to the therapy for post-traumatic stress disorder.
  • parameters 44 e.g., weights
  • one or more of the above identified elements are stored in one or more of the previously mentioned memory devices and correspond to a set of instructions for performing a function described above.
  • the above identified modules, data, or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures, datasets, or modules, and thus various subsets of these modules and data may be combined or otherwise re-arranged in various implementations.
  • the non-persistent memory 107 optionally stores a subset of the modules and data structures identified above.
  • the memory stores additional modules and data structures not described above.
  • one or more of the above identified elements is stored in a computer system, other than that of the system 100, that is addressable by the system 100 so that the system 100 may retrieve all or a portion of such data when needed.
  • FIG. 1 depicts a “system 100,” the figure is intended more as a functional description of the various features that may be present in computer systems than as a structural schematic of the implementations described herein. In practice, and as recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. Moreover, although FIG. 1 depicts certain data and modules in non-persistent memory 107, some or all of these data and modules instead may be stored in persistent memory 109 or in more than one memory.
  • a subject is amendable to a therapy for post-traumatic stress disorder.
  • the PTSD is caused by war trauma, sexual assault, a road traffic accident, a refugee status, being a disaster worker, or experiencing a natural disaster such as an earthquake. See, for example Kar, 2011, “Cognitive behavioral therapy for the treatment of post-traumatic stress disorder: a review,” Neuropsychiatric Disease and Treatment 2011 :7 167-181, which is hereby incorporated by reference.
  • a PTSD subject exhibits symptoms such as insomnia, malignancy, cardiovascular disorders, physical trauma, or brain injuries. Id.
  • the therapy for the post-traumatic stress disorder is prolonged exposure.
  • the therapy for the PTSD is prolonged exposure (PE), stress inoculation training (SIT), combined treatment (PE-SIT), or wait-list control.
  • PE prolonged exposure
  • SIT stress inoculation training
  • PE-SIT combined treatment
  • wait-list control wait-list control.
  • the therapy is administered daily, three times a week, two times a week, or weekly.
  • the therapy is administered for one month, two months, three months, six months, 1 year, 2 years, or 3 years or more.
  • the therapy for the post-traumatic stress disorder is cognitive processing therapy (CPT).
  • CPT is described in Resick, 2001, “Cognitive therapy for posttraumatic stress disorder,” Journal of Cognitive Psychotherapy 15, 321-329, which is hereby incorporated by reference.
  • the therapy for PTSD is supportive psychotherapy, problem-solving therapy, present-centered therapy, psychodynamic therapy, hypnotherapy, acupuncture, or structured writing therapy.
  • Such therapies are described in Cottraux et al., 2008, “Randomized controlled comparison of cognitive behavior therapy with Rogerian supportive therapy in chronic post-traumatic stress disorder: A 2-year follow-up,” Psychother Psychosom 77(2): 101-110; Van Emmerik et al., 2008, “Treating acute stress disorder and posttraumatic stress disorder with cognitive behavioral therapy or structured writing therapy: A randomized controlled trial,” Psychother Psychosom. 77(2):93-100; and Hollifield et cd.. “Acupuncture for posttraumatic stress disorder: A randomized controlled pilot trial,” J Nerv Ment Dis. 195(6):504-513, each of which is hereby incorporated by reference.
  • an electroencephalogram of the subject undergoing a cognitive-emotional task comprising a stimulus.
  • bad epochs are rejected by thresholding the magnitude of each epoch.
  • bad channels are rejected based on thresholding the spatial correlations among channels.
  • the rejected bad channels are then interpolated from the EEG of adjacent channels via the spherical spline interpolation as disclosed in Perrin et al., 1989, “Spherical splines for scalp potential and current density mapping,” Electroencephalography and Clinical Neurophysiology 72, 184-187, which is hereby incorporated by reference.
  • remaining artifacts are removed using independent component analysis such as, for example, disclosed in Bell and Sejnowski, 1995, “An information-maximization approach to blind separation and blind deconvolution,” Neural Computation 7, pp. 1129-1159, which is hereby incorporated by reference.
  • independent components related to the scalp muscle artifact, ocular artifact, and ECG artifact are detected as bad components and rejected using a pattern classifier trained on expert-labeled independent components from another independent EEG dataset disclosed in Wu et al., 2018, “ARTIST: A fully automated artifact rejection algorithm for single-pulse TMS-EEG data,” Human Brain Mapping 39, 1607-1625, which is hereby incorporated by reference.
  • the paradigm for the color identification task follows the design detailed in Etkin et al., 2004, “Individual Differences in Trait Anxiety Predict the Response of the Basolateral Amygdala to Unconsciously Processed Fearful Faces,” Neuron 44, 1043-1055, which is hereby incorporated by reference, with some timing changes to adapt to an event related design.
  • the color identification task comprises a 400ms fixation cue followed by 200ms of face presentation, and inter-stimulus intervals are jittered between 1800ms to 3000ms.
  • the cognitive-emotional task is an emotional conflict task.
  • Example 4 details an example of an emotional conflict task.
  • a participant views fearful or happy facial expressions with superimposed congruent or incongruent words (“happy” or “fear”) and are asked to identify the emotional expression while ignoring the words.
  • the emotional conflict task is administered via PsychoPy, an open-source python stimulus presentation and control package. 1,2 See Peirce, 2009, “Generating stimuli for neuroscience using PsychoPy,” Front. Neuroinformatics 2, 10; and Peirce, 2007, “PsychoPy — psychophysics software in Python,” J. Neurosci. Methods 162, 8-13, each of which is hereby incorporated by reference.
  • the electroencephalogram of the subject includes between 300 milliseconds prior to a time of application of the stimulus to 1000 milliseconds after the time of application of the stimulus.
  • the time of application of the stimulus is a time in which a first image or signal associated with the cognitive-emotional task is presented to the user.
  • Example 3 where the cognitive-emotional task is a color identification task, the time of application of the stimulus is when the fixation cue of the color identification task is first presented to the user.
  • Example 4 where the cognitive-emotional task is an emotional conflict task, the time of application of the stimulus is when the first facial expression is first presented to the user.
  • the electroencephalogram of the subject includes between 500 milliseconds prior to a time of application of the stimulus to 2000 milliseconds after the time of application of the stimulus.
  • electrode amplitudes are extracted for a plurality of samples of the electroencephalogram starting at 300ms prior to- and up to 1000ms post-stimulus onset. For instance, in some embodiments, electrode amplitudes are extracted every 4ms; down-sampled from 1000Hz to 250hz, and each such extraction represents a sampling in the plurality of samples.
  • electrode amplitudes are extracted every 1ms, every 2ms, every 3ms, every 4ms, every 5ms, or every 6m to form a plurality of samples of the electroencephalogram starting at 500 ms prior to, 400ms prior to, or 300ms prior to- and up to 500sm, 750ms, 1000ms or 2000ms post-stimulus onset. In some embodiments this results in 100 or more, 200 or more, 300 or more, 400 or more, 500 or more, or 600 or more samplings of the electroencephalogram. In some embodiments these amplitudes are baselined to the mean amplitude from -100ms to 0ms relative to stimulus onset.
  • these amplitudes are baselined to the mean amplitude from -200ms to 0ms relative to stimulus onset. In some embodiments these amplitudes are baselined to the mean amplitude from -300ms to 0ms relative to stimulus onset. In some embodiments these amplitudes are baselined to the mean amplitude from -50ms to 0ms relative to stimulus onset.
  • the behavioral performance is a measure of accuracy at performing the cognitive-emotional task.
  • clustering is used to determine clusters within the plurality of electrodes in order to identify the subset of the plurality of electrodes used to obtain the electroencephalogram event-related potential.
  • the clustering is k-means clustering.
  • the plurality of clusters consists of four clusters: the first cluster corresponds to a frontal cortical region of the subject, the second cluster corresponds to a median cortical region of the subject, the third cluster corresponds to a posterior cortical region of the subject, and the fourth cluster corresponds to an occipital cortical region of the subject.
  • the clustering used to generate the plurality of clusters referred to above is k-means clustering and the plurality of clusters consists of between 3 and 6 clusters. In some embodiments, the plurality of clusters consists of 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 clusters. In any such embodiments, the subset of clusters used to generate the electroencephalogram event-related potential are the electrodes in any one of these clusters.
  • At least the behavioral performance and the first electroencephalogram event-related potential is inputted into a model thereby obtaining, as output from the model, a prediction of whether the subject is amendable to the therapy for post-traumatic stress disorder.
  • the model provides a predicted CAPS-IV IV (Blake et al., 1995, “The development of a Clinician-Administered PTSD Scale,” Journal of Traumatic Stress, 8(1), 75-90, which is hereby incorporated by reference) or CAPS-5 (Weathers et al., 2013, “The Clinician-Administered PTSD Scale for DSM-5 (CAPS-5), which is hereby incorporated by reference) score.
  • the subject is considered amendable to therapy if the predicted CAPS-IV or CAPS-5 score represents greater than a threshold reduction relative to the subject’s present score. In some embodiments, this threshold reduction is 30%.
  • Still another aspect of the present disclosure provides a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors, the one or more programs comprising instructions for performing any of the methods and/or embodiments disclosed herein. In some embodiments, any of the presently disclosed methods and/or embodiments are performed at a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors.
  • the present disclosure found that relative to healthy-controls, PTSD patients show impaired behavioral performance (slower reaction-time and lower accuracy) and differential neural response (ERP) to a cognitive-emotional task. See Figures 5 and 7.
  • the present disclosure further found that pre-treatment behavioral performance and frontal ERPs predicted the response to PE and CPT. See Figures 14-17. Participants who showed both poor task performance and reduced frontal ERPs were most likely to be treatment resistant.
  • the translational potential of the present disclosure is therefore promising, specifically with respect to the ability to define patients unlikely to respond to current standard-of-care treatment using a procedure applicable at the point of care.
  • These treatment resistant PTSD patients may be an important population to study both mechanistically and with respect to new treatments. Indeed, the concept of treatment-resistant PTSD is much less developed in the literature relative to treatment-resistant depression.
  • the healthy-control group consisted of 65 healthy veterans. Inclusion criteria were no history of PTSD, no history of any Axis I psychiatric disorder and no use of psychotropic medication, or illicit drugs. All study assessments took place at Stanford University.
  • Example 2 Clinical Assessments. Assessments for patients were carried out at baseline, during treatment, and post-therapy. At baseline, both patient and healthy-control s underwent a Structured Clinical Interview for DSM-5 for the presence of Axis I diagnoses outside of PTSD (20). Diagnosis and severity of PTSD was assessed with CAPS for DSM- IV and DSM-5 (21). Self-report questionnaires included the PTSD Checklist for DSM-IV and 5 (PCL) (22), Beck Depression Inventory (BDI-II) (23), and the Patient Health Questionnaire (PHQ-9) (24).
  • Baseline and post-treatment assessments were conducted in-person at Stanford University, the Palo Alto VA, or over the phone, depending on the patient’s location and availability.
  • Midpoint assessments were conducted via phone. Baseline assessments were completed during the period of several weeks pre-treatment up until just before the second treatment session. The second treatment session was chosen as the cutoff for the baseline period because following this session the trauma account is assigned, and more active treatment begins, after the initial informational and psychoeducational sessions. This allowed better capture of patients as they were brought into this real-world treatment study.
  • Midpoint assessments were conducted following 5 therapy sessions. Post-treatment assessments were conducted 4-6 weeks after the patient's final treatment session or last attended session in case of drop-out to most accurately measure the effect of treatment without confounding it with proximity to the final treatment session.
  • PE and CPT were delivered according to manualized procedures with training/oversight conducted by VA clinicians outside the scope of this study. All clinicians were employed by the VA and were obligated to follow nationwide VA regulations on treatment standards.
  • Example 3 Cognitive-Emotional Tasks - Color Identification (13). This task probes goal-irrelevant emotional reactivity via randomized presentation of fearful, happy and neutral faces. The goal is to identify the color-tint of the emotional face. Behavioral outcomes were accuracy and reaction-times on correct trials.
  • This task was administered via PsychoPy, an open-source python stimulus presentation and control package. 1,2 See Peirce, 2009, “Generating stimuli for neuroscience using PsychoPy,” Front. Neuroinformatics 2, 10; and Peirce, 2007, “PsychoPy — psychophysics software in Python,” J. Neurosci. Methods 162, 8-13, each of which is hereby incorporated by reference.
  • Example 4 Cognitive-Emotional Tasks - Emotional Conflict Task (14). Participants viewed fearful or happy facial expressions with superimposed congruent or incongruent words (“happy” or “fear”) and were asked to identify the emotional expression while ignoring the words. This task was administered via PsychoPy, an open-source python stimulus presentation and control package. 1,2 See Peirce, 2009, “Generating stimuli for neuroscience using PsychoPy,” Front. Neuroinformatics 2, 10; and Peirce, 2007, “PsychoPy — psychophysics software in Python,” J. Neurosci. Methods 162, 8-13, each of which is hereby incorporated by reference.
  • Magnetic Resonance Imaging-Inspired Electroencephalography Improves Implicit Emotion Regulation,” Biol Psychiatry. Sep 15;80(6):490-6.
  • the present invention can be implemented as a computer program product that includes a computer program mechanism embedded in a non-transitory computer readable storage medium.
  • the computer program product could contain the program modules shown in Figure 1. These program modules can be stored on a CD-ROM, DVD, magnetic disk storage product, or any other non-transitory computer readable data or program storage product.

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Abstract

L'invention concerne des systèmes et des procédés pour évaluer si un sujet peut obtenir ou non une amélioration par une thérapie pour un trouble de stress post-traumatique. Un électroencéphalogramme du sujet réalisant une tâche cognitive-émotionnelle comprenant un stimulus est obtenu. Une performance comportementale du sujet au niveau de la tâche cognitive-émotionnelle est également obtenue. L'électroencéphalogramme est utilisé pour obtenir un premier potentiel lié à un événement d'électroencéphalogramme. Au moins les performances comportementales et le premier potentiel lié à un événement d'électroencéphalogramme sont entrés dans un modèle, ce qui permet d'obtenir, en tant que sortie à partir du modèle, une prédiction quant à savoir si le sujet peut obtenir ou non une amélioration par la thérapie pour un trouble de stress post-traumatique.
PCT/US2023/086469 2022-12-30 2023-12-29 Prédiction du résultat de traitement d'interventions focalisées sur le traumatisme dans le tspt Ceased WO2024145603A1 (fr)

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