Functional magnetic resonance imaging

Abstract

It is proposed to obtain information about the stress or anxiety level of a subject within a predetermined time period before, during and / or after the capturing time of an fMRI scan image. Using this information, embodiments can provide additional / complementary information which can assist, aid or otherwise improve the interpretation of said fMRI scan image.

Description

Technical Field

[0001] This invention relates to magnetic resonance imaging, and more particularly, to the interpretation of functional magnetic resonance imaging (fMRI) scans. Background Technology

[0002] Functional magnetic resonance imaging (fMRI) is commonly used to study brain activity in human subjects. Analysis of fMRI scans typically seeks to identify correlations between brain activation in the subject and specific behavioral tasks (i.e., memory tasks) performed by the subject during the scan (i.e., at or during the capture time of the MRI scan). fMRI scan analysis also aims to discover correlations with specific cognitive states (such as memory and recognition) induced in the subject.

[0003] Therefore, the diagnosis and treatment of the subject can be based on the interpretation of information collected during fMRI exploration. Summary of the Invention

[0004] This invention is defined by the claims.

[0005] According to an example of one aspect of the invention, a method is provided for generating modified fMRI scan images to support the interpretation of functional magnetic resonance imaging (fMRI) scan images of brain regions of a subject, the method comprising:

[0006] Obtain subject data, including information about the subject's determined stress or anxiety level at an assessment time within a predetermined time period before, during, and / or after the acquisition of fMRI scan images; and

[0007] The fMRI scan image is modified based on the object data to generate a modified fMRI scan image, wherein modifying the fMRI scan image includes modifying one or more values ​​of brain activity in the fMRI scan image based on the determined stress or anxiety of the object at the evaluation time.

[0008] Therefore, the proposed concepts aim to provide schemes, solutions, ideas, designs, methods, and systems related to assisting or supporting the analysis and / or interpretation of fMRI scan images of a subject's brain. In particular, embodiments of the invention propose that neuroimaging results can be influenced by the psychological nature or state of the subject. Specifically, it is proposed that stress or anxiety in the subject before, during, or after an fMRI scan can affect measurement results, potentially influencing the interpretation and / or diagnosis of mental disorders.

[0009] Currently, standard analysis of fMRI scans is performed without considering the context of the subject before or during the MRI scan. For example, subjects with claustrophobia tendencies often experience anxiety or high levels of stress during fMRI investigations. Furthermore, subjects may experience anxiety for a period of time (minutes, hours, or even days) before the MRI scan. Alternatively or additionally, some subjects may experience high levels of stress or anxiety before the start of the MRI scan. Moreover, most subjects become anxious during the MRI scan.

[0010] The proposed concept may be based on the understanding that an object's fMRI scan may be affected by the object's stress or anxiety for a period of time before, during, and / or after the fMRI capture process. According to this implementation, it is proposed to obtain information about the object's stress or anxiety level within a predetermined time period before, during, and / or after the capture of the fMRI scan image. Using this information, embodiments can provide additional / supplementary information that can help, assist, or improve the interpretation of the fMRI scan image (e.g., by enabling the understanding of the object's stress or anxiety, which in turn enables the correction, modification, compensation, and / or consideration of the scenario in the fMRI scan data for more accurate analysis).

[0011] The embodiments attempt to obtain information about a subject's stress or anxiety before, during, and / or after the capture time of fMRI scan images of the subject's brain. This information can then be used to aid in the interpretation or analysis of the fMRI scan images. The embodiments can therefore be particularly used to support clinical decision-making. Exemplary applications may, for example, involve the assessment, diagnosis, or prediction of seizures, the treatment (outcome) or development of medical conditions and / or medical procedures. Therefore, the embodiments can be particularly used for purposes related to, for example, the assessment or treatment of neurological disorders.

[0012] In other words, the embodiments propose generating supplementary information from fMRI scans of the subject's brain based on the subject's stress or anxiety levels during a predetermined time period before, during, and / or after the capture of the fMRI scan images. The generated supplementary information may aid in clinical decision-making. Therefore, the embodiments can relate to treatment options to support medical professionals in selecting treatments for subjects. Such embodiments can also support clinical planning. Thus, the proposed concept can provide improved clinical decision support (CDS).

[0013] The suggestion is that information about the stress or anxiety level of the MRI scan subject can be used to support improved (e.g., more accurate) interpretation of the subject's fMRI scans.

[0014] For example, some embodiments propose a method or system for measuring induced scene stress to aid in the interpretation of fMRI brain scans, the method / system comprising: (i) an fMRI scan; (ii) components for assessing the stress level of a subject; and (iii) an analysis unit for interpreting the fMRI brain scan based on the subject's stress level. Various methods can be used to assess the subject's stress level, including methods based on: cortisol measurements; brain region activity; and peripheral physiological parameters (such as skin conductivity and heart rate).

[0015] Specifically, a method is proposed to generate modified fMRI scans by modifying fMRI scans based on object data to generate supplementary information. In this way, embodiments can generate one or more compensated, corrected, or scenario-considered fMRI scans that support interpretation (e.g., by reducing or eliminating the effects of the object's stress / anxiety or by indicating portions of the scan image that may be affected by the object's stress / anxiety).

[0016] More specifically, generating supplementary information includes modifying (e.g., increasing or decreasing) one or more values ​​of brain activity in the fMRI scan images based on determined stress or anxiety measurements of the subject at the assessment time. In this way, the fMRI scan images can be corrected or compensated for value variations caused by the subject's anxiety or stress. Therefore, embodiments can provide more accurate fMRI scan images, thereby supporting more accurate analysis / interpretation.

[0017] Examples of methods can be used to obtain subject data. Stress / anxiety can manifest in many different forms, and this can vary from subject to subject. As an example only, stress is known to involve significant responses in the amygdala, hippocampus, and inferior frontal gyrus. Furthermore, adrenaline is a major player in the body's stress response (as it addresses stress by secreting hormones such as adrenaline, testosterone, aldosterone, and cortisol). A commonly accepted measure of stress is the level of the stress hormone cortisol. Following a stressful event, cortisol concentrations in the body gradually increase until they peak after approximately 20–30 minutes (e.g., as measured in saliva).

[0018] Therefore, in one embodiment, obtaining subject data may include: obtaining cortisol data, including a measure of the subject's cortisol level at the assessment time; and determining a measure of the subject's stress or anxiety at the assessment time based on the obtained cortisol data. Thus, embodiments may employ generally accepted methods to determine the measure of the subject's stress or anxiety.

[0019] In some embodiments, obtaining subject data may include: obtaining brain activity data, including measures of the subject's brain activity at the time of capture; and determining, based on the obtained brain activity data, a measure of the subject's stress or anxiety at the time of assessment. For example, the subject's stress or anxiety level may be estimated via a concurrent fMRI scan. In this way, immediate and / or continuous information about the subject's stress or anxiety level can be obtained from the fMRI scan. In doing so, it may be preferable to synchronize the stress / anxiety measurement with one or more tasks performed by the subject during the MRI scan. As an example only, the timing of the task (e.g., a stressor) may be identified based on the simultaneous increase in hypothalamic, amygdala, and / or pituitary activity or physiological activity.

[0020] Furthermore, obtaining brain activity data may include analyzing second fMRI scans of one or more regions of the subject's brain, the second fMRI scans being captured at a capture time. The second fMRI scans may be second fMRI scans of multiple different regions, each of which is sampled at a different spatial and / or temporal resolution. Analyzing the second fMRI scans may then include compensating for the different spatial and / or temporal resolutions. Therefore, for scans of multiple brain regions, embodiments may take into account that scans of each region / area may occur at different resolutions and temporal scales. For example, a brain region / area may be sampled more frequently and with a higher spatial resolution. Embodiments can therefore be configured to compensate for this. Embodiments may also take into account non-uniform signal distortion in the brain, thereby improving accuracy.

[0021] Stress / anxiety can also manifest in the subject's body through the autonomic nervous system. This system has two branches: the parasympathetic branch (the "rest and digest" system) and the sympathetic branch (the "fight or flight" system). Activity of the sympathetic nervous system is considered a manifestation of stress. Well-known physiological parameters that change under stress include heart rate, heart rate variability, skin conductance, respiration, and adrenaline secretion. Therefore, in some embodiments, obtaining subject data may include: obtaining physiological data, which includes measurements of one or more physiological parameters of the subject at the assessment time; and determining, based on the obtained physiological data, a measure of the subject's stress or anxiety at the assessment time. For example, one or more physiological parameters of the subject may include at least one of the following: skin conductance; heart rate; respiratory rate; adrenaline level; heart rate variability; skin temperature; and pupillary dilation.

[0022] In some embodiments, obtaining subject data may include: obtaining questionnaire data, which includes information about the subject's answers to questions related to his / her stress or anxiety level at the time of assessment; and determining the subject's stress or anxiety level at the time of assessment based on the obtained questionnaire data. Therefore, stress and anxiety levels can be assessed using self-report information / questionnaires. For example, digital questionnaires can be completed at different times before and after an MRI scan, such as via a dedicated software application on a smartphone, tablet, or portable computing device.

[0023] Modifying (150) the fMRI scan image may include: processing the fMRI scan image and the object data using a machine learning algorithm to generate a prediction of how the object's stress or anxiety affects the fMRI scan image; and generating supplementary information based on the generated prediction. Embodiments can therefore leverage machine learning and artificial intelligence concepts to provide improved (e.g., more accurate) information to support the interpretation of the fMRI scan image.

[0024] According to another aspect of the invention, a method for interpreting fMRI scan images of a subject's brain is provided, the method comprising: generating a modified fMRI scan image to support the interpretation of fMRI scan images of a brain region of the subject, according to the proposed embodiment; obtaining the fMRI scan image, the fMRI scan image being captured at a capture time; and interpreting the obtained fMRI scan image based on the generated modified fMRI scan image.

[0025] According to another aspect of the present invention, a computer program product is provided that includes computer program code units, which, when executed on a computing device having a processing system, cause the processing system to perform all the steps of the method described above.

[0026] According to an example of another aspect of the invention, a system is provided for supporting the interpretation of functional magnetic resonance imaging (fMRI) scan images of brain regions of a subject. The system includes: an interface configured to acquire subject data, including information about a determined level of stress or anxiety in the subject at an assessment time, said assessment time being within a predetermined time period before, during, and / or after the acquisition time of the fMRI scan image; and a processor arrangement configured to modify the fMRI scan image based on the subject data to generate a modified fMRI scan image, wherein modifying the fMRI scan image includes modifying one or more values ​​of brain activity in the fMRI scan image based on the determined stress or anxiety in the subject at the assessment time.

[0027] These and other aspects of the invention will become apparent and will be explained with reference to the embodiments described below. Attached Figure Description

[0028] To better understand the invention and to more clearly illustrate how it can be practiced, reference will now be made to the accompanying drawings by way of example only, wherein,

[0029] Figure 1 A flowchart is depicted for a method for acquiring information to support the interpretation of fMRI scan images of brain regions of an object, according to an exemplary embodiment;

[0030] Figure 2 A simplified block diagram depicts a system 200 for interpreting fMRI scan images of brain regions supporting an object, according to an exemplary embodiment.

[0031] Figure 3 This is a simplified block diagram of a system according to another proposed embodiment; and

[0032] Figure 4 An example of a computer in which one or more parts of an embodiment may be employed is illustrated. Detailed Implementation

[0033] The invention will be described with reference to the accompanying drawings.

[0034] It should be understood that while the detailed description and specific examples indicate exemplary embodiments of the devices, systems, and methods, they are intended for illustrative purposes 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 be better understood from the following description, the appended claims, and the accompanying drawings. Although specific measures are recited in dissimilar dependent claims, this does not imply that combinations of these measures cannot be advantageously used.

[0035] Those skilled in the art, through studying the accompanying drawings, the disclosure, and the claims, will be able to understand and implement variations of the disclosed embodiments when practicing the claimed invention. In the claims, the word "comprising" does not exclude other elements or steps, and the words "a" or "an" do not exclude a plurality.

[0036] It should be understood that the accompanying drawings are schematic only and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the drawings to denote the same or similar parts.

[0037] Embodiments according to this disclosure relate to various techniques, methods, approaches, and / or solutions related to assisting in the analysis and / or interpretation of fMRI scan images of a subject's brain. Based on the proposed concepts, numerous possible solutions can be implemented individually or in combination. That is, while these possible solutions may be described individually below, two or more of these possible solutions may be implemented in one or another combination.

[0038] Specifically, the proposed concept can be based on the understanding that stress or anxiety in a subject before, during, and / or after an fMRI scan can affect the scan images. Utilizing this, embodiments are configured to determine the subject's stress or anxiety levels over predetermined time periods before, during, and / or after the capture time of the fMRI scan images, and then generate information that can aid in the interpretation of the fMRI scan images. Therefore, the proposed embodiments can provide a method for generating and providing useful information for fRMI scan analysis. Consequently, the embodiments can be used for treatment selection and / or to provide improved clinical decision support (CDS).

[0039] The proposed embodiments can utilize known methods to assess a subject's stress or anxiety levels. The results of such assessment can then be used to generate supplementary information that can aid, assist, or improve the interpretation of fMRI scan images (e.g., by enabling an understanding of the subject's stress or anxiety, which in turn allows for the correction, modification, compensation, and / or consideration of the scenario in the fMRI scan data for more accurate analysis). For example, such embodiments can be used to diagnose, treat, and / or predict neurological disorders.

[0040] Figure 1 A flowchart is depicted for a method for acquiring information to support the interpretation of fMRI scan images of brain regions of an object, according to an exemplary embodiment.

[0041] The method begins with step 110, which involves obtaining subject data. Specifically, the subject data includes information about the subject's determined stress or anxiety level at an assessment time. The assessment time is within a predetermined period before, during, and / or after the acquisition of the fMRI scan images.

[0042] Step 110, which involves obtaining object data, can be performed using different processes (alone or in combination). This is just one example. Figure 1 The embodiments include three separate methods / procedures for obtaining object data. One, two, or all of the three methods / procedures may be used during the execution of step 110.

[0043] The first process includes steps 115 and 120. In step 115, cortisol data, including a measure of the subject's cortisol level at the assessment time, is obtained (e.g., using conventional methods to measure the level of the stress hormone cortisol in the subject). Based on the obtained cortisol data, in step 120, a measure of the subject's stress or anxiety at the assessment time is determined.

[0044] The second process includes steps 125 and 130. Step 125 includes acquiring brain activity data, which includes measures of the subject's brain activity at the time of capture of the fMRI scan images. Then, step 130 includes determining a measure of the subject's stress or anxiety at the time of assessment based on the acquired brain activity data.

[0045] As an example only, obtaining brain activity data may include second fMRI scans of one or more regions of the subject's brain, captured at a specific time. If the second fMRI scans represent multiple distinct regions, each sampled at a different spatial and / or temporal resolution, the step of analyzing the second fMRI scans may include compensating for the different spatial and / or temporal resolutions. This allows for the interpretation of situations where each region / area scan uses different resolutions and / or temporal scales. For example, a brain region / area may be sampled more frequently and at a higher spatial resolution, and this can be compensated for.

[0046] The third process includes steps 135 and 140. Step 135 includes obtaining physiological data, including measurements of one or more physiological parameters of the subject at the assessment time. For example, one or more physiological parameters of the subject may include at least one of the following: skin conductance; heart rate; respiratory rate; adrenaline level; heart rate variability; skin temperature; and pupillary dilation. Step 140 then includes determining a measure of the subject's stress or anxiety at the assessment time based on the obtained physiological data.

[0047] After completing step 110 of acquiring object data, the method proceeds to step 130. Step 130 includes generating supplementary information to support the interpretation of fMRI scan images based on the object data. For example, in Figure 1 In the embodiment, generating supplementary information includes three sub-steps: 155; 160; and 165.

[0048] In step 155, the obtained object data (from step 110) and fMRI scan images are provided to a machine learning algorithm. In step 160, the machine learning algorithm processes the object data and fMRI scan images to generate predictions (e.g., predictions of how the object's stress / anxiety level changes / affects the fMRI scan images). Based on the generated predictions, supplementary information is generated in step 165. For example, in step 165, the fMRI scan images are modified to generate modified fMRI scan images. Such modifications may include reducing one or more values ​​in the fMRI scan images based on a determined stress or anxiety measure of the object at the assessment time. In this way, modified fMRI scan images with corrected or adjusted values ​​can be generated to address the object's anxiety and stress.

[0049] Although the above has already stated Figure 1 The embodiments described herein employ one or more of three different processes to obtain information about a subject's stress or anxiety level; however, it should be understood that other processes may be employed in the proposed embodiments. For example, obtaining subject data may include obtaining questionnaire data, which includes information about the subject's answers to questions related to his / her stress or anxiety level during the assessment. That is, information about the subject's stress or anxiety may be provided to the embodiments by the subject.

[0050] Figure 2 A simplified block diagram of a system 200 for supporting the interpretation of fMRI scan images of a subject's brain region, according to an exemplary embodiment, is depicted. System 200 includes an interface 210 (e.g., a signal interface and / or a user input interface) configured to acquire subject data 215. The subject data includes information about a determined level of stress or anxiety in the subject at an assessment time (e.g., before, during, and / or after the capture time of the fMRI scan image). System 200 also includes a processor arrangement 220 (of one or more microprocessors) configured to generate supplementary information to support the interpretation of the fMRI scan images based on the subject data. Processor arrangement 220 is adapted to output the generated supplementary information to, for example, a user, a display device, and / or another system.

[0051] Figure 3 A simplified block diagram of a system 300 according to another proposed embodiment is depicted. The system includes a monitoring module 310, an fMRI imaging module 320, and a processor device 330. Various aspects of the various parts of the system 300 will now be described in the following corresponding sections.

[0052] Monitoring module 310 – which is used to assess the patient’s stress level.

[0053] This module 310 is used to measure, monitor, and determine a patient's stress and anxiety levels. Therefore, it is configured to determine stress and anxiety levels based on measurements of objective parameters.

[0054] For example, physiological signals can be measured using different methods and can be collected in two different ways: at a single moment or multiple moments; or continuously.

[0055] Stress / anxiety levels can be determined during and / or up to 24 hours before and after the fMRI scan (at the time of assessment). This may help establish a more accurate baseline for stress: i.e., lower stress levels may be found in the 24 hours prior to the scan.

[0056] In addition, stress and anxiety levels can be assessed using self-reported information / questionnaires. For example, digital questionnaires can be completed at different times before and after the scan, using a dedicated software application available to the subject (e.g., via smartphone, tablet, or laptop).

[0057] Imaging Module 320 – for fMRI imaging

[0058] Imaging module 320 is configured to measure and monitor brain activity during cognitive, perceptual, or behavioral tasks. To facilitate diagnosis using multiple brain regions, the module can take into account the fact that scanned brain regions may have different resolutions and temporal scales. For example, a brain region can be sampled more frequently and at a higher spatial resolution, and this can be taken into account / compensated for. Non-uniform signal distortion in the brain can also be considered.

[0059] Processor layout 330

[0060] To analyze data from monitoring module 310 and imaging module 320, processor arrangement 330 employs a reference library of known one-to-one scientific learning about specific brain regions associated with specific forms of mental stress. Using such reference information, the processor device can analyze the received data and determine the subject's stress / anxiety level.

[0061] By demonstration Figure 3 Possible implementations of the system 300 will now be described in detail in the following sections, including various exemplary embodiments.

[0062] Exemplary Example 1: Patient stress level estimated based on cortisol measurement.

[0063] Monitoring module 310 is configured to determine cortisol levels. For example, cortisol levels can be estimated as follows:

[0064] (i) Hair samples taken from the last few months prior to the fMRI capture time, representing chronic (i.e., long-term) cortisol levels. These can be used to determine baseline or scalar values;

[0065] (ii) Via single or multiple saliva swabs measuring acute cortisol levels. Sampling may begin in the morning before the MRI scan to correct for daytime fluctuations and continue during the examination;

[0066] (iii) Plasma cortisol is measured via a continuous blood sampling line, preferably starting 30 minutes before the scan; or

[0067] (iv) Indirect continuous sampling using physiological parameters, preferably starting 30 minutes before the scan.

[0068] The processor arrangement 330 is configured to synchronize various cortisol measurements and correct for stress-induced biases in fMRI measurements. This correction can be performed on single or multiple cortisol values ​​by (initially) reducing the activity of the scanned image by 5% for every 1 nmol / L of cortisol present. This 5% figure can be periodically adjusted on a cumulative scan basis to determine it with increasing accuracy. That is, while a standard value of 5% is detailed, this is merely exemplary, and other values ​​can be employed, for example, depending on the brain region of interest and / or specific stress-related neural networks.

[0069] Therefore, alternatively, single or multiple cortisol values ​​can be corrected in a similar manner: for every 1 nmol / L difference in cortisol levels measured before and during a scan, the activity (initial) of the scan image is reduced by y%. Similarly, the y% figure can be periodically adjusted based on cumulative scans to determine it more accurately.

[0070] The correction using continuous cortisol measurements can be performed similarly: for every 1 nmol / L of cortisol present in a time window lasting at least 20–30 minutes before the start of the MRI procedure, the activity (initial) of the scanned image is reduced by z%. Therefore, the image can be corrected for the stress induced by the MRI scanner itself. Furthermore, the z% figure can be periodically adjusted based on cumulative scans to determine it with increasing accuracy.

[0071] Alternatively, continuous measurements can be used to create a delayed time series for (slowly) changing cortisol measurements. This continuous cortisol time series can be included in the confounding regression factors in the brain activity estimation process.

[0072] Long-term hair cortisol levels can be used as a scalar to modify brain activity levels. Alternatively, long-term hair cortisol levels can be used to measure acute cortisol levels. For example, chronic stress can increase skin conductance responses to stressors or actually reduce acute responses in saliva.

[0073] Several of these cortisol-based correction methods can be used in combination.

[0074] For example, for stress associated with PTSD trauma, the relevant parameter is volume, and the relevant region of interest is the hippocampus (associated with placing memories in the correct temporal and spatial context). For stress associated with major depressive disorder (MDD), the parameter would be functional connectivity, and the network of interest would be the default mode network.

[0075] Exemplary Example 2: Patient stress level estimated based on peripheral physiological parameters (such as skin conductance) during the imaging process.

[0076] Stress levels are estimated based on continuous information provided by peripheral physiological parameters such as skin conductance or heart rate. Skin conductance and heart rate information can be collected via wearable devices (i.e., smartwatches). Heart rate information can also be collected using a photoplethysmography (PPG) camera. Such measurements involve the autonomic nervous system responses involved in stress responses.

[0077] Furthermore, skin conductance measurements (using known techniques) can be processed to estimate the amount of cortisol in the body, and thus provide a pressure indication approximately 20-30 minutes prior. When physiological measurements are converted into cortisol estimates using this technique, these cortisol estimates can be considered as part of Example 1 above.

[0078] As a further example, embodiments of system 300 may employ an artificial intelligence (AI) module (e.g., via processor device 330) using machine learning algorithms to assess stress levels and interpret brain scans. The stress level estimate can be used as an absolute indicator of stress, suggesting the extent to which stress is expected to affect the scanned brain images. Furthermore, the stress level can be compared to the stress level prior to the (direct)presentation task, thereby separating the stress effects of the scanning process from those of the presentation task. These relative or absolute stress levels can then be used to correct the brain scans in various ways, such as:

[0079] (a) Stress levels (based on task-specific measurements) are used to correct estimates of task-induced brain activity and to identify brain signals that are amplified or reduced under stress levels. It is well known that brain activity levels vary under stress, and we can use this information to refine brain activity estimates. For continuous stress estimates, this can be done in real time.

[0080] (b) Correction for the absolute pressure level can be accomplished by reducing the activity (initial) of the scanned image by x% for each pressure unit present. This x% figure can be periodically adjusted on a cumulative scan basis to determine it with increasing accuracy.

[0081] (c) Correction for relative pressure values ​​can be done in a similar manner: for each unit difference in pressure levels measured before and during the scan, the activity (initial) of the scanned image is reduced by y%. Again, the y% figure can be periodically adjusted based on cumulative scans to determine it more accurately.

[0082] (d) Similarly, the stress caused by the scanning protocol can be corrected: for each unit of stress present before the start of the task, the activity (initial) of the scanned image is reduced by z%. Thus, the image is corrected for the stress effects on the scanner itself. Likewise, the number z% can be periodically adjusted based on cumulative scans to determine it with increasing accuracy.

[0083] The same method can be used to correct for stress differences between the two (diagnostic) groups while keeping the relative variances related to stress within the groups similar.

[0084] Potential guidance for the clinical interpretation of standardized brain measurements is then based on corrected rather than raw signals.

[0085] Example 3 – Estimation of patient stress levels based on adrenal activity measurements taken during the same imaging process

[0086] Acute or subacute stress can be indicated from fMRI scans of adrenal activity (both cortex and medulla). For a sufficiently wide double diaphragm, this adrenal activity can be measured during the same scan as that used to measure brain activity. Stress levels can be estimated using different methods: (i) adrenal activity values ​​are used to label images taken when adrenal activity levels are estimated to be high, indicating that the task was stressful for the individual and that the current outcome of the task may be influenced by stress; (ii) adrenal activity levels (as measured for a specific task) are used to enhance / correct the intensity of brain activity during that task. It is well known that brain activity levels are affected by stress, and this information can be used to correct / enhance brain activity estimates. This can be done in real time.

[0087] Furthermore, a distinction was made between two regions of adrenal activity: when the adrenal medulla (nucleus) is active, this indicates the production of adrenaline, which typically occurs immediately after a stressful event; therefore, adrenal activity in the medulla is an indicator of acute stress (thus applying similar corrections as in the examples above). When the adrenal cortex is active, this indicates the production of cortisol, which is typically somewhat delayed; and therefore, adrenal activity in the cortex is a measure of past stress (thus applying similar corrections as in Example 1 above).

[0088] As can be understood from the above description, a method is proposed to assist and / or improve the interpretation of brain activity and thus optimize neuroimaging interpretation. Methods and systems are proposed for providing supplemental information on patient stress levels during fMRI examinations. For example, said information can be used to correct the brain measurement itself, or to correct the interpretation / analysis of the brain measurement.

[0089] Although various exemplary embodiments have been described, it should be understood that alternative embodiments and / or various modifications may be implemented. For example, stress and anxiety levels may be assessed using self-reported information / questionnaires.

[0090] Furthermore, stress information can be used to improve the patient experience: reducing the patient's stress level due to brain scans, i.e., providing breathing exercises based on the subject's stress level, can in turn improve the MRI scan process.

[0091] MRI examinations may include scans other than task-based fMRI scans. Pressure will also be measured during these scans (this should be non-task-related pressure).

[0092] In some embodiments, it is suggested to monitor twin subjects (i.e., twins). Such a suggestion is based on the assumption that twins have similar brain activity. For example, if one of the twins has claustrophobia and has associated brain activity, that activity can be used to identify brain regions associated with claustrophobia.

[0093] As another example, Figure 4 An example of computer 400 is illustrated, in which one or more portions of the embodiments may be employed. The various operations discussed above can utilize the capabilities of computer 400. For example, a system for supporting the interpretation of fMRI scan images of specific brain regions can be incorporated into any of the elements, modules, applications, and / or components discussed herein. In this regard, it should be understood that system functional blocks may operate on a single computer or may be distributed across multiple computers and locations (e.g., via an Internet connection).

[0094] Computer 400 includes, but is not limited to, PCs, workstations, laptops, PDAs, handheld devices, servers, memory, etc. Typically, in terms of hardware architecture, computer 400 may include one or more processors 410, memory 420, and one or more I / O devices 470 communicatively coupled via a local interface (not shown). The local interface may be, for example, but not limited to, one or more buses or other wired or wireless connections, as known in the art. The local interface may have additional elements, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communication. Furthermore, the local interface may include addressing, control, and / or data connections to enable appropriate communication between the aforementioned components.

[0095] Processor 410 is a hardware device for running software that can be stored in memory 420. Processor 410 can actually be any custom or commercial processor, central processing unit (CPU), digital signal processor (DSP), or auxiliary processor among several processors associated with computer 400, and processor 410 can be a semiconductor-based microprocessor (in the form of a microchip) or microprocessor.

[0096] Memory 420 may include any or a combination of volatile memory elements (e.g., random access memory (RAM), such as dynamic random access memory (DRAM), static random access memory (SRAM), etc.) and non-volatile memory elements (e.g., ROM, erasable programmable read-only memory (EPROM), electronically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic tape, optical disc read-only memory (CD-ROM), magnetic disk, floppy disk, cassette, cassette tape, etc.). Furthermore, memory 420 may contain electronic, magnetic, optical, and / or other types of storage media. Note that memory 420 may have a distributed architecture, where various components are geographically separated but accessible by processor 410.

[0097] The software in memory 420 may include one or more individual programs, each including an ordered list of executable instructions for implementing logical functions. According to an exemplary embodiment, the software in memory 420 includes a suitable operating system (O / S) 450, a compiler 440, source code 430, and one or more application programs 460. As shown, application program 460 includes numerous functional components for implementing the features and operations of the exemplary embodiment. According to an exemplary embodiment, application program 460 of computer 400 may represent various applications, computing units, logic, functional units, processes, operations, virtual entities, and / or modules, but application program 460 is not intended to be limiting.

[0098] Operating system 450 controls the execution of other computer programs and provides scheduling, input / output control, file and data management, memory management, and communication control and related services. The inventors anticipate that application program 460 for implementing exemplary embodiments can be applied to all commercially available operating systems.

[0099] Application 460 can be a source program, an executable program (object code), a script, or any other entity including a set of instructions to be executed. When it is a source program, the program is typically translated by a compiler (e.g., compiler 440), assembler, interpreter, etc., which may or may not be included in memory 420 to operate correctly with operation O / S 450. Furthermore, application 460 can be written in an object-oriented programming language with classes of data and methods, or a flow programming language with routines, subroutines, and / or functions, such as, but not limited to, C, C++, C#, Pascal, BASIC, API calls, HTML, XHTML, XML, ASP scripts, JavaScript, FORTRAN, COBOL, Perl, Java, ADA, .NET, etc.

[0100] I / O device 470 may include input devices, such as, but not limited to, a mouse, keyboard, scanner, microphone, camera, etc. Furthermore, I / O device 470 may also include output devices, such as, but not limited to, a printer, monitor, etc. Finally, I / O device 470 may also include devices for transmitting both input and output, such as, but not limited to, a NIC or modulator / demodulator (for accessing remote devices, other files, devices, systems, or networks), radio frequency (RF) or other transceivers, telephone interfaces, bridges, routers, etc. I / O device 470 also includes components for communication over various networks such as the Internet or intranets.

[0101] If the computer 400 is a PC, workstation, intelligent device, etc., the software in the memory 420 may also include a Basic Input / Output System (BIOS) (omitted for simplicity). The BIOS is a set of basic software routines that initialize and test the hardware at startup, start the O / S450, and support data transfer between hardware devices. The BIOS is stored in some kind of read-only memory, such as ROM, PROM, EPROM, EEPROM, etc., so that it runs when the computer 400 starts.

[0102] When the computer 400 is in operation, the processor 410 is configured to run software stored in the memory 420 to transfer data to and from the memory 52, and typically controls the operation of the computer 400 based on the software. Application programs 460 and operating systems 450 are read, possibly buffered within the processor 410, and then executed.

[0103] When application 460 is implemented as software, it should be noted that application 460 can be stored on virtually any computer-readable medium for use by or in connection with any computer-related system or method. In the context of this document, a computer-readable medium can be an electronic, magnetic, optical, or other physical device or apparatus that can contain or store computer programs for use by or in connection with a computer-related system or method.

[0104] Application 460 can be implemented on various computer-readable media for use by or in conjunction with an instruction execution system, apparatus, or device, such as a computer-based system, a processor-containing system, or other system capable of retrieving and executing instructions from and from the instruction execution system, apparatus, or device. In the context of this document, "computer-readable medium" can mean any means by which a program can be stored, communicated, propagated, or transmitted for use by or in conjunction with an instruction execution system, apparatus, or device. Computer-readable media can be, for example, but not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, devices, or propagation media.

[0105] This invention can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium (or medium) having computer-readable program instructions thereon for causing a processor to execute aspects of the invention.

[0106] A computer-readable storage medium can be a tangible device capable of retaining and storing instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing.

[0107] Aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0108] A single processor or other unit can perform the functions of several items described in the claims.

[0109] It should be understood that the disclosed methods are computer-implemented methods. Thus, the concept of a computer program is also introduced, which includes code units for implementing any described method when the program is run on a processing system.

[0110] Technicians will be able to easily develop computers for performing any of the methods described herein. Therefore, each step of the flowchart can represent a different action performed by a processor and can be executed by the corresponding module of the processor.

[0111] As described above, the system utilizes a processor to perform data processing. A processor can be implemented in various ways, using software and / or hardware, to perform a variety of required functions. A processor typically employs one or more microprocessors, which can be programmed using software (e.g., microcode) to perform the desired functions. A processor can be implemented as a combination of dedicated hardware for performing some functions and one or more programmed microprocessors and associated circuitry for performing other functions.

[0112] Examples of circuits that may be used in the various embodiments of this disclosure include, but are not limited to, conventional microprocessors, application-specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).

[0113] In various implementations, the processor may be associated with one or more storage media, such as volatile and non-volatile computer memories, such as RAM, PROM, EPROM, and EEPROM. The storage media may be encoded with one or more programs that, when run on one or more processors and / or controllers, perform the required functions. The various storage media may be fixed within the processor or controller, or they may be portable, allowing one or more programs stored thereon to be loaded into the processor.

[0114] Those skilled in the art, through studying the accompanying drawings, disclosure, and claims, will understand and implement variations of the disclosed embodiments when practicing the claimed invention. In the claims, the word "comprising" does not exclude other elements or steps, and the words "a" or "an" do not exclude multiple. A single processor or other unit can perform the functions of several items recited in the claims. Although specific measures are recited in dissimilar dependent claims, this does not imply that combinations of these measures cannot be advantageously used. Computer programs can be stored / distributed on suitable media such as optical storage media or solid-state media provided with or as part of other hardware, but can also be distributed in other forms such as via the Internet or other wired or wireless telecommunications systems. If the term "suitable" is used in the claims or description, it should be noted that the term "suitable" is intended to be equivalent to the term "configured as." Any reference numerals in the claims should not be construed as limiting the scope.

Claims

1. A method for generating modified functional magnetic resonance imaging (fMRI) scan images to support the interpretation of fMRI scan images of brain regions of a subject, the method comprising: (110) Subject data, which includes information about the stress or anxiety level of the subject at an assessment time determined within a predetermined time period before, during, and / or after the capture time of the fMRI scan images; and Modifying (150) the fMRI scan image based on the object data to generate a modified fMRI scan image, wherein modifying the fMRI scan image includes modifying one or more values ​​of brain activity in the fMRI scan image based on the stress or anxiety level determined by the object at the assessment time, such that the fMRI scan image is corrected or compensated in a manner that reduces value changes caused by the stress or anxiety of the object.

2. The method according to claim 1, wherein, Obtaining object data (110) includes: Obtain (115) cortisol data, which includes a measure or estimate of the subject's cortisol level at the assessment time; and The obtained cortisol data are used to determine (120) the measure of the stress or anxiety level of the subject at the time of assessment.

3. The method according to any one of claims 1 to 2, wherein, Obtaining object data (110) includes: (125) Brain activity data is obtained, said brain activity data including a measure of the subject's brain activity at the assessment time; and The measure of the stress or anxiety level of the subject at the assessment time is determined based on the obtained brain activity data (130).

4. The method according to claim 3, wherein, Brain activity data obtained includes: Analyze a second fMRI scan image of one or more regions of the subject's brain, the second fMRI scan image being captured at the capture time.

5. The method according to claim 4, wherein, The second fMRI scan image is a second fMRI scan image of multiple different regions, each of which is sampled at a different spatial and / or temporal resolution, and wherein analyzing the second fMRI scan image includes compensating for the different spatial and / or temporal resolutions.

6. The method according to any one of claims 1 to 5, wherein, Obtaining object data (110) includes: (135) Obtain physiological data, said physiological data including measurements of one or more physiological parameters of the subject at the assessment time; and The measure of the stress or anxiety level of the subject at the assessment time is determined based on the obtained physiological data (140).

7. The method according to claim 6, wherein, The one or more physiological parameters of the object include at least one of the following: skin conductance; heart rate; respiratory rate; adrenaline level; Heart rate variability; skin temperature; and pupil dilation.

8. The method according to any one of claims 1 to 7, wherein, Obtaining object data (110) includes: Obtain questionnaire data, which includes information about the subject's answers to questions related to his / her stress or anxiety level at the time of the assessment; and The survey data obtained is used to determine a measure of the subject's stress or anxiety level at the assessment time.

9. The method according to any one of claims 1 to 8, wherein, Modifying the fMRI scan image described in (150) includes: The fMRI scan images and the object data are processed using machine learning algorithms (160) to generate predictions of how the object's stress or anxiety affects the fMRI scan images; and The fMRI scan image (150) is modified based on the generated prediction.

10. A method for interpreting fMRI scan images of a subject's brain, the method comprising: According to any one of claims 1 to 9, modified fMRI scan images are generated to support the interpretation of fMRI scan images of brain regions of the subject; The fMRI scan image is obtained, and the fMRI scan image is captured at the capture time; and The obtained fMRI scan images are interpreted based on the generated modified fMRI scan images.

11. The method according to claim 10, wherein, The interpretation of the acquired fMRI scan images is also based on the time difference between the evaluation time and the capture time.

12. A computer program product comprising computer program code units, which, when run on a computing device having a processing system, cause the processing system to perform all the steps of the method according to any one of claims 1 to 11.

13. A system (200) for supporting the interpretation of functional magnetic resonance imaging (fMRI) scan images of brain regions of a subject, the system comprising: An interface (210) is configured to obtain object data, the object data including information about the stress or anxiety level of the object at an assessment time determined at a predetermined time period before, during and / or after the capture time of the fMRI scan images; as well as A processor (220) is arranged and configured to modify the fMRI scan image based on the object data to generate a modified fMRI scan image, wherein modifying the fMRI scan image includes modifying one or more values ​​of brain activity in the fMRI scan image based on the stress or anxiety level determined by the object at the assessment time, such that the fMRI scan image is corrected or compensated in a manner that reduces value changes caused by the stress or anxiety of the object.

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