Method of increasing cognitive function with glutamate receptor agonist

Abstract

To provide a treatment for increasing cognitive function including working memory, reasoning / problem solving, and attention / arousal.SOLUTION: Provided is a method of increasing cognitive function and / or treating a disease or disorder associated with decreased cognitive function in a subject. Treating the subject with a glutamate receptor agonist can include identifying the subject as a glutamate receptor agonist responder. The method can further include obtaining electroencephalogram (EEG) signals from the subject. The method can further include measuring one or more EEG metrics, thereby identifying the subject as a glutamate receptor agonist responder. Further provided is a non-transitory processor-readable medium storing code with instructions for identifying glutamate receptor agonist responders.SELECTED DRAWING: None

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application is based on and claims priority from U.S. Provisional Application No. 62 / 895,081, entitled "EEG Biomarkers," filed September 3, 2019, the disclosure of which is incorporated herein by reference in its entirety.

[0002] Technical Field The present disclosure relates to the field of treatments for cogitative function. [Background technology]

[0003] background There is a need in the art for treatments that increase cognitive function, including working memory, reasoning / problem solving, and attention / alertness. Summary of the Invention [Means for solving the problem]

[0004] Abstract Pre-treatment analysis of electroencephalogram (EEG) data of subjects who are subsequently administered glutamate receptor agonists shows that treatment with glutamate receptor agonists increases cognitive function in human subjects. Furthermore, analysis of EEG data identifies a subpopulation of subjects for whom glutamate receptor agonists have particularly strong effects on cognitive function.

[0005] Therefore, in various embodiments, provided herein is a method for increasing cognitive function and / or treating diseases or disorders associated with decreased cognitive function in a subject, comprising administering an effective amount of a glutamate receptor agonist.Subject can be a healthy subject, or can be suffering from or at risk of suffering from psychiatric disorder.

[0006] Further provided in various embodiments is a non-transitory processor-readable medium storing code representing instructions executed by a processor, the code including code for causing the processor to receive electroencephalogram (EEG) signals recorded from one or more brain locations of the subject; convert the EEG signals into one or more EEG metrics; and execute a model configured to receive the one or more EEG metrics and identify the subject as a glutamate receptor agonist responder based on the one or more EEG metrics. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a schematic illustration of a treatment-response prediction device according to one embodiment.

[0008] [Figure 2] FIG. 2 is a flow chart illustrating a method for treatment-response prediction according to one embodiment.

[0009] [Figure 3] FIG. 3 is a flow chart illustrating a method for treatment-response prediction according to one embodiment.

[0010] [Figure 4] Figure 4 shows the montage for the EEG recording.

[0011] [Figure 5] Figure 5 shows determining the power law exponent (PLE) of the EEG signal.

[0012] [Figure 6] Figure 6 shows the correlation between pre-treatment low-gamma (30 Hz) activity and treatment response based on MCCB attention-alertness domain scores. Subjects received photic stimulation at 30 Hz. Color indicates the correlation coefficient r, and the scale is shown on the right. Selected r and p-values ​​are shown in Table 1A.

[0013] [Figure 7] Figure 7 shows the correlation between pre-treatment low beta (15 Hz) activity and treatment response based on the MCCB reasoning problem-solving domain score. Subjects received light stimulation at 15 Hz. Color indicates the correlation coefficient r, and the scale is shown on the right. Selected r and p-values ​​are shown in Table 1A.

[0014] [Figure 8] Figure 8 shows the correlation between the pretreatment power law index and treatment response based on the MCCB working memory domain score. EEG readings were taken in the resting state. Color indicates the correlation coefficient r, and the scale is shown on the right. Selected r values ​​and significance are shown in Table 1B.

[0015] [Figure 9] Figure 9 shows the receiver operating curve (ROC) for the effect shown in Figure 8 on EEG lead C3: sensitivity = 0.750, specificity = 0.897 (area under the curve [AUC] = 0.809, p = 0.039). DETAILED DESCRIPTION OF THE INVENTION

[0016] Detailed Description Non-limiting examples of various aspects and variations of these embodiments are described herein and illustrated in the accompanying drawings.

[0017] In one aspect, the present disclosure provides a method for increasing cognitive function and / or treating a disease or disorder associated with decreased cognitive function in a subject, the method comprising administering an effective amount of a glutamate receptor agonist.

[0018] The method can include obtaining or having obtained an electroencephalogram (EEG) signal from the subject, thereby identifying the subject as a glutamate receptor agonist responder. The method includes measuring or having measured one or more EEG metrics, thereby identifying the subject as a glutamate receptor agonist responder, and if the subject is a glutamate receptor agonist responder, then administering a glutamate receptor agonist.

[0019] In another aspect, one or more embodiments described herein generally relate to devices, methods, and systems for dynamically processing structured and semi-structured data, and in particular to devices, methods, and systems that use models (e.g., neural network models) to efficiently and reliably predict outcomes based on structured and semi-structured data. Treatment-response prediction devices, methods, and systems are disclosed. In some embodiments, treatment-response can be used to process EEG signals in the form of, for example, time series, stationary data, non-stationary data, linear data, non-linear data, etc.

[0020] Described herein are treatment-response prediction devices and methods for predicting treatment response based on EEG signals collected from a subject. By enabling a subject to be identified as a glutamate receptor agonist responder prior to treatment, the methods described herein can avoid unnecessary adverse events and side effects of treatment. Furthermore, the methods described herein can increase the response rate to glutamate receptor agonists. In certain embodiments, the methods described herein enable the safe and effective use of pomaglumetad, pomaglumetad methionyl, or pharmaceutically acceptable salts thereof in the treatment of psychiatric disorders (e.g., psychotic disorders). In some embodiments, individual EEG metrics predicting glutamate receptor agonist responder status are disclosed. In some embodiments, responder prediction is improved by training a machine learning model on multiple EEG metrics.

[0021] FIG. 1 is a schematic illustration of a treatment-response prediction device 110 according to one embodiment. The treatment-response prediction device 110 can identify a subject as a glutamate receptor agonist responder prior to treatment. The treatment-response prediction device 110 can also be configured to execute a model (e.g., an artificial intelligence model) that predicts treatment response based on EEG signals collected from the subject. A set of EEG signals is analyzed by the treatment-response prediction device 110 to generate EEG metrics. The treatment-response prediction device 110 can be coupled to a server computing device 160, a clinician programmer device 170, and / or a subject computing device 180 via a network 150, as needed. The treatment-response prediction device 110, the clinician programmer device 170, and / or the subject computing device 180 can each be a hardware-based computing device and / or a multimedia device, such as, for example, a computer, a desktop, a laptop, a smartphone, a tablet, a wearable device, etc.

[0022] The treatment-response prediction device 110 includes a memory 111, a communication interface 112, and a processor 113. The treatment-response prediction device 110 can receive data including EEG signals from an EEG machine (not shown) that records the subject's brain activity. In some examples, the subject's brain activity can be / include electrical activity, which can be recorded as EEG signals by a set of electrodes connected to the EEG machine, which can be operably coupled to the treatment-response prediction device 110. The EEG machine can transmit a set of EEG signals to the treatment-response prediction device 110. The EEG signals can be recorded in the memory 111 and analyzed by the processor 113 to treat the subject with a glutamate receptor agonist.

[0023] Network 150 may be a digital telecommunications network of servers and / or computing devices. The servers and / or computing devices on the network may be connected via one or more wired or wireless communication networks (not shown) to share resources, such as data storage and / or computing power. The wired or wireless communication networks between the servers and / or computing devices of network 150 may include one or more communication channels, such as radio frequency (RF) communication channels, extremely low frequency (ELF) communication channels, extremely low frequency (ULF) communication channels, low frequency (LF) communication channels, medium frequency (MF) communication channels, ultra high frequency (UHF) communication channels, extremely high frequency (EHF) communication channels, fiber optic communication channels, electronic communication channels, satellite communication channels, etc. Network 150 may be, for example, the Internet, an intranet, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a Worldwide Interoperability for Microwave Access Network (WiMAX®), a virtual network, any other suitable communication system, and / or a combination of such networks.

[0024] The server computing device 160 may be / include a computing device medium dedicated to data storage and / or computation purposes, which may include, for example, a network of electronic memory, a network of magnetic memory, a server, a blade server, a storage area network, a network-attached storage device, a deep learning computation server, a deep learning storage server, etc. Each server device 160 may include memory (not shown), a communications interface (not shown), and / or a processor (not shown). The communications interface may receive / transmit data from / to the prediction device 110 via the network 150, the memory may store the data, and the processor may analyze the data. In some examples, the server computing device 160 may be a biobank server that stores data for a long period of time (e.g., 2 years, 5 years, 10 years, 100 years, etc.).

[0025] Clinician computing device 170 and / or subject computing device 180 can be / include a computing device operably coupled and configured to send and / or receive data and / or analytical models to treatment-response prediction device 110. A user of subject computing device 180 and / or clinician computing device 170 can use treatment-response prediction device 110 (partially or fully) to select a treatment and / or treatment-response prediction. In some examples, subject computing device 180 and / or clinician computing device 170 can be / include a personal computer, laptop, smartphone, custom personal assistant device, etc., each including, for example, memory (not shown), a communications interface (not shown), and / or a processor (not shown). The processor of subject computing device 180 and / or clinician computing device 170 can include a hardware-based integrated circuit (IC) or any other suitable processing device configured to implement and / or execute a set of instructions or code. The memory of subject computing device 180 and / or clinician computing device 170 may include hardware-based charge-storing electronic devices or any other suitable data storage medium configured to store data for long-term or batch processing of the data by a processor. The communication interfaces of subject computing device 180 and / or clinician computing device 170 may include hardware-based devices configured to receive / transmit electrical, electromagnetic, and / or optical signals.

[0026] The memory 111 of the treatment-response prediction device 110 can be, for example, a memory buffer, random access memory (RAM), read-only memory (ROM), hard drive, flash drive, secure digital (SD) memory card, compact disc (CD), external hard drive, erasable programmable read-only memory (EPROM), embedded multi-time programmable (MTP) memory, embedded multimedia card (eMMC), universal flash storage (UFS) device, etc. The memory 111 can store, for example, one or more software modules and / or code including instructions for causing the processor 113 to perform one or more processes or functions (e.g., signal analyzer 114, data preprocessor 115, predictor model 116, etc.).

[0027] The memory 111 may store a set of files associated with the signal analyzer 114, the data preprocessor 115, and / or the predictor model 116 (e.g., generated by executing the signal analyzer 114, the data preprocessor 115, and / or the predictor model 116). The set of files associated with the signal analyzer 114, the data preprocessor 115, and / or the predictor model 116 may include data generated by the signal analyzer 114, the data preprocessor 115, and / or the predictor model 116 during operation of the treatment-response prediction device 110. In some examples, the predictor model 116 may be / include a machine learning model. The machine learning model may store in the memory 111 temporary variables, return memory addresses, variables, the machine learning model's graph (e.g., a set of arithmetic operations or a representation of the set of arithmetic operations used by the machine learning model), graph metadata, assets (e.g., external files), electronic signatures (e.g., specifying the type of machine learning model being exported, and input / output arrays and / or tensors), etc.

[0028] The communication interface 112 of the treatment-response prediction device 110 may include software and / or hardware components (e.g., executed by the processor 113) of the treatment-response prediction device 110 to facilitate data communication between the treatment-response prediction device 110 and external devices (e.g., the server computing device 160, the clinician platform 170, the subject computing device 180, etc.) or internal components (e.g., the memory 111, the processor 113) of the treatment-response prediction device 110. The communication interface 112 is operatively coupled to and used by the processor 113 and / or the memory 111. The communication interface 112 may be, for example, a network interface card (NIC), a Wi-Fi™ module, a Bluetooth® module, an optical communication module, and / or any other suitable wired and / or wireless communication interface. In some examples, the communication interface 112 may facilitate receiving or transmitting data over the network 150. More specifically, in some embodiments, communications interface 112 can facilitate receiving or transmitting data containing EEG signals, models, etc. from / to server computing device 160, clinician platform 170, subject computing device 180, etc. over network 150, each of which is communicatively coupled to treatment-response prediction 110 via network 150. In some examples, communications interface 112 can facilitate receiving or transmitting data from an EEG machine.

[0029] The processor 113 may be a hardware-based integrated circuit (IC) or any other suitable processing device configured to perform or execute a set of instructions or code. For example, the processor 113 may include a general-purpose processor, a central processing unit (CPU), an accelerated processing unit (APU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a programmable logic array (PLA), a complex programmable logic device (CPLD), a programmable logic controller (PLC), a graphics processing unit (GPU), a neural network processor (NNP), etc. The processor 113 may be operatively coupled to the memory 111 via a system bus (e.g., an address bus, a data bus, and / or a control bus, not shown).

[0030] The processor 113 includes a signal analyzer 114, a data pre-processor 115, and a predictor model 116. Each of the signal analyzer 114, the data pre-processor 115, and / or the predictor model 116 may include software stored in the memory 111 and executed by the processor 113. For example, code for causing the signal analyzer 114 to fetch / process high-dimensional and large-scale data may be stored in the memory 111 and executed by the processor 113. Alternatively, each of the signal analyzer 114, the data pre-processor 115, and / or the predictor model 116 may be a hardware-based device. For example, the process for the predictor model 116 to predict clinical outcomes may be implemented on a separate integrated circuit chip (e.g., an ASIC).

[0031] The signal analyzer 114 can receive the EEG signal and perform signal analysis on the EEG signal. In some examples, the signal analyzer can perform Fourier analysis (e.g., used for stationary signals) and / or wavelet analysis (e.g., used for non-stationary signals) to transform the EEG signal from the time domain to the frequency domain. The transformed EEG signal can be further analyzed by the signal analyzer 114 to extract significant frequency components of the EEG signal. Fourier analysis of the EEG signal generates a power spectrum, which includes an indication of which frequencies are present in the EEG signal and the relative intensity (or power) of the frequencies. Known power spectrum analyses of EEG or magnetoencephalography (MEG) signals have not revealed specific frequency peaks that reliably distinguish schizophrenic subjects from controls.

[0032] The signal analyzer 114 can further generate EEG metrics. In some examples, the EEG metrics can include an index based on the ratio of EEG signal power at a first frequency to the EEG signal power at a second frequency (e.g., the ratio of power at 20 Hz to power at 40 Hz). The first and / or second frequencies can be selected from one frequency band or multiple frequency bands. In some examples, the EEG metrics can include a power-law index (also called "1 / f noise" or "fractal index") of the EEG signal. The power spectrum of an EEG signal (in a resting state or under stimulation), when displayed in the frequency domain (i.e., frequency of oscillation is plotted on the x-axis and power is plotted on the y-axis), can be approximated by a straight line when displayed on a log-log plot (see FIG. 5). Mathematically, the power spectrum can be expressed as log(P) = k-βlog(f), or equivalently, Pαf-β (= 1 / fβ), where P represents power, f represents frequency, -β represents the slope of the fitted line, and k represents a constant. The power law exponent β is constant regardless of the resolution at which it is calculated ("scale-invariant" or "fractal" behavior) (see inset in Figure 5). A steeper slope of the power law exponent may reveal a higher degree of "structure" or "memory" in the underlying brain interactions.

[0033] Oscillatory activity at multiple frequencies and brain locations can be observed in the human brain. EEG signals recorded by an EEG machine can be collected for frequency and brain location. Brain location can be based on EEG electrode maps. Frequencies can include the delta band (1-3 cycles per second [Hz]), theta band (4-7 Hz), alpha band (8-12 Hz), beta band (12-30 Hz), and gamma band (40-80 Hz).

[0034] The data pre-processor 115 receives data (e.g., including the signal analyzed by the signal analyzer 114) and may be used to further prepare the data for processing by the predictor model 116. In some implantations, the data pre-processor 115 may normalize the data, perform feature extraction, size reduction, etc. In some examples, normalizing the data may include amplitude matching, frequency matching, file format (e.g., txt format, CSV format, etc.) adjustment, data format (e.g., comma-separated, semi-colon-separated, etc.) adjustment, etc.

[0035] In some examples, the data pre-processor 115 may be configured to receive the set of signals, convert the format of the set of signals, remove measurement artifacts (e.g., generated due to eye blinking or scalp muscle movement of the subject from whom the set of signals is acquired), and / or filter the set of signals (e.g., to reduce noise in the set of signals (denoising)). The data pre-processor 115 may also be configured to perform independent component analysis (ICA) to decompose the set of signals into functionally and spatially separated signals.

[0036] The predictor model 116 (also referred to as a “model”) may be / include a machine learning model, as described in further detail herein. The predictor model 116 may include a forward propagation machine learning model, a convolutional neural network (CNN), a graph neural network (GNN), an autoencoder, a transformer neural network, a logistic regression model, a naive Bayes classifier, a support vector machine (SVM), a random forest, a decision tree, an extreme gradient boosting (XGBoost) model, etc. The predictor model 116 may be configured to include a set of model parameters, including a set of weights, a set of biases, and / or a set of activation functions, that, once trained, may be executed to generate a prediction of a clinical outcome of the EEG signal (e.g., responder, non-responder, 20% responder, 99% responder, response score, etc.). For example, the predictor model 116 may be configured to predict glutamate receptor agonist treatment responders.

[0037] In some embodiments, clinical outcomes can be classified as "responders" versus "non-responders." Such classification can be defined in several ways, including the following: • Based on percentage improvement, averaged across all items in the MCCB cognitive assessment battery. • Based on percentage improvement averaged across all items on the Positive Symptoms Scale. • Based on percentage improvement averaged across all items on the negative symptom rating scale. • Based on the average of (i), (ii) and (iii) above and the percentage change in Clinical Global Impression-Severity Scale (CGI-S). In all of the above cases, the percentage improvement from baseline is used as the outcome measure. "Response" can be defined using several different cutoffs (e.g., 20%, 30%, 40%, 50%, and 60% improvement).

[0038] In one example, the predictor model 116 can be / can include a feedforward neural network or deep learning model including an input layer, an output layer, and multiple hidden layers (e.g., 5 layers, 10 layers, 20 layers, 50 layers, 100 layers, 200 layers, etc.), where the multiple hidden layers can include normalization layers, fully connected layers, activation layers, convolutional layers, recurrent layers, and / or any other layers suitable for representing the correlation between EEG signals and clinical outcomes, and each score represents an energy term.

[0039] In one example, the predictor model 116 may be an XGBoost model that includes a set of hyperparameters, such as a number of boosting rounds that defines the number of boosting rounds or trees in the XGBoost model, a max depth that defines the maximum number of nodes allowed from the root of a tree to a leaf of the tree in the XGBoost model, etc. The XGBoost model may include a set of trees, a set of nodes, a set of weights, a set of biases, and other parameters useful for describing the XGBoost model.

[0040] In some embodiments, the predictor model 116 can be configured to iteratively receive EEG signals and / or EEG metrics and generate an output that predicts a clinical outcome (e.g., a binary response where 1 represents a responder and 0 represents a non-responder). The EEG signals and / or EEG metrics can be associated with one clinical outcome. The true clinical outcome can be compared to the output from the predictor model 116 using an optimization model and an objective function (also referred to as a "cost function") to generate a training loss value. The objective function can include, for example, mean squared error, mean absolute error, mean absolute percentage error, log-hyperbolic cosine (logcosh), multi-class cross entropy, etc. A set of model parameters of the predictor model 116 can be modified in multiple iterations, and a first objective function can be run in each iteration until the training loss value converges to a first predetermined training threshold (e.g., 80%, 85%, 90%, 97%, etc.).

[0041] In some embodiments, the predictor model 116 can integrate the EEG metrics and / or EEG signals to generate a composite score that identifies a subject as a glutamate receptor agonist responder. In some examples, the composite score can be a normalized range of 0 to 100. A threshold value within the normalized range can be set to determine whether a subset of a subject's EEG metrics and / or EEG signals can identify the subject as a glutamate receptor agonist responder.

[0042] FIG. 2 is a flowchart illustrating a method 200 of treatment-response prediction according to one embodiment. Method 200 can be performed by a treatment-response prediction device (such as the treatment-response prediction device shown and described in connection with FIG. 1). Method 200 can include receiving 201 electroencephalogram (EEG) signals recorded from one or more brain locations of a subject. Method 200 can further include converting 202 the EEG signals into a set of EEG metrics. The EEG metrics can include electrophysiological behavior at the set of brain locations under stimulation or at rest. The stimuli can include optical, electrical, magnetic, tactile, and / or acoustic stimuli. Method 200 can further include receiving 203 the set of EEG metrics and executing a model to identify the subject as a glutamate receptor agonist responder based on the set of EEG metrics. In some embodiments, the model is a machine learning model.

[0043] FIG. 3 is a flowchart illustrating a method 300 of treatment-response prediction according to one embodiment. Method 300 can be performed by a treatment-response prediction device (such as the treatment-response prediction device shown and described in connection with FIG. 1). Method 300 can include receiving 301 electroencephalogram (EEG) signals for a set of electrodes. Method 300 can further include determining 302 a peak frequency, power in one or more frequency ranges (also referred to as power bands) (e.g., delta band, 1-4 Hz; gamma band (30-80 Hz)), and / or a fractal index (power law index) of the EEG signals. Method 300 can further include determining 303 a set of indices based on the peak frequency, power in one or more frequency ranges, and / or fractal index of the EEG signals.

[0044] The method 300 may further include identifying 304 a set of EEG metrics that have statistical significance in the set of clinical treatment outcomes (which may be calculated and represented by a p-value as shown in Tables 1A and 1B). Each EEG metric may be identified / generated based on (a) power in a particular frequency band (e.g., delta band, beta band, gamma band, etc.), (b) the ratio of power in two different frequency bands (e.g., first power in beta band / second power in gamma band), (c) a power law index, (d) the power of the EEG signal at the drive frequency (i.e., stimulation frequency), and / or (e) the ratio of power at two different drive frequencies. In some examples, principal component analysis (PCA) may be performed to arrive at a set of principal components (PCs) that contain 75% of the variance in the independent variables (e.g., pre-treatment EEG indices) from the EEG metrics. In some examples, a multivariate analysis of covariance (MANCOVA) can be performed using all clinical outcomes as dependent variables and the following independent factors: (i) treatment class, indicating treatment with pomaglumetad versus placebo; (ii) principal components (PCs); and (iii) interactions between (i) and (ii). This determines whether there was a relationship between any principal components and any clinical response, and more importantly, whether there was a significant interaction indicating a difference between treated and untreated subjects. A significance level of p<0.05 can be used.

[0045] In some examples, analysis of covariance (ANCOVA) can be performed on PCs that show significant interactions to determine whether there was a significant relationship (e.g., p<0.05) between the PCs and any number of clinical outcome measures for treated subjects. For clinical outcome measures identified in the above steps, correlations can be determined between the outcome measure and the independent variables for that subanalysis (e.g., specific EEG indices at specific EEG electrodes). All predictor variables that show correlations with the outcome measure at a significance level of p<0.05 can be considered for analysis. These can be ranked from lowest (most significant) to highest, and the relationships showing the highest level of significance for each subanalysis can be examined in more detail by constructing a topographic cortical map containing electrode-level correlations and significance levels for that independent-dependent variable pair.

[0046] The method 300 can further include training 305 a machine learning model based on the set of EEG metrics and the set of clinical treatment outcomes, where each EEG metric from the set of EEG metrics is associated with one clinical treatment outcome from the set of clinical treatment outcomes. After training, the method 300 can further include running 306 the machine learning model to generate clinical treatment outcomes based on the EEG metrics.

[0047] In one aspect, the present disclosure provides a method for treating a subject with glutamate receptor agonist.The method comprises obtaining or obtaining electroencephalogram (EEG) signals from the subject, thereby identifying the subject as a glutamate receptor agonist responder.The method comprises measuring or measuring one or more EEG metrics, thereby identifying the subject as a glutamate receptor agonist responder, and if the subject is a glutamate receptor agonist responder, then administering a glutamate receptor agonist.

[0048] As used herein, the term "subject" refers to a human subject suffering from or at risk of suffering from a mental disorder. The subject exhibits one or more symptoms of a mental disorder. Exemplary mental disorders are those listed in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (5), which is incorporated by reference for purposes of defining mental disorders and symptoms that can identify a subject as suffering from or at risk of suffering from a mental disorder. th (ed.) (2013). In some embodiments, the psychiatric disorder is schizophrenia spectrum or other psychotic disorder.

[0049] The term "effective amount" refers to an amount or dose of a glutamate receptor agonist or a pharmaceutically acceptable salt, the administration of which to a patient in single or multiple doses provides the desired treatment and / or increase in cognitive function.

[0050] According to the methods of the present disclosure, subjects can be identified as potential responders to glutamate receptor agonists. Other factors, including symptoms of psychiatric disorders, can be considered by the treating physician or other medical professional. It is understood that glutamate receptor agonists can be used to treat other disorders, and the methods of the present disclosure are not limited to schizophrenia spectrum disorders or other psychotic disorders. Without being bound by theory, it is believed that the EEG metrics disclosed herein predict response through correlations between EEG signals and the underlying biochemistry of the human brain. The brain physiological characteristics underlying treatment responsiveness in psychiatric subjects may extend to other disorders, including neurodevelopmental disorders, bipolar and related disorders, depressive disorders, anxiety disorders, and the like.

[0051] Given the observed prediction of response to treatment in certain clinical domains (attention-alertness, reasoning problem solving, and working memory), the methods of the present disclosure may be applied to psychiatric disorders affecting these clinical domains, including but not limited to schizophrenia spectrum and other psychotic disorders.

[0052] Glutamate receptor agonists have previously been unable to achieve the clinical benefits predicted from preclinical studies. The disclosed methods, along with associated computational methods and instruments, provide successful treatment with glutamate receptor agonists that would not otherwise be safe and effective.

[0053] Exemplary glutamate receptor agonists include D-serine, CTP-692 (deuterated D-serine), SAGE-718 (a positive allosteric modulator [PAM] at the NMDA receptor), sarcosine (a GlyT-1 inhibitor; also increases glycine), LY379268, eglumegumad, pomaglumetad (LY2140023), and pomaglumetad methionyl, or pharmaceutically acceptable salts thereof.

[0054] In some embodiments, the glutamate receptor agonist is D-serine or a pharmaceutically acceptable salt thereof.

[0055] In some embodiments, the glutamate receptor agonist is CTP-692 or a pharmaceutically acceptable salt thereof.

[0056] In some embodiments, the glutamate receptor agonist is SAGE-718 or a pharmaceutically acceptable salt thereof.

[0057] In some embodiments, the glutamate receptor agonist is GlyT-1 inhibitor or its pharmaceutically acceptable salt.In some embodiments, the glutamate receptor agonist is bitopertin, PF-3463275, GSK1018921, Org25935, AMG747, SSR504734, SSR103800, DCCCyB, R231857, R213129.ASP2535 or any of the above-mentioned derivatives, or its pharmaceutically acceptable salt.The chemical structures of these molecules are provided below. [ka]

[0058] In some embodiments, the glutamate receptor agonist is sarcosine or a pharmaceutically acceptable salt thereof. Sarcosine is a GlyT-1 inhibitor; sarcosine also increases glycine.

[0059] In some embodiments, the glutamate receptor agonist is LY379268 or a pharmaceutically acceptable salt thereof.

[0060] In some embodiments, the glutamate receptor agonist is eglumegad or a pharmaceutically acceptable salt thereof.

[0061] In some embodiments, the glutamate receptor agonist is pomaglumetad or a pharmaceutically acceptable salt thereof.

[0062] In some embodiments, the glutamate receptor agonist is pomaglumetad methionyl or a pharmaceutically acceptable salt thereof.

[0063] Pomaglumetad is an amino acid analog drug that acts as a highly selective agonist for metabotropic glutamate receptor group II subtypes mGluR2 and mGluR3. Because pomaglumetad exhibits poor oral absorption and bioavailability in humans, human studies investigating the therapeutic use of pomaglumetad have focused on the prodrug, pomaglumetad methionyl. The dose of pomaglumetad methionyl given to subjects varied across clinical trials, but doses typically ranged between 10 mg and 40 mg twice daily (BID). In early phase II monotherapy trials, the dose shown to be effective was 40 mg BID. Subsequent studies investigating the use of pomaglumetad methionyl as an adjunct to glutamate receptor agonist drugs already being used by subjects participating in these studies used a lower dose of 20 mg BID. If treatment was well tolerated after 1 week at this target dose, the dose was increased to 40 mg BID. However, if the 20 mg dose was not well tolerated, the dose was reduced to 10 mg.

[0064] Clinical trials of pomaglumetad methionil were discontinued because glutamate receptor agonists were not significantly more effective than placebo, as determined using the PANSS total score. The disclosed devices and methods relate to the surprising discovery that EEG metrics, alone or in combination with the use of machine learning-based models, can select subjects who will respond to glutamate receptor agonist (e.g., pomaglumetad methionil) therapy and / or predict clinical outcomes based on EEG metrics.

[0065] Depending on the specific condition being treated, such agents may be formulated into liquid (e.g., solutions, suspensions, or emulsions) or solid dosage forms (capsules or tablets) and may be administered systemically or locally. The agents may be delivered, for example, in timed-, controlled-, or sustained-release forms, as known to those skilled in the art. Techniques for formulation and administration are described in Remington: The Science and Practice of Pharmacy (2007). th (ed.) Lippincott, Williams & Wilkins (2000). Suitable routes may include oral, buccal, by inhalation spray, sublingual, rectal, transdermal, vaginal, transmucosal, nasal or intestinal administration; intramuscular, subcutaneous, intramedullary injection, and parenteral delivery, including intrathecal, direct intraventricular, intravenous, intraarticular, intrasternal, intrasynovial, intrahepatic, intralesional, intracranial, intraperitoneal, intranasal, or intraocular injection, or other modes of delivery. In some embodiments, the pharmaceutical composition is administered orally. In some embodiments, the pharmaceutical composition is administered intravenously. In some embodiments, the pharmaceutical composition is administered intramuscularly. In some embodiments, the pharmaceutical composition is administered intrathecally. In some embodiments, the pharmaceutical composition is administered subcutaneously.

[0066] The pharmaceutical compositions or combinations of the present invention may be in unit dosage form (e.g., tablets, capsules, caplets or particulates), and the appropriate dosage of the active ingredient may vary depending on various factors such as age, weight, sex, route of administration or salt used, for example.

[0067] Generally, the methods of treatment disclosed herein result in a reduction in the severity of a disease or condition in a subject. The term "reduce" means to inhibit, suppress, attenuate, weaken, arrest, or stabilize the symptoms of a disease or condition.

[0068] As used herein, the terms "treat," "treating," "treatment," or "therapy" refer to obtaining beneficial or desired results, e.g., clinical results. Beneficial or desired results may include, but are not limited to, alleviating one or more symptoms of a psychiatric disorder. One embodiment of treatment is, for example, that the treatment should have minimal adverse effects on the subject, e.g., should have a high level of safety. The term "alleviation," as used herein, refers to, for example, reducing at least one of the frequency and magnitude of symptoms of a symptom in a subject, e.g., with respect to the symptoms of a symptom. In one embodiment, the term "method of treatment" as used herein refers to a "method for treating."

[0069] In some embodiments of the disclosed methods, the glutamate receptor agonist is administered in an amount effective to produce the desired therapeutic effect (ie, a therapeutically effective amount).

[0070] The term "antipsychotic drug" as used herein refers to a neuroleptic drug used to treat psychotic disorders such as schizophrenia. In one embodiment, the antipsychotic drug is selected from the group including, for example, typical antipsychotic drugs and atypical antipsychotic drugs. In another embodiment, the antipsychotic drug is a typical antipsychotic drug. In yet another embodiment, the antipsychotic drug is an atypical antipsychotic drug.

[0071] The term "typical antipsychotic" as used herein refers to a first-generation antipsychotic selected from the group including, for example, butyrophenones (e.g., haloperidol), diphenylbutylpiperidines (e.g., pimozide), phenothiazines (e.g., chlorpromazine, fluphenazine, perphenazine, prochlorperazine, trifluoperazine), and thioxanthenes (e.g., thiothixene). In one embodiment, the typical antipsychotic is selected from the group including haloperidol, pimozide, chlorpromazine, fluphenazine, perphenazine, prochlorperazine, trifluoperazine, and thiothixene, or a salt thereof.

[0072] The term "atypical antipsychotic" as used herein refers to a second-generation antipsychotic selected from the group including, for example, benzamides (e.g., sultopride), benzisoxazoles / benzisothiazoles (e.g., lurasidone, paliperidone, risperidone), phenylpiperazines / quinolinones (e.g., aripiprazole, brexpiprazole, cariprazine), tricyclic antidepressants (e.g., clozapine, olanzapine, quetiapine, asenapine, zotepine). In one embodiment, the atypical antipsychotic is selected from the group including sultopride, lurasidone, paliperidone, risperidone, brexpiprazole, cariprazine, clozapine, olanzapine, quetiapine, asenapine, and zotepine, or a salt thereof.

[0073] The definition of specific clinical domains of cognition and the standard human tests for assessing them are well known in the art. Test batteries developed for schizophrenia, such as the MATRICS (National Institute for Mental Health's Measurement and Treatment Research to Improve Cognition in Schizophrenia) Consensus Cognitive Battery ("MCCB"), can be used. (Nuechterlein et al., Am J Psychiatry 165(2):203-213(2008)). The MCCB has seven subdomains: processing speed, attention / alertness, working memory, verbal learning, visual learning, reasoning and problem solving, and social cognition. In embodiments of the present disclosure, the increase in memory / cognition in response to the administration of glutamate receptor agonists can be assessed using one or more of the following criteria (listed in order of recommendation): [Table 1]

[0074] It is further known in the art that experimental tasks for mice and rats are located on the MCCB battery as "preclinical MATRICS." In embodiments of the present disclosure, the effectiveness of glutamate receptor agonists in improving cognitive function can be preclinically evaluated using the following seven rodent tasks: a five-choice sequential response task (for attention / alertness), an olfactory discrimination task (for speed of processing), attentional set shifting (for reasoning / problem solving), a novel object recognition test (for visual learning / memory), a radial arm maze (for working memory-spatial), an odor span task (for working memory-nonspatial), and a social interaction task (for social cognition).

[0075] In some embodiments, the subject suffers from a disease or disorder associated with reduced cognitive function, or is at risk of a disease or disorder associated with reduced cognitive function. In some embodiments, the disease or disorder associated with reduced cognitive function is dementia, Alzheimer's disease, major depression, bipolar depression, post-traumatic stress disorder (PTSD), panic disorder, generalized anxiety disorder (GAD), attention deficit hyperactivity disorder (ADHD), Parkinson's disease, schizophrenia, autism spectrum disorder (ASD), or obsessive-compulsive disorder (OCD). In some embodiments, the subject suffers from intellectual disability, including but not limited to intellectual disability caused by Down syndrome or fragile X syndrome. [Example]

[0076] Example The following specific examples are illustrative, and do not limit the scope of the claimed invention.

[0077] method Pretreatment After receiving the data, the data format can be converted (e.g., file format change). The data can be further processed to remove and filter measurement artifacts (e.g., due to eye blinks or scalp muscle movements). Additionally or alternatively, independent component analysis (ICA) can be performed. ICA is a process that allows recorded EEG data to be decomposed into functionally and spatially separated signals (Onton et al., (2006) Neurosci Biobehav Rev 30(6):808-822), which can also help denoise the signal. The following analysis techniques can be applied to the filtered data, free of artifacts, as well as to the estimated sources revealed by ICA.

[0078] Power Spectral Analysis Oscillatory activity at many different frequencies has been observed in the human brain. These are conventionally divided into the slow frequency ranges of delta (1–3 cycles per second [Hz]) and theta (4–7 Hz); the mid-frequency, alpha (8–12 Hz) and beta (12–30 Hz); and the gamma band (40–80 Hz).

[0079] Fourier analysis of EEG signals produces what is called a power spectrum, i.e., a measure of which frequencies are present and their relative intensities (or power). To our knowledge, this power spectrum analysis has never been used successfully to predict drug response or to subclassify schizophrenic subjects.

[0080] In some examples, wavelet analysis can be used to analyze EEG signals. Wavelet analysis provides similar information but uses a rolling time window to determine the frequencies present. This is particularly well suited to shorter (<10 seconds) sets of data segments that may occur after artifact removal.

[0081] Power-law behavior The power spectrum of a resting-state EEG signal, when displayed in the frequency domain (i.e., with frequency of oscillation on the x-axis and power on the y-axis), forms an approximately straight line when displayed on a log-log plot; see Figure 5 (from Miller et al. (2019) PLoS Comput Biol 5(12):e1000609).

[0082] Mathematically, this can be expressed as log(P) = k-βlog(f), or equivalently, Pαf-β (= 1 / fβ), where P = power, f = frequency, -β is the slope of the fitted line, and k is a constant. This phenomenon is known by various terms, such as "power law" spectra or "1 / f noise." Because the power law exponent β is constant regardless of the resolution at which it is calculated, this is often referred to as "scale-invariant" or "fractal" behavior (Lowen and Teich, 2005).

[0083] Treatment response Response to treatment is determined by calculating the change from baseline in key clinical outcome measures as follows:

[0084] Positive and Negative Symptom Scale (PANSS). Correlations consist of the positive subscale (range 7-49), negative subscale (7-49), general psychopathology subscale (16-112), and PANNS total score (30-210).

[0085] Clinical Global Impression-Severity Scale (CGI-S), which measures the overall severity of a subject's symptoms and ranges from 1 to 7.

[0086] The 16-item Negative Symptom Assessment (NSA-16) uses a total score ranging from 16 to 96.

[0087] Measurement and Treatment Studies to Improve Cognition in Schizophrenia: The Unified Cognitive Assessment Battery (MCCB) assesses cognitive function in seven domains: speed of processing, working memory, verbal learning, visual learning, reasoning and problem solving, attention / alertness, and social cognition.

[0088] The Personal and Social Performance (PSP) scale ranges from 1 to 100 and uses a total score assessing four domains of functioning.

[0089] Example 1 Pomaglumetad methionil (LY-2140023, or "POMA") is an experimental glutamate receptor agonist that agonizes metabotropic (mGluR2 / 3) glutamate receptors and has no known effects on dopamine receptors. Multiple Phase II and Phase III clinical trials have suggested that while this drug may not be effective for all subjects with schizophrenia, there may be specific subgroups for whom this drug is uniquely useful. Our goal was to develop novel EEG biomarkers to identify subjects more likely to respond positively to treatment with POMA. Furthermore, we examined additional EEG measures, such as the magnitude of the power law exponent (PLE), a response to light stimulation, which, to our knowledge, has not been used in previous predictive studies.

[0090] method This study used data from clinical trials NCT00845026 (N = 117) and NCT01052103 (N = 196) to study male and female subjects with schizophrenia treated with antipsychotic medication versus standard of care. EEG recordings were taken during the pretreatment period using a standard 19-lead montage (Figure 4) both in the resting state and when subjects were exposed to photic stimulation (light flashes) at frequencies ranging from 1 to 30 Hz. We subjected resting EEG data to known power spectral analysis to determine the intensity of activity in the delta (1-4 Hz), theta (4-7 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-80 Hz) frequency bands. We calculated only the power at the stimulated frequencies and, for resting-state recordings, the power law index (PLE; also known as the "fractal index"). The power spectrum of a resting-state EEG signal, when displayed in the frequency domain, i.e., graphing power as a function of oscillation frequency, often forms an approximately straight line when displayed on a log-log plot, with the slope of the fitted line being the PLE (see, e.g., Figure 5). We calculated all predictive measures for each EEG electrode.

[0091] Response to POM treatment was reported as a percentage change from baseline to study endpoint on several clinical outcome measures, including the Positive and Negative Symptom Scale (PANSS) and the MATRICS Consensus Cognitive Assessment Battery (MCCB) and its seven individual cognitive domain scales. We performed statistical analyses to determine whether significant relationships existed between any of our computed EEG metrics in the pretreatment condition and treatment outcome. Positive symptoms of the PANNSS include delusions, impaired conceptual integration, hallucinations, agitation, grandiosity, suspiciousness / paranoia, and hostility. Negative symptoms of the PANNSS include flat affect, emotional withdrawal, impaired communication, social withdrawal due to passivity / hypoactivity, difficulty with abstract thinking, lack of spontaneity and fluency in speech, and stereotyped thinking.

[0092] result Surprisingly, we identified several pre-treatment EEG metrics that correlated with clinical outcome and predicted response to treatment with POMA (Table 1A and Table 1B). [Table 1A] [Table 1B]

[0093] For example: (1) In the 30 Hz light-stimulated condition, there was a positive correlation between pre-treatment low-gamma (30 Hz) activity in EEG lead T6 and post-treatment improvement in cognition, as measured by the Attention-Alertness domain score of the MCCB (r = 0.385, p = 0.000001) (Table 1A, line 6). The correlation coefficients ranged from 0.34 to 0.39, with similar levels of significance in other occipital and inferior leads (Figure 6). (2) In the 15 Hz light-stimulated condition, there was a positive correlation between pre-treatment low-beta (15 Hz) activity in EEG lead T5 and post-treatment improvement in cognition, as measured by the Inference-Problem-Solving domain score of the MCCB (r = 0.244, p = 0.004932) (Table 1A, line 10). The correlation coefficients ranged from 0.19 to 0.24, with similar levels of significance in other left temporo-parietal leads (Figure 7). (3) For the left central electrode C3, a positive correlation existed between pretreatment PLE and improvement in the MCCB working memory (WM) domain score (r = 0.288, p = 0.000558) (Table 1B, line 17). Correlation coefficients of 0.25 to 0.31 were found in other fronto-central leads, with similar significance levels (Figure 8).

[0094] Taking this effect (pretreatment PLE at left central electrode C3) as an example, and using a 50% improvement in WM performance as the definition of treatment response, receiver operating curve (ROC) analysis revealed that PLE at lead C3 could identify POM responders with a sensitivity of 0.750 and a specificity of 0.897 (AUC = 0.809, p = 0.039) (Figure 9). In total, 23 lead-level relationships were determined to have relatively high effect sizes and robust statistical significance, and thus potential clinical utility (Tables 1A and 1B). Notably, all response variables we identified included improvement in cognitive function, rather than positive or negative symptoms or other measures of psychopathology. Positive EEG predictors did not emerge within a single specific cortical region.

[0095] In summary, this data demonstrates that there are subject subgroups that show unique benefits in terms of cognitive improvement and that can be characterized by specific EEG "spectral fingerprints."

[0096] Example 2 The inventors have identified individual EEG-based pretreatment metrics that serve as prospective biomarkers of treatment outcome. The methodology described herein can be applied to other psychotropic drugs and various conditions other than schizophrenia, as these drugs may also result in unique patterns of EEG activity in responders versus non-responders that may be difficult to understand using known methods alone. In the treatment setting, there is clear value in identifying individuals likely to respond to a drug, thereby reducing the time and resources spent on pharmacological trial and error. Furthermore, the types of markers developed by the inventors can be used to narrow the target sample for clinical trials, potentially leading to smaller studies and shorter trials, as well as overall lower drug development costs.

[0097] Example 3 Specific brain phenotypes that respond uniquely to POMA are characterized by a combination of the effects described in Examples 1 and 2. To identify such patterns, we developed an artificial intelligence (AI) approach using deep learning artificial neural networks (ANNs). This methodology is well suited to complex, nonlinear pattern recognition tasks involving multiple inputs. The resulting "composite biomarker" takes into account all of the identified effects and is a more robust predictor than any single predictor.

[0098] We propose to create a deep neural network consisting of four layers: an input layer of 23 nodes, each corresponding to one of the biomarkers in Table 1; two hidden layers, hidden layer 1 of 33 nodes and hidden layer 2 of 7 nodes; and an output layer of one node that represents a "responder" when active and a "non-responder" when inactive. The model can be a four-layer model, where the number of nodes in the first and second hidden layers is

number

[0099] Achieving an optimal network architecture (e.g., number of layers, number of nodes per layer) can be approached as an optimization problem. Several different methodologies have been proposed to address this problem, including the "evolutionary approach," the "constructive approach," or the "pruning approach" (Thomas and Suhner, 2015), which is the method we chose to use. According to this approach, a large network is started. During the training process, it may become clear that certain parameters are not being utilized (e.g., some connection weights are zero or near zero). These are then removed. While this approach can yield very effective networks, the drawback is that the process can be computationally intensive. Given the 72-processor computer cluster that our lab owns and will be used to run these models, this is not a significant consideration. If the pruning approach fails to produce suitable results, other model-building approaches can be used, which is necessarily a trial-and-error process.

[0100] Example 4 Our analyses (detailed in Examples 1-3 above) demonstrate that subgroups of patients who receive pomaglumetad methionil exhibit significant improvements in cognitive function on measures of working memory, reasoning / problem solving, and attention / alertness, and our methodology allows us to identify such subgroups. Importantly, the novel metrics we developed that identify these functional subgroups make sense from a neurocognitive perspective. Because of this apparent selective effect of pomaglumetad methionil, we propose a novel use of this drug as a nootropic, i.e., memory enhancer, in normal (non-psychiatric) subjects.

[0101] Preclinical studies of glutamate receptor agonists as agents for increasing cognitive function are conducted using pomaglumetad methionil (LY-2140023, or "Poma") as a representative of the entire class. Results are extended to other agents by testing them in the same or similar animal models. Animal models used include those described in Dahhan et al., Front. Behav. Neurosci., 14 (2019) doi:10.3389 / fnbeh.2019.00048, which is incorporated herein by reference.

[0102] Testing of pomaglumetad methionil and other glutamate receptor agonists includes the following:

[0103] Morris Water Task. This test is used to assess learning and memory in rodents. The apparatus consists of a pool filled with water with a hidden escape platform beneath the surface. When an animal is released into the water, it searches for the platform to escape from the water. Spatial memory is assessed separately in a probe test without a platform.

[0104] Y-maze. This test is used to evaluate spatial learning and memory. The spontaneous alteration test is used to assess hippocampal damage, quantify cognitive deficits in transgenic mice, and evaluate the effects of drugs on cognition. The recognition memory test is used to test memory function in mice.

[0105] Radial Arm Maze. This test assesses reference and working memory. Animals are placed on the central platform of the radial arm maze and allowed to explore the maze toward reinforcers (food) placed at the end of each arm. Failure to visit the maze arm containing the reward is considered a reference memory error. Failure to re-enter an arm is considered a working memory error.

[0106] Novel Object Recognition (NOR) Task. This test evaluates the effects of drug candidates on short-term, medium-term, and long-term memory. This test measures the amount of time an animal has to retain memory of a sample object placed during a recognition phase prior to a test phase in which one of the familiar objects is replaced by a novel object.

[0107] Fear Conditioning Test: This test assesses associative fear learning and memory and can be used to evaluate the ability of drug compounds to reverse drug-induced memory deficits.

[0108] Passive avoidance test. This test is a fear-motivated test for assessing long-term memory based on negative reinforcement. Memory performance is assessed by recording the latency to escape from the white compartment.

[0109] Elevated Plus Maze. This test is used to evaluate drugs that affect learning and memory. This test uses the entry latency (TL) as a parameter for the acquisition and retention of memory processes.

[0110] Example 5 Clinical trials of glutamate receptor agonists as agents to increase cognitive function are conducted using pomaglumetad methionil (LY-2140023, or "Poma") as a representative of the class. Clinical trial endpoints may include:

[0111] Clinical Global Impression-Severity Scale (CGI-S). Clinical Global Impression-Severity Scale (CGI-S). This measures the overall severity of a patient's symptoms and ranges from 1 to 7.

[0112] The 16-item Negative Symptom Assessment (NSA-16) uses a total score ranging from 16 to 96.

[0113] Measurement and Treatment Studies to Improve Cognition in Schizophrenia: The Unified Cognitive Assessment Battery (MCCB) assesses cognitive function in seven domains: speed of processing, working memory, verbal learning, visual learning, reasoning and problem solving, attention / alertness, and social cognition.

[0114] A dose escalation study is conducted to identify an effective amount of glutamate receptor agonist for each candidate agonist. The present invention provides, for example, the following items. (Item 1) A method for increasing cognitive function and / or treating a disease or disorder associated with decreased cognitive function in a subject, comprising administering an effective amount of a glutamate receptor agonist. (Item 2) Item 10. The method of item 1, wherein the cognitive function is selected from the group consisting of working memory, reasoning / problem solving, and attention / alertness. (Item 3) The method of item 1 or item 2, wherein the subject is a human. (Item 4) 4. The method according to any one of items 1 to 3, wherein the subject is a healthy subject. (Item 5) 4. The method of any one of items 1 to 3, wherein the subject is suffering from a disease or disorder associated with reduced cognitive function or is at risk of a disease or disorder associated with reduced cognitive function. (Item 6) 6. The method of item 5, wherein the disease or disorder associated with decreased cognitive function is selected from the group consisting of dementia, Alzheimer's disease, major depression, bipolar depression, post-traumatic stress disorder (PTSD), panic disorder, generalized anxiety disorder (GAD), attention deficit hyperactivity disorder (ADHD), Parkinson's disease, schizophrenia, autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), or intellectual disability. (Item 7) 7. The method according to any one of items 1 to 6, wherein the glutamate receptor agonist is a group II metabotropic glutamate receptor (mGluR2 / 3) agonist. (Item 8) 8. The method according to any one of items 1 to 7, wherein the glutamate receptor agonist is pomaglumetad or a pharmaceutically acceptable salt thereof. (Item 9) 8. The method according to any one of items 1 to 7, wherein the glutamate receptor agonist is pomaglumetad methionyl or a pharmaceutically acceptable salt thereof. (Item 10) 10. The method according to any one of items 1 to 9, wherein the effective amount of the glutamate receptor agonist is between about 10 mg and 120 mg. (Item 11) 10. The method according to any one of items 1 to 9, wherein the effective amount of the glutamate receptor agonist is between about 20 mg and 80 mg. (Item 12) 12. The method of any one of items 1 to 11, wherein an effective amount of said glutamate receptor agonist is administered twice daily (BID). (Item 13) 12. The method of any one of items 1 to 11, wherein an effective amount of said glutamate receptor agonist is administered once daily (QD). (Item 14) obtaining or having obtained electroencephalogram (EEG) signals from the subject; and measuring or thereby identifying said subject as a glutamate receptor agonist responder; identifying the subject as a glutamate receptor agonist responder by if the subject is a glutamate receptor agonist responder, then administering the glutamate receptor agonist; 14. The method according to any one of items 1 to 13, further comprising: (Item 15) Item 15. The method of item 14, wherein the measuring is performed before treatment. (Item 16) 16. The method of any one of items 14-15, wherein the one or more EEG metrics comprise one or more electrophysiological behaviors at one or more brain locations. (Item 17) 17. The method of any one of items 14-16, wherein the one or more EEG metrics comprise one or more electrophysiological behaviors at one or more brain locations under sensory stimulation of the subject. (Item 18) Item 18. The method of item 17, wherein the sensory stimulus is a light stimulus, an electrical stimulus, a magnetic stimulus, a tactile stimulus, or an acoustic stimulus. (Item 19) The electrophysiological behavior under sensory stimulation is [Table 2] 19. The method according to item 17 or 18, wherein the method is selected from the group consisting of: (Item 20) The one or more EEG metrics include one or more electrophysiological behaviors at one or more brain locations in a resting state, the electrophysiological behaviors at the brain locations being: [Table 3] 17. The method according to any one of items 14 to 16, wherein the method is selected from the group consisting of: (Item 21) 21. The method of any one of items 14 to 20, wherein each clinical treatment outcome from the plurality of clinical treatment outcomes is classified as responsive and non-responsive based on a threshold or a receiver operating characteristic (ROC) curve. (Item 22) The identifying step is performed by a non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the code causing the processor to: receiving the EEG signals recorded from the one or more brain locations of the subject; converting the EEG signals into the one or more EEG metrics; and 22. The method of any one of items 14 to 21, comprising code for receiving the EEG metrics and running a model configured to identify the subject as a glutamate receptor agonist responder. (Item 23) the model is a machine learning model, and the non-transitory processor-readable medium comprises: 23. The method of claim 22, further comprising code for training the machine learning model based on a training set comprising a plurality of EEG metrics and a plurality of clinical treatment outcomes associated with the plurality of EEG metrics. (Item 24) 24. The method of any one of items 1 to 23, wherein the glutamate receptor agonist increases working memory performance. (Item 25) 24. The method of any one of items 1 to 23, wherein the glutamate receptor agonist increases attention-alertness. (Item 26) 24. The method of any one of items 1 to 23, wherein the glutamate receptor agonist improves reasoning problem solving. (Item 27) 1. A non-transitory processor-readable medium storing code representing instructions for execution by a processor, the code causing the processor to: receiving electroencephalogram (EEG) signals recorded from one or more brain locations of the subject; converting the EEG signals into one or more EEG metrics; and A non-transitory processor-readable medium comprising code for executing a model configured to receive the one or more EEG metrics and identify the subject as a glutamate receptor agonist responder based on the one or more EEG metrics. (Item 28) 28. The non-transitory processor-readable medium of item 27, wherein when administered, the glutamate receptor agonist improves cognitive function. (Item 29) Item 29. The non-transitory processor-readable medium of item 28, wherein the cognitive function is selected from the group consisting of working memory, reasoning / problem solving, and attention / alertness. (Item 30) 30. The non-transitory processor-readable medium of any one of items 27 to 29, wherein the subject is a human. (Item 31) 31. The non-transitory processor-readable medium of any one of items 27 to 30, wherein the subject is a healthy subject. (Item 31) 31. The non-transitory processor-readable medium of any one of items 27 to 30, wherein the subject is suffering from or at risk for a disease or disorder associated with reduced cognitive function. (Item 32) 32. The non-transitory processor-readable medium of Item 31, wherein the disease or disorder associated with decreased cognitive function is selected from the group consisting of dementia, Alzheimer's disease, major depression, bipolar depression, post-traumatic stress disorder (PTSD), panic disorder, generalized anxiety disorder (GAD), attention deficit hyperactivity disorder (ADHD), Parkinson's disease, schizophrenia, autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), or intellectual disability. (Item 33) 33. The non-transitory processor-readable medium of any one of items 27 to 32, wherein the glutamate receptor agonist is a group II metabotropic glutamate receptor (mGluR2 / 3) agonist. (Item 34) 34. The non-transitory processor-readable medium of any one of items 27 to 33, wherein the glutamate receptor agonist is pomaglumetad or a pharmaceutically acceptable salt thereof. (Item 35) 34. The non-transitory processor-readable medium of any one of items 27 to 33, wherein the glutamate receptor agonist is pomaglumetad methionyl or a pharmaceutically acceptable salt thereof. (Item 36) 36. The non-transitory processor-readable medium of any one of items 27 to 35, wherein the EEG with the EEG signal is recorded before treatment. (Item 37) 37. The non-transitory processor-readable medium of any one of items 27-36, wherein the one or more EEG metrics include a power law index. (Item 38) 38. The non-transitory processor-readable medium of any one of items 27 to 37, further comprising code for recording the EEG signals from the subject. (Item 39) removing measurement artifacts from the EEG signals before the EEG signals are converted, including periods when the subject moves and periods when the subject blinks their eyes; and performing independent component analysis (ICA) before the EEG signal is transformed to decompose the EEG signal and remove noise from the EGG signal; 39. The non-transitory processor-readable medium of any one of items 27 to 38, further comprising code for: (Item 39) 39. The non-transitory processor-readable medium of any one of items 27 to 38, wherein the recording of the EEG signal is in a resting state. (Item 40) 39. The non-transitory processor-readable medium of any one of items 27 to 38, wherein the recording of the EEG signal is during exposure to a sensory stimulus. (Item 41) Item 41. The non-transitory processor-readable medium of item 40, wherein the stimulus is a light stimulus. (Item 42) Item 42. The non-transitory processor-readable medium of item 41, wherein the stimulus is an electrical stimulus, a magnetic stimulus, a tactile stimulus, or an acoustic stimulus. (Item 43) the model is a machine learning model, and the non-transitory processor-readable medium comprises: 43. The non-transitory processor-readable medium of any one of items 27 to 42, further comprising code for training the machine learning model based on a training set comprising a plurality of EEG metrics and a plurality of clinical treatment outcomes associated with the plurality of EEG metrics, the plurality of EEG metrics comprising the one or more EEG metrics. (Item 44) 44. The non-transitory processor-readable medium of any one of items 27 to 43, wherein each clinical treatment outcome from the plurality of clinical treatment outcomes is classified as responsive and non-responsive based on a threshold or a receiver operating characteristic (ROC) curve. (Item 45) The electrophysiological behavior under sensory stimulation is [Table 4] 45. The non-transitory processor-readable medium of any one of items 27 to 44, selected from: (Item 46) The one or more EEG metrics include one or more electrophysiological behaviors at one or more brain locations in a resting state, the electrophysiological behaviors at the brain locations being: [Table 5] 46. ​​The non-transitory processor-readable medium of any one of items 27 to 45, selected from:

Claims

[Claim 1] The invention described in this specification.

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