Systems and methods for analyzing brain activity

Abstract

A system and method for analyzing brain activity of a subject during transitions between brain states, wherein brain activity data, having data representative of transitions between brain states, is received from a brain monitoring system and processed to obtain values ​​of one or more transition parameters, the one or more transition parameters being parameters that characterize the transitions between brain states and that may be used as biomarkers for identifying psychiatric disorders.

Description

[Technical Field]

[0001] The present invention relates to the field of analyzing brain activity in a subject, and in particular to analyzing brain activity in a subject during transitions between brain states. [Background technology]

[0002] Currently, the diagnosis of mental disorders is based on a combination of psychiatric interviews, questionnaires, and the patient's own considerations. Therefore, the diagnosis of a patient is somewhat subjective. The accounts and responses given by the patient are colored by the patient's views and perceptions, and the interpretation of these accounts and responses into a diagnosis depends to some extent on the views and perceptions of the diagnosing clinician.

[0003] Furthermore, some patients may be unable to provide clinicians with the information necessary for a diagnosis as a result of their psychiatric disorder. Some psychiatric disorders affect a patient's memory, and anxious patients may stress out over their interviews with clinicians, resulting in the omission of important information. In some cases, clinicians may rely on information from third parties, but this may only provide a partial picture, and the information is subjective. Summary of the Invention [Problem to be solved by the invention]

[0004] Therefore, there is a need for objective diagnostic tools to accurately and reliably diagnose mental disorders. [Means for solving the problem]

[0005] The invention is defined by the claims.

[0006] According to an example according to one aspect of the present invention, a processing system for analyzing brain activity of a subject during transitions between brain states is provided.

[0007] The processing system is configured to receive, from a brain monitoring system for monitoring brain activity, brain activity data of a subject acquired during a transition from a first brain state of the subject to a second brain state of the subject, wherein at least one of the first brain state and / or the second brain state is a sleep state, and process the brain activity data to obtain values ​​of one or more transition parameters of the brain activity data, the transition parameters being parameters representing the transition.

[0008] The one or more transition parameters may include transition timing, transition duration, transition stability, and / or transition Frequency of The transition timing is the amount of time between when a transition is detected in the subject and when a discernible neural network change is first detected in the subject's brain activity data in response to a change in sleep state.

[0009] This system can be used to aid in the diagnosis of mental disorders. The inventors have recognized that brain activity differs in different states. For example, resting-state waking activity differs from brain activity during sleep.

[0010] The inventors have further recognized that psychiatric patients generally exhibit distinct, attenuated, or delayed transitions between different brain states, and therefore, parameters derived from monitoring a subject's brain activity during transitions can be used as biomarkers to assess or identify any psychiatric disorder in the patient.

[0011] The processing system may be further configured to display the obtained values ​​of the parameters, which provides a clinician with useful clinical information for assessing the subject's state and / or condition, and in particular aids in assessing whether the subject is associated with a psychiatric disorder and / or condition.

[0012] The brain monitoring system may be a magnetic resonance imaging (MRI) scanner. However, it will be appreciated that the brain monitoring system may alternatively or additionally use other brain scanning techniques, such as electroencephalography (EEG) and / or positron emission tomography (PET). In some embodiments, the brain monitoring system may use a combination of different brain scanning techniques, for example, a combination of EEG and MRI.

[0013] Thus, the brain activity data may be MRI data, EEG data, PET data, or any combination thereof. Of course, the type of brain activity data will depend on the type of brain monitoring system used.

[0014] The processing system may be further configured to process values ​​of the one or more transition parameters of the brain activity data and corresponding values ​​of the one or more transition parameters of the subjects in the plurality of groups to identify which subjects in the plurality of groups are most similar.

[0015] In this way, the brain activity of a subject during a transition can be compared to the brain activity of healthy subjects and subjects with a psychiatric disorder to determine which subjects' brain activity is most similar. In particular, the subjects (in multiple groups of subjects) can be grouped based on at least the psychiatric disorder (or lack thereof) in each subject. Thus, each group of subjects can represent a group of subjects sharing the same (particular) psychiatric disorder and / or lack of (particular) psychiatric disorder.

[0016] In some embodiments, the step of processing the values ​​of the one or more transition parameters of the brain activity data for the multiple groups of subjects and the corresponding values ​​of the one or more transition parameters further comprises using one or more characteristics of the subjects to identify which of the multiple groups the subject most closely resembles.

[0017] The transitions a subject experiences may depend on factors other than those used to group the groups of subjects. For example, subjects may be grouped based on psychiatric disorders, but the transitions a subject experiences may also be affected by the use of sleep medications. Accounting for these factors reduces the likelihood of providing information that would result in a healthy subject being misdiagnosed with a psychiatric disorder or a subject with a psychiatric disorder being misdiagnosed as healthy.

[0018] The one or more characteristics of the subject may include at least one of the subject's sleep medication use, age, sex, left-handedness or right-handedness, past sleep quality, and / or chronotype. It is recognized that each of these parameters may also affect or influence the patient's transitions between sleep states. Therefore, it is advantageous to take such characteristics into account when assessing the patient's mental state.

[0019] For example, sleeping pills can put subjects to sleep faster, and taking this into account reduces the likelihood that the group whose brain activity is determined to be most similar to the subjects' will not be affected by the sleeping pills taken by the subjects.

[0020] The one or more characteristics may be considered by subdividing each of a plurality of groups of the subject according to the one or more characteristics, and the one or more transition parameters of the brain activity data and corresponding values ​​of the one or more transition parameters of the subdivided groups having the same one or more characteristics as the subject may be processed to identify which of the subdivided groups the subject most closely resembles.

[0021] In some embodiments, processing the values ​​of the one or more transition parameters of the brain activity data and the corresponding values ​​of the one or more transition parameters to identify which of the plurality of groups of subjects are most similar comprises inputting the brain activity data and / or the values ​​of the one or more transition parameters into an artificial neural network.

[0022] The artificial neural network can be trained using a training algorithm configured to receive an array of training inputs and known outputs, where the training inputs include brain activity data and / or values ​​of one or more transition parameters during a transition from a first brain state to a second brain state, and the known outputs include a determination of which of a plurality of groups of subjects the brain activity data belongs to. In other words, the artificial neural network can classify the brain activity data.

[0023] In this way, an artificial neural network can be trained to identify features in the transitions that represent particular groups of subjects and can be used to distinguish between different groups, which may include, for example, a group of healthy subjects and one or more groups of subjects with a psychiatric disorder.

[0024] The transition may be one of a transition from a wakefulness state to a sleepful state, a transition from a sleepful state to a wakefulness state, or a transition from a first sleep state to a second, different sleep state.

[0025] Different neural networks are involved in different sleep and wakefulness states, and therefore the transitions between wakefulness and sleep states and between different sleep states can be used to obtain transition parameters in the brain activity data.

[0026] The one or more transition parameters may comprise at least one of transition timing, transition duration, transition stability, transition frequency, and / or one or more networks active during the transition.

[0027] The transition timing may be defined as the time between the instant of the detected transition and the instant the neural network associated with the subject's first brain state begins to weaken in the brain activity image data.

[0028] Transition duration may be defined as the time duration from when the neural network associated with a first brain state of the subject begins to weaken to when the neural network associated with a second brain state of the subject is fully established.

[0029] Transitional stability may be defined as the number of transitions between various neural networks between the moment when the neural network associated with a subject's first brain state begins to weaken and the moment when the neural network associated with the subject's second brain state is fully established.

[0030] Transition frequency can be defined as the number of times that transitions between different brain states occur within a particular period of time.

[0031] Subjects with a psychiatric disorder can be expected to have delayed timing of transitions, longer transition durations, higher transition instability, more frequent transitions, and different active neural networks during transitions compared to healthy subjects. The specific differences in these parameters exhibited by a subject will depend on the particular psychiatric disorder the subject has.

[0032] The multiple groups of subjects may include at least a first group and a second group, where the first group includes healthy subjects and the second group includes subjects with a (particular) mental disorder. In this way, the system can be used to determine whether the brain activity of the subjects more closely resembles that of healthy subjects or subjects with a (particular) mental disorder.

[0033] In some embodiments, the processing system is configured to continue receiving the subject's brain activity data until a predetermined number of transitions have been recorded, and to obtain values ​​of one or more transition parameters of the brain activity data for each detected transition.

[0034] This can improve the reliability of the obtained values ​​of one or more parameters.

[0035] Also proposed is a system comprising a sensing and monitoring unit adapted to detect a transition of a subject from a first brain state to a second brain state of the subject, wherein at least one of the first brain state and / or the second brain state is a sleep state, and a processing system as mentioned above further configured to receive information corresponding to the detected transition from the sensing and monitoring unit.

[0036] The sensing and monitoring unit may be used to accurately detect a subject's transitions between brain states. Information corresponding to a detected transition may include, for example, the time at which the transition is detected.

[0037] The sensing and monitoring unit may be adapted to detect the transition based on at least one of brain activity information, cardiorespiratory information, cardiolithography information, respiratory rate, behavioral information, and / or information corresponding to the subject's performance on a repetitive task.

[0038] The sensing and monitoring unit may obtain brain activity information from a brain monitoring system or may directly image the ventral preoptic nucleus of the hypothalamus. Cardiorespiratory information may be obtained from a PPG sensor or a vital signs camera. Respiration rate may be measured using radar technology. Behavioral information may be obtained from a vital signs camera. Information corresponding to the subject's performance on a repetitive task may include reaction time and / or percentage of correct answers.

[0039] In some embodiments, the system further comprises a sleep regulation unit adapted to induce a change in the brain state of the subject.

[0040] The sleep adjustment unit may be adapted to induce sleep in the subject, to awaken the subject from sleep, or to induce a change from a first sleep state to a second, different sleep state. Inducing a change in the subject's brain state ensures that the subject undergoes the transition being recorded quickly. If the subject is left to fall asleep, awaken, or change from one sleep state to another without assistance, this may take a long time. Therefore, the use of a sleep adjustment unit provides a more controlled environment for obtaining values ​​of transition parameters for the patient.

[0041] The sleep regulation unit may, for example, induce sleep by using haptic feedback and / or by generating rhythmic sounds. The sleep regulation unit may also be adapted to induce sleep by reducing the subject's breathing rate.

[0042] In some embodiments, the sleep adjustment unit is adapted to alternately induce sleep in the subject and wake the subject from sleep for a predetermined number of wake / sleep cycles.

[0043] In this way, a large number of transitions can be efficiently recorded. The sleep regulation unit may be adapted to wake a sleeping subject once the neural network associated with the sleep state is fully established, and to induce sleep in an awake subject once the neural network associated with the awake state is fully established.

[0044] According to another aspect of the present invention, a computer-implemented method for analyzing brain activity of a subject during transitions between brain states is provided.

[0045] The computer-implemented method includes receiving, from a brain monitoring system for monitoring brain activity, brain activity data of a subject acquired during a transition of the subject from a first brain state to a second brain state, where at least one of the first brain state and / or the second brain state is a sleep state; and processing the brain activity data to obtain values ​​of one or more transition parameters of the brain activity data, where the transition parameters are parameters representative of a detected transition.

[0046] Also proposed is a computer program product comprising computer program code means which, when executed on a computing device having a processing system, causes the processing system to perform all of the steps of the method described above.

[0047] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter.

[0048] Embodiments of the present invention will now be described in detail with reference to the accompanying drawings. [Brief explanation of the drawings]

[0049] [Figure 1] 1 illustrates a system for analyzing a subject's brain activity during transitions between brain states, according to an embodiment of the present invention. [Figure 2] 1 illustrates a method for analyzing a subject's brain activity during transitions between brain states, according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0050] The present invention will now be described with reference to the drawings.

[0051] It will be understood that the detailed description and specific examples, while indicating exemplary embodiments of the devices, systems, and methods, are for purposes of illustration only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the devices, systems, and methods of the present invention will become better understood from the following description, appended claims, and accompanying drawings. It will be understood that the figures are schematic only and are not drawn to scale. It will also be understood that the same reference numerals will be used throughout the figures to indicate the same or similar parts.

[0052] In accordance with the concepts of the present invention, a system and method are proposed for analyzing brain activity of a subject during transitions between brain states. Brain activity data, including data representative of transitions between brain states, are received from a brain monitoring system and processed to obtain values ​​of one or more transition parameters. The one or more transition parameters are parameters that characterize the transitions between brain states and can be used as biomarkers for identifying psychiatric disorders.

[0053] Embodiments are based, at least in part, on the recognition that transitions between brain states are different, attenuated, or delayed in subjects with psychiatric disorders compared to healthy subjects, and therefore parameters representative of transitions between brain states can act as biomarkers of psychiatric disorders, allowing for more accurate and reliable diagnosis of psychiatric disorders.

[0054] Exemplary embodiments may be employed, for example, in clinical decision support systems.

[0055] 1 shows a system 100 for analyzing a subject's brain activity during transitions between brain states, comprising a processing system 110, according to one embodiment of the present invention. The processing system 110 itself is one embodiment of the present invention.

[0056] The processing system 110 is in communication with a brain monitoring system 120. In FIG. 1, the brain monitoring system 120 is an MRI scanner. While MRI scanners are frequently used to visualize activity in neural networks, any brain monitoring system suitable for monitoring brain activity can be used. For example, the brain monitoring system may be a PET scanner or an EEG machine. The brain monitoring system may include two or more brain monitoring devices. For example, the brain monitoring system may include an MRI scanner and an EEG machine.

[0057] The brain monitoring system 120 acquires brain activity data 125 of the subject during a time period that includes at least one transition from a first brain state to a second brain state of the subject. At least one of the first and second brain states is a sleep state, i.e., a brain state that corresponds to a sleep stage of the subject, e.g., stage 1 sleep, stage 2 sleep, slow-wave sleep, or REM sleep. For example, the transition may be from a wakefulness state to a sleep state, from a sleep state to a wakefulness state, or from a first sleep state to a second, different sleep state.

[0058] Brain activity data 125 includes information indicative of the activity of multiple neural networks over time, including at least one transition between brain states. Different neural networks are active in different brain states, and thus the activity of the neural networks can be used to monitor transitions between brain states.

[0059] The processing system 110 receives the brain activity data 125 from the brain monitoring system 120 and processes the brain activity data to obtain values ​​of one or more transition parameters of the brain activity data. The transition parameters are parameters that describe transitions between brain states. Because subjects with a mental disorder exhibit different transitions between brain states than healthy subjects, the parameters that describe transitions between brain states are different for healthy subjects and subjects with a mental disorder.

[0060] The brain activity data can indicate the sleep state of the subject. In particular, the subject has different neural activity levels depending on the sleep state. Therefore, it is possible to detect the subject's current sleep state and detect the transition between one sleep state and another sleep state. Mechanisms for detecting sleep states and / or sleep state transitions are well known to those skilled in the art, for example, as described in international patent applications having WO 2016 / 193030 A1, WO 2015 / 118415 A1, or WO 2013 / 061185 A1.

[0061] Processing system 120 can use additional parameters of the patient (e.g., other physiological signals) to derive transition parameters. Thus, processing system 120 can use information received from sensing and monitoring unit 140. Unit 140 can be used to detect physical indicators of changes in transition state (e.g., changes in heart rate, breathing rate, etc.), and brain activity data can be used to detect neurological indicators of changes in sleep state (e.g., when active neural networks change).

[0062] The one or more transition parameters may include, for example, transition timing, transition duration, transition stability, transition frequency, and / or one or more networks active during the transition. Other suitable transition parameters will be apparent to those skilled in the art.

[0063] The transition timing is the length of time between when a transition is (first) detected in a subject (e.g., by the sensing and monitoring unit 140) and when a discernible neural network change is first detected in the subject's brain activity data 125 in response to a change in sleep state. In other words, the transition timing may be the delay between when the subject's sleep state begins to change (as indicated by a physiological signal) and when the brain activity data first indicates a change in neural network activity. The change in neural network may be indicated, for example, by a neural network associated with a first brain state beginning to weaken.

[0064] Various methods for detecting a transition in a subject are contemplated and are described in more detail below. The onset of a transition in brain activity data can be considered to be the moment when activity in the neural network associated with a first brain state begins to become weaker.

[0065] Subjects with psychiatric disorders tend to experience delayed transitions between brain states compared to healthy subjects, and therefore, long transition timings may be indicative of a psychiatric disorder. Transition timings on the order of tens of seconds can be considered long transition timings, e.g., greater than 10 s, e.g., greater than 50 s.

[0066] Transition duration is the length of time that a transition takes in brain activity data 125. It may be measured as the time from the moment activity in the neural network associated with a first brain state begins to weaken to the moment the network associated with a second brain state is fully established.

[0067] The exact mechanism by which "weaker" and "fully established" neural networks are detected may depend on the form of the brain activity data.

[0068] Consider a scenario in which brain activity data is represented by a stream of (2D / 3D) images, such as MR images. The strength of a neural network can be defined by the number of voxels (representing a particular neural network) that display as active in the brain monitoring data, e.g., the number of voxels that identify ongoing brain activity within a particular neural network. A neural network can be considered to be weak when the (average) number of voxels associated with the neural network that display as active decreases by more than a predetermined amount and / or percentage, or falls below some predetermined threshold. A neural network can be considered to be fully established when the number of voxels associated with the neural network that display as active exceeds some predefined threshold.

[0069] In another example, the brain activity data may include EEG data, and the neural network may be considered active if a particular combination of signals (e.g., representing activity within the neural network) meets some predetermined criteria.

[0070] Transitions between brain states can be expected to take longer in subjects with a psychiatric disorder than in healthy subjects, and therefore, a long transition period can indicate that the subject has a psychiatric disorder.

[0071] Transition stability is a measure of how many transitions occur between neural networks during the transition from a first brain state to a second brain state. For example, transition stability may be measured as the number of transitions between neural networks in brain activity data 125 between the instant when activity in the neural network associated with the first brain state begins to weaken and the instant when the network associated with the second brain state is fully established.

[0072] Subjects with psychiatric disorders generally exhibit less stable transitions between brain states than healthy subjects, and therefore a large number of transitions between neural networks during transitions between brain states may indicate that the subject has a (particular) psychiatric disorder.

[0073] Transition frequency is a measure of the number of times a predetermined transition occurs within a set period of time. For example, in the case of a transition from wakefulness to sleep, the number of times sleep onset is detected within a certain period of time, for example, within 5 minutes, in the brain activity data 125 is detected.

[0074] Specific neural networks that are active during transitions between brain states can also be used as biomarkers for psychiatric disorders. In particular, brain networks that are active during transitions between sleep states are thought to be different between patients with a particular psychiatric disorder and patients without the same psychiatric disorder. For example, if a subject's brain activity data shows that a neural network that is not typically associated with a particular type of transition is active during that transition for that subject, this can be used to indicate that the subject has a psychiatric disorder.

[0075] In some embodiments, system 100 may further include a display device 130 in communication with processing system 110. Display device 130 may receive and display the obtained values ​​of one or more transition parameters from processing system 110. A clinician may use the displayed values ​​to aid in diagnosing a subject with a psychiatric disorder, for example, by comparing the displayed values ​​to reference values.

[0076] Alternatively, or in addition, the processing system 110 may process the obtained values ​​of one or more transition parameters to determine whether the obtained values ​​indicate that the subject is healthy or has a mental disorder. The processing system may also determine which one or more mental disorders are indicated by the obtained values.

[0077] The processing system 110 determines what the acquired value(s) indicate about the mental health of the subjects by processing the acquired value(s) and corresponding values ​​of one or more transition parameters for the multiple groups of subjects for which the acquired value(s) for the subjects most resemble the acquired value(s). The processing system can send an indication to the display device 130 of which group of subjects is most similar.

[0078] For example, the processing system 110 can compare the obtained values ​​of one or more transition parameters for the subject with corresponding values ​​for a group of healthy subjects and corresponding values ​​for a group of subjects with a mental disorder to determine whether the obtained values ​​more closely resemble the corresponding values ​​for the group of healthy subjects or the corresponding values ​​for the group of subjects with a mental disorder. In this manner, the processing system can determine whether the obtained values ​​indicate that the subject is healthy or has a mental disorder.

[0079] The processing system 110 can compare the obtained value for the subject with corresponding values ​​for a group of healthy subjects and multiple groups of subjects with a mental disorder, each group consisting of subjects with a different mental disorder. For example, the obtained value for the subject can be compared with corresponding values ​​for a group of subjects with a first mental disorder and corresponding values ​​for a group of subjects with a second, different mental disorder. The multiple groups of subjects may also include a group of subjects with a particular combination of two or more mental disorders. In this way, the processing system can determine which mental disorder (or disorders) is indicated by the obtained value.

[0080] In some embodiments, the processing system 110 can identify which of multiple groups the subject most closely resembles by using a lookup table containing values ​​of the transition parameter, the group of the subject corresponding to that value.

[0081] In some embodiments, the processing system 110 can provide the brain activity data and / or the transition parameter values ​​to the artificial neural network 115 to identify which of multiple groups the subject most closely resembles.

[0082] The structure of an artificial neural network (or simply a neural network) is inspired by the human brain. A neural network is composed of layers, each layer containing multiple neurons. Each neuron contains a mathematical operation. In particular, each neuron may comprise a different weighted combination of a single type of transformation (e.g., the same type of transformation, such as sigmoid, but with different weightings). In the process of processing input data, the mathematical operation of each neuron is performed on the input data to generate a numerical output, and the output of each layer in the neural network is sequentially fed to the next layer. The final layer provides the output.

[0083] Methods for training machine learning algorithms are well known. Typically, such methods involve obtaining a training dataset including training input data entries and corresponding training output data entries. An initialized machine learning algorithm is applied to each input data entry to generate a predicted output data entry. The error between the predicted output data entry and the corresponding training output data entry is used to modify the machine learning algorithm. This process can be repeated until the error converges and the predicted output data entries are sufficiently similar to the training output data entries (e.g., within ±1%). This is commonly known as a supervised learning technique.

[0084] For example, the mathematical operations (weightings) of each neuron can be modified until the error converges. Known methods for modifying neural networks include gradient descent, backpropagation algorithms, etc.

[0085] The training input data entries for the artificial neural network 115 correspond to example brain activity data and / or values ​​of transition parameters during a transition from a first brain state to a second brain state, and the training output data entries correspond to identification information of which of a plurality of groups of subjects the brain activity data belongs to.

[0086] Other methods for determining which of a plurality of groups a subject most closely resembles based on the value of one or more transition parameters will be apparent to those skilled in the art.

[0087] In some embodiments, the processing system may be adapted to take into account one or more characteristics of the subject that may affect the transitions between brain states experienced by the subject. Characteristics that may affect the transitions between brain states will be apparent to those skilled in the art and may include sleep medication use (or other drug use), age, sex, left- or right-handedness, previous sleep quality, and / or chronotype.

[0088] For example, each of the multiple groups of subjects may be further subdivided according to one or more characteristics. The values ​​obtained for one or more transition parameters for the subject can then be compared to corresponding values ​​for subdivided groups of subjects having the same one or more characteristics. For example, for a subject taking a particular medication, the values ​​obtained for one or more transition parameters may be compared to corresponding values ​​for a group of healthy subjects taking the same medication as the subject, a group of subjects with a first psychiatric disorder taking the same medication, a group of subjects with a second psychiatric disorder taking the same medication, etc. In this way, the determination of whether a subject most closely resembles a healthy subject, a subject with a first psychiatric disorder, a subject with a second psychiatric disorder, etc. is less likely to be affected by the effect of medication on the subject's transitions between brain states.

[0089] In some embodiments, system 100 is configured to acquire and process brain activity data 125 across multiple transitions between brain states. For example, processing system 110 may be configured to continue receiving brain activity data from brain monitoring system 120 until a predetermined number of transitions have been recorded. The predetermined number of transitions to record may be a predetermined number of a particular type of transition. For example, processing system 110 may be configured to continue receiving brain activity data until a predetermined number of transitions from a wakefulness state to a sleep state have been recorded. Alternatively, the predetermined number of transitions to record may be a predetermined number of various different transitions. For example, processing system 110 may be configured to continue receiving brain activity data until a predetermined number of each of a plurality of transition types, or a predetermined number of total transitions, have been recorded.

[0090] It will be clear to one skilled in the art how to select an appropriate predetermined number of transitions to record. For example, the predetermined number may be determined using power analysis to find the number of transitions that can have a significant effect.

[0091] The processing system 110 may obtain values ​​of one or more transition parameters for each of a plurality of transitions. The processing system may determine a mean value (e.g., a median, mean, or mode value) for each of the one or more transition parameters based on the obtained values. The processing system may compare the mean value to corresponding values ​​for multiple groups of subjects to determine the group to which the subjects are most similar.

[0092] In some embodiments, system 100 may further comprise a sensing and monitoring unit 140. The sensing and monitoring unit detects transitions between a first brain state and a second brain state of the subject. For example, the sensing and monitoring unit may detect when the subject falls asleep, when the subject wakes up, and / or when the subject experiences a transition from one sleep stage to another.

[0093] Methods for detecting transitions between brain states will be apparent to those skilled in the art. For example, the sensing and monitoring unit 140 can detect transitions based on brain activity data. This may be brain activity data 125 acquired by the brain monitoring system 120 or brain activity data acquired from another brain monitoring device. The brain activity data used to detect transitions may include, for example, imaging data of the ventral preoptic nucleus of the hypothalamus of the subject. The sensing and monitoring unit can detect transitions between brain states based on cardiopulmonary information, for example, by using a PPG sensor and / or a vital signs camera. The sensing and monitoring unit can detect transitions between brain states based on cardiac aristography information. The sensing and monitoring unit can detect transitions between brain states based on respiratory rate. The respiratory rate may be measured, for example, using radar technology. The sensing and monitoring unit can detect transition-based behavioral information, such as eye movement, eye blinks, arousal measurements, reaction times, and responses to stimuli. The behavioral information may be obtained, for example, using a vital signs camera. In the case of a transition between a wakefulness state and a sleep state, the sensing and monitoring unit may detect the transition using an indirect method, such as measuring information corresponding to the subject's performance on a repetitive task. For example, the sleep and monitoring unit may monitor the subject's reaction time or percentage of correct answers while the subject performs the repetitive task.

[0094] The sensing and monitoring unit 140 is in communication with the processing system 110. The processing system receives information from the sensing and monitoring unit corresponding to a transition detected by the sensing and monitoring unit. The information may include, for example, a time at which the transition is detected by the sensing and monitoring unit. The information may further include additional information about the detected transition, such as a transition type. The time at which the transition is detected by the sensing and monitoring unit may be used by the processing system to determine the transition timing of the detected transition.

[0095] As previously described, the processing system 110 may further use information from the sensing and monitoring unit 140 to determine values ​​of one or more transition parameters. Thus, processing the brain activity data to determine values ​​of one or more transition parameters may include processing the brain activity data and information from the sensing and monitoring unit to determine values ​​of the one or more transition parameters.

[0096] For example, one of the transition parameter(s) may be differential transition timing between sleep state transitions indicated by the sensing and monitoring unit 140 and neural network changes (from brain activity data) indicative of transitions between sleep states. A delay between a detected sleep state transition and a corresponding transition in neuronal state may be indicative of a psychiatric disorder.

[0097] In some embodiments, transitions between brain states in a subject can occur spontaneously, for example, if a subject is undergoing a brain scan without engaging in sensory and / or cognitive tasks, the subject may spontaneously fall asleep.

[0098] In other embodiments, system 100 may further comprise a sleep adjustment unit 150 adapted to induce a change in a brain state in the subject. For example, the sleep adjustment unit may induce sleep in the subject, awaken the subject from sleep, and / or induce a change in the subject from a first sleep state to a second, different sleep state.

[0099] Methods for inducing brain state changes will be apparent to those skilled in the art and may depend on the type of transition being induced. For example, the sleep adjustment unit 150 can induce sleep using tactile feedback, such as the use of a respiratory balloon on the subject's hand or chest. The subject may be asked to breathe so that the respiratory balloon expands and expands as the respiratory balloon deflates. Finger PPG can be used to measure the subject's respiratory rate and the measured respiratory rate supplied to the respiratory balloon. The respiratory balloon begins to expand and contract at the subject's current respiratory rate, and the subject's respiratory rate can be slowly decreased via a feedback loop of the finger PPG signal. A decrease in respiratory rate generally induces relaxation in the subject, increasing the likelihood of the subject falling asleep. The sleep adjustment unit can use sound to induce sleep in the subject, for example, by generating a rhythmic ticking sound. The sleep adjustment unit can awaken the subject from sleep, for example, by using sound, tactile feedback, and / or changes in light levels. The sleep adjustment unit may induce a change in the subject from a first sleep state to a second, different sleep state through the use of sensory stimuli such as light, or gentle auditory or tactile signals.

[0100] Other mechanisms for facilitating sleep state transitions can use techniques such as those suggested by international patent applications having publication numbers WO 2015 / 087188 A1 or WO 2018 / 104309 A1.

[0101] In some embodiments, the sleep adjustment unit 150 alternately induces sleep in the subject and wakes the subject from sleep. The sleep adjustment unit may continue to induce sleep and wake for a predetermined number of wake / sleep cycles to allow multiple transitions between brain states to be monitored. The predetermined number of wake / sleep cycles may be equal to the predetermined number of transitions recorded by the system 100 described above.

[0102] When the sleep adjustment unit 150 induces multiple transitions between brain states in a subject, it is desirable to leave sufficient time between induced transitions for a first transition to complete before a second transition is induced. For example, if the sleep adjustment unit induces sleep in a subject and then awakens the subject from sleep, it is desirable that the sleep adjustment unit not begin awakening the subject until the neural networks associated with the sleep states are fully established in the patient, and the brain activity data associated with the awakening-to-sleep transition contains all the information necessary for the processing system 110 to derive values ​​for one or more transition parameters.

[0103] The sleep adjustment unit 150 may be configured to induce the transition at predetermined time intervals, such as every 5 minutes, 10 minutes, or 20 minutes. The predetermined time interval may be greater than or equal to 5 minutes, such as greater than or equal to 10 minutes. Alternatively, the sleep adjustment unit may be configured to induce the transition upon instruction from the processing system 110. For example, the processing unit may determine, based on the brain activity data 125 and / or information from the sensing and monitoring unit 140, that a sleep network is fully established in the subject and send an instruction to the sleep adjustment unit to wake the subject.

[0104] In one example, a subject whose brain activity is monitored by brain monitoring system 120 is assisted to fall asleep by sleep adjustment unit 150. Sensing and monitoring unit 140 detects when the subject falls asleep and sends this information to processing system 110. Once the neural networks associated with sleep states are fully established in the subject, the sleep adjustment unit awakens the subject. The sensing and monitoring unit detects when the subject awakens. This process continues with the brain monitoring system monitoring the subject's brain activity throughout the process until a predetermined number of sleep instances have been recorded. Once the process is complete, processing system 110 processes brain activity data 125 acquired by the brain monitoring system to obtain values ​​of one or more transition parameters for each sleep instance recorded. Based on the obtained values, the processing system determines multiple groups of subjects that the subject is most similar to and outputs this information to display device 130.

[0105] FIG. 2 illustrates a computer-implemented method 200 for analyzing a subject's brain activity during transitions between brain states, according to one embodiment of the present invention.

[0106] Method 200 begins at step 210, where brain activity data is received from a brain monitoring system. The brain activity data includes data acquired during a subject's transition from a first brain state to a second brain state, where at least one of the first brain state and / or the second brain state is a sleep state.

[0107] In step 220, the brain activity data is processed to obtain values ​​of one or more transition parameters of the brain activity data. A transition parameter is a parameter that describes a transition.

[0108] The method 200 may also include step 230 of processing values ​​of one or more transition parameters of the brain activity data and corresponding values ​​of one or more transition parameters of the plurality of subject groups to identify which subjects of the plurality of groups are most similar.

[0109] It will be understood that the disclosed methods are computer-implemented methods, and therefore the concept of a computer program comprising code means for performing any of the described methods when the program is run on a processing system is also proposed.

[0110] As described above, the system utilizes a processor to perform data processing. The processor can be implemented in numerous ways using software and / or hardware to perform the various functions required. The processor typically uses one or more microprocessors that can be programmed using software (e.g., microcode) to perform the necessary functions. The processor can also be implemented as a combination of dedicated hardware to perform some functions and one or more programmed microprocessors and associated circuitry to perform other functions.

[0111] Examples of circuitry that may be used in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field programmable gate arrays (FPGAs).

[0112] In various implementations, a processor may be associated with one or more storage media, such as volatile and non-volatile computer memory, including RAM, PROM, EPROM, and EEPROM. The storage media may be encoded with one or more programs that, when executed on the one or more processors and / or controllers, perform the necessary functions. The various storage media may be fixed within the processor or controller, or may be portable, such that the one or more programs stored thereon can be loaded into the processor.

[0113] Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite articles "a" or "an" do not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be interpreted as limiting the scope.

Claims

1. 1. A processing system for analyzing brain activity of a subject during transitions between brain states, the processing system comprising: receiving, from a brain monitoring system for monitoring brain activity, brain activity data of the subject acquired during a transition of the subject from a first brain state to a second brain state, wherein at least one of the first brain state and / or the second brain state is a sleep state; receiving, from a sensing and monitoring unit adapted to detect a transition of the subject from a first brain state to a second brain state of the subject, information corresponding to the detected transition; processing the brain activity data and information from the sensing and monitoring unit to obtain values ​​for one or more transition parameters of the brain activity data, the transition parameters being parameters indicative of the transition; The one or more transition parameters are: a transition timing, the transition timing being the length of time between a time when a discernible neural network change is first detected in the subject's brain activity data in response to a change in sleep state and a time when a transition is detected in the subject by the sensing and monitoring unit; a transition duration, the transition duration being the time duration from the moment a neural network associated with a first brain state of the subject starts to become weaker to the moment a neural network associated with a second brain state is fully established; transition stability, the transition stability being the number of transitions between neural networks in the brain activity data from the moment activity in the neural network associated with the first brain state starts to become weaker until the moment the network associated with the second brain state is fully established; and / or a transition frequency, the transition frequency being a measure of the number of times a transition from the first brain state to the second brain state occurs within a set period of time; and processing values ​​of one or more transition parameters of the brain activity data and corresponding values ​​of one or more transition parameters of a plurality of groups of the subject to identify to which of the plurality of groups the subject is most similar, the plurality of groups of subjects comprising at least one first group and a second group, the first group comprising healthy subjects and the second group comprising subjects with a psychiatric disorder; Processing system.

2. 2. The processing system of claim 1, wherein processing the values ​​of one or more transition parameters of the brain activity data and corresponding values ​​of one or more transition parameters for the plurality of groups of the subject further uses one or more characteristics of the subject to identify which of the plurality of groups the subject most closely resembles.

3. processing the values ​​of one or more transition parameters of the brain activity data and corresponding values ​​of the one or more transition parameters for a plurality of groups of the subject to identify to which of the plurality of groups the subject most closely resembles; inputting said brain activity data and / or said one or more transition parameter values ​​into an artificial neural network.

3. The processing system according to claim 1, further comprising:

4. 4. The processing system of claim 3, wherein the artificial neural network is trained using a training algorithm configured to receive an array of training inputs and known outputs, the training inputs comprising values ​​of one or more transition parameters and / or brain activity data during a transition from a first brain state to a second brain state, and the known outputs comprising a determination of which of a plurality of groups the brain activity data belongs to for the subject.

5. The transition is transition from wakefulness to sleep, Transition from sleep to wakefulness, or Transition from a first sleep state to a second, different sleep state 5. The processing system according to claim 1, wherein the processing system is one of:

6. 6. The processing system of claim 1, wherein the one or more transition parameters further comprise the one or more networks that are active during the transition.

7. 7. The processing system of claim 1 , wherein the processing system is configured to continue receiving brain activity data of the subject until a predetermined number of transitions are recorded, and to obtain values ​​for one or more transition parameters of the brain activity data for each detected transition.

8. a sensing and monitoring unit adapted to detect a transition of the subject from a first brain state to a second brain state, wherein at least one of the first brain state and / or the second brain state is a sleep state; A processing system according to any one of claims 1 to 7; A system having:

9. 10. The system of claim 8, wherein the sensing and monitoring unit is adapted to detect transitions based on at least one of brain activity information, cardiorespiratory information, cardioverter-respiratory information, cardioverter-ballistic information, respiratory rate, behavioral information, and / or information corresponding to the subject's behavior on a repetitive task.

10. 10. The system of claim 8 or 9, wherein the system further comprises a sleep regulation unit adapted to induce a change in the brain state of the subject.

11. The system of claim 10 , wherein the sleep adjustment unit is adapted to alternately induce sleep in the subject and wake the subject from sleep for a predetermined number of wake / sleep cycles.

12. 1. A computer-implemented method for analyzing brain activity of a subject during transitions between brain states, the computer-implemented method comprising: receiving, from a brain monitoring system for monitoring brain activity, brain activity data of the subject acquired during a transition of the subject from a first brain state to a second brain state, wherein at least one of the first brain state and / or the second brain state is a sleep state; receiving, from a sensing and monitoring unit adapted to detect a transition of the subject from a first brain state to a second brain state of the subject, information corresponding to the detected transition; processing the brain activity data and information from the sensing and monitoring unit to obtain values ​​of one or more transition parameters of the brain activity data, the transition parameters being parameters representative of the transitions; and The one or more transition parameters are: a transition timing, the transition timing being the length of time between a time when a neural network change discernible in the subject's brain activity data is first detected in response to the sleep state change and a time when a transition is detected in the subject; a transition duration, the transition duration being the time duration from the moment a neural network associated with a first brain state of the subject starts to become weaker to the moment a neural network associated with a second brain state is fully established; transition stability, the transition stability being the number of transitions between neural networks in the brain activity data from the moment activity in the neural network associated with the first brain state starts to become weaker until the moment the network associated with the second brain state is fully established; and / or a transition frequency, the transition frequency being a measure of the number of times a transition from the first brain state to the second brain state occurs within a set period of time; and processing values ​​of one or more transition parameters of the brain activity data and corresponding values ​​of one or more transition parameters of a plurality of groups of the subject to identify to which of the plurality of groups the subject is most similar, the plurality of groups of subjects comprising at least one first group and a second group, the first group comprising healthy subjects and the second group comprising subjects with a psychiatric disorder; Computer-implemented methods.

13. 13. A computer program product comprising computer program code means, when executed on a computing device having a processing system, that causes said processing system to perform all of the steps of the method of claim 12.

Images