Systems and methods for treating psychiatric disorder

Abstract

To provide systems and methods for treating a psychiatric disorder using a computer.SOLUTION: In a therapy session for a patient having a psychiatric disorder, each of multiple expression images is sequentially displayed. Each expression image is associated with an individual expression. The successive display of images is construed as a tiled series of expression image subsets, each consisting of N expression images. Upon completion of the display of each subset, the user is asked whether the first image and the last image in the subset exhibit the same emotion. A score is determined for each subset based on whether the patient has learned to respond correctly. The number of images in each subset is adjusted to a new number based on these scores. A treatment regimen is prescribed to the patient having the psychiatric disorder based at least in part on the scores.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 62 / 054,371, filed September 23, 2014, entitled "Systems and Methods for Treating Mental Disorders," the entire contents of which are incorporated herein by reference.

[0002] Statement of Federally Funded Research and Development This invention was made with government support under 1K23MH099223-01A1 awarded by the National Institutes of Health. The government has certain rights in this invention.

[0003] The present invention relates to the treatment of psychiatric disorders, more particularly to treatments associated with affective disorders (AD). [Background technology]

[0004] There is an urgent need for more effective treatments for psychiatric disorders characterized by negative affect (affective disorders), such as major depressive disorder (MDD), post-traumatic stress disorder (PTSD), and anxiety disorders. Such disorders are typically disabling and costly. In fact, an estimated 350 million people worldwide suffer from depression, making it the leading cause of disability among Americans aged 15-44. Novel therapeutic interventions are needed, as available relief-promoting therapies reach only one-third of patients. Summary of the Invention [Problem to be solved by the invention]

[0005] Psychiatric disorders, including affective disorders (AD), can be treated with neurobehavioral therapy (NBT), which stimulates networks of brain regions involved in these diseases. In particular, cognitive-emotional training can enhance cognitive control of emotional information processing by simultaneously activating brain regions impaired in AD, such as the dorsolateral paraplegia fasciculus (DLPFC) and amygdala. Without being bound by specific operational or mechanistic theories, we believe that such cognitive-emotional training, which exercises the ability to manipulate emotional information in working memory, enhances cognitive control of emotional substances and emotion regulation, and thus has an antidepressant effect. [Means for solving the problem]

[0006] One aspect provided herein is a computing system for treating a mental disorder. The computing system includes one or more processors, a memory, and one or more programs. The one or more programs are stored in the memory and configured to be executed by the one or more processors to treat a patient in need of treatment for a mental disorder. The one or more programs include instructions for conducting a treatment session and prescribing a treatment regimen for the mental disorder to the patient. The treatment session includes sequentially displaying each expression image in a plurality of expression images for a predetermined time period. Each expression image in the plurality of expression images (i) is independently associated with a respective expression in a set of expressions, and (ii) is designed to display a predetermined intensity of each expression according to an intensity scale ranging from low intensity to high intensity for each expression. That is, the expression images are designed to display a certain intensity of the expression (e.g., level 1 includes expression images at 90% intensity, level 2 includes expression images at 80% intensity, level 3 includes expression images at 70% intensity, level 4 includes expression images at 60% intensity, and level 5 includes expression images with 50% emotional intensity in the image). This contributes to the difficulty of the task across levels. This is intended to increase participation and learning throughout the session.

[0007] Each expression image independently represents a sliding window of the last N expression images, but is referred to as an "expression image subset." Thus, the multiple expression image subsets are necessarily displayed once all expression images have been displayed. Thus, in a treatment session, in response to the completion (e.g., display) of each expression image in each expression image subset of the multiple expression images, a response from the patient to a query regarding whether the first and last expression images in each expression image subset suggest the same emotion is received. That is, after each expression image subset is displayed, the patient is asked whether the first and last expression images in the expression image subset suggest the same expression. Each expression image subset of the multiple expression images is comprised of N expression images displayed sequentially. The value of N is a predetermined integer determined by the stage of the treatment session achieved by the patient. In a treatment session, a score is determined for each expression image subset of the multiple expression image subsets based at least in part on (a) the response to the query for each expression image subset, (b) the expression associated with the first expression image in each expression image subset, and (c) the expression associated with the last expression image in each expression image subset. In this manner, multiple scores are determined. The treatment session continues by resetting the value of N to a new positive integer value based at least in part on the plurality of scores. In some optional embodiments, the treatment session continues by prescribing a treatment regimen to the patient based at least in part on the reset value of N or some other aspect of the particular scoring process described above.

[0008] In some embodiments, the psychiatric disorder is affective disorder (AD).In some embodiments, AD is major depressive disorder (MDD), bipolar disorder, post-traumatic stress disorder (PTSD), generalized anxiety disorder, social phobia, obsessive-compulsive disorder, treatment-resistant depression or borderline personality disorder.In some embodiments, the affective disorder is MDD.

[0009] In some embodiments, the sequence of displaying, receiving, determining, and resetting is repeated multiple times during a treatment session, hi one embodiment, the sequence of displaying, receiving, determining, and resetting is repeated 10-20 times during a treatment session.

[0010] In some embodiments, the value of N before the initial reset is 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10. In one embodiment, the value of N before the initial reset is 2.

[0011] In some embodiments, there are 10-20 individual representation image subsets. In some embodiments, there are 20 individual representation image subsets.

[0012] In some embodiments, each representational image subset overlaps. In one embodiment, a representational image subset overlaps each other image subset by N-1 images.

[0013] In some embodiments of the computing system, the set of expressions includes happy, worried, angry, and sad, hi some embodiments, there are two, three, four, or five different expression images that are independently associated with each expression in the set of expressions.

[0014] In some embodiments, the treatment sessions are repeated on a repeat basis over several weeks. In some embodiments, the treatment sessions are repeated on a repeat basis over several weeks before prescribing a treatment regimen to the patient. In certain embodiments, the treatment sessions are repeated 2-10 times over several weeks before prescribing.

[0015] In some embodiments, a treatment regimen is characterized by the frequency with which treatment sessions are performed as well as the absolute number of times treatment sessions are performed.

[0016] In some embodiments, the treatment regimen is characterized by the frequency of treatment sessions and the absolute number of treatment sessions, and further characterized by the use of a pharmaceutical composition. In some embodiments, the affective disorder is MDD, and the pharmaceutical composition is a selective serotonin reuptake inhibitor (SSRI), a serotonin norepinephrine reuptake inhibitor (SNRI), a cognitive enhancer, ketamine or a ketamine derivative, or a combination thereof. In some embodiments, the affective disorder is post-traumatic stress disorder (PTSD), and the pharmaceutical composition is an SSRI, an SNRI, a cognitive enhancer, or a combination thereof. In some embodiments, the affective disorder is generalized anxiety disorder, and the pharmaceutical composition is an SSRI, an SNRI, a cognitive enhancer, ketamine or a ketamine derivative, or a combination thereof. In some embodiments, the affective disorder is social phobia, and the pharmaceutical composition is an SSRI, an SNRI, a cognitive enhancer, or a combination thereof. In some embodiments, the affective disorder is obsessive-compulsive disorder, and the pharmaceutical composition is an SSRI, a cognitive enhancer, or a combination thereof. In some embodiments, the affective disorder is borderline personality disorder and the pharmaceutical composition is an SSRI, an SNRI, ketamine or a ketamine derivative, a cognitive enhancer, or a combination thereof.

[0017] In some embodiments, the treatment regimen is characterized by the frequency per treatment session as well as the absolute number of treatment sessions, and further characterized by the use of a brain stimulation intervention. In some embodiments, the brain stimulation intervention stimulates the dorsolateral prefrontal cortex (DLPFC). In some embodiments, the brain stimulation intervention is transcranial direct current stimulation.

[0018] In some embodiments, the treatment regimen is characterized by the frequency with which treatment sessions are administered as well as the absolute number of treatment sessions administered, and further characterized by the use of psychotherapy for the affective disorder. In some embodiments, the psychotherapy is empirically supported psychotherapy for the affective disorder. In some embodiments, the psychotherapy is cognitive-behavioral psychotherapy.

[0019] In some embodiments, the predetermined time for which each image is displayed is less than 10 seconds. In some embodiments, the predetermined time for which each image is displayed is between 0.2 seconds and 10 seconds.

[0020] In some embodiments, each representation image in the plurality of representation images is displayed in grayscale. In other embodiments, each representation image in the plurality of representation images is in color. In yet other embodiments, the plurality of representation images includes color and grayscale representation images.

[0021] In some embodiments, prescribing includes transmitting the score to a remote server for evaluation by the prescribing physician.

[0022] In some embodiments, the step of sequentially displaying includes retrieving the plurality of expressive images from a database storing the plurality of expressive images, such database storing, for each expressive image in the plurality of expressive images, an emotion associated with each expressive image.

[0023] In some embodiments, the plurality of scores is determined as a total number of correct responses from the patient to a query about whether the first and last representations in each representation image subset are the same. In an embodiment, resetting N is based at least in part on the proportion of correct responses from the patient to the query compared to the total number of responses. In some embodiments, N is reset to N+Y when the proportion of correct responses is greater than a first threshold percentage, N is reset to N-Y when the proportion of correct responses is less than a second threshold percentage, and N is not reset when the proportion of correct responses is between the first and second threshold percentages. In some embodiments, the value of Y is 1. In some embodiments, the value of Y is an integer value determined by the size of N. For example, in one embodiment, when N is 5 or less, Y is 1, while when N is 6 or more, Y is 2.

[0024] In some embodiments, an initial assessment of the patient is obtained prior to conducting a therapy session to select an appropriate therapy session treatment protocol from among multiple therapy session treatment protocols, hi certain embodiments, the initial assessment includes obtaining information regarding the patient's cognitive and / or emotional functioning.

[0025] In some embodiments, the prescription of a treatment regimen is based at least in part on the patient's cognitive and emotional functioning after the training session. In some embodiments, the patient is evaluated after administering the treatment. In such embodiments, the patient evaluation is used, at least in part, as a basis for prescribing a prescription therapy for the patient. In certain embodiments, the evaluation is an evaluation of the patient's cognitive and / or emotional functioning.

[0026] In some embodiments, the patient receives a performance evaluation based at least in part on a score determined for each representational image subset during a treatment session. In some embodiments, the patient receives a performance evaluation based at least in part on a plurality of scores for a treatment session. In some embodiments, the patient receives a performance evaluation based at least in part on a plurality of scores for a plurality of treatment sessions.

[0027] In some embodiments, treatment sessions are repeated on a repeat basis over several weeks before prescribing a treatment regimen to the patient, and further, the patient is intermittently assessed for the presence of one or more symptoms of the psychiatric disorder to be treated. In certain embodiments, the intermittent assessment for the presence of one or more symptoms of the psychiatric disorder to be treated comprises a Patient Health Questionnaire (PHQ) test (e.g., PHQ-2, PHQ-9, PHQ-15, GAD-7, etc.).

[0028] The computing system of any one of claims 1-35, wherein at least one representation image in the plurality of representation images is not a facial expression.

[0029] The computing system of any one of claims 1-35, wherein each representation image in the plurality of representation images is not a facial expression.

[0030] The computing system of any one of claims 1-35, wherein at least one representation image in the plurality of representation images is a facial expression.

[0031] The computing system of any one of claims 1-35, wherein each representation image in the plurality of representation images is a representation of a face.

[0032] In some embodiments, the predetermined intensity of each representation on the intensity scale of each displayed representation image increases from low to high intensity over the course of the treatment session, hi some embodiments, the treatment session reduces the patient's depressive symptoms by at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, or at least 70%.

[0033] In some embodiments, at least one representation image in the plurality of representation images is not a facial expression. In some embodiments, each representation image in the plurality of representation images is not a facial expression. In some embodiments, at least one representation image in the plurality of representation images is a facial expression. In some embodiments, each representation image in the plurality of representation images is a facial expression. [Brief explanation of the drawings]

[0034] For a better understanding of the above-described implementations of the present invention, as well as additional implementations thereof, please refer to the following description of implementations in conjunction with the following drawings, in which like reference numerals designate corresponding parts throughout the drawings: [Figure 1] FIG. 1 is a block diagram of an electronic network for providing treatment to patients in need of treatment for a psychiatric disorder, according to some embodiments. [Figure 2] FIG. 2 is a block diagram of the patient device memory shown in FIG. 1 according to some embodiments. [Figure 3] 3A-3C are flowcharts of methods for treating psychiatric disorders according to some embodiments. [Figure 4] Figure 4A shows the change in depression severity over time between the group with MDD receiving EFMT treatment sessions and the CT comparison group. As shown in this graph, the change over time in the EFMT group (21.55-10.91; p<0.001, d=2.67) was significantly greater than the change over time in the CT group (19.80-14.10; p=0.01, d=1.08). The difference in the change in MDD symptoms over time between the groups (EFMT=-10.64; CT=-5.7) was significant, d=0.82. Figure 4B shows the change over time in negative emotional bias in short-term memory between the group with MDD receiving EFMT treatment sessions and the CT comparison group. As shown in this graph, negative self-referential information (the proportion of negative self-descriptions accurately recalled after a delay) showed a significant decrease in EFMT participants (.1852-.1697, p = .037, d = -.79), while the decrease in CT participants was small and non-significant (.1845-.1885, p = .535, d = .27). The difference in change between groups was moderate (d = .60). DETAILED DESCRIPTION OF THE INVENTION

[0035] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] The following detailed description of the preferred embodiments is provided with reference to the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without these details. Description of the Examples

[0036] The embodiments described herein provide various technical solutions for improving the health of human subjects with mental disorders by providing treatments directed at the mental disorders. Details of the embodiments are described below with reference to the drawings.

[0037] FIG. 1 is a schematic diagram of an electronic network 100 for treating mental disorders according to some embodiments. Network 100 includes a series of points or nodes interconnected by communication paths. Network 100 can include subnetworks that can interconnect with other networks, specifically, local area networks (LANs), metropolitan area networks (MANs), wide area networks (WANs), or global networks (Internet). Furthermore, network 100 can use WAP (Wireless Application Protocol), TCP / IP (Transmission Control Protocol / Internet Protocol), NetBEUI (NetBIOS Extended User Interface), or IPX / SPX (Internetwork Packet Switching / Sequenced Packet Switching). Furthermore, network 100 can be characterized by what type of signals it carries (voice, data, or both), by whom it is used (public or private), and the typical attributes of its connections (e.g., dial-up, dedicated, switched, non-switched, or virtual connections).

[0038] The network 100 connects a plurality of patient devices 110 to at least one mental disorder treatment server 102. This connection is made via a communications or electronic network 106, including an intranet, a wireless network, a cellular data network, or preferably the Internet. The connection may also be made via a communications link 108, such as, for example, coaxial cable, copper wire (including but not limited to PSTN, ISDN, and DSL), optical fiber, radio, microwave, or satellite link. Communication between the devices and the server is preferably via Internet Protocol (IP) or, optionally, a secure synchronization protocol, and may alternatively be via electronic mail (email).

[0039] The psychiatric treatment server 102 is shown in FIG. 1 as separate from the patient device 110 and is described below. The psychiatric treatment server 102 comprises at least one data processor or central processing unit (CPU) 212, server memory 220, (optionally) a user interface device 218, a communication interface circuit 216, and at least one bus 214 interconnecting these elements. The server memory 220 includes an operating system 222 that stores instructions for communication, data processing, data access, data storage, data retrieval, etc. The server memory 220 also includes a remote access module 224 and an expressive image library (database) 226. In some embodiments, the remote access module 224 is used to communicate (send and receive) data between the psychiatric treatment server 102 and the communication network 106. In some embodiments, the expressive image database 226 is used to store expressive images used by one or more programs (e.g., programs for conducting therapy sessions) of the computing system provided herein.

[0040] In some embodiments, the server memory 220 further comprises a patient database 228 including a plurality of patient profiles 230-1-230-Z. In some embodiments, each patient profile 230-1-230-Z includes patient information 232, such as, but not limited to, treatment session performance scores, pre- and post-treatment session assessments, and / or treatment session history, as described herein. In some embodiments, the patient profile 230 further includes the patient's contact details, information regarding the patient's medical history, details of the patient's medical insurance, etc. In some embodiments, the patient database 228 further includes information regarding the mental disorder treatment plan, such as, but not limited to, the frequency with which the treatment sessions described herein are conducted, the absolute number of treatment sessions conducted, and / or any prescribed medications or medications administered concomitantly with treatment provided in other treatments (e.g., drugs and other psychotherapies targeting brain regions and neural networks associated with the mental disorder being treated).

[0041] In some embodiments, the patient device 110 is a device used by a patient in need of treatment for a psychiatric disorder as described herein. The patient device 110 accesses the communications network 106 via a remote client computing device, such as a desktop computer, laptop computer, notebook computer, handheld computer, tablet computer, smartphone, or the like. In some embodiments, the patient device 110 includes a data processor or central processing unit (CPU), user interface device, communications interface circuitry, and buses similar to those described in connection with the psychiatric disorder treatment server 102. The device 110 includes a display 121 for displaying representational images, as described below. The patient device 110 also includes a memory 120, as described below. The memories 220 and 120 can include both volatile memory, such as random access memory (RAM), and non-volatile memory, such as a hard disk or flash memory.

[0042] Figure 2 is a block diagram of the patient device memory 120 shown in Figure 1 according to some embodiments. The patient device memory 120 includes an operating system 122 and a remote access module 124, which is compatible with the remote access module 224 (Figure 1) in the server memory 220 (Figure 1).

[0043] In some embodiments, the patient device memory 120 includes a treatment session module 126. The treatment session module 126 includes instructions for executing a treatment program, as described in more detail below. In some embodiments, the treatment session module 126 includes one or more modules for conducting a treatment session. For example, in some embodiments, the treatment session module 126 included in the patient device memory 120 includes a display module 128, a receiving module 130, a determination module 132, and a reset module 134.

[0044] In some embodiments, the patient device memory 120 includes a prescription program 136, which includes instructions for prescribing a treatment regimen for a patient receiving the treatment described herein. In some embodiments, the prescribing is based at least in part on results generated by the treatment session program 126 (e.g., a reset value for N, as described in more detail below).

[0045] In some embodiments, the patient database 138 includes a patient database 138 that stores data about patients using the patient device 110. In some embodiments, the patient database 138 stores data about a patient-user's performance scores for a treatment session or multiple treatment sessions as described herein, pre- and post-treatment session evaluations, and / or treatment session history. In some embodiments, the patient database 138 stores information related to the patient's psychiatric disorder treatment regimen, such as, but not limited to, the frequency with which treatment sessions as described herein are conducted, the absolute number of treatment sessions conducted, and / or any prescribed medications or medications administered concomitantly with treatment provided in other therapies (e.g., drugs and other psychotherapies that target brain regions and neural networks associated with the treated psychiatric disorder).

[0046] In some embodiments, the patient device memory 120 also includes a representational image database 140. In one embodiment, the representational image database includes representational images used in a treatment session of the computing system, as described below.

[0047] Note that the above databases hold data organized in a way that allows their contents to be easily accessed, managed, and updated. Databases include, for example, flat file databases (databases in which only one table is used for each database, taking the form of a table type representation), relational databases (databases in which data is defined so that it can be reorganized and accessed in multiple different ways), or object-oriented databases (databases in which data is defined in terms of object classes and subclasses). The databases may be hosted on a single server or distributed across multiple servers. In some embodiments, representational image database 226 is present, but representational image database 140 is not.

[0048] 3A-3C are flow charts illustrating a method 300 for treating a psychiatric disorder, according to some embodiments of the computer system of the present invention. In some embodiments, the method is performed by one or more programs of the patient computer system described herein.

[0049] In some embodiments, the method is for the treatment of affective disorders (AD). As used herein, "affective disorder" refers to a psychological disorder characterized by an abnormal emotional state (i.e., a mood disorder). Affective disorders include, but are not limited to, attention deficit hyperactivity disorder, bipolar disorder, body dysmorphic disorder, bulimia nervosa and other eating disorders, catalepsy, dysthymia, generalized anxiety disorder, irritability, irritable bowel syndrome, panic disorder, post-traumatic stress disorder, premenstrual dysphoric disorder, treatment-resistant depression, and social anxiety disorder. In certain embodiments, the system and method described herein is for the treatment of major depressive disorder (MDD), post-traumatic stress disorder (PTSD), generalized anxiety disorder, social phobia, obsessive-compulsive disorder, treatment-resistant depressive borderline personality disorder. In certain embodiments, the system and method described herein is for the treatment of MDD.

[0050] In some embodiments, the subject method includes A) conducting a treatment session 302-326 and B) prescribing a treatment regimen 334 to a patient with a mental disorder. Conducting the treatment session includes sequentially displaying 304 each representation image in a plurality of representation images for a predetermined time period, where each representation image in the plurality of representation images is associated with one representation in a set of representations. This sequential display of representation images can be thought of as sequential display of representation image subsets in a plurality of representation image subsets, each representation image subset consisting of the N most recently displayed representation image subsets. For example, in some embodiments, each representation image subset is tiled with respect to each other, each overlapping a temporally adjacent representation image by one representation image.

[0051] In some embodiments, the set of expressions evoked by the plurality of expression images includes four or more different expressions (e.g., happy, anxious, angry, and sad) (306). In some embodiments, the amount of time between the display of any two consecutive expression images is random and independent of the amount of time other images are displayed (308). In some embodiments, the amount of time between the display of any two consecutive expression images is random and independent of the amount of time between the display of any two consecutive images (310).

[0052] During a treatment session, upon completion of each expressive image subset within the plurality of expressive images, the patient is queried (312) as to whether the emotions of the first and last expressive images in each image subset are the same. For example, by pressing a first key (e.g., "1") if the expressions are the same and a second key (e.g., "2") if the expressions are different. Because each expressive image subset is temporally tiled, in some embodiments, this is equivalent to querying the patient after each expressive image once the first expressive image subset is displayed. Thus, in response to completion of each expressive image subset, a response to the query is received from the patient as to whether the first and last expressive images in each expressive image subset indicate the same emotion. In such embodiments, each expressive image subset within the plurality of expressive images consists of N consecutively displayed expressive images, where N is a predetermined integer (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or more). In some embodiments, each representation image subset overlaps, with each subset (temporally) overlapping with another respective image subset by N-1 images (where N is the number of representation images in each image subset). In some embodiments, each representation image subset overlaps, with each subset overlapping with another respective image subset by N-2 images (temporally). In some embodiments, each representation image subset overlaps, with each subset overlapping with another respective image subset by N-3 images (temporally). In some embodiments, each representation image subset overlaps, with each subset overlapping with N-Y images (temporally) with a respective other image subset, where N is the number of images in each image subset and Y is an integer less than N.

[0053] In some embodiments, there are 10-20 representational image subsets in each of the plurality of representational images (316). In some embodiments, there are 3-100 representational image subsets in each of the plurality of representational images. In some embodiments, there are 3-50 representational image subsets in each of the plurality of representational images. In some embodiments, there are 3-30 representational image subsets in each of the plurality of representational images.

[0054] The treatment session continues by determining a score for each representational image subset in the plurality of representational images based at least in part on (a) the response to the query for each representational image subset, (b) the expression associated with the first representational image in each representational image subset, and (c) the expression associated with the last representational image in each representational image subset, thereby determining a plurality of scores 318. In some embodiments, the plurality of scores is determined as a total number of correct responses by the patient to the query 320.

[0055] The treatment session continues by resetting the value of N to a new positive integer value based at least in part on the plurality of scores (322). In some embodiments, the resetting of the positive integer N is based at least in part on the proportion of correct responses from the patient to the queries relative to the total number of responses (324). In some embodiments, N is reset to N+Q if the proportion of correct responses is greater than a first threshold percentage, to NQ if the proportion of correct responses is less than a second threshold percentage, and not reset if the proportion of correct responses is between the first and second threshold percentages. Typically, the value of Q is "1," but other values ​​(e.g., 2, 3, 4, or any value less than N) are possible.

[0056] In some embodiments, a treatment regimen is defined based at least in part on the reset value of N (334).

[0057] Now that an overview of a treatment session has been detailed, some specific embodiments will be described. In some embodiments, a treatment session (e.g., as shown in steps 302-326) includes: i) sequentially displaying 304, for a predetermined period of time, each representational image in a plurality of representational images, where each representational image in the plurality of representational images is independently associated with one representation in a set of representations. In some embodiments of the computing system provided herein, the sequential displaying is performed according to instructions contained in the display module 128 stored in the patient device memory 120 of the patient device 110.

[0058] In some embodiments, the sequential displaying includes retrieving from a database (e.g., expressive image database 140, 226) that stores a plurality of expressive images. In some embodiments, the expressive image database further stores an emotion associated with each expressive image stored in the database. In one embodiment, the database that stores the expressive images is included in the server memory 220 of the mental disorder treatment server 102. In other embodiments, the database that stores the expressive images is located in the patient device memory 120 of the patient device 110.

[0059] In some embodiments, after retrieving the plurality of representational images from the representational image database 140, 226, the images are sequentially displayed to the patient via the display 121 of the patient device 110. In some embodiments, the predetermined time for which each particular representational image in the plurality of representational images is displayed is 0.1-5.0 seconds. In some embodiments, the predetermined time for which each particular representational image in the plurality of representational images is displayed is 0.5-3.5 seconds. In some embodiments, all of the representational images are sequentially displayed for the same predetermined time. In some embodiments, each representational image in the plurality is displayed for a predetermined time period that is random and independent of the length of time for which other images are displayed (308). In some embodiments, the predetermined time for which a particular representational image is displayed is a time period randomly selected from a set of predetermined time periods ranging from 2-10 seconds. In some embodiments, the amount of time between the display of two consecutive images is 0.1-5.0 seconds. In some embodiments, the amount of time between the display of two consecutive images is 0.75-3.5 seconds. In some embodiments, the amount of time between the display of two consecutive images is the same for all consecutive images displayed sequentially. In other embodiments, the amount of time between the display of any two consecutive images is random and unrelated to the amount of time between the display of any two other consecutive images 310. In some embodiments, the amount of time between the display of two consecutive images is randomly selected from a set of 2-10 predetermined lengths of time.

[0060] In some embodiments, the expressive images include images of human facial expressions. Images of any human facial expressions may be used in the subject systems and methods provided herein. The expressive images can represent human facial expressions of male and / or female adults and / or children, and of humans of the same or different ages and ethnicities. In some embodiments, the expressive images of the plurality of expressive images are displayed in grayscale. In other embodiments, the expressive images of the plurality of expressive images are in color. In still other embodiments, the expressive images of the plurality of expressive images are a combination of grayscale and color images. In some embodiments, the expressive images are photographs. In some embodiments, the expressive images are illustrations. In some embodiments, the expressive images are animated expressive images. In some embodiments, the expressive images are designed to display a certain intensity of expression (e.g., level 1 includes expressive images with 90% intensity, level 2 includes expressive images with 80% intensity, level 3 includes expressive images with 70% intensity, level 4 includes expressive images with 60% intensity, and level 5 includes expressive images with 50% emotional intensity in the image). This contributes to the difficulty of the task between levels. This aims to increase participation and learning throughout the session.

[0061] In some embodiments, the expression image is associated with one expression in an expression set, hi some embodiments, the expression set includes 2, 3, 4, 5, 6, 7, 8, 9 or 10 different emotional expressions. In some embodiments, the expression set comprises the following expressions: {pain, anger, annoyance, shame, shyness, gloom, desolation, bliss, cheerfulness, cruelty, melancholy, caution, annoyance, irritability, boldness, confused, contemptuous, timid, depressed, inquisitive, expressionless, dejected, depressed, pessimistic, sulky, melancholic, dreary, withered, overjoyed, stunned, ecstatic, expressionless, secretive, gloomy, somber, gloomy, sad, saddened, cruel, solemn, troubled, impatient, frightened, despairing, hostile, apathetic, indignant, emotionless, fearful, angry, mocking, inert, nasty, malicious, abusive, The expressive image may comprise two, three, four, five, six, seven, eight, nine, ten, or ten or more emotional expressions selected from the following: annoyed, pale, irritated, angry, pleading, absent-minded, pouting, suspicious, suspicious, cheerful, angry, sad, cheerful, cynical, scornful, unfriendly, inquiring, serious, embarrassed, contemptuous, somber, cynical, straight-faced, sulky, surprised, suspicious, stern, insensitive, sneering, tense, grim, vengeful, pale, wary, wistful, withering, miserable, bewildered, enraged, grimacing, and yearning. In some embodiments, the expressive image is a human facial expression. However, the present disclosure is not limited thereto. In some embodiments, the expressive image may be any image (emotionally salient or evocative image) that elicits activation of the amygdala.

[0062] In some embodiments, the expression set includes four different expressions. In particular embodiments, the expression set is {happy, anxious, angry, sad} (306). In some embodiments, the plurality of expression images consists of one image associated with a particular expression in the expression set. In other embodiments, the plurality of images consists of 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more different expression images associated with each expression in the expression set.

[0063] In some embodiments, conducting the therapy session (302-326) includes receiving 312, in response to completion of each expressive image subset in the plurality of expressive images, a response from the patient to a query regarding whether the first and last expressive image in each expressive image subset exhibits the same emotion, where each expressive image subset in the plurality of expressive images consists of N sequentially displayed expressive images, where N is a predetermined integer. In certain embodiments of the computing system provided herein, receiving is performed according to instructions contained in a receiving module 130 stored in a patient device memory 120 of the patient device 110.

[0064] In some embodiments, N is 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10. In some embodiments, each representation image subset is non-overlapping, i.e., there are no representation images in common between each representation image subset. In some embodiments, each representation subset is overlapping. In certain embodiments, each representation subset consists of N images, and N-1 of these images are found in another representation subset among the plurality of representation subsets.

[0065] In certain embodiments, each representational image subset overlaps by N-1 representational images 314. Thus, in these embodiments, successive representational image subsets differ by one representational image. For example, for multiple images displayed sequentially, such as A, B, C, D, E, F, G, H, I, J, K, L, M,

[0066] If each representation image subset contains N=4 representation images that are displayed sequentially, then the representation image subsets that overlap by N-1 representation images are: 1) A, B, C, D; 2) B, C, D, E; 3) C, D, E, F; 4) D, E, F, G, etc.

[0067] Thus, for N-1 overlapping representational image subsets, completion of display of a representational image subset after completion of the first representational image subset occurs after display of each image after the first N images.

[0068] In certain embodiments, there are 10-20 representational image subsets in each of the plurality of representational images displayed sequentially (316). That is, in such embodiments, 10-20 responses are received from the patient. In certain embodiments, there are 20 representational image subsets in each of the plurality of representational images displayed sequentially (316).

[0069] In some embodiments, conducting the therapy session (302-326) includes determining a score for each expressive image subset of the plurality of expressive images based at least in part on (a) a response to a query for each expression, (b) an expression associated with a first expressive image in each expressive image subset, and (c) an expression associated with a last expressive image in each expressive image subset, thereby determining a plurality of scores (318). In some embodiments of the computing system provided herein, the determining is performed according to instructions contained in a determination module 132 stored in the patient device memory 120 of the patient device 110. In some embodiments, the plurality of scores is determined (320) as a total number of correct responses by the patient to a query about whether the first and last expressive images in each expressive image subset indicate the same emotion.

[0070] In some embodiments, conducting the treatment session (302-326) includes resetting (322) the value of N to a new positive integer value based at least in part on the plurality of scores. In certain embodiments of the computing system provided herein, the resetting (322) is performed according to instructions contained in a reset module 134 stored in the patient device memory 120 of the patient device 110.

[0071] In certain embodiments, resetting N is based at least in part on the percentage of correct responses by the patient to the query compared to the total number of responses (324). In some embodiments, if the percentage of correct responses is greater than a predetermined threshold percentage, N is reset to N+X, where X is 1, 2, 3, 4, or 5. In certain embodiments, if the percentage of correct responses is greater than 80%-90%, N is reset to N+1. In certain embodiments, if the percentage of correct responses is greater than 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, or 90%, N is reset to N+1. In some embodiments, if the percentage of correct responses is less than a set threshold percentage, N is reset to N+X, where X is 1, 2, 3, 4, or 5. In some embodiments, N is reset to N-1 when the percentage of correct responses is less than 55-70%. In some embodiments, N is reset to N-1 when the percentage of correct answers is less than 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, or 70%. In some embodiments, N is not reset if the percentage of correct answers is within a certain percentage range. In one particular embodiment, N is reset to N+1 if the percentage of correct answers is greater than a first threshold percentage, N is reset to N-1 if the percentage of correct answers is less than a second threshold percentage, and N is not reset if the percentage of correct answers is between the first and second threshold percentages (326). In one particular embodiment, the first threshold percentage is approximately 85% and the second threshold percentage is approximately 65%.

[0072] In some embodiments, the treatment session includes repeating i) displaying, ii) receiving, iii) deciding, and iv) resetting multiple times (328). In certain embodiments, the repeating occurs prior to prescribing a treatment regimen (334). In certain embodiments, the repeating occurs on an iterative basis over the course of a period of time prior to prescribing a treatment regimen. In certain embodiments, the repeating occurs on an iterative basis over the course of several weeks prior to prescribing a treatment regimen.

[0073] In some embodiments, the repetition occurs on a recurring basis over a period of time prior to prescribing a treatment regimen, wherein the patient undergoes treatment sessions and is intermittently assessed for one or more symptoms of the psychiatric disorder being treated. In certain embodiments, the computer system disclosed herein includes one or more programs including instructions for intermittently assessing a patient for one or more symptoms of the psychiatric disorder being treated. In certain embodiments, the instructions include instructions for administering a Patient Health Questionnaire (PHQ) test (e.g., PH-QA, PHQ-2, PHQ-8, PHQ-9, PHQ-15, GAD-2, GAD-7, etc.). See, e.g., Kroenke et al., General Hospital Psychiatry 32:345-359 (2010) (incorporated herein by reference). For example, in certain embodiments, patients undergoing treatment sessions are intermittently assessed for cognitive biases and / or neurological cognition. Tests of cognitive biases include, for example, the Self-Referencing Information Processing (SRIP) task, the Cognitive Style Questionnaire (CSQ), and the Repetitive Response Scale (RRS). Neurocognitive tests include tests for working memory, such as tests testing forward and backward digit span and letter-number sequencing. Also, in certain embodiments, the computing systems disclosed herein include one or more programs containing instructions for intermittently assessing a patient's cognitive biases and / or neurocognition.

[0074] As used herein, the term "block" refers to a single iteration (for example) of the following steps of a treatment session: i) display, ii) receive, iii) decide, and iv) reset. In some embodiments, a treatment session includes 3-200 blocks. In certain embodiments, a treatment session includes 3-15 blocks. In certain embodiments, a treatment session includes 3-15 blocks that occur prior to prescribing a treatment regimen (334).

[0075] In some embodiments, the methods provided herein include prescribing a treatment regimen to the patient based at least in part on the reset value of N (334). In some embodiments, the prescribing step includes transmitting the score to a remote server (e.g., the psychiatric treatment server 102) for evaluation by a prescribing clinician (336). In some embodiments, the transmitting is performed by a prescription program 136 located in the patient device memory 120 of the patient device 110 described herein. In certain embodiments, the treatment regimen includes a frequency at which the treatment sessions are to be conducted and an absolute number of times the treatment sessions are to be conducted (338). In certain embodiments, the treatment session includes three or more blocks, with the sequential blocks grouped into levels, each level including an equal number of blocks. For example, in certain embodiments, the treatment session includes conducting 15 blocks and five levels, with each of the five levels consisting of three blocks. In certain embodiments, prescribing the treatment regimen is based at least in part on an average of the reset value of N in the last block of each level.

[0076] In some embodiments, the treatment regimen includes the frequency of treatment sessions, the absolute number of treatment sessions, and the use of a pharmaceutical composition (340), brain stimulation intervention (342), and / or empirically supported psychotherapy (344). Without being bound by any particular treatment theory, it is believed that combining different approaches (e.g., pharmacotherapy, brain stimulation intervention, and psychotherapy) that target overlapping nervous systems can provide additive benefits in the treatment of psychiatric disorders. In certain embodiments, the treatment regimen includes the frequency of treatment sessions (e.g., once per week, twice per week, three times per week, daily, biweekly), the absolute number of treatment sessions (e.g., 10, 20, 30), and the use of a pharmaceutical composition (340). In certain embodiments, the treatment regimen includes the frequency of treatment sessions (e.g., once per week, twice per week, three times per week, daily, weekly), the absolute number of treatment sessions (e.g., 10, 20, 30), and the use of a brain stimulation intervention (342). In certain embodiments, the treatment regimen includes a frequency of treatment sessions (e.g., once a week, twice a week, three times a week, daily, every other week), an absolute number of treatment sessions (e.g., 10, 20, 30), and the use of empirically supported psychotherapy (344). In other embodiments, the treatment regimen includes a frequency of treatment sessions (e.g., once a week, twice a week, three times a week, daily, every other week), an absolute number of treatment sessions (e.g., 10, 20, 30), the use of a pharmaceutical composition (340), and the use of a brain intervention mechanism (342). In other embodiments, the treatment regimen is characterized by including the frequency at which treatment sessions are conducted (e.g., once per week, twice per week, three times per week, daily, every other week), the absolute number of treatment sessions conducted (e.g., once per week, twice per week, three times per week, daily, every other week), the absolute number of treatment sessions conducted (e.g., 10, 20, 30), the use of pharmaceutical compositions (340), and the use of empirically supported psychotherapy (344).In yet other embodiments, the treatment regimen includes a frequency at which the treatment sessions are conducted (e.g., once a week, twice a week, three times a week, daily, every other week), an absolute number of times the treatment sessions are conducted (e.g., 10, 20, 30 times a week), the use of a brain intervention mechanism (342), and the use of an empirically supported psychotherapy (344). In yet other embodiments, the treatment regimen includes a frequency at which the treatment sessions are conducted (e.g., once a week, twice a week, three times a week, daily, every other week), an absolute number of times the treatment sessions are conducted (e.g., 10, 20, 30 times a week), the use of a pharmaceutical composition (340), and the use of a brain intervention mechanism (342), and the use of an empirically supported psychotherapy (344).

[0077] In certain embodiments, the treatment regimen is characterized by the frequency with which treatment sessions are administered (once a week, twice a week, three times a week, daily, every other week), the absolute number of treatment sessions administered (e.g., 10, 20, 30), and the use of the pharmaceutical composition 340. The pharmaceutical composition prescribed depends on the psychiatric disorder being treated. Pharmaceutical compositions known for treating affective disorders include, but are not limited to, selective serotonin reuptake inhibitors (SSRIs, e.g., fluoxetine, sertraline, paroxetine, citalopram, escitalopram, dapoxetine, cecloxetine, mesembrine, and zimelidine), serotonin norepinephrine reuptake inhibitors (SNRIs, e.g., venlafaxine, desvenlafaxine, duloxetine, milnacipran, levomilnacipran, and sibutramine), nootropics (e.g., Adderall, Ritalin, Dexadrine, modafinil), ketamine and ketamine derivatives, atypical antipsychotics, benzodiazepines, bupropion, amotrigine, lithium, monoamine oxidase inhibitors, tricyclic antidepressants, valproic acid, nefazodone, trazodone, and pramipexole. For proper dosing and selection of such compositions, see U.S. Patent No. 8,785,500, entitled "Intranasal Administration of Ketamine to Treat Depression," which is incorporated herein by reference in its entirety for such purposes.

[0078] In certain embodiments, the psychiatric disorder is MDD, and the pharmaceutical composition is an SSRI, SNRI, cognitive enhancer, ketamine or a ketamine derivative, an atypical antipsychotic, a benzodiazepine, bupropion, amotrigine, lithium, a monoamine oxidase inhibitor, valproic acid, nefazodone, trazodone, pramipexole, or a combination thereof. In certain embodiments, the psychiatric disorder is post-traumatic stress disorder (PTSD), and the pharmaceutical composition is an SSRI, SNRI, cognitive enhancer, ketamine or a ketamine derivative, or a combination thereof. In other embodiments, the psychiatric disorder is general anxiety disorder, and the pharmaceutical composition is an SSRI, SNRI, cognitive enhancer, or a combination thereof.

[0079] In some embodiments, the psychiatric disorder is social phobia, and the pharmaceutical composition is an SSRI, an SNRI, a cognitive enhancer, or a combination thereof.In other embodiments, the psychiatric disorder is obsessive-compulsive disorder, and the pharmaceutical composition is an SSRI, a cognitive enhancer, or a combination thereof.In some embodiments, the psychiatric disorder is borderline personality disorder, and the pharmaceutical composition is an SSRI, an SNRI, ketamine or a ketamine derivative, a cognitive enhancer, or a combination thereof.

[0080] In some embodiments, the treatment regimen is characterized by the frequency of treatment sessions, the absolute number of treatment sessions, and brain stimulation intervention (342). In some embodiments, the target of the brain intervention includes the amygdala and the anterior lateral prefrontal cortex (DLPFC) brain regions. Without being bound by any particular theory of operation, it is believed that neural circuit abnormalities within these regions cause biased and prolonged processing of negative emotional information associated with some affective disorders (e.g., MDD). In some embodiments, the brain-targeted intervention activates the amygdala and DLPFC regions. In some embodiments, the brain-targeted intervention is transcranial direct current stimulation, deep brain stimulation, and / or transcranial magnetic stimulation (TMS).

[0081] In some embodiments, the treatment session includes multiple cycles of i) displaying, ii) receiving, iii) deciding, and iv) resetting (328) before prescribing a treatment regimen, and one or more brain-targeted interventions disclosed herein are administered concurrently during or prior to administering the treatment session. Further, in some embodiments, one or more pharmaceutical compositions disclosed herein are administered as part of the treatment regimen, either before or after administering the treatment session.

[0082] In certain embodiments, the treatment regimen is characterized by the frequency of treatment sessions (e.g., once a week, twice a week, three times a week, daily, every other week), the absolute number of treatment sessions (e.g., 10, 20, 30), and the use of an empirically supported psychotherapy. In certain embodiments, the empirically supported psychotherapy is cognitive behavioral therapy.

[0083] In certain embodiments, the computing system disclosed herein includes one or more programs including instructions for conducting an initial assessment of a patient (302) prior to treatment to select a treatment session from among multiple treatment session protocols. In certain embodiments, the initial assessment includes assessing the user-patient for one or more symptoms of the affective disorder being treated. In certain embodiments, the instructions include instructions for administering a patient health questionnaire (e.g., PHQ-2, PHQ-9, PHQ-15, GAD-7, etc.). Also, in certain embodiments, the patient is initially assessed for cognitive bias, neurocognition, and / or emotional functioning. In certain embodiments, the computing system disclosed herein also includes one or more programs including instructions for intermittently assessing the patient's cognitive bias and / or neurocognition.

[0084] In some embodiments, a performance rating is provided to the user after completion of a block, multiple blocks, or a treatment session. In certain embodiments, one or more programs of a computing system provided herein include instructions for generating a performance rating. In some embodiments, the performance rating is based on the reset value of N at the completion of a block, multiple blocks, or a treatment session. In some embodiments where a treatment session is performed at the block level, the performance rating is based on the average of the reset value of N for the last block at each level. [Example]

[0085] Example 1 relates to cognitive-affective training as an intervention for major depressive disorder.

[0086] For example, more effective treatments for affective disorders, including major depressive disorder (MDD), are urgently needed. As our understanding of the cognitive and affective neuroscience underlying these disorders expands, opportunities exist to advance interventions that harness the brain's capacity for plasticity. Cognitive training is one such strategy. Below, we present a novel cognitive-affective training exercise designed to enhance cognitive control over emotional information processing and target components of neural networks implicated in MDD.

[0087] E-1-1 Methods and Equipment

[0088] E-1-1-1 Participants. Twenty-one currently unmedicated MDD participants were recruited through advertisements for a depression research study. Participants, ages 18-55, were diagnosed by trained master's- and doctoral-level clinicians using the Structured Clinical Interview for DSM-IV-TR Axis I Disorders (SCID). Participants met diagnostic criteria for MDD and, if MDD was their primary diagnosis, could have other Axis I disorders (excluding psychotic disorders, bipolar disorders, and substance abuse or dependence within the past 6 months). MDD severity, as measured by the Hamilton Depression Rating Scale (Ham-D)-17 item version, had to be moderate (Ham-D > 16); participants with very severe MDD (Ham-D > 27) were excluded from treatment due to safety concerns regarding participation in an unproven antidepressant study. Participants with a history of treatment non-response (failed two or more adequate trials of standard antidepressants) or chronic, non-episodic MDD were excluded from participation, as were patients with visual or motor impairments that would interfere with performance on the computerized exercises. Thirty potential participants were screened for the study; six did not meet the inclusion / exclusion criteria, and three were eligible but decided not to participate.

[0089] The Mount Sinai Program for the Protection of Human Subjects approved the protocol and study procedures, which were conducted in accordance with the Declaration of Helsinki. After screening, eligible participants were informed of the study procedures and signed informed consent. Participants were informed that the study would assess the effects of two different memory training methods on memory and MDD symptoms. This was not presented as an intervention study, and participants were unaware of the differences between the cognitive-affective and control training paradigms, thereby maintaining the blindness of the study and minimizing placebo effects. After completing the study, participants were informed of the blindness of the study, including its underlying rationale. Participants were reimbursed for each completed study session to compensate for their time and travel expenses.

[0090] E-1-1-2 Procedure. The study included 11 sessions. First, the SCID and Ham-D-17 were administered. A baseline session was then conducted to assess attention and working memory, cognitive processing biases, and MDD symptoms. To assess attention span and working memory, composite scores were obtained from the Wechsler Adult Intelligence Scale, Digit-Span Forward (DSF), Digit-Span Backward (DSB), and Letter-Number Sequencing (LNS), Third Edition (Wechsler D (1997): Wechsler Adult Intelligence Scale, Third Edition. San Antonio: The Psychological Corporation). Measures of cognitive processing bias included the Repetition Rating Scale (RRS) (Treynor et al., Cognitive Ther Res 27:247-259 (2003)) and the Reference Information Processing Task (SRIP) (Murray et al., Memory 7:175-196 (1999)). The SRIP involves presenting self-descriptors (positive and negative) and asking participants whether the word "sounds like me." Participants were later asked to recall as many of the words as they could remember. The percentage of negative self-descriptors accurately recalled is used as an index of the negative bias in short-term memory in MDD. The percentage of negative self-descriptors accurately recalled is used as an index of the negative bias in short-term memory in MDD. MDD symptoms were assessed using the Ham-D-17.

[0091] Participants were randomly assigned to the cognitive-affective or control training group by the study coordinator using a predetermined sequence for group assignment generated by an independent biostatistician. Participants completed eight training sessions (30-45 minutes each, twice per week) over a four-week period. Weekly Ham-D assessments were administered by PhD- or MD-level clinicians blinded to group assignment. Ham-D assessors received extensive training to administer the assessment and demonstrated an ICC > 0.8 in two separate training interviews. The outcome session was conducted within one week of completing the training session, at which point the baseline assessment was repeated.

[0092] E-1-1-3 Cognitive Training Exercise. The cognitive-emotional training exercise combines an emotion identification task with a working memory task. In the Emotional Faces Memory Task (EFMT), participants identify the emotion observed in a series of photographs of faces presented on a computer screen and remember the order of the emotions. Using an N-back working memory training paradigm, observed participants indicate whether the emotion is the same as the N-backed emotion for each face. The N level varies across blocks depending on performance. N may decrease or increase across blocks. Participants complete 15 blocks per session. Session 1 begins with N = 1, and N for subsequent sessions is determined by performance in the previous session. The task consistently challenges participants, focusing on their ability level. In a single, non-progressive session in healthy volunteers, this task simultaneously activated the DLPFC and amygdala (Neta et al., NeuroImage 56:1685-1692 (2011)). Although progressively challenging n-back working memory tasks have been shown to improve working memory performance (Jaeggi et al., Proc Natl Acad Sci USA 105:6829-6833 (2008)), a progressively more challenging working memory paradigm involving emotional stimuli has yet to be reported. The control training (CT) task is an active comparator consisting of an identical cognitive training paradigm to the EFMT, except that the stimuli are neutral shapes (e.g., circles, squares), thereby isolating coactivation in the amygdala and DLPFC for the EFMT group.

[0093] E-1-2 Study Design: This study was designed as a double-blind, randomized, proof-of-concept trial to determine the effects of eight cognitive-affective training sessions on cognitive processing biases, working memory, and MDD symptoms in MDD participants. Outcome measures, changes in rumination (RRS), short-term memory, positive and negative self-descriptors (SRIP), attention and working memory (DSF, DSB, and LNS), and MDD symptoms (Ham-D) were assessed before and after the training treatment.

[0094] E-1-3 Data analysis procedure. The effect of EFMT training versus CT training on MDF symptoms was the main analysis of interest in this proof-of-concept investigation. To examine the impact on MDD symptoms, a repeated measures ANOVA of Ham-D change with the time difference between factors (baseline and outcome) as the group (EFMT v. CT) was planned. The secondary analysis was aimed at evaluating changes in cognitive processing bias and working memory. In this investigation, due to the small sample size and limited power to detect significant effects, an exploratory analysis of within-group changes (EFMT or CT) over time was planned, along with an effect size procedure for differences in change scores between groups. The ability to detect a significant p-value (α <.05) was limited, but the magnitude of the effect was the main concern in describing the possible effects of the cognitive training intervention, and t-tests were conducted within groups. The effect size was interpreted by Cohen 1988 (Cohen J: Behavioral Sciences, Statistical Power Analysis, 2nd Edition, Lawrence Erlbaum Associates, New Jersey (1998)): "small" =.2 < d <.3; "medium" = d =.5; "large" = d <.8. Due to the limited power and exploratory nature of the analysis, between-group analysis of cognitive variables was not planned.

[0095] E-1-4 Outcome. Table 1 provides the demographic and clinical characteristics of the study sample. Eleven participants were assigned to EFMT and ten to CT. All 21 participants completed all eight training sessions over four weeks. Task performance (average N level achieved in each session) improved over time in both groups. In week 4, the EFMT group achieved an average N level of 5.5 and the CT group an average N level of 5.6.

[0096]

Table 1

[0097] Figure 4A shows the decrease in mean Ham-D scores between baseline and outcome; the EFMT group's decrease (21.55-10.91) was significantly greater than the CT group's decrease (19.80-14.10) (Group [EFMT vs. CT] × Time [Baseline vs. Outcome] ANOVA: F(1,19) = 5.605, p = .029). At week 4, the difference in Ham-D scores between groups approached significance, t(19) = 1.91, p = .07. Both groups showed a predominant effect of time on Ham-D reduction (EFMT: t(10) = 8.86, p < .001; CT: t(3.27), p = .01). There was a significant effect size difference between groups, d = 0.82, for change in MDD symptoms over time (ΔHam-D: EFMT = -10.64; CT = -5.7). Six of 11 EFMT participants and one of 10 CT participants achieved a 50% or greater reduction in Ham-D score between baseline and outcome, the criterion for defining "responders" in clinical MDD trials.

[0098] The EFMT group showed a moderate but nonsignificant decrease in rumination (RRS) scores (28.45-25.45, t(10) = 1.54, p = .14, d ≤ 10, dΔ = -0.66). The CT group showed a nonsignificant increase (28.8-30.6, t(9) = .88, p = .39, dΔ = .39). The magnitude of the difference in change in rumination over time between groups was moderate (d = 0.64). In the self-referential information processing (SRIP) task, the EFMT group showed a significant decrease in short-term memory for negative self-referential information (proportion of negative self-descriptors accurately recalled after a delay) (0.1852-0.1697, t(10) = 2.23, p = 0.037, dΔ = -0.79). The CT group showed a small, non-significant increase of 0.1845-0.1885, t(9) = 0.63, p = 0.535, dΔ = 0.27 (see Figure 4B). The effect size for the difference in change scores between groups was moderate, d = 0.60.

[0099] Both groups showed similar small improvements in attention span and working memory (DSF, DSB, and LNS composite scores) after training. The EFMT group showed an increase from 10.3 to 11.36 (t(10) = 1.855, p = 0.09, d = 0.31), while the CT group showed an increase from 10.8 to 11.25 (t(9) = 1.01, p = 0.34, d = 0.20). Composite scores were not significantly different between groups at baseline or week 4.

[0100] E-1-5 Conclusion. The differential effects of EFMT vs. CT in this study demonstrate that cognitive-emotional training is an intervention strategy for MDD that targets impaired cognitive control and neural network dysfunction for emotional information processing. These interventions are feasible, relatively low-cost, and ultimately can be widely distributed to participants' homes via computer. Such interventions can serve as an augmentation strategy for traditional interventions such as medication and cognitive-behavioral therapy. Furthermore, these types of interventions may be accessible to populations that cannot utilize traditional interventions (e.g., pregnant women or medically ill individuals for whom antidepressants are not prescribed). [Example]

[0101] Example 2 relates to a clinical trial.

[0102] The aim of this study was to investigate the effectiveness of the disclosed computerized training paradigm to correct the negative bias in working memory and to investigate whether improvements in this domain transfer to other areas of cognitive processes that are also impaired in depression (facial emotion recognition, self-referential information processing, and attributional style).

[0103] Therefore, one specific aim of this study is to measure the effectiveness of an emotional face memory task, compared to a placebo-controlled task (PCT), in reducing depressive symptoms in participants with major depressive disorder (MDD). MDD participants undergoing 6 weeks of EFMT training are expected to experience greater improvement in depressive symptoms than MDD participants undergoing PCT.

[0104] Another specific aim of this study is to measure the effect of EFMT on negative emotional bias in information processing. EFMT training is believed to reduce negative emotional bias in information processing compared to PCT.

[0105] Another specific aim of this study is to measure the neurocognitive effects of EFMT training. Both EFMT and PCT training groups are expected to show improvements across neurocognitive domains.

[0106] Major depressive disorder (MDD) affects approximately 17% of the general population and is typically a severe, chronic, and often life-threatening illness. The impairments in physical and social functioning resulting from MDD are as severe as those of other chronic medical illnesses. Despite significant advances in the treatment of depression, many patients with this illness often receive inappropriate treatment due to treatment maladjustment. It is estimated that only 60–70% of patients treated with antidepressants respond to an initial trial of pharmacotherapy. Because MDD continues to pose a significant public health problem with high morbidity and mortality, new well-tolerated interventions are needed.

[0107] Research has consistently demonstrated that depressed individuals exhibit a negative emotional bias, a tendency to bias perception and processing toward negative information compared with positive or neutral information. This negative emotional bias has been demonstrated in several domains of perception and processing, including working memory, emotion processing, self-referential information processing, and attributional style. Importantly, the presence of a negative emotional bias across each of these therapeutic domains in depression may be due to shared underlying neural circuits. Indeed, brain regions involved in each of these domains of perception and processing are part of a larger network involved in normal emotion perception and the pathophysiology of depression. Recent research has aimed to modify various cognitive biases in depression and anxiety disorders through cognitive training, with promising initial results.

[0108] E-2-1 Research design.

[0109] E-2-2 Recruitment Methods. Subjects were recruited through advertisements in local newspapers and on websites such as Craigslist and clinical sites. Participants were recruited through an IRB-approved ongoing MAP recruitment and selection protocol.

[0110] E-2-2-1 Inclusion and Exclusion Criteria. Inclusion criteria are (i) a current Axis I diagnosis of a primary major depressive disorder according to DSM-IV criteria and a Ham-D-17 score of 16-27, (ii) age between 18-55 years, and (iii) the ability to provide informed consent. Therefore, patients with a primary diagnosis of major depressive disorder will be accepted. In addition, some patients with a coexisting (secondary) affective disorder diagnosis, such as post-traumatic stress disorder, social phobia, generalized anxiety disorder, specific phobia, borderline personality disorder, etc., who are expected to experience symptom improvement (at least a 50% reduction in depressive symptoms) as a result of the disclosed test and the disclosed system and method will also be accepted.

[0111] Exclusion criteria were: (i) a history of drug or alcohol abuse or dependence (DSM-IV criteria) within the last 6 months; (ii) visual impairment affecting the ability to observe computerized displays of faces or other images that induce amygdala activation; (iii) movement disorders affecting the ability to provide responses by momentary button presses; (iv) a lifetime history of bipolar or schizophrenia spectrum disorder; (v) a primary, current Axis I diagnosis other than major depression; (vi) a primary, current Axis II personality disorder; (vii) currently participating in cognitive-behavioral psychotherapy; (viii) acute suicidal or homicidal risk (evidenced by a suicide or homicide attempt within the last 6 months prior to screening); and (ix) pregnancy.

[0112] Enrolled participants were currently taking medication but were stable on medication therapy prior to study enrollment (i.e., medication had not been started within 8 weeks, had been stopped within 6 weeks, or had gone up or down within 4 weeks of study entry). Therefore, if a patient's medication status does not change over the course of the study, the study will be discontinued. Medications will not be discontinued for the purposes of enrolling in the study.

[0113] Subjects were to be either non-alcohol users or moderate users of alcohol. Participants with current excessive alcohol use (≥8 oz / day for men and ≥6 oz / day for women) were ineligible because such drug use may confound results. The study recruited 80 participants.

[0114] Timeline of the E-2-2 investigation.

[0115] E-2-2-1 Individual Subject Enrollment. All participants will require baseline behavioral assessment measures. They will be randomly assigned to either the training or control group and will be required to meet for 18 appointments, three times per week, for 6 weeks. The estimated duration of individual subject participation is 10 weeks, depending on flexibility in scheduling diagnostic, baseline, and post-test measures. The approximate recruitment period is 5 years.

[0116] E-2-2-2 Study End Point. This study will end when the target recruitment is achieved.

[0117] E-2-3 Related Procedures: A double-blind randomized design was used in which depressed subjects were assigned to one of two procedures: the Emotional Faces Memory Task (EFMT) training paradigm or a placebo-controlled task (PCT), a computerized task designed to coordinate multiple participant sessions and exposure to computerized stimuli. Patients first underwent screening and diagnostic assessments to determine eligibility. Once participants met the inclusion criteria, they were assigned to either the training or control group based on a predetermined randomization algorithm that included medication status as a factor. Depressive symptoms (BDI-II), visual acuity tests (RRS), neuropsychological function (DS and LNS), verbal memory (HVLT), facial emotion recognition ability (EFRT), cognitive / emotional processing bias (eStroop (AGNG)), and outcome measures (CSQ, SRIP, LOT-R) were administered. All participants participated in 18 sessions and underwent either the training paradigm or the placebo-controlled task. After the 18 sessions, baseline assessments were repeated.

[0118] E-2-4 Screening Interview (approximately 1 hour). Subjects referred for the study or who respond to an advertisement for the study are educated about the protocol. Subjects interested in participating sign a consent form to proceed with the screening and diagnostic interview. This interview includes the collection of demographic information and a psychosocial battery, including the Diagnostic Interview for Depression (SCID) and the Hamilton Rating Scale for Depression (Ham-D-17), to confirm the MDD diagnosis and eliminate exclusion criteria.

[0119] Individuals screened for the study and known to have a previously undiagnosed psychiatric disorder or condition will be made aware of the diagnosis / condition and offered a referral (either to their current primary care physician or another appropriate referral) for treatment or further consultation, if they wish.

[0120] E-2-5 Baseline Assessment (approximately 2.5 hours). Participants participate in a baseline assessment session, during which self-report assessments of depressive symptoms (Beck Depression Inventory-II (BDI-II) and rumination (RRS)), neuropsychological functioning assessments (Digit Span and Letter-Number Sequencing), and verbal memory (HVLT) are administered. During this session, participants are administered secondary measures to achieve baseline. These include the Emotional Face Recognition Task (EFRT), the Emotional Stroop (eStroop), the Emotional Go / No-go (AGNG), the Cognitive Style Questionnaire (CSQ), the Self-Referenced Information Processing (SRIP) task, and the Optimism and Resilience Questionnaire (LOT-R). During this session, participants are asked to rate their acceptability on a single Likert scale item to assess the acceptability of cognitive training as a potential intervention strategy for depression.

[0121] E-2-6 training sessions or control group sessions (35 minutes each). The training condition consisted of three sessions per week for six weeks (18 sessions total). In the training condition, subjects completed the EFMT task with the goal of improving their ability to accurately identify and remember facial emotions. In the control condition, the control group completed a placebo-controlled task (PCT) three sessions per week for six weeks and participated in an adaptive n-back working memory assessment using shapes as stimuli. This controlled for various confounding factors that could potentially affect outcome variables, such as improvements in working memory due to training, the number of sessions attended, interactions with study staff, and placebo effects resulting from expectations of improvement based on participating in cognitive training.

[0122] E-2-7 Midterm Assessment (Week 3; approximately 45 minutes). At the end of participants' third week of training, participants will complete the RRS, eStroop, EFRT, and AGNG to monitor cognitive and emotional changes that precede changes in mood symptoms.

[0123] E-2-8 Outcome Assessment (approximately 2.5 hours). During the outcome assessment session, participants completed the BDI-II, RRS, DS, LNS, HVLT, EFRT, eStroop, AGNG, CSQ, SRIP, and LOT-R. During this session, participants completed a questionnaire regarding 1) the acceptability of cognitive training for depression as a possible intervention strategy and 2) the perceived usefulness of the cognitive training method they participated in. Ratings were provided for each of the two Richards scale items.

[0124] E-2-9 Follow-up Assessments (approximately 0.5 hours each). Study completers (participants who completed at least 15 training sessions and baseline and outcome sessions) will be interviewed at 2, 4, 6, 8, 16, and 24 weeks after the outcome assessment to collect pilot data on the course of MDD symptoms following EFMT and PCT therapy. Follow-up assessments will include a Ham-D interview administered by a trained clinician.

[0125] E-2-10 Suicidality Assessment. Upon enrollment, participants will discuss with the study administrator a plan to monitor and respond to emergent suicidality among study participants. Participants will be provided with the PI's direct contact numbers (including a cell phone number listed on the consent form) and encouraged to call if suicidal thoughts appear or worsen. Participants will also be instructed to identify the emergency room closest to their home and call 911 or 1-800-LIFE-NET if they determine they are at risk of harm and are unable to contact their PI (or feel the emergency cannot wait to contact their PI), or to go to the nearest emergency room.

[0126] The baseline and outcome assessment sessions will also include an assessment of suicidal ideation. Additionally, each week during the study, patients will meet briefly with a clinician (licensed clinical specialist; MD psychiatrist or PhD clinical psychologist) to ask about mood and suicidal ideation experienced since the previous session using a standardized, validated assessment (Columbia-Suicide Severity Rating Scale, C-SSRS) and a clinician-assessed improvement / worsening scale (CGI).

[0127] In addition to weekly depression and suicidality symptom assessments conducted by the principal investigator (including standard rating scales: Columbia Suicide Severity Rating Scale (CSSRS) and Hamilton Depression Rating Scale (Ham-D)), all patients will be interviewed weekly by licensed clinicians within Mount Sinai's Mood and Anxiety Disorders Program (MAP), who are not study investigators, supervisors, or co-investigators (per the K23 application). MAP licensed clinicians have extensive experience assessing and monitoring symptoms and suicidality of serious mood and anxiety disorders. These interviews include assessment of depressive symptoms and the occurrence of passive or active suicidal thoughts, intentions, plans, or actions. Significant worsening of symptoms or increased suicidal ideation, while common among depressed patients, should still be taken very seriously. If a patient exhibits significant suicidal ideation, in the interviewer's judgment, the patient is at risk of harming themselves and therefore will be (i) escorted to a hospital emergency room for inpatient evaluation, or (ii) the patient will be removed from the study to obtain necessary treatment. If the patient is not hospitalized or placed on a treatment regimen through the emergency room, (a) the patient will receive three months of free outpatient treatment. This free follow-up period is offered to all subjects enrolled in the MAP study and includes the following treatments: psychiatric evaluation and ongoing treatment (including provision of prescriptions to be filled at the patient's local pharmacy), psychotherapy with a psychologist or social worker, or both pharmacological and psychotherapy if warranted. The free follow-up period will be used by clinicians to advise patients about finding long-term care in their local community, depending on their insurance coverage / ability to pay. Clinicians will suggest long-term treatment options and make appropriate referrals. If a patient is already receiving care from a mental health care provider and does not wish to receive three months of free treatment through MAP, the patient will be referred to the provider in consultation with the provider regarding the reasons for the study, including investigator concerns about patient safety. All study patients sign a consent to provide contact information for their current treatment provider upon study enrollment and grant study investigators the right to contact their current treatment provider in the event of an emergency or threat of harm.

[0128] E-2-11 Data Analysis Strategy. Objective 1: Measure the effectiveness of EFMT in reducing depressive symptoms in MDD participants compared with PCT. Hypothesis 1: MDD participants receiving 6 weeks of EFMT training will demonstrate greater improvement in depressive symptoms than MDD participants receiving PCT. Analytical Strategy: An intent-to-care analysis will be conducted, including all participants who completed at least 1 week of training. The primary analysis for this proof-of-concept study is based on a Bayesian approach focused on estimating the posterior probability distribution of the mean between-group difference in Ham-D change from baseline to final assessment. This flexible approach allows for the estimation of the probability that EFMT is superior to PCT in reducing Ham-D scores by any amount (e.g., >0, >2, etc.) in addition to providing tests of corresponding frequent null hypotheses, providing no difference. This approach assumes a non-informative diffuse normal prior probability distribution for the difference in Ham-D scores between groups, reflecting the null hypothesis of no difference (e.g., N(0, 25) prior). This prior distribution is updated by incorporating the observed outcomes, resulting in an updated posterior distribution of outcomes, which is used to calculate the probability that EFMT is superior to PCT by a specified amount. In addition to the Bayesian analysis, a more traditional analysis of covariance (ANCOVA) is performed to determine the adjustment in Ham-D scores (adjusted for baseline and number of training sessions) between the EFMT and PCT groups. In this ANCOVA, the factor is group (EFMT or PCT), final Ham-D score is the dependent variable, and baseline Ham-D score and number of completed training sessions are covariates.

[0129] E-2-12 Objective 2: Measure the effect of EFMT on negative emotional bias in information processing. Hypothesis 2: EFMT training will result in a decrease in negative emotional bias in information processing. Analytical Strategy: ANCOVA will be conducted to evaluate changes in cognitive / emotional processing assessments between the EFMT and PCT groups. (EFMT or PCT), final scores (RRS, eStroop, AGNG, EFRT, LOT-R, SRIP, or CSQ) are dependent variables, while baseline scores and number of training sessions completed are covariates.

[0130] E-2-13 Objective 3. Measure the neurocognitive effects of EFMT training. Hypothesis 3: The EFMT and PCT groups will show similar improvements in neurocognition. Analytical Strategy: ANCOVAs will be conducted for training effects on (1) attention and storage (DSF), (2) working memory (DSB and LNS composite score), and (3) verbal memory (HVLT). Participants unable to reach an average performance level of 2.5 on the cognitive training tasks for at least one week of training (during any week of the study) will be excluded from the final data analysis.

[0131] E-2-14 Interim Results. Table 2 provides interim results for the 23-patient group, with 12 patients in Group A and 11 in Group B. The goal of this blinded study data is for one participant in the study group to achieve at least 50% clinical response in depressive symptoms, much more frequently than the other groups. This indicates that the intervention administered to this group is more effective in reducing MDD symptoms. If the data were uninterpreted, this group would be expected to be the active cognitive-affective training group. To date, there have been no adverse side effects or other adverse events resulting from clinical trial procedures. Many patients have shown significant improvement as a result of the cognitive-affective training intervention and as a result of their participation in the study, and the intervention is expected to be effective when the final data is analyzed. In Table 2, the "Baseline" and "Outcome" numbers are the total scores of the depression rating scale used in the study (the Hamilton Depression Rating Scale, 17-item version (HAM-D-17)). The two values ​​for each patient ("Baseline" and "Outcome") represent the baseline (beginning of the study) and outcome (endpoint). A "change" score is provided as outcome minus baseline. "% change" is the change score (multiplied by 100) divided by the baseline score.

[0132] [Table 2]

[0133] In Group A, 1 of 12 patients "responded" (had at least a 50% reduction in symptoms). In Group B, 5 of 11 patients responded. Although formal statistical analysis has not been performed at this point (not until the data have been decoded for interim analysis), the data indicate a statistically significant difference between the groups.

[0134] The terminology used in the detailed description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Also, as used herein, the term "and / or" is understood to refer to and include any and all possible combinations of one or more of the associated listed items. As used herein, the terms "comprises" and / or "comprising" specify the presence of stated features, steps, operations, elements, and / or components, but may include one or more of the steps, operations, elements, components, and / or groups thereof.

[0135] All references cited herein are incorporated by reference in their entirety for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes.

[0136] The foregoing description has been set forth with reference to specific implementations for purposes of explanation. However, the illustrative discussion above is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and variations are possible in light of the above teachings. The embodiments described herein have been chosen and described in order to best explain the principles of the invention and its practical application, and thus enable others skilled in the art to easily understand the invention and its particular intended use.

Claims

1. 1. A system for displaying an image, comprising:

1. A computing system having one or more processors coupled to a memory, comprising: displaying a first plurality of images to a patient, at least one image in the first plurality of images being associated with each representation in a plurality of representations; receiving a response from the patient to an output requesting the patient to identify whether a first image and a second image in the first plurality of images depict the same emotion in response to completion of displaying at least one image in the first plurality of images; modifying the display of a second plurality of images displayed to the patient based at least on the response; a computing system configured to: system.

2. 2. The system of claim 1, wherein the computing system is further configured to sequentially display the first plurality of images, each image in the first plurality of images being displayed at an intensity on an intensity scale for each of the representations according to a plurality of levels.

3. 2. The system of claim 1, wherein the computing system is further configured to set a value N that defines a number of representational images between the first image and the second image in the first plurality of images.

4. 2. The system of claim 1, wherein the computing system is further configured to reset a value N that defines a number of representational images between the first image and the second image in the second plurality of images that are displayed to the patient.

5. The system of claim 1 , wherein the computing system is further configured to provide a performance score based on the response.

6. The system of claim 1 , wherein the computing system is further configured to display the second plurality of images with at least a partial multiple-image overlap with the first plurality of images.

7. 10. The system of claim 1, wherein the computing system is further configured to generate a regimen including a particular frequency or number of treatment sessions, the treatment sessions including a plurality of blocks at a plurality of levels, each block in the plurality of blocks sequentially displaying a respective plurality of representational images to the patient.

8. The system of claim 1 , wherein the computing system is further configured to determine an assessment of at least one symptom of the patient's psychiatric disorder based at least on the response.

9. 10. The system of claim 1, wherein the patient is in need of treatment for a psychiatric disorder, the psychiatric disorder comprising at least one of major depressive disorder (MDD), post-traumatic stress disorder (PTSD), generalized anxiety disorder, social phobia, obsessive-compulsive disorder, treatment-resistant depression, or borderline personality disorder.

10. 10. The system of claim 1, wherein the patient is using a pharmaceutical composition to treat a psychiatric disorder, and the pharmaceutical composition comprises at least one of a selective serotonin reuptake inhibitor (SSRI), a serotonin norepinephrine reuptake inhibitor (SNRI), a nootropic, ketamine and ketamine derivatives, an atypical antipsychotic, a benzodiazepine, a bupropion, a lamotrigine, lithium, a monoamine oxidase inhibitor, a tricyclic antidepressant, a valproic acid, a nefazodone, a trazodone, or a pramipexole.

11. 1. A method for displaying an image, comprising: a computing system displaying a first plurality of images to a patient, at least one image in the first plurality of images being associated with a respective representation in a plurality of representations; receiving, from the patient, a response to an output from the computing system in response to completion of displaying at least one of the first plurality of images, the output prompting the patient to identify whether a first image and a second image in the first plurality of images depict the same emotion; the computing system modifying a display of a second plurality of images displayed to the patient based at least on the response; and A method comprising:

12. 12. The method of claim 11, further comprising the computing system sequentially displaying the first plurality of images, each image in the first plurality of images being displayed at an intensity on an intensity scale for each of the representations according to a plurality of levels.

13. The method of claim 11 , further comprising the computing system setting a value N that defines a number of representational images between the first image and the second image in the first plurality of images.

14. 12. The method of claim 11, further comprising the computing system resetting a value N that defines a number of representational images between the first image and the second image in the second plurality of images that are displayed to the patient.

15. The method of claim 11 , further comprising the computing system providing a performance score based on the response.

16. The method of claim 11 , further comprising the computing system displaying the second plurality of images with at least a partial multiple image overlap with the first plurality of images.

17. 12. The method of claim 11, further comprising: generating a regimen including treatment sessions at either a particular frequency or number, the treatment sessions including a plurality of blocks at a plurality of levels, each block in the plurality of blocks sequentially displaying a respective plurality of representational images to the patient.

18. The method of claim 11 , further comprising the computing system determining an assessment of at least one symptom of the patient's psychiatric disorder based at least on the response.

19. 12. The method of claim 11, wherein the patient is in need of treatment for a psychiatric disorder, the psychiatric disorder comprising at least one of major depressive disorder (MDD), post-traumatic stress disorder (PTSD), generalized anxiety disorder, social phobia, obsessive-compulsive disorder, treatment-resistant depression, or borderline personality disorder.

20. 12. The method of claim 11, wherein the patient is using a pharmaceutical composition to treat a psychiatric disorder, and the pharmaceutical composition comprises at least one of a selective serotonin reuptake inhibitor (SSRI), a serotonin norepinephrine reuptake inhibitor (SNRI), a nootropic, ketamine and ketamine derivatives, an atypical antipsychotic, a benzodiazepine, a bupropion, a lamotrigine, lithium, a monoamine oxidase inhibitor, a tricyclic antidepressant, a valproic acid, a nefazodone, a trazodone, or a pramipexole.

Images